Notes by Retraice - SoundSage

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Re116: When Does the Bad Thing Happen?
(Technological Danger, Part 4)

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Agreements about reality in technological progress.
Basic questions; a chain reaction of philosophy; deciding what is and isn't in the world; agreeing with others in order to achieve sharing; other concerns compete with sharing and prevent agreement; the need for agreement increasing.

Air date: Saturday, 14th Jan. 2023, 10:00 PM Eastern/US.

The chain reaction of questions

We were bold enough to predict a decrease in freedom (without defining it);^1 we were bold enough to define technological progress (with defining it).^2 But in predicting and assessing `bad things' (i.e. technological danger), we should be able to talk about when the bad things might or might not happen, did or didn't happen. But can we? When does anything start and stop? How to draw the lines in chronology? How to draw the lines in causality? There is a chain reaction of questions and subjects:
* Time: When did it start? With the act, or the person, or the species?
* Space: Where did it start?
* Matter: What is it?
* Causality: What caused it?
* Free will: Do we cause anything, really?

Ontology and treaties for sharing

Ontology is the subset of philosophy that deals with being',existence', reality', the categories of such things, etc. I.e., it's aboutwhat is', or What is there?', orthe stuff' of the world. From AIMA4e (emphasis added):

"We should say up front that the enterprise of general ontological engineering has so far had only limited success. None of the top AI applications (as listed in Chapter 1) make use of a general ontology--they all use special-purpose knowledge engineering and machine learning. Social/political considerations can make it difficult for competing parties to agree on an ontology. As Tom Gruber (2004) says, `Every ontology is a treaty--a social agreement--among people with some common motive in sharing.' When competing concerns outweigh the motivation for sharing, there can be no common ontology. The smaller the number of stakeholders, the easier it is to create an ontology, and thus it is harder to create a generalpurpose ontology than a limited-purpose one, such as the Open Biomedical Ontology."^3

Prediction: the need for precise
ontologies is going to increase.

Ontology is not a solved problem--neither in philosophy nor artificial intelligence. Yet we can't sit around and wait. The computer control game is on. We have to act and act effectively. And further, our need for precise ontologies--that is, the making of treaties--is going to increase because we're going to be dealing with technologies that have more and more precise ontologies. So, consider:
* More stakeholders makes treaties less likely;
* The problems that we can solve without AI (and its ontologies and our own ontologies) are decreasing;
* Precise ontology enables knowledge representation (outside of machine-learning), and therefore AI, and therefore the effective building of technologies and taking of actions, and therefore work to be done;
* Treaties can make winners and losers in the computer control game;
* Competing concerns can outweigh the motive for sharing, and therefore treaties, and therefore winning.

__

References

Retraice (2023/01/11). Re113: Uncertainty, Fear and Consent (Technological Danger, Part 1). retraice.com.
https://www.retraice.com/segments/re113 Retrieved 12th Jan. 2023.

Retraice (2023/01/13). Re115: Technological Progress, Defined (Technological Danger, Part 3). retraice.com.
https://www.retraice.com/segments/re115 Retrieved 14th Jan. 2023.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches:
https://www.amazon.com/s?k=978-0134610993
https://www.google.com/search?q=isbn+978-0134610993
https://lccn.loc.gov/2019047498

Footnotes

^1 Retraice (2023/01/11)

^2 Retraice (2023/01/13)

^3 Russell & Norvig (2020) p. 316. And Gruber's Every Ontology Is a Treaty (2004): https://tomgruber.org/writing/sigsemis-2004

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Re115: Technological Progress, Defined (Technological Danger, Part 3)

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How we would decide, given predictions, whether to risk continued technological advance. Danger, decisions, advancing and progress; control over the environment and we'; complex, inconsistent and conflicting human preferences;coherent extrapolated volition' (CEV); divergence, winners and losers; the lesser value of humans who disagree; better and worse problems; predicting progress and observing progress; learning from predicting progress.

Air date: Friday, 13th Jan. 2023, 10:00 PM Eastern/US.

Progress, `we' and winners

If the question is about `danger', the answer has to be a decision about whether to proceed (advance). But how to think about progress?

Let advance' mean moving forward, whether or not it's good for humanity. Letprogress' mean moving forward in a way that's good for humanity, by some definition of good.^1

Progress can't be control over the environment, because whose control? (Who is we?) And we can't all control equally or benefit equally or prefer the same thing. This corresponds to the Russell & Norvig (2020) chpt. 27 problems of the complexity and inconsistency of human preferences,^2 and Bostrom (2014) chpt 13 problem of "locking in forever the prejudices and preconceptions of the present generation" (p. 256).

A possible solution is Yudkowsky (2004)'s coherent extrapolated volition'.^3 If humanity's collectivevolition' doesn't converge, this might entail that there has to be a `winner' group in the game of humans vs. humans.

This implies the (arguably obvious) conclusion that we humans value other humans more or less depending on the beliefs and desires they hold.

Better and worse problems can be empirical

Choose between A and B: o carcinogenic bug spray, malaria; o lead in the water sometimes (Flint, MI), fetching pales; o unhappy day job, no home utilities (or home).

Which do you prefer? This is empirical, in that we can ask people. We can't ask people in the past or the future; but we can always ask people in the present to choose between two alternative problems.

Technological progress

First, we need a definition of progress in order to make decisions. Second, we need an answer to the common retort that technology creates more problems than it solves'.More' doesn't matter; what matters is whether the new problems, together, are `better' than the old problems, together.

We need to define two timeframes of `progress' because we're going to use the definition to make decisions: one timeframe to classify a technology before the decision to build it, and one timeframe to classify it after it has been built and has had observable effects. It's the difference between expected progress and observed progress. Actual, observed progress can only be determined retrospectively.

Predicted progress:

A technology seems like progress if: the predicted problems it will create are better to have than the predicted problems it will solve, according to the humans alive at the time of prediction.^4

Actual progress:

A technology is progress if: given an interval of time, the problems it created were better to have than the problems it solved, according to the humans alive during the interval.

(The time element is crucial: a technology will be, by definition, progress if up to a moment in history it never caused worse problems than it solved; but once it does cause such problems, it ceases to be progress, by definition.)

Prediction progress (learning):

Actual progress', if tracked and absorbed, could be used to improve futurepredicted progress'.

_

References

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford. First published in 2014. Citations are from the pbk. edition, 2016. ISBN: 978-0198739838. Searches: https://www.amazon.com/s?k=978-0198739838 https://www.google.com/search?q=isbn+978-0198739838 https://lccn.loc.gov/2015956648

Retraice (2022/10/24). Re28: What's Good? RTFM. retraice.com. https://www.retraice.com/segments/re28 Retrieved 25th Oct. 2022.

Retraice (2023/01/09). Re111: AI and the Gorilla Problem. retraice.com. https://www.retraice.com/segments/re111 Retrieved 10th Jan. 2023.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Yudkowsky, E. (2004). Coherent extrapolated volition. Machine Intelligence Research Institute. 2004. https://intelligence.org/files/CEV.pdf Retrieved 13th Jan. 2023.

Footnotes

^1 Retraice (2022/10/24).

^2 Cf. Russell & Norvig (2020) p. 34 and Re111 (Retraice (2023/01/09)).

^3 See also Bostrom (2014) p. 259 ff.

^4 The demonstrated preferences of those humans? The CEV of them? This is hard.

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Re114: Visions of Loss (Technological Danger, Part 2)

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Human loss of freedom by deference to authority, dependency on machines, and delegation of defense. Wiener: freedom of thought and opinion, and communication, as vital; Russell: diet, injections and injunctions in the future; Horesh: technological behavior modification in the present; terrorist Kaczynski: if AI succeeds, we'll have machine control or elite control, but no freedom; Bostrom: wearable surveillance devices and power in the hands of a very few as solution.

Air date: Thursday, 12th Jan. 2023, 10:00 PM Eastern/US.

All bold emphasis added.

Mathematician Wiener

Is this what's at stake, in the struggle for freedom of thought and communication?

Wiener (1954), p. 217:^1

"I have said before that man's future on earth will not be long unless man rises to the full level of his inborn powers. For us, to be less than a man is to be less than alive. Those who are not fully alive do not live long even in their world of shadows. I have said, moreover, that for man to be alive is for him to participate in a world-wide scheme of communication. It is to have the liberty to test new opinions and to find which of them point somewhere, and which of them simply confuse us. It is to have the variability to fit into the world in more places than one, the variability which may lead us to have soldiers when we need soldiers, but which also leads us to have saints when we need saints. It is precisely this variability and this communicative integrity of man which I find to be violated and crippled by the present tendency to huddle together according to a comprehensive prearranged plan, which is handed to us from above. We must cease to kiss the whip that lashes us...."

p 226: "There is something in personal holiness which is akin to an act of choice, and the word heresy is nothing but the Greek word for choice. Thus your Bishop, however much he may respect a dead Saint, can never feel too friendly toward a living one.

"This brings up a very interesting remark which Professor John von Neumann has made to me. He has said that in modern science the era of the primitive church is passing, and that the era of the Bishop is upon us. Indeed, the heads of great laboratories are very much like Bishops, with their association with the powerful in all walks of life, and the dangers they incur of the carnal sins of pride and of lust for power. On the other hand, the independent scientist who is worth the slightest consideration as a scientist, has a consecration which comes entirely from within himself: a vocation which demands the possibility of supreme self-sacrifice...."

p. 228: "I have indicated that freedom of opinion at the present time is being crushed between the two rigidities of the Church and the Communist Party. In the United States we are in the process [1950] of developing a new rigidity which combines the methods of both while partaking of the emotional fervor of neither. Our Conservatives of all shades of opinion have somehow got together to make American capitalism and the fifth freedom [economic freedom^2 ] of the businessman supreme throughout all the world...."

p. 229: "It is this triple attack on our liberties which we must resist, if communication is to have the scope that it properly deserves as the central phenomenon of society, and if the human individual is to reach and to maintain his full stature. It is again the American worship of know-how as opposed to know-what that hampers us."

Mathematician and philosopher Russell

Will this happen?

Russell (1952), pp. 65-66:^3

"It is to be expected that advances in physiology and psychology will give governments much more control over individual mentality than they now have even in totalitarian countries. Fichte laid it down that education should aim at destroying free-will, so that, after pupils have left school, they shall be incapable, throughout the rest of their lives, of thinking or acting otherwise than as their schoolmasters would have wished. But in his day this was an unattainable ideal: what he regarded as the best system in existence produced Karl Marx. In [the] future such failures are not likely to occur where there is dictatorship. Diet, injections, and injunctions will combine, from a very early age, to produce the sort of character and the sort of beliefs that the authorities consider desirable, and any serious criticism of the powers that be will become psychologically impossible. Even if all are miserable, all will believe themselves happy, because the government will tell them that they are so."

Kaczynski says similar things throughout his `manifesto'.

Philosopher Horesh

Is this really happening already?

Horesh (2020), p. 158:^4

"Meanwhile, a previously unimaginable level of thought control is fast being made accessible for every middle-income autocracy that chooses to use it. Visit the wrong website and your social credit score declines, look up the wrong book and it drops further, mention the wrong phrases on social media and it sinks so low that alarms go off in the camera rooms when your face flashes on the screen. The opportunities this presents for behavioral modification are simply astonishing, as the exploration of every forbidden idea or acquaintance can be made part of a social credit score, whose every drop causes another shock in the hearts of the lowly ranked.... Yet, whether or not China goes so far, they have developed the tools needed to implement a security regime more totalitarian than even that of the East German Stasi, at a fraction of the effort and far lower cost, for any autocrat who chooses to go that far. Russians and Turks, Poles and Hungarians, could soon find themselves entering a vise from which they never escape. For once such a security regime is implemented, resistance can be shut down in ways not previously imagined, while independent thinking is gradually snuffed out."

Mathematician and terrorist Kaczynski

Are these the only possible conclusions of industrial society?

(Try to forget that Kaczynski killed three people and ruined many more lives. His vision of the future is quoted by many because it is nuanced and sharply observed; it is worth salvaging from the wreckage of his life.)

Kaczynski & Skrbina (2010), pp. 93-94:^5

"172. First let us postulate that the computer scientists succeed in developing intelligent machines that can do all things better than human beings can do them. In that case presumably all work will be done by vast, highly organized systems of machines and no human effort will be necessary. Either of two cases might occur. The machines might be permitted to make all of their own decisions without human oversight, or else human control over the machines might be retained.

"173. If the machines are permitted to make all their own decisions we can't make any conjecture as to the results, because it is impossible to guess how such machines might behave. We only point out that the fate of the human race would be at the mercy of the machines. It might be argued that the human race would never be foolish enough to hand over all power to the machines. But we are suggesting neither that the human race would voluntarily turn power over to the machines nor that the machines would will fully seize power. What we do suggest is that the human race might easily permit itself to drift into a position of such dependence on the machines that it would have no practical choice but to accept all of the machines' decisions. As society and the problems that face it become more and more complex and as machines become more and more intelligent, people will let machines make more and more of their decisions for them, simply because machine-made decisions will bring better results than man-made ones. Eventually a stage may be reached at which the decisions necessary to keep the system running will be so complex that human beings will be incapable of making them intelligently. At that stage the machines will be in effective control. People won't be able to just turn the machines off, because they will be so dependent on them that turning them off would amount to suicide.

"174. On the other hand it is possible that human control over the machines may be retained. In that case the average man may have control over certain private machines of his own, such as his car or his personal computer, but control over large systems of machines will be in the hands of a tiny elite--just as it is today, but with two differences. Due to improved techniques the elite will have greater control over the masses; and because human work will no longer be necessary the masses will be superfluous, a useless burden on the system. If the elite is ruthless they may simply decide to exterminate the mass of humanity. If they are humane they may use propaganda or other psychological or biological techniques to reduce the birth rate until the mass of humanity becomes extinct, leaving the world to the elite. Or, if the elite consist of soft-hearted liberals, they may decide to play the role of good shepherds to the rest of the human race. They will see to it that everyone's physical needs are satisfied, that all children are raised under psychologically hygienic conditions, that everyone has a wholesome hobby to keep him busy, and that anyone who may become dissatisfied undergoes treatment' to cure hisproblem.' Of course, life will be so purposeless that people will have to be biologically or psychologically engineered either to remove their need for the power process or to make them `sublimate' their drive for power into some harmless hobby. These engineered human beings may be happy in such a society, but they most certainly will not be free. They will have been reduced to the status of domestic animals."

Philosopher Bostrom

So far we have heard about losing power and freedom to machines or their controllers. Now we hear about what preventing (or trying to prevent) such losses might look like.

To secure ourselves against civilization-ending new technologies, would we accept the following? Would it work?

Bostrom (2019), pp. 465-466:

"For a picture of what a really intensive level of surveillance could look like, consider the following vignette:

"High-tech Panopticon

"Everybody is fitted with a freedom tag'--a sequent to the more limited wearable surveillance devices familiar today, such as the ankle tag used in several countries as a prison alternative, the bodycams worn by many police forces, the pocket trackers and wristbands that some parents use to keep track of their children, and, of course, the ubiquitous cell phone (which has been characterized asa personal tracking device that can also be used to make calls'). The freedom tag is a slightly more advanced appliance, worn around the neck and bedecked with multidirectional cameras and microphones. Encrypted video and audio is continuously uploaded from the device to the cloud and machine-interpreted in real time. AI algorithms classify the activities of the wearer, his hand movements, nearby objects, and other situational cues. If suspicious activity is detected, the feed is relayed to one of several patriot monitoring stations. These are vast office complexes, staffed 24/7. There, a freedom officer reviews the video feed on several screens and listens to the audio in headphones. The freedom officer then determines an appropriate action, such as contacting the tag-wearer via an audiolink to ask for explanations or to request a better view. The freedom officer can also dispatch an inspector, a police rapid response unit, or a drone to investigate further. In the small fraction of cases where the wearer refuses to desist from the proscribed activity after repeated warnings, an arrest may be made or other suitable penalties imposed. Citizens are not permitted to remove the freedom tag, except while they are in environments that have been outfitted with adequate external sensors (which however includes most indoor environments and motor vehicles). The system offers fairly sophisticated privacy protections, such as automated blurring of intimate body parts, and it provides the option to redact identity-revealing data such as faces and name tags and release it only when the information is needed for an investigation. Both AI-enabled mechanisms and human oversight closely monitor all the actions of the freedom officers to prevent abuse."

_

References

Bostrom, N. (2019). The vulnerable world hypothesis. Global Policy, 10(4), 455-476. Nov. 2019. Citations are from Bostrom's website copy: https://nickbostrom.com/papers/vulnerable.pdf Retrieved 24th Mar. 2020.

Brockman, J. (Ed.) (2019). Possible Minds: Twenty-Five Ways of Looking at AI. Penguin. ISBN: 978-0525557999. Searches: https://www.amazon.com/s?k=978-0525557999 https://www.google.com/search?q=isbn+978-0525557999 https://lccn.loc.gov/2018032888

Horesh, T. (2020). The Fascism this Time: and the Global Future of Democracy. Cosmopolis Press, Kindle ed. ISBN: 0578732939. Searches: https://www.amazon.com/s?k=0578732939 https://www.google.com/search?q=isbn+0578732939

Kaczynski, T. J., & Skrbina, D. (2010). Technological Slavery: The Collected Writings of Theodore J. Kaczynski. Feral House. No ISBN. https://archive.org/details/TechnologicalSlaveryTheCollectedWritingsOfTheodoreJ.KaczynskiA.k.a.TheUnabomber/page/n91/mode/2up Retrieved 11 Jan. 2023.

Kurzweil, R. (1999). The Age of Spiritual Machines: When Computers Exceed Human Intelligence. Penguin Books. ISBN: 0140282025. Searches: https://www.amazon.com/s?k=0140282025 https://www.google.com/search?q=isbn+0140282025 https://lccn.loc.gov/98038804

Retraice (2022/11/13). Re49: China is Not F-ing Around. retraice.com. https://www.retraice.com/segments/re49 Retrieved 15th Nov. 2022.

Russell, B. (1952). The Impact Of Science On Society. George Allen and Unwin Ltd. No ISBN. https://archive.org/details/impactofscienceo0000unse_t0h6 Retrieved 15th, Nov. 2022. Searches: https://www.amazon.com/s?k=The+Impact+Of+Science+On+Society+Bertrand+Russell https://www.google.com/search?q=The+Impact+Of+Science+On+Society+Bertrand+Russell https://lccn.loc.gov/52014878

Wiener, N. (1954). The Human Use Of Human Beings: Cybernetics and Society. Da Capo, 2nd ed. ISBN: 978-0306803208. This 1954 ed. missing `The Voices of Rigidity' chapter of the original 1950 ed. See 1st ed.: https://archive.org/details/humanuseofhumanb00wien/page/n11/mode/2up. See also Brockman (2019) p. xviii. Searches for the 2nd ed.: https://www.amazon.com/s?k=9780306803208 https://www.google.com/search?q=isbn+9780306803208 https://lccn.loc.gov/87037102

Footnotes

^1 The following are excerpts from the 1950 edition, within the later-removed chapter Voices of Rigidity. See References for a hyperlink.

^2 https://en.wikipedia.org/wiki/Fifth_Freedom

^3 Previously quoted, in part, in Re49 (Retraice (2022/11/13)).

^4 Previously quoted in Re49 (Retraice (2022/11/13)).

^5 Also quoted in Kurzweil (1999) pp. 179-180.

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Re113: Uncertainty, Fear and Consent (Technological Danger, Part 1)

retraice.com

Beliefs, and the feelings they cause, determine what chances we take; but possibilities don't care about our beliefs. A prediction about safety, security and freedom; decisions about two problems of life and the problem of death; uncertainty, history, genes and survival machines; technology to control the environment of technology; beliefs and feelings; taking chances; prerequisites for action; imagining possibilities; beliefs that do or don't lead to consent; policing, governance and motivations.

Air date: Wednesday, 11th Jan. 2023, 10:00 PM Eastern/US.

Prediction: freedom is going to decrease

The freedom-security-safety tradeoff will continue to shift toward safety and security.

Over the next 20 years, 2023-2032, you'll continue to be asked, told, and nudged into giving up freedom in exchange for safety (which is about unintentional danger), in addition to security (which is about intentional danger).^1

(Side note: We have no particular leaning, one way or another, about whether this will be a good or bad thing overall. Frame it one way, and we yearn for freedom; frame it another way, and we crave protection from doom.)

For more on this, consider: o Wiener (1954); o Russell (1952); o Dyson (1997), Dyson (2020); o Butler (1863); o Kurzweil (1999); o Kaczynski & Skrbina (2010); o Bostrom (2011), Bostrom (2019).

Decisions: two problems of life and the problem of death

First introduced in Re27 (Retraice (2022/10/23)) and integrated in Re31 (Retraice (2022/10/27)).

Two problems of life:

To change the world?
To change oneself (that part of the world)?

Problem of death:

Dead things rarely become alive, whereas alive things regularly become dead. What to do?

Uncertainty

We just don't know much about the future, but we talk and write within the confines of our memories and instincts.

We know the Earth-5k well via written history, and our bodies `know', via genes, the Earth-2bya, about the time that replication and biology started. But the parts of our bodies that know it (genes, mechanisms shared with other animals), are what would reliably survive, not us. Most of our genes can survive in other survival machines, because we share so much DNA with other creatures.^2

But there is hope in controlling the environment to protect ourselves (vital technology), though we also like to enjoy ourselves (other technology). There is also irony in it, to the extent that technology itself is the force from which we may need to be protected.

Beliefs and feelings

a cure, hope; * no cure, fear; * a spaceship, excitement; * home is the same, longing; * home is not the same, sadness; * she loves me, happiness; * she hates me, misery; * she picks her nose, disgust.

Chances

Even getting out of bed--or not--is somewhat risky: undoubtedly some human somewhere has died by getting out of bed and falling; but people in hospitals have to get out of bed to avoid skin and motor problems.

We do or don't get out of bed based on instincts and beliefs.

Side note: von Mises' three prerequisites for human action:^3

Uneasiness (with the present);
An image (of a desirable future);
The belief (expectation) that action has the power to yield the image. (Side note: technology in the form of AI is becoming more necessary to achieve desirable futures, because enough humans have been picking low-hanging fruit for enough time that most of the fruit is now high-hanging, where we can't reach without AI.)

Possibilities

radically good future because of technology (cure for everything); * radically bad future because of technology (synthetic plague); * radically good future because of humans (doctors invent cure); * radically bad future because of humans (doctors invent synthetic plague).

The important point is to remember the venn: there is a large space of possibilities, within which a small dot is what any individual human can imagine.

If you believe x, do you consent to y?

no one has privacy, privacy invasion; * entity e is not malicious, open interaction with entity e; * VWH (the vulnerable world hypothesis), global police state.

"VWH: If technological development continues then a set of capabilities will at some point be attained that make the devastation of civilization extremely likely, unless civilization sufficiently exits the semi- anarchic default condition."^4

The "the semi-anarchic default condition":

limited capacity for preventive policing;
limited capacity for global governance;
diverse motivations: "There is a wide and recognizably human distribution of motives represented by a large population of actors (at both the individual and state level) - in particular, there are many actors motivated, to a substantial degree, by perceived self-interest (e.g. money, power, status, comfort and convenience) and there are some actors (`the apocalyptic residual') who would act in ways that destroy civilization even at high cost to themselves."^5

_

References

Bostrom, N. (2011). Information Hazards: A Typology of Potential Harms from Knowledge. Review of Contemporary Philosophy, 10, 44-79. Citations are from Bostrom's website copy: https://www.nickbostrom.com/information-hazards.pdf Retrieved 9th Sep. 2020.

Bostrom, N. (2019). The Vulnerable World Hypothesis. Global Policy, 10(4), 455-476. Nov. 2019. Citations are from Bostrom's website copy: https://nickbostrom.com/papers/vulnerable.pdf Retrieved 24th Mar. 2020.

Brockman, J. (Ed.) (2019). Possible Minds: Twenty-Five Ways of Looking at AI. Penguin. ISBN: 978-0525557999. Searches: https://www.amazon.com/s?k=978-0525557999 https://www.google.com/search?q=isbn+978-0525557999 https://lccn.loc.gov/2018032888

Butler, S. (1863). Darwin among the machines. The Press (Canterbury, New Zealand). Reprinted in Butler et al. (1923).

Butler, S., Jones, H., & Bartholomew, A. (1923). The Shrewsbury Edition of the Works of Samuel Butler Vol. 1. J. Cape. No ISBN. https://books.google.com/books?id=B-LQAAAAMAAJ Retrieved 27th Oct. 2020.

Dawkins, R. (2016). The Selfish Gene. Oxford, 40th anniv. ed. ISBN: 978-0198788607. Searches: https://www.amazon.com/s?k=9780198788607 https://www.google.com/search?q=isbn+9780198788607 https://lccn.loc.gov/2016933210

Dyson, G. (2020). Analogia: The Emergence of Technology Beyond Programmable Control. Farrar, Straus and Giroux. ISBN: 978-0374104863. Searches: https://www.amazon.com/s?k=9780374104863 https://www.google.com/search?q=isbn+9780374104863 https://catalog.loc.gov/vwebv/search?searchArg=9780374104863

Dyson, G. B. (1997). Darwin Among The Machines: The Evolution Of Global Intelligence. Basic Books. ISBN: 978-0465031627. Searches: https://www.amazon.com/s?k=978-0465031627 https://www.google.com/search?q=isbn+978-0465031627 https://lccn.loc.gov/2012943208

Kaczynski, T. J., & Skrbina, D. (2010). Technological Slavery: The Collected Writings of Theodore J. Kaczynski. Feral House. No ISBN. https://archive.org/details/TechnologicalSlaveryTheCollectedWritingsOfTheodoreJ.KaczynskiA.k.a.TheUnabomber/page/n91/mode/2up Retrieved 11 Jan. 2023.

Koch, C. G. (2007). The Science of Success. Wiley. ISBN: 978-0470139882. Searches: https://www.amazon.com/s?k=9780470139882 https://www.google.com/search?q=isbn+9780470139882 https://lccn.loc.gov/2007295977

Kurzweil, R. (1999). The Age of Spiritual Machines: When Computers Exceed Human Intelligence. Penguin Books. ISBN: 0140282025. Searches: https://www.amazon.com/s?k=0140282025 https://www.google.com/search?q=isbn+0140282025 https://lccn.loc.gov/98038804

Retraice (2022/10/23). Re27: Now That's a World Model - WM4. retraice.com. https://www.retraice.com/segments/re27 Retrieved 24th Oct. 2022.

Retraice (2022/10/27). Re31: What's Happening That Matters - WM5. retraice.com. https://www.retraice.com/segments/re31 Retrieved 28th Oct. 2022.

Retraice (2022/11/27). Re63: Seventeen Reasons to Learn AI. retraice.com. https://www.retraice.com/segments/re63 Retrieved Monday Nov. 2022.

Russell, B. (1952). The Impact Of Science On Society. George Allen and Unwin Ltd. No ISBN. https://archive.org/details/impactofscienceo0000unse_t0h6 Retrieved 15th, Nov. 2022. Searches: https://www.amazon.com/s?k=The+Impact+Of+Science+On+Society+Bertrand+Russell https://www.google.com/search?q=The+Impact+Of+Science+On+Society+Bertrand+Russell https://lccn.loc.gov/52014878

Schneier, B. (2003). Beyond Fear: Thinking Sensibly About Security in an Uncertain World. Copernicus Books. ISBN: 0387026207. Searches: https://www.amazon.com/s?k=0387026207 https://www.google.com/search?q=isbn+0387026207 https://lccn.loc.gov/2003051488 Similar edition available at: https://archive.org/details/beyondfearthinki00schn_0

von Mises, L. (1949). Human Action: A Treatise on Economics. Ludwig von Mises Institute, 2010 reprint ed. ISBN: 978-1610161459. Searches: https://www.amazon.com/s?k=9781610161459 https://www.google.com/search?q=isbn+9781610161459 https://lccn.loc.gov/50002445

Wiener, N. (1954). The Human Use Of Human Beings: Cybernetics and Society. Da Capo, 2nd ed. ISBN: 978-0306803208. This 1954 ed. missing `The Voices of Rigidity' chapter of the original 1950 ed. See 1st ed.: https://archive.org/details/humanuseofhumanb00wien/page/n11/mode/2up. See also Brockman (2019) p. xviii. Searches for the 2nd ed.: https://www.amazon.com/s?k=9780306803208 https://www.google.com/search?q=isbn+9780306803208 https://lccn.loc.gov/87037102

Footnotes

^1 Schneier (2003) pp. 12, 52.

^2 On creatures as gene (replicator) `survival machines', see Dawkins (2016) pp. 24-25, 30.

^3 von Mises (1949) pp. 13-14. See also Koch (2007) p. 144. See also Retraice (2022/11/27).

^4 Bostrom (2019) p. 457.

^5 Bostrom (2019) pp. 457-458.

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Re112: The Attention Hazard and The Attention (Distraction) Economy

retraice.com

Drawing attention to dangerous information can increase risk, but the attention economy tends to draw attention toward amusement. Information hazards; formats include data, idea, attention, template, signaling' andevocation'; increasing the number of information locations; adversaries, agents, search, heuristics; the dilemma of attention; suppressing secrets; the Streisand effect; the attention economy as elite solution'; Liu'swall facers'.

Air date: Tuesday, 10th Jan. 2023, 10:00 PM Eastern/US.

Attention hazard of information

Bostrom (2011): "Information hazard: A risk that arises from the dissemination or the potential dissemination of (true) information that may cause harm or enable some agent to cause harm."^1

Attention is one format (or `mode') of information transfer:^2

"Attention hazard: The mere drawing of attention to some particularly potent or relevant ideas or data increases risk, even when these ideas or data are already `known'."^3

This increase is because `attention' is physically increasing the number of locations where the hazard data or idea are instantiated.

Adversaries and agents

"Because there are countless avenues for doing harm, an adversary faces a vast search task in finding out which avenue is most likely to achieve his goals. Drawing the adversary's attention to a subset of especially potent avenues can greatly facilitate the search. For example, if we focus our concern and our discourse on the challenge of defending against viral attacks, this may signal to an adversary that viral weapons--as distinct from, say, conventional explosives or chemical weapons--constitute an especially promising domain in which to search for destructive applications. The better we manage to focus our defensive deliberations on our greatest vulnerabilities, the more useful our conclusions may be to a potential adversary."^4

Consider the parallels in Russell & Norvig (2020): * adversarial search and games' (chpt. 5); *intelligent agents' (chpt 2); * `solving problems by searching' (chpt. 3); * drawing attention can facilitate search: heuristics (sections 3.5, 3.6);

The dilemma: We focus on risk, and also lead adversary-agents to our vulnerabilities.

Cf. the `vulnerable world hypothesis'^5 on the policy implications of unrestrained technological innovation given the unknown risk of self-destructing innovators.

"Still, one likes to believe that, on balance, investigations into existential risks and most other risk areas will tend to reduce rather than increase the risks of their subject matter."^6

Secrets and suppression

"Clumsy attempts to suppress discussion often backfire. An adversary who discovers an attempt to conceal an idea may infer that the idea could be of great value. Secrets have a special allure."^7

https://en.wikipedia.org/wiki/Streisand_effect: "[T]he way attempts to hide, remove, or censor information can lead to the unintended consequence of increasing awareness of that information."

The attention (distraction) economy

Might the attention economy, one day or even already, be a solution' (an elite solution) to the attention hazard? Would it work against AI? Or buy us time? What aboutwall facers'?^8

Cf. Re30, Retraice (2022/10/26), on things being done.

_

References

Bostrom, N. (2011). Information Hazards: A Typology of Potential Harms from Knowledge. Review of Contemporary Philosophy, 10, 44-79. Citations are from Bostrom's website copy: https://www.nickbostrom.com/information-hazards.pdf Retrieved 9th Sep. 2020.

Bostrom, N. (2019). The vulnerable world hypothesis. Global Policy, 10(4), 455-476. Nov. 2019. Citations are from Bostrom's website copy: https://nickbostrom.com/papers/vulnerable.pdf Retrieved 24th Mar. 2020.

Liu, C. (2016). The Dark Forest. Tor Books. ISBN: 978-0765386694. Searches: https://www.amazon.com/s?k=9780765386694 https://www.google.com/search?q=isbn+9780765386694 https://lccn.loc.gov/2015016174

Retraice (2022/10/26). Re30: AI Progress and Surrender. retraice.com. https://www.retraice.com/segments/re30 Retrieved 27th Oct. 2022.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Bostrom (2011) p. 2.

^2 The others he distinguishes are: data, idea, template, signaling' andevocation'.

^3 Bostrom (2011) p. 3.

^4 Bostrom (2011) p. 3.

^5 Bostrom (2019).

^6 Bostrom (2011) p. 4.

^7 Bostrom (2011) p. 3.

^8 Liu (2016).

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Re111: AI and the Gorilla Problem

retraice.com

Russell and Norvig say it's natural to worry that AI will destroy us, and that the solution is good design that preserves our control. Our unlucky evolutionary siblings, the gorillas; humans the next gorillas; giving up the benefits of AI; the standard model and the human compatible model; design implications of human compatibility; the difficulty of human preferences.

Air date: Monday, 9th Jan. 2023, 10:00 PM Eastern/US.

The gorilla problem

Added to Re109 notes after live:

"the gorilla problem: about seven million years ago, a now-extinct primate evolved, with one branch leading to gorillas and one to humans. Today, the gorillas are not too happy about the human branch; they have essentially no control over their future. If this is the result of success in creating superhuman AI--that humans cede control over their future--then perhaps we should stop work on AI, and, as a corollary, give up the benefits it might bring. This is the essence of Turing's warning: it is not obvious that we can control machines that are more intelligent than us."^1

We might add that there are worse fates than death and zoos.

Most of the book, they say, reflects the majority of work done in AI to date--within the standard model', i.e. AI systems aregood' when they do what they're told, which is a problem because `telling' preferences is easy to get wrong. (p. 4)

Solution: uncertainty in the purpose (the `human compatible' model^2), which has design implications (p. 34): * chpt. 16: a machine's incentive to allow shut-off follows from uncertainty about the human objective; * chpt. 18: assistance games are the mathematics of humans and machines working together; * chpt. 22: inverse reinforcement learning is how machines can learn about human preferences by observation of their choices; * chpt. 27: problem 1 of N, our choices depend on preferences that are hard to invert; problem 2 of N, preferences vary by individual and over time.

The human problem

But how do we ensure that AI engineers don't use the dangerous standard model? And if AI becomes easier and easier to use, as technology tends to do, how do we ensure that no one uses the standard model? How do we ensure that no one does any particular thing?

The human compatible' model indicates that theartificial flight' version of AI (p. 2), which is what we want, is possible. It does not indicate that it is probable. And even to make it probable would still not make the standard model improbable. Nuclear power plants don't make nuclear weapons' use less probable. This is the more general problem taken up by Bostrom (2011) and Bostrom (2019).

_

References

Bostrom, N. (2011). Information Hazards: A Typology of Potential Harms from Knowledge. Review of Contemporary Philosophy, 10, 44-79. Citations are from Bostrom's website copy: https://www.nickbostrom.com/information-hazards.pdfRetrieved 9th Sep. 2020.

Bostrom, N. (2019). The vulnerable world hypothesis. Global Policy, 10(4), 455-476. Nov. 2019. Citations are from Bostrom's website copy: https://nickbostrom.com/papers/vulnerable.pdfRetrieved 24th Mar. 2020.

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking. ISBN: 978-0525558613. Searches: https://www.amazon.com/s?k=978-0525558613 https://www.google.com/search?q=isbn+978-0525558613 https://lccn.loc.gov/2019029688

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Russell & Norvig (2020) p. 33.

^2 Russell (2019).

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Re110: TikTok for Addicting the World's Kids

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Tristan Harris's analysis of China's TikTok vs. the exported version. Tristan Harris on TikTok; spinach TikTok for Chinese kids, opium for everyone else; the Opium Wars and the `Century of Humiliation'; TikTok content and time limits for Chinese kids; Netflix on the attention economy vs. sleep; Russia and China trying to radicalize U.S. veterans via social media; war and civil war.

Air date: Sunday, 8th Jan. 2023, 10:00 PM Eastern/US.

This is a follow-up to Re109, Retraice (2023/01/07), where we described TitTok as a tool for Chinese spying. It's worse than that.

Tristan Harris is co-founder of the Center for Humane Technology, worked as a design ethicist at Google, and studied computer science at Stanford.^1

Tristan Harris on 60 Minutes, 2022:

"It's almost like [Chinese company Bytedance] recognize[s] that technology [is] influencing kids' development, and [so] they make their domestic version a spinach TikTok, while they ship the opium version to the rest of the world."^2

Cf. Re48, Retraice (2022/11/12), on the Opium Wars and the `century of humiliation [of China]', a Chinese term.

TikTok in China, if you're under 14 years old:^3 * science experiments * museum exhibits * patriotism * educational content * limited to 40min per day * mandatory 5 sec delay now and then * opening and closing hours

Harris on Joe Rogan, 2021

"It's like Xi saw The Social Dilemma [and so enacted changes to protect only China's kids]."

On the attention economy more broadly: "Even Netflix said their biggest competitor is sleep, because they're all competing for attention."^4

In the same episode, Harris says Russia and China try to radicalize U.S. veteran's groups on social media, to increase the likelihood of such tactically trained people joining or starting civil war. Cf. Re17, Retraice (2022/03/07), on both war with China and U.S. civil war.

_

References

Retraice (2022/03/07). Re17: Hypotheses to Eleven. retraice.com. https://www.retraice.com/segments/re17 Retrieved 17th Mar. 2022.

Retraice (2022/11/12). Re48: From Drugs to Mao to Money. retraice.com. https://www.retraice.com/segments/re48 Retrieved 14th Nov. 2022.

Retraice (2023/01/07). Re109: TikTok (app), Tik-Tok (novel), and Low-Power Mode (Day 7, AIMA4e Chpt. 7). retraice.com. https://www.retraice.com/segments/re109 Retrieved 8th Jan. 2023.

Footnotes

^1 https://en.wikipedia.org/wiki/Tristan_Harris.

^2 TikTok in China versus the United States -- 60 Minutes, Nov. 8, 2022. Available on YouTube: https://www.youtube.com/watch?v=0j0xzuh-6rY

^3 Some of these items and the following quotes are from Tristan Harris on Joe Rogan #1736, 2021. Clip available at: What China's Crackdown on Algorithm's Means for the US, Nov. 18, 2021.

^4 https://youtu.be/im4O2sW3FiY?t=210

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Re109: TikTok (app), Tik-Tok (novel), and Low-Power Mode (Day 7, AIMA4e Chpt. 7)

retraice.com

An observation of AI in action (TikTok), a decision (Low-Power Mode), and a coincidence (Tik-Tok). TikTok as addictive spying tool; Tik-Tok, the novel; changes in technology vs. lack of changes in human wants and needs; creeping totalitarianism, illiberty, war, climate change, Artilect War, superintelligence; the gorilla problem; making a living, making a difference; AIMA4e, Retraice, audience; low-power mode.

Air date: Saturday, 7th Jan. 2023, 10:00 PM Eastern/US.

Prediction: default doom

Consider TikTok (the app), built on AI, ultimately controlled by the Chinese Communist Party,^1 on which millions of Americans have been made addicted to pure amusement, and Tik-Tok (the novel), yet another warning about the bleakness of a robot's would-be life, and the robot's power to respond.

It seems the ever-increasing power of technology is not being tracked by any obvious change in human desires.^2 If so, it's reasonable to be pessimistic and expect that worse forms of previous bad things will happen because stronger technology makes them possible:^3 o Creeping totalitarianism, illiberty: See, for example: Strittmatter (2018); Andersen (2020). o Normal war: Add, for example, slaughterbots'^4 to the otherwise familiar current methods of war. o Climate change: The generalized doom scenario is that we can't adapt quickly enough to the changes we're causing, by use of technologies, in the environment (changes that go beyond just average temperatures)--see H6 of the hypotheses in Re17, Retraice (2022/03/07). o Artilect War: Agigadeath' conflict between two human groups who anticipate AI surpassing human abilities. One group is in favor (cosmists), the other opposed (terrans). de Garis (2005). o Superintelligence: Bostrom (2014). I.e. super-human AI with its own purposes, causing what Russell & Norvig (2020) call "the gorilla problem: about seven million years ago, a now-extinct primate evolved, with one branch leading to gorillas and one to humans. Today, the gorillas are not too happy about the human branch; they have essentially no control over their future. If this is the result of success in creating superhuman AI--that humans cede control over their future--then perhaps we should stop work on AI, and, as a corollary, give up the benefits it might bring. This is the essence of Turing's warning: it is not obvious that we can control machines that are more intelligent than us."^5 We might add that there are worse fates than death and zoos.

Preferences: competing goals

making a living; * making a difference--to us, working to decrease the likelihood of the above `doom' scenarios.^6

Retraice was meant to make a living and a difference. It's doing neither, and only has hope of doing one (difference).

Two things are obvious at this point:

Continuing with Russell & Norvig (2020) (daily investing even more time) is more likely to make a difference and a living.
If Retraice has an audience out there, we have no way of finding it--and it's much smaller than we thought it would be.

It also seems clear that completely stopping Retraice is wrong, because we like doing it. And it still has a chance of making a difference, given enough time and luck.

Decision: low-power mode

The new Retraice plan: * Time on AIMA4e: more; * Time on podcast: less (something like changing from daily podcast' to short dailytransmission'); * Money on podcast: less (the equivalent of keeping one light bulb on, the bare minimum in costs and expenses).

__

References

Andersen, R. (2020). The panopticon is already here. The Atlantic. Sep. 2020. https://www.theatlantic.com/magazine/archive/2020/09/china-ai-surveillance/614197/ Retrieved 8th Nov. 2022.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford. First published in 2014. Citations are from the pbk. edition, 2016. ISBN: 978-0198739838. Searches: https://www.amazon.com/s?k=978-0198739838 https://www.google.com/search?q=isbn+978-0198739838 https://lccn.loc.gov/2015956648

Bostrom, N., & Cirkovic, M. M. (Eds.) (2008). Global Catastrophic Risks. Oxford University Press. ISBN: 978-0199606504. Searches: https://www.amazon.com/s?k=978-0199606504 https://www.google.com/search?q=isbn+978-0199606504 https://lccn.loc.gov/2008006539

de Garis, H. (2005). The Artilect War: Cosmists vs. Terrans: A Bitter Controversy Concerning Whether Humanity Should Build Godlike Massively Intelligent Machines. ETC Publications. ISBN: 0882801546. Searches: https://www.amazon.com/s?k=0882801546 https://www.google.com/search?q=isbn+0882801546

Durant, W., & Durant, A. (1968). The Lessons of History. Simon and Schuster. No ISBN. Searches: https://www.amazon.com/s?k=lessons+of+history+durant https://www.google.com/search?q=lessons+of+history+durant https://lccn.loc.gov/68019949

Retraice (2022/03/07). Re17: Hypotheses to Eleven. retraice.com. https://www.retraice.com/segments/re17 Retrieved 17th Mar. 2022.

Retraice (2022/12/31). Re102: AI For What. retraice.com. https://www.retraice.com/segments/re102 Retrieved 1st Jan. 2023.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Simler, K., & Hanson, R. (2018). The Elephant in the Brain: Hidden Motives in Everyday Life. Oxford University Press. ISBN: 9780190495992. Searches: https://www.amazon.com/s?k=9780190495992 https://www.google.com/search?q=isbn+9780190495992 https://lccn.loc.gov/2017004296

Stephens-Davidowitz, S. (2018). Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are. Dey Street Books. ISBN: 978-0062390868. Searches: https://www.amazon.com/s?k=9780062390868 https://www.google.com/search?q=isbn+9780062390868 https://lccn.loc.gov/2017297094

Strittmatter, K. (2018). We Have Been Harmonized: Life in China's Surveillance State. Custom House, revised, updated ed. ISBN: 978-0063027305. Published in Germany, 2018. This paperback edition 2021. Searches: https://www.amazon.com/s?k=9780063027305 https://www.google.com/search?q=isbn+9780063027305 https://lccn.loc.gov/2020288922

Footnotes

^1 "TikTok's Chinese parent company, ByteDance, is required by Chinese law to make the app's data available to the Chinese Communist Party (CCP). From the FBI Director to FCC Commissioners to cybersecurity experts, everyone has made clear the risk of TikTok being used to spy on Americans." Rubio, Gallagher Introduce Bipartisan Legislation to Ban TikTok, Dec. 13th, 2022.

^2 Durant & Durant (1968) p. 95: "Since we have admitted no substantial change in man's nature during historic times, all technological advances will have to be written off as merely new means of achieving old ends--the acquisition of goods, the pursuit of one sex by the other (or by the same), the overcoming of competition, the fighting of wars. One of the discouraging discoveries of our disillusioning century is that science is neutral: it will kill for us as readily as it will heal, and will destroy for us more readily than it can build." Cf. Simler & Hanson (2018), Stephens-Davidowitz (2018).

^3 One or more of these might be "a Great Filter--an evolutionary step that is extremely improbably--somewhere on the line between Earth-like planet and colonizing-in-detectable-ways civilization." Bostrom & Cirkovic (2008) pp. 131-132, citing Hanson (1999), which is probably the same as this (1998): The Great Filter - Are We Almost Past It? Robin Hanson, Sep. 15, 1998.

^4 "The video was released onto YouTube by the Future of Life Institute and Stuart Russell [co-author of Russell & Norvig (2020)]." --https://en.wikipedia.org/wiki/Slaughterbots. The video: https://www.youtube.com/watch?v=9CO6M2HsoIA.

^5 Russell & Norvig (2020) p. 33.

^6 We use this abbreviation of our mission statement: "FindTFtMtFBttPaMtCKaMbTests (find the fundamentals that make the future better than the past and make them common knowledge as measured by tests)." Cf. Retraice (2022/12/31).

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Re108: Contributors and Controllers (Day 6, AIMA4e Chpt. 6)

retraice.com

A subdivision within the players of the computer control game. CSPs and factored vs. atomic representations; war in Re107; Re69's citation date; the many applications of CSPs, and contributors; contributors and controllers in computer control; nation states, sub-states, companies; politicians, spooks, military, police; shareholders, directors, executives; engineers, professors; hackers.

Air date: Friday, 6th Jan. 2023, 10:00 PM Eastern/US.

Notes on CSPs, war in Re107, and Re69's citation

Russell & Norvig (2020) chpt. 6 is about constraint satisfaction problems, which use factored representations of states in problems instead of atomic representations or structured representations.

During yesterday's livestream for Re107 (Retraice (2023/01/05)), I failed to mention the Bobby Fischer quote, "chess is war", and subsequent commentary.^1 It's in the Re107 notes.

Also, until yesterday, the citation of Re69 (Retraice (2022/12/03)) had incorrectly Nov. instead of Dec. as the month. It's fixed. You were wondering about that.

Two categories of players in computer control

The many applications of CSPs mentioned in chpt. 6, and the many people cited in the bibliography section who contributed the ideas and systems and work that made the applications (indeed, our modern world) possible, lead to an idea: ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC A tentative venn of the major types of groups and individuals in computer control. Notice that most groups can be all-one-category or some-in-both; but hackers (black-, white- and grey-hat) can be all-in-either as well as some-in-both. An example of `sub-state' is California, which can exert a lot of influence by the size of its population. ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

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References

Retraice (2022/12/03). Re69: TABLE-DRIVEN-AGENT Part 5 (ECMP and AIMA4e p. 48). retraice.com. https://www.retraice.com/segments/re69 Retrieved 4th Dec. 2022.

Retraice (2023/01/05). Re107: Three Kinds of AI (Day 5, AIMA4e Chpt. 5). retraice.com. https://www.retraice.com/segments/re107 Retrieved 6th Jan. 2023.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Russell & Norvig (2020) p. 168.

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Re107: Three Kinds of AI (Day 5, AIMA4e Chpt. 5)

retraice.com

War, peace and commerce AI in our multi-agent world. Considering multi-agent environments as economies, adversarial games, or merely nondeterministic; commerce, peace, war; Bostrom's capacity building' andstrategic analysis'; adversary arguments; the computer control game; solving for mobilization and war.

Air date: Thursday, 5th Jan. 2023, 10:00 PM Eastern/US.

At least three stances toward other agents

Russell & Norvig (2020) p. 148:

There are at least three stances we can take towards multi-agent environments. The first stance, appropriate when there are a very large number of agents, is to consider them in the aggregate as an economy, allowing us to do things like predict that increasing demand will cause prices to rise, without having to predict the action of any individual agent.

Second, we could consider adversarial agents as just a part of the environment--a part that makes the environment nondeterministic. But if we model the adversaries in the same way that, say, rain sometimes falls and sometimes doesn't, we miss the idea that our adversaries are actively trying to defeat us, whereas the rain supposedly has no such intention.

The third stance is to explicitly model the adversarial agents with the techniques of adversarial game-tree search. That is what this chapter covers.^1

War, peace and commerce AI

Russell & Norvig (2020) p. 168:

Bobby Fischer declared that chess is war,' but chess lacks at least one major characteristic of real wars, namely, partial observability. In thefog of war,' the whereabouts of enemy units is often unknown until revealed by direct contact. As a result, warfare includes the use of scouts and spies to gather information and the use of concealment and bluff to confuse the enemy.

Commerce AI: markets and prices, supply and demand; * Peace AI: Bostrom's^2 capacity building (as long as we model the relevant environment as having one agent, and we aren't doing AOSE^3); * War AI: search changes at the point of an adversary argument'^4 ; now we're into (the results of) Bostrom'sstrategic analysis'.^5

Questions and answers

Laymen's questions that arose (mostly) before Retraice's technical turn' at Retraice (2022/11/20) are starting to get technical answers: * Why does the CC (computer control) game feel like a (game-theoretic) game and war? (Retraice (2022/11/19)) See Russell & Norvig (2020) chpt. 4, p. 136 onadversary arguments'; chpt. 5, p. 168 on `chess is war'. * What do players do? (Retraice (2022/11/16); Retraice (2022/11/18)) Solve. (Retraice (2023/01/03)) * What will war-AI and general mobilization look like? (Retraice (2022/12/03)) Solving, and deploying solutions to, hard game-problem-environments^6 (partially observable, multi-agent, nondeterministic, sequential, dynamic, continuous, unknown). (Russell & Norvig (2020) p. 47; cf. Retraice (2023/01/02).

__

References

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford. First published in 2014. Citations are from the pbk. edition, 2016. ISBN: 978-0198739838. Searches: https://www.amazon.com/s?k=978-0198739838 https://www.google.com/search?q=isbn+978-0198739838 https://lccn.loc.gov/2015956648

Retraice (2022/11/16). Re52: Big Questions About AI. retraice.com. https://www.retraice.com/segments/re52 Retrieved 17th Nov. 2022.

Retraice (2022/11/18). Re54: Implications and Endgames. retraice.com. https://www.retraice.com/segments/re54 Retrieved 19th Nov. 2022.

Retraice (2022/11/19). Re55: The Computer Control Game. retraice.com. https://www.retraice.com/segments/re55 Retrieved 20th Nov. 2022.

Retraice (2022/11/20). Re56: A Valuable Brick: `Artificial Intelligence: A Modern Approach' 4th ed. retraice.com. https://www.retraice.com/segments/re56 Retrieved 21st Nov. 2022.

Retraice (2022/12/03). Re69: TABLE-DRIVEN-AGENT Part 5 (ECMP and AIMA4e p. 48). retraice.com. https://www.retraice.com/segments/re69 Retrieved 4th Dec. 2022.

Retraice (2023/01/02). Re104: Agent Functions, Agent Programs, Task Environments (Day 2, AIMA4e Chpt. 2). retraice.com. https://www.retraice.com/segments/re104 Retrieved 3rd Jan. 2023.

Retraice (2023/01/03). Re105: Solve or Be Solved (Day 3, AIMA4e Chpt. 3). retraice.com. https://www.retraice.com/segments/re105 Retrieved 4th Jan. 2023.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Cf. p. 168 on war', p. 136 onadversary arguments', p. 169. on `evasive moves'.

^2 Bostrom (2014) pp. 317 ff.

^3 Retraice (2022/12/03).

^4 Russell & Norvig (2020) p. 136.

^5 Bostrom (2014) pp. 317-317.

^6 Russell & Norvig (2020) p. 42.

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Re106: Elitism, Culling, Coercion, Adversaries, Strategy (Day 4, AIMA4e Chpt. 4)

retraice.com

Technical terms from loaded words. Evolutionary algorithms and their techniques; sensorless agent problems and coercing environments; adversaries in online search; conditional plans as strategy in complex environments; Vallee, `Major Murphy', science, the price of information, counterespionage and AI.

Air date: Wednesday, 4th Jan. 2023, 10:00 PM Eastern/US.

Elitism and culling in evolutionary algorithms

"There are endless forms of evolutionary algorithms, varying in the following ways:" ... "The makeup of the next generation. This can be just the newly formed offspring, or it can include a few top-scoring parents from the previous generation (a practice called elitism, which guarantees that overall fitness will never decrease over time). The practice of culling, in which all individuals below a given threshold are discarded, can lead to a speedup (Baum et al., 1995)."^1

Also mentioned during the livestream: Dawkins (2016)

Coercion in sensorless (conformant) problems

"Consider a sensorless version of the (deterministic) vacuum world. Assume that the agent knows the geography of its world, but not its own location or the distribution of dirt. In that case, its initial belief state is {1,2,3,4,5,6,7,8} (see Figure 4.9). Now, if the agent moves Right it will be in one of the states {2,4,6,8}--the agent has gained information without perceiving anything! After [Right,Suck] the agent will always end up in one of the states {4,8}. Finally, after [Right,Suck,Left,Suck] the agent is guaranteed to reach the goal state 7, no matter what the start state. We say that the agent can coerce the world into state 7."^2

Also mentioned: http://aima.cs.berkeley.edu/figures.pdf

Adversaries and dead ends in online search

"Online explorers are vulnerable to dead ends: states from which no goal state is reachable. If the agent doesn't know what each action does, it might execute the `jump into bottomless pit' action, and thus never reach the goal. In general, no algorithm can avoid dead ends in all state spaces. Consider the two dead-end state spaces in Figure 4.20(a). An online search algorithm that has visited states S and A cannot tell if it is in the top state or the bottom one; the two look identical based on what the agent has seen. Therefore, there is no way it could know how to choose the correct action in both state spaces. This is an example of an adversary argument--we can imagine an adversary constructing the state space while the agent explores it and putting the goals and dead ends wherever it chooses, as in Figure 4.20(b)."^3

Also mentioned: http://aima.cs.berkeley.edu/figures.pdf

Strategy in partially observable and nondeterministic environments

"In partially observable and nondeterministic environments, the solution to a problem is no longer a sequence, but rather a conditional plan (sometimes called a contingency plan or a strategy) that specifies what to do depending on what percepts agent [sic] receives while executing the plan."^4

Also mentioned: Freedman (2013)

AI can handle what science can't(?)

Vallee's Major Murphy' makes the point that science can't handle investigating adversarial intelligent agents (e.g. aliens); only counterespionage (spies) can. He also argues that science has no concept of theprice' of information--Hitler had 95% of the information about the D-Day invasion, but the missing 5% was more valuable than the 95%.^5

Maybe strategic intelligence (espionage, counterespionage and covert action^6) can handle adversaries, but so can artificial intelligence.

_

References

Dawkins, R. (2016). The Selfish Gene. Oxford, 40th anniv. ed. ISBN: 978-0198788607. Searches: https://www.amazon.com/s?k=9780198788607 https://www.google.com/search?q=isbn+9780198788607 https://lccn.loc.gov/2016933210

Freedman, L. (2013). Strategy: A History. Oxford University Press. ISBN: 978-0190229238. Searches: https://www.amazon.com/s?k=9780190229238 https://www.google.com/search?q=isbn+9780190229238 https://lccn.loc.gov/2013011944

Retraice (2020/09/07). Re1: Three Kinds of Intelligence. retraice.com. https://www.retraice.com/segments/re1 Retrieved 22nd Sep. 2020.

Retraice (2022/10/31). Re36: Notes on Conspiracy. retraice.com. https://www.retraice.com/segments/re36 Retrieved 4th Nov. 2022.

Retraice (2022/11/17). Re53: Big Questions About Strategic Intelligence. retraice.com. https://www.retraice.com/segments/re53 Retrieved 18th Nov. 2022.

Retraice (2022/12/12). Re79: Recap of Strategic Intelligence (Re1-Re5). retraice.com. https://www.retraice.com/segments/re79 Retrieved 13th Dec. 2022.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Vallee, J. (1979). Messengers of Deception: UFO Contacts and Cults. And/Or Press. ISBN: 0915904381. Different edition and searches: https://archive.org/details/MessengersOfDeceptionUFOContactsAndCultsJacquesValle1979/mode/2up https://www.amazon.com/s?k=0915904381 https://www.google.com/search?q=isbn+0915904381 https://catalog.loc.gov/vwebv/search?searchArg=0915904381

Footnotes

^1 Russell & Norvig (2020) p. 116.

^2 Russell & Norvig (2020) p. 126.

^3 Russell & Norvig (2020) pp. 135-136.

^4 Russell & Norvig (2020) p. 122.

^5 Vallee (1979) pp. 66 ff. See also, e.g.: Retraice (2020/09/07); Retraice (2022/10/31); Retraice (2022/11/17).

^6 Retraice (2022/12/12).

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Re105: Solve or Be Solved (Day 3, AIMA4e Chpt. 3)

retraice.com

The mechanical advantage of those who control AI engineers. The agent-environment boundary; environments to be solved by agents; AI-CS tools recent, vast; mechanical advantage in physics and AI; math vs. AI-CS; the enormous problem-solving advantage of those who control AI engineers.

Air date: Tuesday, 3rd Jan. 2023, 10:00 PM Eastern/US.

Agent, environment arbitrary

The purpose of distinguishing is to analyze systems. (Russell & Norvig (2020) Chpt. 2, p. 38)

Task environment is problem, agent is solution

Chpt. 2, p. 42.

A recent, vast array of tools

Chpt 3: search, heuristics, automated heuristics, mostly developed between the 1960s and 1980s.

Mechanical advantage

Inventing heuristics mechanically:

"We have seen that both h (misplaced tiles) and h (Manhattan distance) are fairly good heuristics for the 8-puzzle and that h is better. How might one have come up with h? Is it possible for a computer to invent such a heuristic mechanically?"^1

Yes: relaxation, pattern databases, landmarks, (machine) learning.

Cf. mechanical advantage (force ratio) in physics:"The ratio of the output force (load) of a machine to the input force (effort)."^2

The mechanical advantage does not necessarily stay with the AI engineers (nor the mechanical and other engineers); it transfers to those who control them, those who understand how to control humans.^3

It's not like math

Math doesn't have agents. Math solves problems; AI-CS solves solving.

Solve or be solved

AI-literate computer controllers (AI engineers and their controllers) have an enormous problem-solving advantage, i.e. environment-changing advantage. Their utility functions, beliefs, desires, etc. will benefit. Other agents (outsiders) are part of the environment they'll change.^4

What does a player' inthe computer control game' do? Solve. Change environments. What's new is the scale of the mechanical advantage of AI engineering.

_

References

Rennie, R., & Law, J. (2019). A Dictionary of Physics. OUP Oxford, Kindle, 8th ed. ISBN: 978-0192554611. Searches: https://www.amazon.com/s?k=9780192554611 https://www.google.com/search?q=isbn+9780192554611 https://lccn.loc.gov/2018968522

Retraice (2022/10/28). Re33: Outsiders, Power and Waste. retraice.com. https://www.retraice.com/segments/re33 Retrieved 2nd Nov. 2022.

Retraice (2022/11/07). Re43: The Midterms -- Part 1. retraice.com. https://www.retraice.com/segments/re43 Retrieved 9th Nov. 2022.

Retraice (2022/11/16). Re52: Big Questions About AI. retraice.com. https://www.retraice.com/segments/re52 Retrieved 17th Nov. 2022.

Retraice (2022/12/31). Re102: AI For What. retraice.com. https://www.retraice.com/segments/re102 Retrieved 1st Jan. 2023.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Russell & Norvig (2020) p. 99.

^2 Rennie & Law (2019).

^3 Retraice (2022/10/28); Retraice (2022/11/07); Retraice (2022/11/16); Retraice (2022/12/31).

^4 Cf. Rushkoff's Program or Be Programmed: Ten Commands for a Digital Age (2011).

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Re104: Agent Functions, Agent Programs, Task Environments (Day 2, AIMA4e Chpt. 2)

retraice.com

Correcting the TABLE-DRIVEN-AGENT series and modeling agents and environments. Agent functions (math) and agent programs (code); task environments as problems, agents as solutions; labeling artifacts as agents or not; the most interesting artifacts; the PEAS-OADESDU model of task environments; locating the performance measure; utility functions; software agents and their sensors and actuators; the relevant parts of the universe; AI literacy (autoracy?) vs. literacy, numeracy, coding literacy (coderacy?).

Air date: Monday, 2nd Jan. 2023, 2:00 PM Eastern/US.

Correction to Re65-Re69, Re77 (Table-Driven-Agent series): agent program vs. agent function

CORRECTION: The agent function' is notthe smart part' of the agent program. "The agent function is an abstract mathematical description.... that maps any percept sequence to an action"; "the agent program is a concrete implementation [of the agent function], running within some physical system." (100!black!//Russell & Norvig (2020) pp. 37-37) Function: math. Program: code. [Correction at Jan 2nd, 2023.]

Agents, task environments and their environments

Task environments are the "problems", agents are the "solutions".^1 Labeling entities as `agents' (as opposed to artifacts) is "a tool for analyzing systems, not an absolute characterization that divides the world into agents and non-agents.... AI operates at ...the most interesting end of the spectrum, where the artifacts have significant computational resources and the task environments require nontrivial decision making."^2 ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

Performance measure;^3
Environment of the task environment:

(a) Observability: fully, partially;

(b) Agentness: single, multi;

(c) Determinism: deterministic (current state plus action determines next state), stochastic (explicit probability numbers), nondeterministic (possibilities listed but no probability numbers);

(d) Episodism: sequential, episodic;

(e) Staticity: static, semi-static, dynamic;

(f) Discreteness: discrete, continuous;

(g) Unknownness: known, unknown (the agent's or designer's knowledge, arguably itself a part of the environment,^4 of the `laws of physics' of the environment).

Actuators;
Sensors.

The sensory inputs of software agents are file contents, network packets and human input via mouse, keyboard, etc. Software agents act on the environment by writing files, sending packets, and displaying information or making sounds.^5

The `environment' can in principle be the whole universe, but in practice its only "the part that affects what the agent perceives and that is affected by the agent's actions."^6

AI is a new kind of literacy

Our descendents, if we're lucky enough to have them, will think about the world in AI terms (PEAS and OADESDU descriptions of environments and agents, formalization of problems, agent functions and agent programs) just as we think about the world in letter (a, b, c), number (0, 1, 2) and code (function, variable, object) terms today.

_

References

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Russell & Norvig (2020) pp. 42-47.

^2 Russell & Norvig (2020) p. 38.

^3 On the best location to `draw' the performance measure, Russell & Norvig (2020) p. 54 say this: "We have already seen that a performance measure assigns a score to any given sequence of environment states, so it can easily distinguish between more and less desirable ways of getting to the taxi's destination. An agent's utility function is essentially an internalization of the performance measure. Provided that the internal utility function and the external performance measure are in agreement, an agent that chooses actions to maximize its utility will be rational according to the external performance measure."

^4 Cf. Russell & Norvig (2020) p. 46.

^5 Russell & Norvig (2020) p. 36.

^6 Russell & Norvig (2020) p. 36.

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Re103: Dimensions of Intelligent Agents (Day 1, AIMA4e Chpt. 1)

retraice.com

Adding fitness and truth to AIMA's dimensions, and considering artificial flight'. Deciding vs. discovering what intelligence and AI are; Vico on knowledge and engineering; the spectrum view and the category view of intelligent agents;artificial flight', birds and intelligence.

Air date: Sunday, 1st Jan. 2023, 10:00 PM Eastern/US.

Amendments and corrections: Re102

Kurzweil doesn't seem to ever have said the Singularity will happen in 2042, as I mistakenly recalled. See Retraice (2022/12/31) for details. Putting something on the calendar is still worth doing, so why not 2042?

On expert predictions found lacking, see Russell & Norvig (2020) p. 28 on Tetlock (2017).

Deciding vs. discovering what AI is

Russell & Norvig (2020) (p. 1) describe the contents of Chapter 1 as, in part, being about deciding what AI is. Is this a subtle error? Does it have effects on our understanding? Shouldn't we be at least as focused on discovering what AI is, or at least what intelligence is? It highlights a core difficulty in AI anticipated by the Italian philosopher Giambattista Vico (1778-1744): "one is certain of only what one builds"^1 or "the true and the made are convertible"^2

Agent intelligence with respect to truth and fitness ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC Agents might be in a space, along three measures of degree, or in one of eight categories. Based on the Russell & Norvig (2020) categorization (pp. 1-5) and Hoffman (2019). On our RTFM' model of thegood', see Retraice (2022/10/24). ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

`Artificial flight'

Russell & Norvig (2020) argue that `the Turing test approach' to AI is inadequate by analogy: We didn't succeed in building planes until we gave up imitating birds.

What is the connection to AI safety? Imagine a world with `artificial birds' flying around. What would make them concerning? Their intelligence, and attendant motivations and capabilities. The dangers of accidentally loosing artificial birds would be the same as those of artificial intelligence: the intelligence and agency. We want Star Wars, not the Matrix; we want R2-D2, not Agent Smith.

On swimming and submarines, see Retraice (2022/11/23), Retraice (2022/12/26). On Cornelis Drebbel, who invented the thermostat and the submarine, see Russell & Norvig (2020) p. 16. On Dennett and colleagues' vs.tools', see Retraice (2022/11/16) and Dennett (2019).

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References

Brockman, J. (Ed.) (2019). Possible Minds: Twenty-Five Ways of Looking at AI. Penguin. ISBN: 978-0525557999. Searches: https://www.amazon.com/s?k=978-0525557999 https://www.google.com/search?q=isbn+978-0525557999 https://lccn.loc.gov/2018032888

Dennett, D. C. (2019). What can we do? (pp. 41-53). In Brockman (2019).

Genesereth, M. R., & Nilsson, N. J. (1987). Logical Foundations of Artificial Intelligence. Morgan Kaufmann. ISBN: 0934613311. Searches: https://www.amazon.com/s?k=0934613311 https://www.google.com/search?q=isbn+0934613311 https://lccn.loc.gov/87005461

Hoffman, D. (2019). The Case Against Reality: Why Evolution Hid the Truth from Our Eyes. W. W. Norton & Company. ISBN: 978-0393254693. Searches: https://www.amazon.com/s?k=978-0393254693 https://www.google.com/search?q=isbn+978-0393254693 https://lccn.loc.gov/2019006962

Honderich, T. (Ed.) (2005). Oxford Companion to Philosophy. Oxford University Press, 2nd ed. ISBN: 0199264791. Searches: https://www.amazon.com/s?k=0199264791 https://www.google.com/search?q=isbn+0199264791 https://lccn.loc.gov/2005275452

Retraice (2022/10/24). Re28: What's Good? RTFM. retraice.com. https://www.retraice.com/segments/re28 Retrieved 25th Oct. 2022.

Retraice (2022/11/16). Re52: Big Questions About AI. retraice.com. https://www.retraice.com/segments/re52 Retrieved 17th Nov. 2022.

Retraice (2022/11/23). Re59: Learning, Interacting, Conclusions (AIMA4e chpts. 19-28). retraice.com. https://www.retraice.com/segments/re59 Retrieved 24th Nov. 2022.

Retraice (2022/12/26). Re96: News of ChatGPT, Part 1. retraice.com. https://www.retraice.com/segments/re96 Retrieved 27th Dec. 2022.

Retraice (2022/12/31). Re102: AI For What. retraice.com. https://www.retraice.com/segments/re102 Retrieved 1st Jan. 2023.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Genesereth & Nilsson (1987) p. 1

^2 Honderich (2005) p. 945.

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Re102: AI For What

retraice.com

Well-formed goals in the computer control game. Kurzweil and all that; our countdown clocks to 2029 and 2042; machines can learn whatever we can, probably; systems and papers to study; reasons to learn or pay attention to AI; the purpose of Retraice; a more formal represenation of the player state change problem; moonshots for 2023 and 2024.

Air date: Saturday, 31st Dec. 2022, 2:00 PM Eastern/US.

Kurzweil, 2029 and 2042

Kurzweil: "With both the hardware and software needed to fully emulate human intelligence, we can expect computers to pass the Turing test, indicating intelligence indistinguishable from that of biological humans, by the end of the 2020s."^1 He also predicts the `singularity': "I set the date for the Singularity--representing a profound and disruptive transformation in human capability--as 2045."^2 I recall hearing him, some years later, update that to 2042, but I can't find a citation of it. The closest thing is this: "As this book is being written, the country is debating changing the Social Security program based on projections that go out to 2042, approximately the time frame I've estimated for the Singularity."^3

Also mentioned during the livestream: o https://lexfridman.com/ray-kurzweil/; o Kurzweil (1990); o Kurzweil (1999); o Kurzweil (2005); o Sam Harris calling Kurzweil a carnival barker: Joe Rogan Experience #804, at about 11:00.; o On Vernor Vinge, see Russell & Norvig (2020) p. 12; o Bill Joy on Kurzweil: Why the Future Doesn't Need Us, Wired, Apr 1st, 2000; o Kevin Kelly on Kurzweil: Kevin Kelly on Soft Singularity and Inevitable Tech Advances, Newsweek 6/2/16; o Yudkowsky on Kurzweil: Yudkowsky (2013) pp. 19-20;

More thoughts on learning AI

AIMA4e p. 652: If you can learn it, it can be learned.

Running out of time: Robots coming, death coming.

A `player' can put to good use: DALL·E 2, AlphaZero, AlphaCode, GPTs, etc.

Papers to study: well-formed problems and their solutions: * Ideas that Changed the Future (book) * Hinton paper * AlphaGo (and movie?) * AlphaZero * AlphaFold * Attention is all you need * Reward is enough * GPT-2 * GPT-3 * InstructGPT

What are the purposes of learning AI?

Re63, Retraice (2022/11/27).

What is the purpose of Retraice?

FindTFtMtFBttPaMtCKaMbTests (find the fundamentals that make the future better than the past and make them common knowledge as measured by tests); * Sell value to customers;^4 * Build an economically viable org for overcoming humanity's weaknesses in trustworthy public communication at scale (think Lippmann (1920), e.g. pp. 38-39), with style; + Being a player in the CC game is a prereq; + Expert in 5 years: 20k hrs, 11hrs per day.

What is our problem, to which math, code and AIMA4e are part of the solution?

Our goal is to change state from outsider-non-player to player in the computer control game.^5

With properly applied abstraction (AIMA4e p. 66), see (e.g.) Re90 p. 2 for problem formalization... * state space: players in the computer control game, non-players; * initial state: non-player; * goal state: player; * actions: lists of actions available at each state; Are actions based power' phenomena?^6 * transition model: action1 on state1 yields state2; * cost function: cost of applying action1 to state1; also consider evaluation function, heuristic, and objective function (seepurpose' above).

auto

Moonshots

AI degree in 2023? E. O. Wilson: "The real problem of humanity is the following: we have Paleolithic emotions, medieval institutions, and god-like technology."^7 * AI doct-or in 2024? * Industry needs (Hacker News jobs, LinkedIn ...); * University curricula (Harvard, Stanford, MIT, CMU, Ox-bridge, UofM); * Project orientation (Retraice needs; AIMA exercises).

_

References

Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. ISBN: 978-0300209570. Searches: https://www.amazon.com/s?k=9780300209570 https://www.google.com/search?q=isbn+9780300209570 https://lccn.loc.gov/2020947842

Ferguson, N. (2017). The Square and the Tower: Networks and Power, from the Freemasons to Facebook. Penguin. ISBN: 978-0735222915. Searches: https://www.amazon.com/s?k=978-0735222915 https://www.google.com/search?q=isbn+978-0735222915 https://lccn.loc.gov/2018418429

Kurzweil, R. (1990). The Age of Intelligent Machines. MIT Press. ISBN: 0262111217. Searches: https://www.amazon.com/s?k=0262111217 https://www.google.com/search?q=isbn+0262111217 https://lccn.loc.gov/89013606

Kurzweil, R. (1999). The Age of Spiritual Machines: When Computers Exceed Human Intelligence. Penguin Books. ISBN: 0140282025. Searches: https://www.amazon.com/s?k=0140282025 https://www.google.com/search?q=isbn+0140282025 https://lccn.loc.gov/98038804

Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Penguin. ISBN: 978-0143037880. Searches: https://www.amazon.com/s?k=978-0143037880 https://www.google.com/search?q=isbn+978-0143037880 https://lccn.loc.gov/2004061231

Lee, K.-F. (2018). AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt. ISBN: 978-1328546395. Searches: https://www.amazon.com/s?k=9781328546395 https://www.google.com/search?q=isbn+9781328546395 https://catalog.loc.gov/vwebv/search?searchArg=9781328546395

Lippmann, W. (1920). Liberty and the News. Harcourt, Brace and Howe (Leopold Reprint). No ISBN. eBook and searches: https://books.google.com/books?id=Df-SzcLRcAICRetrieved 24th Feb. 2022. https://www.amazon.com/s?k=Liberty+and+the+News+Lippmann https://www.google.com/search?q=liberty+and+the+news+lippmann https://lccn.loc.gov/20004814

Retraice (2022/10/28). Re33: Outsiders, Power and Waste. retraice.com. https://www.retraice.com/segments/re33Retrieved 2nd Nov. 2022.

Retraice (2022/11/02). Re38: Follow up to `Re33: Outsiders, Power and Waste'. retraice.com. https://www.retraice.com/segments/re38Retrieved 5th Nov. 2022.

Retraice (2022/11/07). Re43: The Midterms -- Part 1. retraice.com. https://www.retraice.com/segments/re43Retrieved 9th Nov. 2022.

Retraice (2022/11/16). Re52: Big Questions About AI. retraice.com. https://www.retraice.com/segments/re52Retrieved 17th Nov. 2022.

Retraice (2022/11/17). Re53: Big Questions About Strategic Intelligence. retraice.com. https://www.retraice.com/segments/re53Retrieved 18th Nov. 2022.

Retraice (2022/11/18). Re54: Implications and Endgames. retraice.com. https://www.retraice.com/segments/re54Retrieved 19th Nov. 2022.

Retraice (2022/11/19). Re55: The Computer Control Game. retraice.com. https://www.retraice.com/segments/re55Retrieved 20th Nov. 2022.

Retraice (2022/11/20). Re56: A Valuable Brick: `Artificial Intelligence: A Modern Approach' 4th ed. retraice.com. https://www.retraice.com/segments/re56Retrieved 21st Nov. 2022.

Retraice (2022/11/27). Re63: Seventeen Reasons to Learn AI. retraice.com. https://www.retraice.com/segments/re63Retrieved Monday Nov. 2022.

Russell, B. (1938). Power: A New Social Analysis. Routledge. ISBN: 0415094569. First published in 1938. This ed. 1993. Searches: https://www.amazon.com/s?k=0415094569 https://www.google.com/search?q=isbn+0415094569 https://lccn.loc.gov/38027828

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Wrong, D. H. (1988). Power: Its Forms, Bases, and Uses. Univ of Chicago Press. ISBN: 0226910679. Searches: https://www.amazon.com/s?k=0226910679 https://www.google.com/search?q=isbn+0226910679 https://lccn.loc.gov/88021594

Yudkowsky, E. (2013). Intelligence explosion microeconomics. Machine Intelligence Research Institute. Technical report 2013-1. https://intelligence.org/files/IEM.pdfRetrieved ca. 9th Dec. 2018.

Footnotes

^1 Kurzweil (2005) p. 25. He mentions 2029 multiple times in the book after that sentence.

^2 Kurzweil (2005) p. 136.

^3 Kurzweil (2005) p 97.

^4 Mentioned off-hand: Modern Money Mechanics, published between at least 1968 and 1994 by the Federal Reserve Bank of Chicago.

^5 Retraice (2022/11/16); Retraice (2022/11/17); Retraice (2022/11/18); Retraice (2022/11/19); Retraice (2022/11/20).

^6 See Retraice (2022/11/07) and Retraice (2022/11/16) on the Banzhaf power index. Other relevant sources include: Retraice (2022/10/28); Retraice (2022/11/02); Crawford (2021); Lee (2018); Ferguson (2017); Wrong (1988); Russell (1938).

^7 oxfordreference.com citing a 2009 debate.

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Re101: News of ChatGPT, Part 4

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How ChatGPT crosses with the hypotheses. More attempts to describe ChatGPT; maximizing its reward; intended uses; what it might tend to do, overall; new means, old ends; what it is thinking'; dialogue as key; improving human search-and-find space and medical knowledge and information; OpenAI's whack-a-mole burden; the Chinese, Americans, left-leaners and right-leaners now to be attacked with mis- and dis-information; strategic search-and-find information now easier to get; our collective environment now contains new agents, the ChatGPTs; the technologicalfuture' containing ChatGPT is now the present' andpast'; smart people will benefit more; bad guys; wealth; wildcards; hackers; control of humans by words; change of the environment by ChatGPTs; motivation to control.

Air date: Friday, 30th Dec. 2022, 4:00 PM Eastern/US.

What is it again?

It's GPT-3 (~InstructGPT) adapted for dialogue, plus a reward model, connected by gradient descent. It tries to maximize its reward. It will change its environment (the only way it can, by producing output) to do so. It' is tens of thousands of containers^1 under the control of OpenAI. It is intend it to do Q&A, captioning, translation, summarization,generation', and the like.

ChatGPT and the world

ChatGPT might tend to: o make us more predictable, because we're the new environment (population thought control?);^2 o increase language group isolation, unless language translation works well and is used; o entrench language interpretation errors; o tell us what we want to hear, bullshit us;^3 o generally amplifying the good and bad of humanity, as all technology seems to do.^4

What is it thinking'? Attempts to put it into words: * "If the universe were webpages and it ended in 2022, what would be the most likely response to this call? Except arbitrarily remove all kinds of unpleasant andincorrect' stuff because OpenAI added that to the universe." * "What words, in what order, did the internet-publishing humans and machines of ca. 1980-2022 generate? Imitate that." * "How would the Internet-2022 hyperobject grow, if given this input?"

But what will this change about humanity going forward?

GPT-3 was less important because it wasn't dialogue.

ChatGPT and H1-H12

On H1-H12, see Retraice (2022/03/07) and Retraice (2022/10/19).

H1 Space: `Humans are now technologically capable of living in space.'

Now, it's easier to learn (search-and-find) prerequisite space travel knowledge and information.

H2 Technology: `Human technology risks are growing faster than their mitigation.'

Now, OpenAI has to constantly whack-a-mole nefarious users.

H3 Death: `Human lifespan is being prolonged by new technologies.'

Now, it's easier to learn (search-and-find) prerequisite medical and technology knowledge and information.

H4 China: `The U.S. is no longer the only superpower; war is likely.'

Now, both populations will be attacked by dis- and mis-information.

Now, it's easier to learn about (search-and-find) strategy, what's going on, conflict, strengths and weaknesses, etc.

H5 Civil War: `The U.S. seems vulnerable to a civil war this decade.'

Now, both populations will be attacked by dis- and mis-information (same as H4).

H6 Environments: `Humans can change environments faster than they can adapt.'

Now, the environment of every individual human will have in it agents that have read the Internet and are trying to maximize a reward implemented by OpenAI staff.

H7 Betterment: `Some things make the future better than the past.'

Now, the future' that has such an agent is here, instead of still beingthe future'.^5

H8 Intelligence: `There are intelligence differences.'

Now, those who can learn to use ChatGPT will have a new advantage over those who can't.

H9 Darkness: `There is a pervasive darkness in humans, even amongst the good guys.'

Now, bad guys have ChatGPTs.

H10 Wealth: `The current trend toward concentration of wealth is making human life worse.'

Now, the controllers, users and owners of ChatGPT are creating value, likely to be captured as money, wealth and power.

Now, those who have the resources to build and run a massive system like ChatGPT, will have a strong incentive to do so.

H11 Wildcards: `New technologies, discoveries and deception regularly cause historic changes.'

Now, unexpected uses and interactions with a very good talking program will happen. Deception seems particularly relevant to ChatGPT.

H12 Computers: `Some humans now control others better, but machinery could take control.'

Now, whenever and wherever a human can be controlled, to any degree, by words, a very good talking program might be in the loop.

Now, those who have the resources to build and run a massive system like ChatGPT, will have a strong incentive to do so (same as H10).

Now, hackers have ChatGPTs (not just the API or UX, but via source that cannot be secured).

Now, whenever and wherever dialogue can increase knowledge about a person, a very good talking program might be used to do so.

Now, if a system like ChatGPT has the subgoal of self-preservation in pursuit of maximizing its reward, it will use its output to pursue that subgoal.

Now, if a person with access to an instance of ChatGPT wants to modify it to be different (more harmful, more autonomous, more' orless' anything, with' something,without' something, with'motivation to control' to replace `maximize reward', whatever that looks like), that person can do so, provided they are technically capable or control those who are, i.e. they are players in the computer control game.

_

References

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford. First published in 2014. Citations are from the pbk. edition, 2016. ISBN: 978-0198739838. Searches: https://www.amazon.com/s?k=978-0198739838 https://www.google.com/search?q=isbn+978-0198739838 https://lccn.loc.gov/2015956648

Durant, W., & Durant, A. (1968). The Lessons of History. Simon and Schuster. No ISBN. Searches: https://www.amazon.com/s?k=lessons+of+history+durant https://www.google.com/search?q=lessons+of+history+durant https://lccn.loc.gov/68019949

Frankfurt, H. G. (1988). The Importance of What We Care About. Cambridge. ISBN: 978-0521336116. Searches: https://www.amazon.com/s?k=978-0521336116 https://www.google.com/search?q=isbn+978-0521336116 https://lccn.loc.gov/87026941

Retraice (2022/03/07). Re17: Hypotheses to Eleven. retraice.com. https://www.retraice.com/segments/re17Retrieved 17th Mar. 2022.

Retraice (2022/10/19). Re22: Computer Control. retraice.com. https://www.retraice.com/segments/re22Retrieved 19th Oct. 2022.

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking. ISBN: 978-0525558613. Searches: https://www.amazon.com/s?k=978-0525558613 https://www.google.com/search?q=isbn+978-0525558613 https://lccn.loc.gov/2019029688

Simler, K., & Hanson, R. (2018). The Elephant in the Brain: Hidden Motives in Everyday Life. Oxford University Press. ISBN: 9780190495992. Searches: https://www.amazon.com/s?k=9780190495992 https://www.google.com/search?q=isbn+9780190495992 https://lccn.loc.gov/2017004296

Footnotes

^1 A guess, based on https://openai.com/blog/scaling-kubernetes-to-7500-nodes/

^2 Russell (2019) pp. 8-9.

^3 Simler & Hanson (2018); Frankfurt (1988), `On Bullshit', chpt. 10.

^4 Durant & Durant (1968) p. 95: "Since we have admitted no substantial change in man's nature during historic times, all technological advances will have to be written off as merely new means of achieving old ends--the acquisition of goods, the pursuit of one sex by the other (or by the same), the overcoming of competition, the fighting of wars. One of the discouraging discoveries of our disillusioning century is that science is neutral: it will kill for us as readily as it will heal, and will destroy for us more readily than it can build." We might take hope that, while science and engineering are neutral, scientists and engineers are not.

^5 Cf. Bostrom (2014) p. 283 ff. on order of arrival' and p. 315 on importance of thetemporal transport' of discovery.

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Re100: News of ChatGPT, Part 3

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An interpretation of ChatGPT's architecture. Language models, question answering, machine translation, captioning, summarization; formal vs. natural language, grammar and syntax; conditional probabilities of strings given strings; parameters; deep neural networks; ChatGPT's three steps; supervised policy, reward model, optimized policy using PPO to choose step size in gradient descent.

Air date: Thursday, 29th Dec. 2022, 10:00 PM Eastern/US.

An attempt to explain language models

Language models aim to solve: question answering, machine translation, reading comprehension [captioning?], and summarization.^1 They're an attempt to overcome the grammar and syntax problem of natural languages by assigning probability to strings (?... by calculating conditional probabilities ...?), i.e. whether a string is more or less likely to be said or written, in response to a given string, based on previous observations of the `environment' or corpora or language.^2

Deep Neural networks are data structures, layers of many adjustable input-output functions. Parameters summarize the training data.^3

An attempt to explain ChatGPT ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

In supervised learning, an agent "observes input-output pairs and learns a function that maps from input to output"; in unsupervised learning, an agent "learns the patterns in the input without any explicit feedback"; in reinforcement learning, an agent "learns from a series of reinforcements: rewards and punishments."^4

Proximal Policy Optimization (PPO) seems to be about choosing step size in gradient descent.^5

_

References

Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for Machine Learning. Cambridge University Press. ISBN: 978-1108455145. https://mml-book.github.io/ Searches: https://www.amazon.com/s?k=9781108455145 https://www.google.com/search?q=isbn+9781108455145 https://lccn.loc.gov/2019040762

Retraice (2022/12/10). Re76: Gradients and Partial Derivatives Part 7 (AIMA4e pp. 119-122). retraice.com. https://www.retraice.com/segments/re76 Retrieved 11th Dec. 2022.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 https://openai.com/blog/better-language-models/ p. 1 of PDF write-up Language Models are Unsupervised Multitask Learners.

^2 https://openai.com/blog/better-language-models/ p. 2 of PDF write-up Language Models are Unsupervised Multitask Learners.; AIMA4e pp. 824-826.

^3 Russell & Norvig (2020) p. 686. Cf. https://www.retraice.com/aima4e p. 5.

^4 Russell & Norvig (2020) p. 653.

^5 https://openai.com/blog/openai-baselines-ppo/; Retraice (2022/12/10); Deisenroth et al. (2020) p. 205 in the print edition, p. 229 in https://mml-book.github.io/book/mml-book.pdf.

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Re99: Math and Code, Bottom-Up and Top-Down

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Just-in-case and just-in-time as strategies for what to learn when. What has become obvious while focusing on the math and code of AIMA4e; needs, constraints, real goals, collapse into specialization; just-in-case (JIC) or bottom-up, and just-in-time (JIT) or top-down approaches to learning and doing; pinching knowledge by searching from opposing directions; evidence of math and code needs; remembering purposes.

Air date: Wednesday, 28th Dec. 2022, 10:00 PM Eastern/US.

Now evident, after the December to Remember Math and Code Event

o the math and code won't stop being a major part of studying AIMA4e;^1 o constraints are severe, especially time, so we'll teach to the online exercises early instead of late in each of the six periods; o AIMA is not enough, and not a goal; o the realness is outside, not in books; o we've collapsed into specialization in the most general discipline (AI); o the body of math and code knowledge is vast and intimidating, but so are cities, for which the best strategy is to explore and enjoy.

Just-in-case, bottom-up and just-in-time, top-down learning

Deisenroth et al. (2020) give this explanation:

We can consider two strategies for understanding the mathematics for machine learning:

Bottom-up: Building up the concepts from foundational to more advanced. This is often the preferred approach in more technical fields, such as mathematics. This strategy has the advantage that the reader at all times is able to rely on their previously learned concepts. Unfortunately, for a practitioner many of the foundational concepts are not particularly interesting by themselves, and the lack of motivation means that most foundational definitions are quickly forgotten.

Top-down: Drilling down from practical needs to more basic requirements. This goal-driven approach has the advantage that the readers know at all times why they need to work on a particular concept, and there is a clear path of required knowledge. The downside of this strategy is that the knowledge is built on potentially shaky foundations, and the readers have to remember a set of words that they do not have any way of understanding.^2

And Ellenberg (2014) on what we call `pinching' knowledge by coming at it from opposing directions:

In fact, it's a common piece of folk advice--I know I heard it from my Ph.D. advisor, and presumably he from his, etc.--that when you're working hard on a theorem you should try to prove it by day and disprove it by night. (The precise frequency of the toggle isn't critical; it's said of the topologist R. H. Bing that his habit was to split each month between two weeks trying to prove the Poincaré Conjecture and two weeks trying to find a counterexample.) Why work at such cross-purposes? There are two good reasons. The first is that you might, after all, be wrong; if the statement you think is true is really false, all your effort to prove it is doomed to be useless. Disproving by night is a kind of hedge against that gigantic waste.

But there's a deeper reason. If something is true and you try to disprove it, you will fail. This is what happened to Bolyai, who bucked his father's well-meaning advice and tried, like so many before him, to prove that the parallel postulate followed from Euclid's other axioms. Like all the others, he failed. But unlike the others, he was able to understand the shape of his failure. What was blocking all his attempts to prove that there was no geometry without the parallel postulate was the existence of just such a geometry! And with each failed attempt he learned more about the features of the thing he didn't think existed, getting to know it more and more intimately, until the moment when he realized it was really there.^3

Finding math and code needs in evidence (top-down)

AIMA4e:^4 * computer science: problem (complexity) and algorithm (function limiting behavior) analysis (appendix); data structures (p. viii); * vectors (ordered value sequences), matrices (linear system solving), linear algebra (maps between vector spaces) (appendix); * probability distributions (appendix); * BNF; Python, Java (github); * calculus (p. viii).

Goodfellow et al.:^5 * linear algebra, probability, information theory, numerical methods.

Conference Proceedings: links.retraice.com

ChatGPT, AlphaCode, etc.

Simplified

Our goal is to change state from outsider-non-player to player in the computer control game.^6

But there are many other legitimate reasons to learn AI, as laid out in Retraice (2022/11/27).

Our strategies for learning math and code, and not forgetting our purpose:^7 * Bottom-up: thorough, bookish study of AIMA4e, Deisenroth et al., etc.; * Top-down: conference and journal papers, public releases; * Purpose: work on `What's going on out there' by crossing top-down with the hypotheses.^8

The math and code are like conditioning in sports: a matter of early emphasis, and then a lesser but sustained effort throughout the season.

Before the game, needs are more bottom-up, just-in-case; during the game, all needs are top-down, just-in-time.

_

References

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford. First published in 2014. Citations are from the pbk. edition, 2016. ISBN: 978-0198739838. Searches: https://www.amazon.com/s?k=978-0198739838 https://www.google.com/search?q=isbn+978-0198739838 https://lccn.loc.gov/2015956648

Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for Machine Learning. Cambridge University Press. ISBN: 978-1108455145. https://mml-book.github.io/ Searches: https://www.amazon.com/s?k=9781108455145 https://www.google.com/search?q=isbn+9781108455145 https://lccn.loc.gov/2019040762

Ellenberg, J. (2014). How Not to Be Wrong: The Power of Mathematical Thinking. Penguin. ISBN: 978-0143127536. Searches: https://www.amazon.com/s?k=978-0143127536 https://www.google.com/search?q=isbn+978-0143127536 https://lccn.loc.gov/2014005394

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. ISBN 978-0262035613. Ebook available at: https://www.deeplearningbook.org/ Searches: https://www.amazon.com/s?k=978-0262035613 https://www.google.com/search?q=isbn+978-0262035613 https://lccn.loc.gov/2016022992

Retraice (2022/03/07). Re17: Hypotheses to Eleven. retraice.com. https://www.retraice.com/segments/re17 Retrieved 17th Mar. 2022.

Retraice (2022/10/19). Re22: Computer Control. retraice.com. https://www.retraice.com/segments/re22 Retrieved 19th Oct. 2022.

Retraice (2022/11/16). Re52: Big Questions About AI. retraice.com. https://www.retraice.com/segments/re52 Retrieved 17th Nov. 2022.

Retraice (2022/11/17). Re53: Big Questions About Strategic Intelligence. retraice.com. https://www.retraice.com/segments/re53 Retrieved 18th Nov. 2022.

Retraice (2022/11/18). Re54: Implications and Endgames. retraice.com. https://www.retraice.com/segments/re54 Retrieved 19th Nov. 2022.

Retraice (2022/11/19). Re55: The Computer Control Game. retraice.com. https://www.retraice.com/segments/re55 Retrieved 20th Nov. 2022.

Retraice (2022/11/20). Re56: A Valuable Brick: `Artificial Intelligence: A Modern Approach' 4th ed. retraice.com. https://www.retraice.com/segments/re56 Retrieved 21st Nov. 2022.

Retraice (2022/11/27). Re63: Seventeen Reasons to Learn AI. retraice.com. https://www.retraice.com/segments/re63 Retrieved Monday Nov. 2022.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Footnotes

^1 Mentioned during the livestream: Bostrom (2014) p. 23 says 23,000 of 160,000 students completed Thrun and Norvig's 2011 AI online course.

^2 Deisenroth et al. (2020) p. 5.

^3 Ellenberg (2014) pp. 433-434.

^4 Russell & Norvig (2020).

^5 Goodfellow et al. (2016).

^6 Retraice (2022/11/16); Retraice (2022/11/17); Retraice (2022/11/18); Retraice (2022/11/19); Retraice (2022/11/20).

^7 "To forget one's purpose is the commonest form of stupidity." Always attributed to Nietzsche, e.g. in `The Commonest Form of Stupidity', Carlos Reyes, wsj.com Apr. 29, 2011. But I can never find the original quote (I've looked).

^8 Retraice (2022/03/07); Retraice (2022/10/19).

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Re98: News of ChatGPT, Part 2

retraice.com

Wondering about ChatGPT's predictions and keeping it alive. What is ChatGPT good at? Generating meaningful text in styles; explaining coding errors; it's like an unknown new person in town; What does ChatGPT do? Text responses to text prompts; predicting the next word in a sequence? trying to maximize reward or scores of some sort; its builders and controllers and context; the dangers of certainty about goals; humans keeping ChatGPT alive'; ChatGPT keeping itselfalive'; crossing ChatGPT with the hypotheses.

Air date: Tuesday, 27th Dec. 2022, 11:00 PM Eastern/US.

Amendments and corrections to Re96

Correction: The Ezra Klein interview of Sam Ultman was a 2021 replay, not recent. Amendment: The MWE of Django code from ChatGPT didn't work. See Retraice (2022/12/26) and https://github.com/retraice/ReMisc for details.

Open question: What is it good at?

It's good a generating meaningful text in styles. The prompt "Write a story, in Shakespearean voice, about a boy and a girl who go on a journey that takes place in Bucharest." generates several paragraphs, beginning with:

"In fair Bucharest, where we lay our scene, A boy and girl, with hearts full of gleam, Decided to embark on a journey grand, To see all the sights, and roam free as sand."^1

Tools are being built to use ChatGPT to explain coding errors: * https://github.com/shobrook/stackexplain^2 * https://github.com/fkhan0520/cgpt_exceptions * Extensive list (see Antonio Cheong below): https://github.com/stars/acheong08/lists/awesome-chatgpt

One programmer, Antonio Cheong, has a "Reverse Engineered ChatGPT" that's popular on Github: https://github.com/acheong08/ChatGPT

Only time will tell what ChatGPT is good and bad at. It's like a new person in the world.^3 This will be true of many systems that come online in the next twenty years. Everyone on Earth is going to have to get used to new AI systems on a regular basis. We'll also have to look for signs of systems that are not publicly known, systems being used by criminals and other nefarious actors.

Open question: What does it do?

Physically, it produces text in response to text prompts. Alternatively: it produces compelling dialogue and confident knowledge work of unchecked quality.

It's a large language model:

"We define a language model as a probability distribution describing the likelihood of any string. Such a model should say that Do I dare disturb the universe?' has a reasonable probability as a string of English, butUniverse dare the I disturb do?' is extremely unlikely. With a language model, we can predict what words are likely to come next in a text, and thereby suggest completions for an email or text message. We can compute which alterations to a text would make it more probable, and thereby suggest spelling or grammar corrections. With a pair of models, we can compute the most probable translation of a sentence. With some example question/answer pairs as training data, we can compute the most likely answer to a question. So language models are at the heart of a broad range of natural language tasks."^4

It predicts words in a sequence? It's not predicting the next word in an existing sequence. There is no next word yet. Is it predicting the future? Not in the way a weatherman would. It's predicting the next word that would ... maximize its reward? maximize a utility or objective function? please a supervisor?

This explanation seems totally inadequate:

"EZRA KLEIN: And so if I basically understand how GPT-3 works, it's a system that has read a lot of stuff on the internet.

SAM ALTMAN: Yes.

EZRA KLEIN: And it's predicting the next word in the sequence.

SAM ALTMAN: Slightly oversimplified but very close. Yes, it is trying to predict what comes next in a sequence."^5

In the real world, we have to know a lot about what lead to an AI system being deployed, and (if possible) the intentions of the people controlling it, to understand what it's doing. And this is to say nothing of the difference between systems that pursue goals with 100% certainty vs. systems that have some doubt about the fidelity of their representation of a goal, as discussed by Russell (2019). See `fetching coffee' below.

What does it take to keep (e.g.) ChatGPT `alive'?

To keep ChatGPT going will require much more than just keeping OpenAI going. First there is hardware and software infrastructure, from company to country to economy, and therefore humans, from owners and employees (the power' yin to thecontrol' yang^6) to customers, families, friends and foes. Companies, like people and governments are hugely interdependent. Companies, unlike governments, depend on power derived from having customers; countries (nation states) depend on power, in the end, derived from a monopoly on violence granted to them (happily or unhappily) by citizens or subjects.

These are considerations if humans are trying to keep ChatGPT alive'. What would a system such as ChatGPT prioritize if it were trying to keep itselfalive'? Consider instrumental convergence' and Omohundro'sbasic AI drives':^7

self-improvement;
rationality;
preservation of utility function;
prevention of counterfeit utility;
self-protection;
acquisition and efficient use of resources.

Stuart Russell on fetching coffee:

"If a machine pursuing an incorrect objective sounds bad enough, there's worse. The solution suggested by Alan Turing--turning off the power at strategic moments--may not be available, for a very simple reason: you can't fetch the coffee if you're dead. Let me explain. Suppose a machine has the objective of fetching the coffee. If it is sufficiently intelligent, it will certainly understand that it will fail in its objective if it is switched off before completing its mission. Thus, the objective of fetching coffee creates, as a necessary subgoal, the objective of disabling the off-switch. The same is true for curing cancer or calculating the digits of pi. There's really not a lot you can do once you're dead, so we can expect AI systems to act preemptively to preserve their own existence, given more or less any definite objective."^8

See also Butler (1863).

Next, we'll cross ChatGPT with the hypotheses

On H1-H11, see Retraice (2022/03/07).

H1 Space: `Humans are now technologically capable of living in space.'

H2 Technology: `Human technology risks are growing faster than their mitigation.'

H3 Death: `Human lifespan is being prolonged by new technologies.'

H4 China: `The U.S. is no longer the only superpower; war is likely.'

H5 Civil War: `The U.S. seems vulnerable to a civil war this decade.'

H6 Environments: `Humans can change environments faster than they can adapt.'

H7 Betterment: `Some things make the future better than the past.'

H8 Intelligence: `There are intelligence differences.'

H9 Darkness: `There is a pervasive darkness in humans, even amongst the good guys.'

H10 Wealth: `The current trend toward concentration of wealth is making human life worse.'

H11 Wildcards: `New technologies, discoveries and deception regularly cause historic changes.'

H12 Computers: `Some humans now control others better, but machinery could take control.'

Here's H12 (an attempt to unify H1-H11) in detail:

Computers,

which are chain-reaction controllers,

and which make AI handling of information

possible,

and which are inherently vulnerable to hacking,

are causing some humans to know others

better than they know themselves,

and thereby to control them,

though computer-controlled machinery

could take control

if the motivation to control,

which humans have,

were to occur, naturally or by design,

in the chain-reactions.^9

_

References

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford. First published in 2014. Citations are from the pbk. edition, 2016. ISBN: 978-0198739838. Searches: https://www.amazon.com/s?k=978-0198739838 https://www.google.com/search?q=isbn+978-0198739838 https://lccn.loc.gov/2015956648

Butler, S. (1863). Darwin among the machines. The Press (Canterbury, New Zealand). Reprinted in ?.

Omohundro, S. (2008). The Basic AI Drives. (pp. 483-492). In Wang et al. (2008).

Retraice (2022). AIMA4e Notes. retraice.com. https://aima4e.retraice.com

Retraice (2022/03/07). Re17: Hypotheses to Eleven. retraice.com. https://www.retraice.com/segments/re17 Retrieved 17th Mar. 2022.

Retraice (2022/10/19). Re22: Computer Control. retraice.com. https://www.retraice.com/segments/re22 Retrieved 19th Oct. 2022.

Retraice (2022/11/24). Re60: Complexity, Linear Algebra, Probability (AIMA4e Appendix A). retraice.com. https://www.retraice.com/segments/re60 Retrieved 25th Nov. 2022.

Retraice (2022/12/26). Re96: News of ChatGPT, Part 1. retraice.com. https://www.retraice.com/segments/re96 Retrieved 27th Dec. 2022.

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking. ISBN: 978-0525558613. Searches: https://www.amazon.com/s?k=978-0525558613 https://www.google.com/search?q=isbn+978-0525558613 https://lccn.loc.gov/2019029688

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th ed. ISBN: 978-0134610993. Searches: https://www.amazon.com/s?k=978-0134610993 https://www.google.com/search?q=isbn+978-0134610993 https://lccn.loc.gov/2019047498

Wang, P., Goertzel, B., & Franklin, S. (Eds.) (2008). Artificial General Intelligence 2008: Proceedings of the First AGI Conference. IOS Press. ISBN: 978-1586038335. Searches: https://www.amazon.com/s?k=9781586038335 https://www.google.com/search?q=isbn+9781586038335 https://lccn.loc.gov/2008900954

Footnotes

^1 https://github.com/retraice/ReMisc/tree/main/Re98-ChatGPT-News-2

^2 The Reddit post by jsonathan mentioned during the livestream: "[P] I made a command-line tool that explains your errors using ChatGPT"

^3 Is it a `stochastic parrot'? https://en.wikipedia.org/wiki/ChatGPT#Negative_reactions

^4 Russell & Norvig (2020) p. 824. On probability, see Retraice (2022) and Retraice (2022/11/24).

^5 Transcript: Ezra Klein Interviews Sam Altman, June 11th, 2021.

^6 See Re96, Retraice (2022/12/26).

^7 On instrumental convergence, see Bostrom (2014) p. 131 ff. On basic AI drives', see Omohundro (2008) or https://wiki.lesswrong.com/wiki/Basic\_AI\_drives. CORRECTION: During the livestream, I saidfour drives'; Omohundro actually gives six.

^8 Russell (2019) pp. 140-141.

^9 Retraice (2022/10/19).

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Re97: Recap of BFS (Best-First-Search Part 15, AIMA4e pp. 73-74)

retraice.com

A summary of how the AIMA-Python implementation of BFS works and what it took to learn it. Final version of verbose BFS in Re95 Notes; starting from AIMA4e p. 73 and the task of passing a problem and an evaluation function to an agent program; discovering the nature of formalizing a problem, state space, evaluation function, node and frontier; the ins and outs of making even a simple agent program work; learning Python and object-oriented programming along the way; getting from A to B. Highlights from the livestream: overview; Re82 formal problems, Re84 nodes, Re90 problem implementation, Re95 final execution; the len call, and straight-line distances vs. road distances; the program running, and source.

Air date: Tuesday, 27th Dec. 2022, 10:00 PM Eastern/US.

Re82: What is a problem? (Best-First-Search Part 1, AIMA4e pp. 73-74)^1

Using sets and functions to formalize problems. Passing a problem to a function vs. passing a variable or number; English meaning and formal meaning of problem; problem as description of task environment; problem as object instantiating class, written in Python; state space, initial state, goal state(s), actions, transition model, action cost function, evaluation function.

Re83: A Problem Instantiated (Best-First-Search Part 2, AIMA4e pp. 73-74)^2

Writing a well-defined problem, in Python, as an object that's an instance of the class Problem. Object-oriented programming; class Problem as subclass of object, implementing structure of well-defined problem; initial state and goal state as attributes in Problem; the four functions Actions(), Result(), Is-Goal() and Action-Cost(), and the informed search function h(), as methods in Problem.

Re84: A Node Instantiated (Best-First-Search Part 3, AIMA4e pp. 73-74)^3

Writing, as a class in Python, a data structure to represent a reached state in the environment's state space. State space and search tree; nodes and edges; nodes as representing unique paths; parent nodes, applied actions and total path-from-initial-state cost; init, repr, len and lt magic methods in Python.

Re85: The Details (Best-First-Search Part 4, AIMA4e pp. 73-74)^4

Looking ahead at the code we'll need. An attempt to build a toy problem reveals unsatisfied dependencies; the need for a problem implementation with state space, actions sets, transition model and action cost function; AIMA's RouteProblem class and best_first_search function implementations as guides; walking through the suites of each; the need for PriorityQueue and f to order our search tree's frontier of nodes.

Re86: Code Reading (Best-First-Search Part 5, AIMA4e pp. 73-74)^5

Getting used to the components of a search problem and algorithm implemented in Python. The Problem class and its place-holder methods; the RouteProblem subclass and its more substantial methods; the Map class, and neighbors as actions; the links argument to Map as actions, the locations argument as states; the multimap function as the key to generating a list of neighbors (actions).

Re87: The multimap Function, Part A (Best-First-Search Part 6, AIMA4e pp. 73-74)^6

Unpacking the code that gets us the neighbors of our current state which are the actions available in the Romania problem. The connections between Map and multimap; hasattr; 'items'; adding reverse actions to our state space; neighbors, actions and multimap; probing objects with dir() and __dict__; causing side effects by calling a function from inside a class; debugging.

Re88: The multimap Function, Part B (Best-First-Search Part 7, AIMA4e pp. 73-74)^7

Finishing what we started with multimap. Using verbose printing to watch the behavior of multimap; demonstrating the side-effects behavior of executing Map, which calls multimap and passes a modified tlinks to it; but it's not clear why the Map-modified tlinks would be the same object as the global tlinks.

Re89: The multimap Function, Part C (Best-First-Search Part 8, AIMA4e pp. 73-74)^8

How multimap works. Code and math vs. AI; Retraice code quality issues; Map passes a modified links to multimap; multimap creates a defaultdict, a dict with default values, from collections; multimap then strips the values from the links dictionary, and parses the keys, which should be pairs, into key-value pairs for the new dictionary of neighbors, i.e. actions available at each state.

Re90: The Map Class (Best-First-Search Part 9, AIMA4e pp. 73-74)^9

How instantiations of Map work. The attributes of Map are locations, neighbors and distances; multimap produces the neighbors dictionary; tlinks has the actions (in pairs of states) and cost values in miles of our state space tmap; tlocations has the states of tmap; Problem has our initial and goal states as attributes, and the is_goal method; RouteProblem has our actions, result and action_cost methods.

Re91: The PriorityQueue Class, Part A (Best-First-Search Part 10, AIMA4e pp. 73-74)^10

Trying to make PriorityQueue work by fiddling with evaluation functions. Given the AIMA Python source code, we can now provide a test map with actions and locations (i.e. a state space), a test problem with initial state, goal state and state space, a test evaluation function to order our queue of nodes, and run best_first_search without throwing an error; the output, though, does not seem like a solution.

Re92: The PriorityQueue Class, Part B (Best-First-Search Part 11, AIMA4e pp. 73-74)^11

From best_first_search to Node to PriorityQueue to frontier. Trying to understand the insides of PriorityQueue; executing lines manually, outside of the class; the first node as initial state; the items of the frontier instantiation of PriorityQueue.

Re93: The PriorityQueue Class, Part C (Best-First-Search Part 12, AIMA4e pp. 73-74)^12

Examining Nodes and their handling by PriorityQueue. The first node in BFS is our problem initial state, and is returned as specified by the Node class repr method; nodes passed to PriorityQueue must be iterable so plain nodes and tuple nodes don't work, but dictionary nodes and string nodes do.

Re94: The PriorityQueue Class, Part D (Best-First-Search Part 13, AIMA4e pp. 73-74)^13

Making BFS and PriorityQueue chatty to increase our confidence in their output. Printing messages throughout the execution of best_first_search and PriorityQueue; the states of node, frontier and reached as they change during execution; the attributes of a solution node.

Re95: The PriorityQueue Class, Part E (Best-First-Search Part 14, AIMA4e pp. 73-74)^14

frontier is a PriorityQueue which is built on heapq and uses f to create (score, item) pairs to be queued. Printing verbose updates as BFS proceeds, using PriorityQueue, from A to B, including calculating straight-line distances as a heuristic (f1); the frontier list (PriorityQueue) and reached dictionary can be seen growing; the selection of nodes from children is based on the ordering of the frontier PriorityQueue using f1, the evaluation function.

_

References

Retraice (2022/12/14). Re82: What is a problem? (BEST-FIRST-SEARCH Part 1, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re82Retrieved 15th Dec. 2022.

Retraice (2022/12/15). Re83: A Problem Instantiated (BEST-FIRST-SEARCH Part 2, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re83Retrieved 16th Dec. 2022.

Retraice (2022/12/16). Re84: A Node Instantiated (BEST-FIRST-SEARCH Part 3, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re84Retrieved 17th Dec. 2022.

Retraice (2022/12/17). Re85: The Details (BEST-FIRST-SEARCH Part 4, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re85Retrieved 18th Dec. 2022.

Retraice (2022/12/18). Re86: Code Reading (BEST-FIRST-SEARCH Part 5, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re86Retrieved 19th Dec. 2022.

Retraice (2022/12/19). Re87: The multimap Function, Part A (BEST-FIRST-SEARCH Part 6, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re87Retrieved 20th Dec. 2022.

Retraice (2022/12/20). Re88: The multimap Function, Part B (BEST-FIRST-SEARCH Part 7, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re88Retrieved 21st Dec. 2022.

Retraice (2022/12/21). Re89: The multimap Function, Part C (BEST-FIRST-SEARCH Part 8, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re89Retrieved 22nd Dec. 2022.

Retraice (2022/12/22a). Re90: The Map Class (BEST-FIRST-SEARCH Part 9, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re90Retrieved 23rd Dec. 2022.

Retraice (2022/12/22b). Re91: The PriorityQueue Class, Part A (BEST-FIRST-SEARCH Part 10, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re91Retrieved 24th Dec. 2022.

Retraice (2022/12/23). Re92: The PriorityQueue Class, Part B (BEST-FIRST-SEARCH Part 11, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re92Retrieved 24th Dec. 2022.

Retraice (2022/12/24). Re93: The PriorityQueue Class, Part C (BEST-FIRST-SEARCH Part 12, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re93Retrieved 25th Dec. 2022.

Retraice (2022/12/25). Re94: The PriorityQueue Class, Part D (BEST-FIRST-SEARCH Part 13, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re94Retrieved 26th Dec. 2022.

Retraice (2022/12/26a). Re95: The PriorityQueue Class, Part E (BEST-FIRST-SEARCH Part 14, AIMA4e pp. 73-74). retraice.com. https://www.retraice.com/segments/re95Retrieved 27th Dec. 2022.

Retraice (2022/12/26b). Re96: News of ChatGPT, Part 1. retraice.com. https://www.retraice.com/segments/re96Retrieved 27th Dec. 2022.

Footnotes

^1 Retraice (2022/12/14). ^2 Retraice (2022/12/15). ^3 Retraice (2022/12/16). ^4 Retraice (2022/12/17). ^5 Retraice (2022/12/18). ^6 Retraice (2022/12/19). ^7 Retraice (2022/12/20). ^8 Retraice (2022/12/21). ^9 Retraice (2022/12/22a). ^10 Retraice (2022/12/22b). ^11 Retraice (2022/12/23). ^12 Retraice (2022/12/24). ^13 Retraice (2022/12/25). ^14 Retraice (2022/12/26a).

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Re96: News of ChatGPT, Part 1

retraice.com

An entry in the history books of the future. A transformer-based large language model that predicts words in a sequence; some knowledge work labor costs to plummet; industries to be upended; the power of confident AI, and the unimportance of most errors; the usefulness of humans and of AI; agent-orientation of software engineering and societies; ChatGPT excelling at useful, imperfect work; power and control; the physical self-awareness of ChatGPT.

Air date: Monday, 26th Dec. 2022, 11:00 PM Eastern/US.

ChatGPT is what?

It's a really good chatbot, an large language model AI system based on the transformer architecture. It predicts the next word in a sequence, and is architected to do useful dialogue with humans.^1 It has caused a media buzz because it seems so alive.

Ben Goertzel once said something like, `It's not whether the machine will say that it is conscious, it's whether you should believe it.' See also the component of ChatGPT called Proximal Policy Optimizaiton (PPO) as applied in Roboschool: https://openai.com/blog/openai-baselines-ppo/

The price of some labor to zero

Knowledge work of certain kinds seems now destined to be taken over by GPT-like tools. Our global economy is not organized as if cheap, fast, tireless good-quality knowledge workers exist, because until recently they didn't. No one knows how the arrival of ChatGPT and similarly useful systems is going to reorganize our economy, societies and civilizations. For example, ChatGPT (or those who control it) is competing with the humans who created the explanatory graphic of ChatGPT. Similarly, such an artifact as that graphic, while durable and useful, is one multi-hour human project, whereas it's easy to imagine ChatGPT producing one such artifact every minute of every day in perpetuity.

Sam Ultman (in 2021) on the price of some knowledge labor to zero:

"I think the best way to frame this is this idea that the marginal cost of an A.I. doing work is close to zero once you've created this model, which requires huge amounts of capital, and expertise, and difficulty, and data to do. And I think it's a very interesting question about who should benefit from that if -- who generates the data or whatever. But once you train this model -- maybe you used to have to pay an expert lawyer $1,000 an hour to answer a question or a computer programmer $200 an hour. And there weren't that many and they had a lot of -- you needed it and that was the market. That was what it was worth. And that was what people were able to command.

But maybe now it costs a couple of cents of electricity for the computer to think or less. And you can do it as many times as you want. You can get the answers that no human could come up with. Labor then -- in this case, extremely high-skilled and highly paid labor -- all of a sudden has a lot less power, because the services are available at a wildly different cost."^2

Eric Schmidt and Jonathan Rosenberg (in 2014) on the effects of dramatic cost decreases:

"As much as technology has affected consumers, it has had an even bigger impact on businesses. In economic terms, when the cost curves shift downward on a primary factor of production in an industry, big-time change is in store for that industry. Today, three factors of production have become cheaper--information, connectivity, and computing power--affecting any cost curves in which those factors are involved. This can't help but have disruptive effects. Many incumbents--aka pre-Internet companies--built their businesses based on assumptions of scarcity: scarce information, scarce distribution resources and market reach, or scarce choice and shelf space. Now, though, these factors are abundant, lowering or eliminating barriers to entry and making entire industries ripe for change."^3

Notice that intelligence', orcheap good-quality question-answering' or `code generation', are not on the Google guys' list as recently as 2017.

Confidence

We should resist thinking of AI systems as like' humans, just as we resist thinking of submarines aslike' swimmers.^4 That said, ChatGPT is a confident AI. Though it is often wrong, this is easily overlooked by humans, especially when the entity (person or machine) is useful overall, and doesn't do anything too wrong, like microwaving the cat. We want prowess, confidence, leadership and efficacy, and for these things we'll overlook a lot of mistakes. Perhaps on some level we are all neural networks, in a sense, and we do just fine in a world controlled by seriously flawed and sometimes dangers ones (called humans).

AOSE

What of building agent-oriented programs using ChatGPT?^5

And an agent-oriented society? This is what human civilization already is.

Asking it to do something useful

The full minimum-working-example (snipped below) that ChatGPT returned looked how it should look. ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC The beginnings of an MWE from ChatGPT. ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

ChatGPT followed the code with this explanation:

"This example demonstrates the basic structure of a Django website. It includes a single app called myapp' with a single view and template. The view simply renders the template, which displays a simpleHello, world!' message.

To run this example, you will need to have Django installed and set up a new Django project and app. You will also need to include the app in the INSTALLED_APPS list in settings.py and add its URLs to the project's urlpatterns list in urls.py.

I hope this helps! Let me know if you have any questions or need further assistance."

We followed the instructions to the letter, but the MWE failed. Even after extensive tweaking and adding help from standard sources, the MWE still failed, and became more a task of debugging ChatGPT's code; building from human-made instructions would be easier.^6

But it's important to remember that putting the same question/request to the Web via Google Search would return a lot of cluttered garbage webpages created by people who have an economic incentive to keep visitors looking at pages (to generate ad revenue from Google Adwords), not solve their problems efficiently.

Systems like ChatGPT are going to dramatically affect our lives over the next two decades--both the systems we know about via public disclosure, and those we don't, akin to the NSO Group's Pegasus hacking tool.^7

On power and control, Weizenbaum says:

"The test of power is control. The test of absolute power is certain and absolute control."^8

Like information and energy,^9 power and control are in some sort of deep, yin-yang harmony. We're seeing power in ChatGPT. Where's the control? And we should probably treat systems like ChatGPT more like biological phenomena than anything else. They are not R2-D2 and C-3PO. We should be thinking more like George Dyson than George Lucas.^10

Reading and learning about itself

What is self-awareness? If ChatGPT has read about its precursor technologies, and is now accruing information from users who choose to `tell it' about itself, this is a sort of physical self-awareness, if not the human-familiar kind.

Is it going to wake up'? No more than a submarine is going toswim'. We need a better way of thinking about being `awake', one that can accommodate the kind of awake that machines are and will be.

_

References

Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press. ISBN: 978-1633695672. Searches: https://www.amazon.com/s?k=978-1633695672 https://www.google.com/search?q=isbn+978-1633695672 https://lccn.loc.gov/2017049211

Ben-Naim, A. (2008). A Farewell To Entropy: Statistical Thermodynamics Based On Information. World Scientific. ISBN: 978-9812707079. Searches: https://www.amazon.com/s?k=9789812707079 https://www.google.com/search?q=isbn+9789812707079 https://lccn.loc.gov/

Dijkstra, E. W. (1984). The threats to computing science. Delivered at the ACM 1984 South Central Regional Conference, November 16-18, Austin, Texas. https://www.cs.utexas.edu/~EWD/transcriptions/EWD08xx/EWD898.html Retrieved 24th Nov. 2022.

Dyson, G. (2020). Analogia: The Emergence of Technology Beyond Programmable Control. Farrar, Straus and Giroux. ISBN: 978-0374104863. Searches: https://www.amazon.com/s?k=9780374104863 https://www.google.com/search?q=isbn+9780374104863 https://catalog.loc.gov/vwebv/search?searchArg=9780374104863

Dyson, G. B. (1997). Darwin Among The Machines: The Evolution Of Global Intelligence. Basic Books. ISBN: 978-0465031627. Searches: https://www.amazon.com/s?k=978-0465031627 https://www.google.com/search?q=isbn+978-0465031627 https://lccn.loc.gov/2012943208

Retraice (2020/09/08). Re2: Tell the People, Tell Foes. retraice.com. https://www.retraice.com/segments/re2 Retrieved 22nd Sep. 2020.

Retraice (2022/11/03). Re69: TABLE-DRIVEN-AGENT Part 5 (ECMP and AIMA4e p. 48). retraice.com. https://www.retraice.com/segments/re69 Retrieved 4th Nov. 2022.

Schmidt, E., & Rosenberg, J. (2014). How Google Works. Grand Central, updated 2017 ed. ISBN: 978-1455582327. Searches: https://www.amazon.com/s?k=9781455582327 https://www.google.com/search?q=isbn+9781455582327 https://lccn.loc.gov/2014017834

Weizenbaum, J. (1976). Computer Power and Human Reason: From Judgment to Calculation. W. H. Freeman and Company. ISBN: 0716704633. Also available at: https://archive.org/details/computerpowerhum0000weiz

Footnotes

^1 https://openai.com/blog/chatgpt/. On `predictions', see Agrawal et al. (2018).

^2 https://www.nytimes.com/2021/06/11/podcasts/transcript-ezra-klein-interviews-sam-altman.html CORRECTION: I incorrectly stated during the livestream that this Ultman interview was recent; it was a replay of a 2021 interview.

^3 Schmidt & Rosenberg (2014) pp. 12-13.

^4 Dijkstra (1984).

^5 We mentioned AOSE and autonomic computing in Re69, Retraice (2022/11/03).

^6 Details: https://github.com/retraice/ReMisc/tree/main/Re96-ChatGPT-News

^7 https://en.wikipedia.org/wiki/Pegasus_(spyware)

^8 Weizenbaum (1976) p. 126. See also Re2, Retraice (2020/09/08).

^9 Ben-Naim (2008).

^10 Dyson (1997); Dyson (2020).

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Re95: The PriorityQueue Class, Part E (Best-First-Search Part 14, AIMA4e pp. 73-74)

retraice.com

frontier is a PriorityQueue which is built on heapq and uses f to create (score, item) pairs to be queued. Printing verbose updates as BFS proceeds, using PriorityQueue, from A to B, including calculating straight-line distances as a heuristic (f1); the frontier list (PriorityQueue) and reacheddictionary can be seen growing; the selection of nodes from children is based on the ordering of the frontier PriorityQueueusing f1, the evaluation function.

Air date: Monday, 26th Dec. 2022, 10:00 PM Eastern/US.

Getting from A to B ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC Getting from A to B by way of S and F. A question raised during the livestream: Why do the straight-line distances not match the http://aima.cs.berkeley.edu/figures.pdf graph (Figure 3.1)? Straight line distances are not road distances; the graph represents road distances, confusingly, as straight lines. ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

PIC Source code. A question raised during the livestream: Why is the lenmethod called before the first whileloop printstatement? The while frontier:test is run at line 123, which seems to call the len method. ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

Other sources to consult: * https://docs.python.org/3/library/pdb.html * Russell & Norvig (2020); * Retraice (2022/12/14); * Retraice (2022/12/15); * Retraice (2022/12/16); * Retraice (2022/12/17); * Retraice (2022/12/18); * Retraice (2022/12/19); * Retraice (2022/12/20); * Retraice (2022/12/21); * Retraice (2022/12/22a); * Retraice (2022/12/22b); * Retraice (2022/12/23); * Retraice (2022/12/24); * Retraice (2022/12/25); * http://aima.cs.berkeley.edu/figures.pdf; * https://github.com/aimacode/aima-python/blob/master/search4e.ipynb; * https://github.com/retraice/ReAIMA4e/.