Speaker 1: Now here's a highlight from Coast to Coast AM on iHeartRadio.
Speaker 2: And welcome back George Doria, doctor Bart costco Bart. Seems
Speaker 2: like the world has gone crazy with artificial intelligence chat systems.
Speaker 3: What's going on here? Sure? Yeah, sure has Well, they've
Speaker 3: gotten more powerful. They're now up to more than a
Speaker 3: trillion parameters in their brain, in their synapses, and they're wiring.
Speaker 3: And that's a good thing in terms of applications are
Speaker 3: more powerful. It's a bad thing because all the inherent
Speaker 3: problems in these key forward black boxes we call them,
Speaker 3: simply get worse. The black boxes get bigger and blacker,
Speaker 3: and they hallucinate and they do a lot of other things.
Speaker 3: But I think, George, it's because of the language interface.
Speaker 3: I mean, the real advances in AI that are ongoing
Speaker 3: and will continue to be for a long time are
Speaker 3: not so much on the language front, but the tie
Speaker 3: end to the language. It's a front end with words,
Speaker 3: it's a back end with words and in between. It's
Speaker 3: straightforward nineteen eighties nineteen nineties processing, but greatly speeded up
Speaker 3: with just a lot of parameters added now courtesy of
Speaker 3: chips like from Nvidia and elsewhere. Chips that were really
Speaker 3: initially designed for imaging and for graphics and game playing
Speaker 3: and that sort of thing. But the other types of
Speaker 3: AI that will continue to be out there, applications of
Speaker 3: data and statistics, I think a little safer. You may
Speaker 3: not realize it for the better and for the worse,
Speaker 3: medical applications and just about anywhere you use data, because
Speaker 3: that's essentially what we mean in this context by AI,
Speaker 3: access to a lot of data. But that language front
Speaker 3: end has always been a big deal, and it's a
Speaker 3: little bit illsory. You know, when someone uses language to
Speaker 3: speak to you, you impute to them human like characteristics,
Speaker 3: Like when you watch a Disney cartoon, you think that
Speaker 3: Vambi is like a person. So we have that effect.
Speaker 3: But I can assure you there is no person, no
Speaker 3: homunculous inside those big black boxes.
Speaker 2: When you coin the phrase fuzzy logic, what does that mean?
Speaker 3: I want to clarify what my PHA advisors. The late
Speaker 3: Great loss the Auto coined that phrase in nineteen sixty five.
Speaker 3: He was subject by the way of a Google Doodle.
Speaker 3: He died in twenty seventeen. Fuzzy logic means thinking in
Speaker 3: shades of gray. It's maybe not the best adjective fuzzy.
Speaker 3: Sometimes the word is vague, that's where the boundaries are
Speaker 3: not black and white, like the name of the series
Speaker 3: Twilight Zone refers to that fuzzy area of twilight between
Speaker 3: night and day, or the rose that's both pink cannot peak.
Speaker 3: And if you look at most human concepts, from cool
Speaker 3: air to green grass and those kinds of things, rarely
Speaker 3: do you get something that you could really say is
Speaker 3: one hundred percent one way or the other. But classical
Speaker 3: binary logic, the modern mathematics from one hundred plus years ago, anyway,
Speaker 3: is based on assume that ran with that the first approximation.
Speaker 3: But that's not how think, and that's not how we talked,
Speaker 3: it's not how we reason. When you park your car
Speaker 3: in a parking lot, in a parking space, you don't
Speaker 3: have to get it exactly precisely right in a little
Speaker 3: rectangular slot. It's approximate. It's fuzzy. And we came to
Speaker 3: learn in the sixties, seventies, eighties, and nineties that demanding
Speaker 3: that extra precision was just too costly and then necessary.
Speaker 3: So what happened with fuzzy logic when it became popular,
Speaker 3: I got involved in it. Forty plus years ago and
Speaker 3: the Japanese started doing applications. Small skill are actually the
Speaker 3: first commercial applications that we have and you probably own one.
Speaker 3: Like I have a subru outback car. It has a
Speaker 3: fuzzy transmission to smoothly shift the gears and soon in
Speaker 3: my class this week told me he had a neural
Speaker 3: fuzzy lights cooker. But thousands of products, largely commercial products
Speaker 3: out of Japan and South Korea, commercial electronics type products,
Speaker 3: and many many other systems are under control of fuzzy
Speaker 3: logic because it's not complete black box like the neural
Speaker 3: networks you're referring to we call AI today and not
Speaker 3: like the old black white decision trees that we called
Speaker 3: AI for about thirty years. It's sort of a compromise.
Speaker 3: It's sort of a gray box reasoning with shades of gray,
Speaker 3: and it has only grown over time. But the essential
Speaker 3: difference between fuzzy Logic and old AI is old AI
Speaker 3: in effect try to get people to think or model
Speaker 3: how people think, like on off binary switches work. Where
Speaker 3: fuzzy Logic tries to get and is getting computers to
Speaker 3: think more like people think.
Speaker 2: Why does so many people bart seem to be afraid
Speaker 2: of AI.
Speaker 3: You know, there has been a lot of hype, and
Speaker 3: I think, just like when you watch that Disney movie
Speaker 3: whatever happens to be, you impute two it human aspects.
Speaker 3: When I ask people to mean by AI number one,
Speaker 3: they don't know what the adjective artificial stands for. And
Speaker 3: it was something the late great AI expert John McCarthy
Speaker 3: just sort of threw out in the late fifties the name,
Speaker 3: and so that's a problem. And intelligence they don't know
Speaker 3: what that stands for. Just a real quick point. There
Speaker 3: is a split between the computer science folks in the
Speaker 3: engineering folks. Actual engineering folks like me. We have renamed
Speaker 3: that to distinguish at CI may not be as catchy
Speaker 3: for computational intelligence. By intelligence we mean behavior that tends
Speaker 3: to increase or decrease a performance measure, like the accuracy
Speaker 3: of a test or something like that. And by computational
Speaker 3: we mean something on a computer. So what do you
Speaker 3: fill in with if you don't know what the adjective
Speaker 3: is the artificial and the noun you fill in with
Speaker 3: what you remember of a lot of good and bad
Speaker 3: old science fiction Movies'm a great one like how the
Speaker 3: computer in two thousand and one of Space, Odyssey to
Speaker 3: the Terminator fantasies and so on, And there's a lot
Speaker 3: of the nature of two hour dramas like that is
Speaker 3: to have a scary problem and then fix it at
Speaker 3: the end. So a lot of the sense of the
Speaker 3: overtaking of the robots and so forth, like the Terminator movies.
Speaker 3: And they're like, I mean, there's some risk of that,
Speaker 3: but it's pretty slim, George, And there's other problems out there,
Speaker 3: but it's pretty slim. That's just your background associative memories.
Speaker 3: We call it filling in teeing off of that word AI.
Speaker 2: Where do you see AI going bar in the next
Speaker 2: fifty years? I know it's a long ways out there.
Speaker 3: Yeah, it's a tough call. And if you go online,
Speaker 3: there's a series call Closer to Truth and tape an
Speaker 3: episode of the late great founder of AI, the true
Speaker 3: father of AI is Marvin Minsky, and we had one
Speaker 3: of the episodes is what do you think it will
Speaker 3: be like at fifty and one hundred years and far out?
Speaker 3: We do think there will be supplements to the brain,
Speaker 3: uploads to the brain at least implants more bringing human
Speaker 3: interface and when it hit they'll hit as fast as
Speaker 3: the smartphones hit. But well before that kind of thing happens, George,
Speaker 3: we're just going to have a kind of AI creep everywhere,
Speaker 3: because what we're really talking about is applying lots of
Speaker 3: data to whatever problem we're looking at. And there's some
Speaker 3: very good things about that. Your systems will become more accurate,
Speaker 3: the scheduling systems and stores how much food they need
Speaker 3: to buy based on much want that day. The all
Speaker 3: griddles will get better and better and more precise. And
Speaker 3: there's some bad things. We already have a big privacy creep,
Speaker 3: and no one realize how much personal privacy we'd leave,
Speaker 3: we would lose just when everyone has a smartphone and
Speaker 3: is taping at will, even though a lot of that's
Speaker 3: not legal at least in California, for example, without consent
Speaker 3: and so on. So it's a mixed bag, and a
Speaker 3: lot of us are very concerned. I'm also a lawyer
Speaker 3: in the law school of USC besides engineering school. We're
Speaker 3: concerned about the loss of digital privacy in the law
Speaker 3: that's changing right in front of us, without let's say,
Speaker 3: judges and justice is really being fully up to speed
Speaker 3: on what's going on, and the law that we have
Speaker 3: is based on paper, old paper, and stuff in dictionaries
Speaker 3: and law books and things like that. It's not the
Speaker 3: way the system is working today. And there's a huge
Speaker 3: clash as we move in effect from atoms or things
Speaker 3: to bits or information. It's a big clash. And we're
Speaker 3: trying to update the law, I mean at a criminal level,
Speaker 3: at the surveillance level, and also now that you're hearing
Speaker 3: more about in all the lawsuits at the civil level,
Speaker 3: but so far not we haven't been very successful with that.
Speaker 2: Are you impressed with the pace of AI.
Speaker 3: I am impressed with the development of computer chips, which
Speaker 3: is behind a lot of it, especially in the large
Speaker 3: language models that people are using out at chat systems,
Speaker 3: and I've been contributing to them. We have a portfolio
Speaker 3: patents for example at USC that we're taking the market,
Speaker 3: so to speak, and so, rightly or wrongly, I'm contributing
Speaker 3: to this process and have been for a long time,
Speaker 3: and it's got problems and we're trying to fix those
Speaker 3: problems in a variety of ways. Fuzzy logic is one
Speaker 3: way to help out to deal with the black box.
Speaker 3: Let me just say real fast, the problem with that
Speaker 3: you can just expose a neural network system and this
Speaker 3: is the lgims from the eighties to lots of pictures
Speaker 3: of your face, and very soon it will learn to
Speaker 3: recognize your face in many, many versions of it, noisy versions,
Speaker 3: your face at night, your face with a killer mustache
Speaker 3: or half covered in a blanket or something like that.
Speaker 3: And that's a good thing. And your brain does that too,
Speaker 3: So it has that ability to become a kind of
Speaker 3: patterned computer, pattern recognized you. The trouble is is generalizing
Speaker 3: that way beyond the scope of what you trained it,
Speaker 3: so you don't know what it's learned. And when I
Speaker 3: learned something new new, you don't know what it is forgotten.
Speaker 3: And that was for small computers we were worried about
Speaker 3: and it was hard to release these earlier for fear
Speaker 3: in the commercial world of lawsuits because you really needed
Speaker 3: ninety nine percent accuracy and you weren't getting that. Well,
Speaker 3: now we have these giant black boxes, say for example,
Speaker 3: the Chat and other systems that are up to way
Speaker 3: beyond now a trillion parameters and growing, and they don't
Speaker 3: just suffer from the problem of garbage garbage out, but
Speaker 3: they have the problem of garbage in hallucinated garbage out,
Speaker 3: or even when it's not garbage in, when it's really
Speaker 3: good stuff going in known patterns. The system hallucinates in ways,
Speaker 3: and it doesn't tell you it's doing. It's as if
Speaker 3: an expert is lying to your faith and you can't tell.
Speaker 3: So one of the things that doesn't do is when
Speaker 3: you ask it a question. Even the chat system that
Speaker 3: doesn't say, hey, I'm giving you an answer, George, but
Speaker 3: I'm only eighty six percent or twenty three percent confident
Speaker 3: of that answer, it spits out all answer is equally
Speaker 3: confident no matter what it's training. Now, the advance of
Speaker 3: fuzzy logic, as I call it fuzzy Logic two point
Speaker 3: zero and discuss this in the re release of the
Speaker 3: book Fuzzy Thinking, is we've gone from the earlier fuzzy
Speaker 3: system of rules to the way the rules map over
Speaker 3: the probability, which is the language of AI. And a
Speaker 3: consequence of that is we can take some of these
Speaker 3: neuralblack boxes, not all of them, but we can take
Speaker 3: them and train on them and build a system of rules.
Speaker 3: Gives us a kind of logical audit trail of inputs
Speaker 3: to outputs to know what's being said, and a probabilistic
Speaker 3: description that tells us for every output what the probability
Speaker 3: is or the uncertainty is and that output or we
Speaker 3: can open up its brain and look at which are
Speaker 3: the rules fired. Now you have to pay for that.
Speaker 3: You need more computation if you want a better XAI system.
Speaker 3: We call it explainable AI. But that's one way to
Speaker 3: do it. And it's hard to keep up with the
Speaker 3: big black boxes. But that's been the contribution of funty logic.
Speaker 3: And with the explosion in ongoing in Moore's law of
Speaker 3: computer chip density, that the number on our circuits have
Speaker 3: been has still been doubling about every two years since
Speaker 3: nineteen sixty. We thought it would run out. It's still
Speaker 3: going strong. And I think we're about to shift from
Speaker 3: electrons to protons, from electricity to light as a lot
Speaker 3: of its computations and some form of More's law may continue.
Speaker 3: So these kind of gains, which are just scaling up
Speaker 3: the old algorithms, are likely to continue at least for
Speaker 3: a decade, if not much more.
Speaker 2: Part I think one of the greatest inventions for mankind
Speaker 2: is the smartphone. I mean the things that they do,
Speaker 2: text messages, sending pictures through the air. It's incredible.
Speaker 3: It is, indeed, and as I said, when I think
Speaker 3: we get to the point, we're not there yet where
Speaker 3: we can bridge this gap in the brain computer interface
Speaker 3: between what's going on in your head and what's going
Speaker 3: on in the smartphone and those kinds of systems, that's
Speaker 3: the next really big advance. And for example, it's one
Speaker 3: of the fourteen objectives or main projects of Modern Engineer
Speaker 3: of the National Academy of Engineering called the Grand Challenges
Speaker 3: for the twenty first century to reverse engineer the brain.
Speaker 3: And that will surely happen. You and I live to see.
Speaker 3: It is a very different matter. But when it does,
Speaker 3: I think it will take off like a California brushfire,
Speaker 3: and you'll have wireless access in effect to your brain
Speaker 3: that you have right now to your smartphone. You may
Speaker 3: not always want that and the fact it has, but
Speaker 3: that will be truly revolutionary. We're not anywhere near that yet,
Speaker 3: just because brains think in these on off spikes, and
Speaker 3: you have one hundred billion neurons in your brain, each
Speaker 3: connected on average to about ten thousand other neurons in
Speaker 3: a totally asynchronous way, and that's completely different in a
Speaker 3: structure than the way these chat systems. For example, the
Speaker 3: chat systems have the form of spaghetti, whereas the human brain,
Speaker 3: or the insect brain or the blue whale brain has
Speaker 3: the form of a fish net. Spaghetti even though it's
Speaker 3: all wound up, you could straighten it out, goes from
Speaker 3: left to right. We call that feed forward, whereas the
Speaker 3: fish net, the thing is linked up. In the case
Speaker 3: of your brain instead of connected just to four other nuts.
Speaker 3: Thereby in the lattice of the net. Again, each neurons
Speaker 3: connected to on the order of ten thousand others, so
Speaker 3: the organizational principles are different. That's the next frontiers computational neuroscience,
Speaker 3: coming very soon to a computer chip near you.
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