The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 16 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
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: Outsiders Are More Likely Than AI Researchers to Create Paradigm Shifts
“Part of my goal in writing the book was to try to get people from outside the field interested in these problems, because in some sense they are more likely, ironically, to have these new ideas than the people who are already professional machine learning rese…”
Domingos: Stock Prediction Neural Networks Decay Within Weeks as Rivals Model Them
“And what typically happens when somebody, you know, deploys a neural network to predict, you know, a certain stock, like for example, you might have 3000 networks each predicting one stock in, in, in the Russell 3000 is that it works for a few weeks and then i…”
Domingos: Startups Can Dominate by Applying Basic ML to Untapped Industries
“Precisely because machine learning is something that can be used just about everywhere, right? In every single industry, in every single part of what a company does, So far, it's only been used for a small fraction of the things that it could be used for. So y…”
Domingos: Technology Growth Curves Are S-Curves, Not Perpetual Exponentials
“I think that argument is actually very dubious, because in reality, no exponential goes on forever. Because there's always a limit because the world is finite. So actually what happens with all of these technology curves is that in the beginning they look like…”
Domingos: Machine Learning Algorithms Do Not Have to Be Black Boxes
“Actually, the learning algorithms don't have to be black boxes. There's actually no reason why I shouldn't be able to say to the Amazon recommender system, why did you recommend that book to me? Or, you know, I just bought a watch. Please don't recommend more …”
Domingos: Mixed Human-Machine Driving Control Is Inherently Dangerous
“This notion of a mix of mixed control between the human and the car is actually very problematic. If someone is not driving the car and then suddenly the computer says like, oh, shoot, I'm confused. Take over. Then the person will not be very well able to take…”
Domingos: A Single ML Algorithm Can Master Multiple Domains via Data
“In traditional computer science, you need to write down a different algorithm for everything that you want to do. So if you want the computer to do diagnosis, you need to explain to it what are the rules of that diagnosis. If you wanted to play chess, you need…”
Domingos: Knowledge Induced From Empirical Data Is Inherently Uncertain
“Any knowledge that you induce from data is necessarily uncertain, because you never know if you generalized correctly or didn't.”
Domingos: Humans Are Systematically Overconfident in Evolutionary and Cultural Knowledge
“Conversely, a lot of the knowledge that we have from evolution and from experience and from culture, we often tend to think of it as much more certain than it really is. We have this great tendency that's been well studied by psychologists to be overconfident …”
Domingos: Machine Learning Masters Single Domains While Humans Synthesize Broadly
“Machine learning today is very good at learning about one thing at a time. The thing that humans have is that they can bring to bear knowledge from all sorts of directions.”
Domingos: Data Network Effects Create Major Competitive Moats for Machine Learning Incumbents
“There's this network effect of data where if you have a good product and people start using it, then you have a lot of use this, for example, you know, how Google has built up such an unassailable position in search, right? Is like you use their search engine.…”
Domingos: Human Competition Will Endure Despite Superhuman Computers
“I think more likely what will happen is that people will still be playing each other, even though the best is our computers in the same way that, you know, there's race cars that go way faster than people. But we still have people doing, you know, in the Olymp…”
Domingos: Autonomous Vehicles Only Need to Beat Humans, Not Reach Perfection
“The cars don't have to be perfect before we start using them instead of people. They just have to get better than people.”
Domingos: Understanding Learning Algorithms Differs From Understanding Their Output Models
“Having said that, you know, we, the machine learning researchers and the data scientists, we actually have a good understanding of how the learning algorithm itself works. You know, what is it that it does to learn, and how could you make it learn better? And …”