61. Ben Goertzel - The unorthodox path to AGI
Towards Data Science
12/09/20
•81m
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No one knows for sure what it’s going to take to make artificial general intelligence work. But that doesn’t mean that there aren’t prominent research teams placing big bets on different theories: DeepMind seems to be hoping that a brain emulation strategy will pay off, whereas OpenAI is focused on achieving AGI by scaling up existing deep learning and reinforcement learning systems with more data, more compute.
Ben Goertzel —a pioneering AGI researcher, and the guy who literally coined the term “AGI” — doesn’t think either of these approaches is quite right. His alternative approach is the strategy currently being used by OpenCog, an open-source AGI project he first released in 2008. Ben is also a proponent of decentralized AI development, due to his concerns about centralization of power through AI, as the technology improves. For that reason, he’s currently working on building a decentralized network of AIs through SingularityNET, a blockchain-powered AI marketplace that he founded in 2017.
Ben has some interesting and contrarian views on AGI, AI safety, and consciousness, and he was kind enough to explore them with me on this episode of the podcast.
Previous Episode
60. Rob Miles - Why should I care about AI safety?
December 2, 2020
•45m
Progress in AI capabilities has consistently surprised just about everyone, including the very developers and engineers who build today’s most advanced AI systems. AI can now match or exceed human performance in everything from speech recognition to driving, and one question that’s increasingly on people’s minds is: when will AI systems be better than humans at AI research itself?
The short answer, of course, is that no one knows for sure — but some have taken some educated guesses, including Nick Bostrom and Stuart Russell. One common hypothesis is that once an AI systems are better than a human at improving their own performance, we can expect at least some of them to do so. In the process, these self-improving systems would become an even more powerful system that they were previously—and therefore, even more capable of further self-improvement. With each additional self-improvement step, improvements in a system’s performance would compound. Where this all ultimately leads, no one really has a clue, but it’s safe to say that if there’s a good chance that we’re going to be creating systems that are capable of this kind of stunt, we ought to think hard about how we should be building them.
This concern among many others has led to the development of the rich field of AI safety, and my guest for this episode, Robert Miles, has been involved in popularizing AI safety research for more than half a decade through two very successful YouTube channels, Robert Miles and Computerphile. He joined me on the podcast to discuss how he’s thinking about AI safety, what AI means for the course of human evolution, and what our biggest challenges will be in taming advanced AI.
Next Episode
62. Nicolai Baldin - AI meets the law: Bias, fairness, privacy and regulation
December 16, 2020
•49m
The fields of AI bias and AI fairness are still very young. And just like most young technical fields, they’re dominated by theoretical discussions: researchers argue over what words like “privacy” and “fairness” mean, but don’t do much in the way of applying these definitions to real-world problems.
Slowly but surely, this is all changing though, and government oversight has had a big role to play in that process. Laws like GDPR — passed by the European Union in 2016 —are starting to impose concrete requirements on companies that want to use consumer data, or build AI systems with it. There are pros and cons to legislating machine learning, but one thing’s for sure: there’s no looking back. At this point, it’s clear that government-endorsed definitions of “bias” and “fairness” in AI systems are going to be applied to companies (and therefore to consumers), whether they’re well-developed and thoughtful or not.
Keeping up with the philosophy of AI is a full-time job for most, but actually applying that philosophy to real-world corporate data is its own additional challenge. My guest for this episode of the podcast is doing just that: Nicolai Baldin is a former Cambridge machine learning researcher, and now the founder and CEO of Synthesized, a startup that specializes in helping companies apply privacy, AI fairness and bias best practices to their data. Nicolai is one of relatively few people working on concrete problems in these areas, and has a unique perspective on the space as a result.
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