In this episode of Cyber Security Inside What That Means, Camille explores intelligent systems and artificial intelligence with Lama Nachman, Intel Fellow and Director of Human & AI Systems Research Lab. The conversation covers:
Intelligent systems using a combination of observation, social science, artificial intelligence and more to improve human experience.
The difference between a full virtual setting and one that is a balance of virtual and analog.
What type of devices are used to observe and improve day-to-day activities, and how they work.
How privacy and ethics play a role in these systems as the digital and physical worlds become more blended.
...and more. Don’t miss it!
The views and opinions expressed are those of the guests and author and do not necessarily reflect the official policy or position of Intel Corporation.
Here are some key takeaways:
Intelligent systems research is focused on using social science, design, AI, hardware engineering, and software engineering to augment and amplify human capabilities and experiences with AI.
This is more than just improving your experience when using your PC or device. It is about improving your day-to-day tasks using technology in the physical environment.
The idea is that humans are really good at some things, and AI is really good at others. And oftentimes these aren’t the same things. For example, AI is really good at processing huge amounts of data, which humans can’t do efficiently. If we can put these things together - what a human is good at and what an AI is good at - it can lead to better problem-solving and development.
Some environments are harder to observe than others, but still could have huge benefits. If we look at early childhood learning, there isn’t a lot of screen usage, but a lot of learning is taking place. If we can get AI to observe that and learn from it, we can then bring a conversation to the physical world that helps that learning. Something that is being developed is a projection in that classroom on the wall that kids have to create a course for the projection to land on or interact with. It increases engagement. It also requires turning the space into a smart space with cameras, servers, etc.
The key thing here is the balance between virtual and analog. If it was full virtual, analyzing the data would be easy. But because we want it to be a balance of both, it is a hard problem to work with, because observing and learning is much harder for the AI. This information comes from cameras, microphones, text, and other things like heart rate and skin temperature. There are also sensors that capture muscle movement and brain waves for people with disabilities.
Wireless sensing, as opposed to cameras, is one way information can be collected about where people are moving, when they are, etc. This removes some hesitation for people not wanting cameras watching them, but still collects data. Movement is an important data point for AI. If someone is walking towards their computer to turn it on, the AI might start turning things on in the background to make boot-up faster. If someone is walking around in the kitchen, the AI can infer they are making dinner and make that process easier.
EEG sensors are also being used for people who can’t communicate traditionally to help learn and interpret what different brain signals mean so that communication can happen.
Privacy and ethics are being taken into consideration when the developers look at how the data is collected, where it is getting sent, and where it is getting analyzed. They are also looking at equity and bias in terms of who is building and looking at the algorithms.
Some interesting quotes from today’s episode:
“In terms of learning in a physical environment or working in a fab or just helping people with disabilities, as an example, what can we utilize as signals in the physical world? Then, with a lot of algorithmic innovation, turn that into understanding so we can better facilitate experiences for people as they traverse their normal life.” - Lama Nachman
“You’re looking at augmenting human experience, so we’re focused on humans here and using technology and using sensors to understand better what is the human experience, and then improve that experience.” - Camille Morhardt summing up intelligent systems
“So if you understood what somebody is doing, and if you understood what is supposed to happen, and the AI system can actually converse well with the human, then you could see how you can start to think of these things as human-AI systems where we’re bringing the best of the human and the best of your AI system.” - Lama Nachman
“It’s amazing, because we’ve done tons of automation in general, especially in chip manufacturing. But you walk into a fab and you still see tons of people. It’s not that the people disappear, they just do different tasks in the fab.” - Lama Nachman
“Technology needs to come into the physical world, observe, and then have a conversation that is actually situated in that physical world.” - Lama Nachman
“Ultimately there are all sorts of experiences within that spectrum - from total virtual reality where everything is virtual to everything analog and everything in between within that spectrum, right? What’s really interesting about this is what is the problem that you’re trying to solve, and what are the concerns that you’re trying to mitigate?” - Lama Nachman
“Actually, to solve some of the privacy issues, one of the things that you could do is reduce the gap between what is being sensed and what is being inferred.” - Lama Nachman
“How do you enable responsible development of AI? That means at the very early stages, you’re asking questions about risk.You’re looking at the project as a whole before you start developing.” - Lama Nachman