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About this Episode
Episode 70 of Voices in AI features host Byron Reese and Jakob Uszkoreit discuss machine learning, deep learning, AGI, and what this could mean for the future of humanity. Jakob has a masters degree in Computer Science and Mathematics from Technische Universität Berlin. Jakob has also worked at Google for the past 10 years currently in deep learning research with Google Brain.
Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.
Byron Reese: This is Voices in AI, brought to you by GigaOm. I’m Byron Reese. Today our guest is Jakob Uszkoreit, he is a researcher at Google Brain, and that’s kind of all you have to say at this point. Welcome to the show, Jakob.
Let’s start with my standard question which is: What is artificial intelligence, and what is intelligence, if you want to start there, and why is it artificial?
Jakob Uszkoreit: Hi, thanks for having me. Let’s start with artificial intelligence specifically. I don’t think I’m necessarily the best person to answer the question what intelligence is in general, but I think for artificial intelligence, there’s possibly two different kind of ideas that we might be referring to with that phrase.
One is kind of the scientific or the group of directions of scientific research, including things like machine learning, but also other related disciplines that people commonly refer to with the term ‘artificial intelligence.’ But I think there’s this other maybe more important use of the phrase that has become much more common in this age of the rise of AI if you want to call it that, and that is what society interprets that term to mean. I think largely what society might think when they hear the term artificial intelligence, is actually automation, in a very general way, and maybe more specifically, automation where the process of automating [something] requires the machine or the machines doing so to make decisions that are highly dynamic in response to their environment and in our ideas or in our conceptualization of those processes, require something like human intelligence.
So, I really think it’s actually something that doesn’t necessarily, in the eyes of the public, have that much to do with intelligence, per se. It’s more the idea of automating things that at least so far, only humans could do, and the hypothesized reason for that is that only humans possess this ephemeral thing of intelligence.
Do you think it’s a problem that a cat food dish that refills itself when it’s empty, you could say has a rudimentary AI, and you can say Westworld is populated with AIs, and those things are so vastly different, and they’re not even really on a continuum, are they? A general intelligence isn’t just a better narrow intelligence, or is it?
So I think that’s a very interesting question. Whether basically improving and slowly generalizing or expanding the capabilities of narrow intelligences, will eventually get us there, and if I had to venture a guess, I would say that’s quite likely actually. That said, I’m definitely not the right person to answer that. I do think that guesses, that aspects of things are today still in the realms of philosophy and extremely hypothetical.
But the one trick that we have gotten good at recently that’s given us things like AlphaZero, is machine learning, right? And it is itself a very narrow thing. It basically has one core assumption, which is the future is like the past. And for many things it is: what a dog looks like in the future, is what a dog looked like yesterday. But, one has to ask the question, “How much of life is actually like that?” Do you have an opinion on that?
Yeah so I think that machine learning is actually evolving rapidly from the initial classic idea of basically trying to predict the future just in the past, and not just the past as a kind of encapsulated version of the past. So it’s basically a snapshot captured in this fixed static data set. You expose machines to that, you allow it to learn from that, train on that, whatever you want to call it, and then you evaluate how the resulting model or machine or network does in the wild or on some evaluation tasks, and tests that you’ve prepared for it.
It’s evolving from that classic definition towards something that is quite a bit more dynamic, that is starting to incorporate learning in situ, learning kind of “on the job,” learning from very different kinds of supervision, where some of it might be encapsulated by data sets, but some might be given to the machine through somewhat more high level interactions, maybe even through language. There is at least a bunch of lines of research attempting that. Also quite importantly, we’re starting slowly but surely to employ machine learning in ways where the machine’s actions actually have an impact on the world, from which the machine then keeps learning. I think that that’s actually something [for which] all of these parts are necessary ingredients, if we ever want to have narrow intelligences, that maybe have a chance of getting more general. Maybe then in the more distant future, might even be bolted together into somewhat more general artificial intelligence.
Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com
Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.