Highlights 1:22 Howie’s Journey Into Artificial Intelligence 3:35 The Gap Between AI Problems and Current Technologies 9:25 Finding Solutions to These AI Problems 13:05 The Application of Natural Selection in AI 17:48 An Overview of the Genetic Programming Process 26:11 Identifying Data Relationships Through Neurodivergence 31:44 The Humanity and Mathematics Behind AI Evolution 37:13 Practical Applications of Genetic Programming 40:01 Howie’s Core Values Are The Driver Behind His Success
Show Notes
Genetic programming is a technique of evolving programs that have the potential to overcome basic limitations and take AI to the next level. In this episode, Melody is joined by Howie Altman, co-founder and CEO of Perceiver AI, where their mission is to elevate the science of artificial intelligence. They have developed a novel form of AI-based on the foundation of genetic programming which uses evolutionary engines to optimize through a process of natural selection. Howie highlights the genetic programming process, the potential that it has to alter the path of AI, and the extensive applications that it can solve today and in the future.
ARTIFICIAL INTELLIGENCE AND GENETIC PROGRAMMING There is a gap between AI problems and the technologies currently available to solve them. Most current AI technologies are based on neural networks and deep learning, which are incredibly effective but utilize pattern matching as their main problem-solving tool. Additionally, the black box problem, human bias, blind spots, and the need to start from scratch every time all contribute to limitations that have yet to be solved within AI. Howie highlights the benefits and alternative solutions offered by genetic programming, using the evolution of breeding dogs as a real-life example of the same process.
AN OVERVIEW OF THE GENETIC PROGRAMMING PROCESS Starting with the genome, Howie walks listeners through the possibilities that are made possible by genetic programming in a matter of nanoseconds. After running information through Perceiver AI, the result is a population of solution candidates that are tested against a fitness function. Data success comes in utilizing more than just error rate, but also measuring information gained from generation to generation, identifying stagnation, and implementing extinction when necessary. This process helps to overcome the major issues of insufficient data and poorly identified goals in a matter of minutes.
UTILIZING INTELLIGENCE ARCHITECTURES At Perciever AI, they believe that the universe has an infinite number of intelligence architectures, i.e. frameworks by which to reason, judge, understand or problem solve. Perceiver AI aims to come up with the optimal intelligence architecture for any number of scenarios with the data at hand. With sufficient data, problems could be solved with minimal human intervention by identifying all weaker solutions and discarding them while simultaneously evolving along the way. Each step in this evolution of genetic programming takes us closer to achieving practical solutions with AI.
PRACTICAL APPLICATIONS OF GENETIC PROGRAMMING From improved crew scheduling and fueling for commercial airlines to minimizing power consumption at telecommunication data centers, there are endless optimization solutions available with genetic programming. Howie highlights carbon credits, fuel savings, CO2 emissions reduction, and waste reduction and offers listeners insights into the variety of real-world possibilities for the future.
Howie’s Insights
“Whereas neural networks are the digital version of the human brain, … genetic programming is the digital version of evolution.” [13:31]
“As long as you can define it mathematically and in code, you can have any goal in AI you want.” [17:22]
“What Perceiver [AI] is doing is coming up with the optimal intelligence architecture for the problem at hand.” [28:20]
“These are practical,...