Pedro Domingos
Professor Emeritus, University of Washington. On 2 shows, 2 appearances. The Shows tab opens the full record on each.
academicscientistauthorexecutive@pmddomingos ↗homes.cs.washington.edu/~pedrod ↗Wikipedia ↗
Pedro Domingos is an artificial intelligence researcher known for introducing Markov logic networks and authoring The Master Algorithm. He co-founded the International Machine Learning Society and previously served as the Head of Machine Learning at D. E. Shaw & Co.
2shows
2appearances
65statements
15resolved
12supported
1contradicted
80%fully supported
Everything Pedro Domingos said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Pedro Domingos predicts Backprop alone will not reach general AI
“I actually do not believe that that's the case. I think Backprop will not get us there.”
Domingos: Mandating total model explainability literally makes deep learning illegal
“And at the end of the day, you know, you can't, like the European Union, mandate that every model has to be explainable, right? Because then, if you read that law literally, it makes deep learning illegal, right?”
Domingos: Doctors Gatekeep Medical AI to Prevent Automating Their Own Jobs
“In the particular case of medicine, it's not used more already because, of course, the doctors are also the gatekeepers Of the system, and they're not very interested in replacing themselves or their job that they like best by machines.”
Domingos: AI Will Plateau Rather Than Experiencing Infinite Runaway Intelligence Growth
“And I think they will be in the case of AI, but I don't think we're going to see this, you know, infinite growth that, you know, goes completely beyond you know, what humans can imagine.”
Pedro Domingos: Very little of current machine learning capability has been deployed
“With the existing machine learning technology, of what we can do with that, how much have we done? Very little so far. So there's enormous amount for, enormous scope to do things there. Not just in finance, but in a lot of other fields, right?”
Domingos: AI is too early to predict 20 years into the future
“I would say we are so much in the beginning that we can't even really picture where we're going to be 20 years from now.”
Domingos: The hardest problem in AI is learning representation
“The hardest problem in AI is coming up with a representation. Once you've done that, the rest follows, and when you don't do that, you don't really get very far”
Domingos: AI is an amazing tool for authoritarianism
“AI can be a great tool for democracy. It is also unfortunately an amazing tool for authoritarianism.”
Domingos: Dumb AI is vastly more dangerous than smart AI
“The real danger of AI is not that computers will get too smart and take over the world. The real danger is that computers are too stupid, and they've already taken over the world, right? Computers are making decisions about us the whole time, and they're dumb,…”
Domingos: Future human work will shift to setting goals and verifying AI output
“So we humans, right, I can see a future where our full-time occupation is to tell the, you know, algorithms what we want to do, set the objective function, set the boundaries, and then verify the solutions all the time, continuously.”
Domingos: Computers Will Soon Discover and Store Most Knowledge on Earth
“So in the not too distant future, the vast majority of the knowledge on earth
Will be discovered and will be stored in computers.”
Domingos: A Venture Capital Fund Appointed an Algorithm as a Voting Director
“For example, there's this venture fund recently that announced that one of their directors is now going to be an algorithm. There's seven directors on the board, and one of them's an algorithm. So their algorithm doesn't decide anything all by itself, but it d…”
Domingos: Researchers Can Develop a Single Universal Master Algorithm for ML
“But what I and others believe is that we can Develop a true master algorithm, meaning an algorithm that is able to solve all the different kinds of learning problems that these different algorithms can.”
Domingos: Achieving a Universal Master Algorithm Requires Entirely New AI Paradigms
“And my gut feeling is that actually it's more the latter. I do believe that we have made a lot of progress, but I think we are still missing some important ideas.”
Domingos: White-Collar Jobs Like Engineering Are Easier to Automate Than Blue-Collar Work
“So people often think that the easiest jobs to automate are like the blue collar ones. But actually our experience in AI is that it's actually more the opposite. It's often white collar jobs that are easy to automate. For example, things like engineering and, …”
Domingos: Machine Learning Algorithms Typically Outperform Human Doctors in Medical Diagnosis
“Machines are remarkably better than human doctors at doing all types of medical diagnosis, not just from x-rays, but from, you know, symptoms, right? You have a patient, you have their symptoms, what is the diagnosis? And even very simple machine learning algo…”
Domingos: Commercial Airplane Cockpits Will Transition from Two Pilots to Zero
“We already have two people in the cockpit instead of three, and then we'll have one and eventually we'll have zero.”
Domingos: Most Cars Will Be Self-Driving Between 2026 and 2031
“But I, my guess is that, you know, five years from now, there will be a lot of self-driving cars around and, you know, maybe 10 years from now, 15, Most cars will be self-driving.”
Domingos: Incremental Autonomy Wins Short Term, But Full Autonomy Prevails Long Term
“So, but I think that in the short term the approach of the Teslas and the Toyotas and whatnot will be the prevalent one. I think in the longer run, it will be the Google approach that prevails, right?”
Domingos: Major deep learning wins come from combining multiple techniques
“A lot of the things that people think of as successes of deep learning are actually successes of combining deep learning with other things.”
Domingos: Geoffrey Hinton hasn't made capsule networks work yet despite right intuition
“I think capsule networks are a very interesting, you know I like the intuition behind capsule networks. I think they point in the right direction. I think Jeff hasn't quite been able to make them work yet.”
Domingos: Machine learning's five major paradigms haven't changed since the 1950s
“There's been enormous progress. The five major paradigms are exactly the same as they were then.”
Domingos: Terminator-style AI scenarios are not happening anytime soon
“Terminator isn't happening anytime soon. First of all, because the technology isn't there, but second of all, because there is this, I mean, like a lot of these, I think, errors that people made come from anthropomorphizing AI.”
Domingos: Basic ML Often Outperforms Highly Trained Human Pathologists
“And the thing that's amazing is that often just by taking a basic machine learning algorithm and applying on a database of, for example, x-rays and diagnosis, you actually wind up with something that is better. That, for example you know, pathology than a high…”
One line per show, most statements first. The link opens Pedro's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 KNOWLEDGE PROJECTLEDGER | Professor Emeritus, University of Washington | 1 | 51 | 77% 10/13 | full record on the Knowledge Project → |
 MADLEDGER | Professor Emeritus, University of Washington | 1 | 14 | 100% 2/2 | full record on the MAD Podcast → |