Our CEO Howie Altman had the pleasure to be a guest on Innodata’s Podcast Absolute AI. They had an excellent conversation about genetic programming and how we at Perceiver AI have solved the previously intractable problems with the approach using our patent pending solutions. There is enormous potential for solving the world’s toughest problems that cannot be adequately addressed with neural networks and deep learning. Listen to hear more!
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
Genetic programming is a technique of evolving programs that have the potential to overcome existing 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 an evolutionary engine 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 enabled by genetic programming in a matter of seconds. After running data through Perceiver AI, the result is millions of generations of solution candidates that are tested against a fitness function. 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 issue of plateauing in a matter of minutes or hours, depending on the complexity of the problem being solved.
UTILIZING INTELLIGENCE ARCHITECTURES
At Perceiver 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 (and generally, far less data is required than for neural networks/deep learning), problems can be solved with minimal human intervention by identifying all weaker solutions and discarding them while simultaneously evolving along the way. Each step in this evolutionary process of genetic programming takes us closer to achieving the sought after solution.
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.
“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, real-world applications, not just research-type things that Perceiver [AI] excels in.” [38:24]
“The technology path that we’re on is just going to continue to explode.” [46:45]