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 38 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
Marcus challenges Andrew Ng: AI cannot automate every one-second mental task
“I don't think it's true at all, so there are some things that people can do in a second that AI can automate, and there are other things that it can't”
Marcus: Drivers should not trust Tesla Autopilot because AI remains unready
“So you should not be trusting a Tesla. Elon might get on 60 minutes and take his hands off the wheel, but you should not be doing the same because the technology is not really there yet.”
Marcus: Current AI techniques are too brittle to earn public trust
“Most of why I don't think we should trust AI right now is that the current techniques that people are using are simply too brittle to earn the trust that we're giving them.”
Marcus: Deep learning models are giant correlation machines without causal understanding
“These systems are just giant correlation machines. They have no understanding of causation.”
Marcus: Deep learning cannot perform common sense, planning, analogy, or reasoning
“Perception is what deep learning does, and actually it only does a small part of perception. There's lots of parts of perception where we use our knowledge about the world that it doesn't capture. And then there are all these other things like common sense and…”
Marcus: Deep learning is a better ladder but cannot reach the moon
“Deep learning is a better ladder for sure, but a better ladder doesn't necessarily get you to the moon.”
Marcus: Siri's technology is not fundamentally different from ELIZA
“But the technology for Siri is not really that different from the technology for ELISA, and Siri is still very far from artificial general intelligence.”
Marcus: Deep learning alone won't solve common-sense AI without symbolic systems
“Deep learning doesn't seem to be getting us there. The old systems, in some ways, we're better at that. We do need a marriage between the two.”
Marcus: Teslas crashed into multiple emergency vehicles due to vision flaws
“It turns out that Teslas have a problem with stopped vehicles, mostly emergency vehicles, on the side of the road. So they have run into, in the last year, three fire trucks, a tow truck a police car, and so forth.”
Marcus: Deep learning requires vast data, quick tasks, and stable domains
“If a typical person can do a mental task with less than one second of thought, and we can gather an enormous amount of data, of directly relevant data, we have a fighting chance. So long as the test data aren't too terribly different from the training data, an…”
Marcus: Deep learning models lack understanding of spatial relationships and silhouettes
“Deep learning doesn't even really fully understand the relations between parts and wholes. It certainly doesn't understand what a silhouette is, and it gets it wrong.”
Marcus: Deep learning systems cannot perform compositionality or combine separate ideas
“Deep learning can't do what we call compositionality. It can't put ideas together.”
Marcus: Deep learning fails at reading comprehension and misses the point
“I think that deep learning doesn't just fail on reading. It completely, entirely misses the point.”
Marcus: General AI requires hybrid models because cognition has distinct components
“There are a lot of different components to cognition, and so we have to have a hybrid model. There's just no way that one system is going to do all of these different things”
Marcus predicts AI developers will increasingly adopt hybrid models in secret
“I suspect that what will actually happen is that people will start to incorporate hybrid models and not call them that.”
Marcus: Current AI systems are fundamentally a collection of idiot savants
“AI is still basically a collection of idiot savants.”
Marcus: AI is stuck due to over-reliance on statistics and big data
“And I have a three-part diagnosis, which is that it's fallen in love with statistics, it's fallen in love with big data, and it's forgotten its roots.”
Marcus: Reinforcement learning fails in physical robotics due to real-world damage costs
“But it's another thing if you import those same techniques into robots. If you have your robots doing reinforcement learning, which is basically trial and error learning, they start knocking over the furniture a 100,000 times, you're probably gonna send it bac…”
Marcus: AI progress will be gradual, not a sudden explosion
“I don't think there's going to be like one special day with this big explosion. They're going to get better and better. It's going to take a while.”
Marcus: Deep learning achievements are limited to perceptual classification
“These successes are examples of one thing. They're all examples of what a cognitive psychologist would call perceptual classification, and that's part of what we do as intelligent human beings, but it's not all that we do.”
Marcus: Deep learning relies primarily on texture, not shape, for recognition
“Deep learning, it mostly cares about texture.”
Marcus: Building human-level AI requires studying cognitive development in children
“If we want to build machines that are as smart as people, we should start by studying small people.”
Marcus predicts deeply neurally inspired AI models are 50 years away
“50 years from now, we will know how to build really deeply neurally inspired models, but right now we have to settle for cognitive science, for psychology, linguistics, fields like that, where we can actually get some insight now before we unravel the brain.”
Marcus: Autonomous driverless cars can drive in Palo Alto but not Manhattan
“In fact, they can drive in Palo Alto, but they can't drive in Manhattan.”
Marcus: AI has made little progress toward strategic general game players
“There's still been very little progress in building a general game player that can play games that require strategy or insight, complex three-dimensional graphics.”
Marcus: AI is not close to achieving strong, human-level intelligence
“But we're not there yet. We're not even close.”
Google's Peter Norvig estimated strong AI is worth up to $2T annually
“I asked Peter Norvik, who at the time was director of research at Google I said, you know, how much money would it really be worth if you could have strong AI? And he wrote back about five minutes later, I'm suddenly a much louder, I think. And estimated somet…”
Marcus: Deep learning models struggle with low-frequency edge cases
“People get very excited every time there's a new deep learning result. But it's always the case of doing better on the high frequency data than the low frequency data.”
Marcus: AI researchers should look to child cognitive development for AGI clues
“So, where I think we should start looking for clues, in a nutshell, is children.”
Marcus: Information retrieval fails at state updates that human-level AI requires
“If you have human-level intelligence, you can make sense of that, but if you just memorize stuff off the web and do information retrieval, you could actually get stuck there.”
Gary Marcus: No great machine learning technique exists for natural language
“And so, as a result, there is no great machine learning technique in natural language.”
Marcus: Facebook M relies mostly on human operators rather than AI
“Facebook's new M service, which they have not rolled out at scale has humans on the back end. There's a little bit of AI in there, but it's mostly, ah, human beings, which is why they haven't rolled it out for a billion customers. They don't have enough human …”
Marcus: Robust AI was formed to add common sense to robotics
“And, in fact, the point of the company that we formed is to add common sense, which the Roomba lacks.”
Marcus: In AI, we wanted Rosie the Robot but got Roomba
“We wanted Rosie the robot, and instead we got Roomba.”
Marcus: Deep learning vision errors are not human-like and resemble hallucinations
“So when you start to look at the errors that these things make, they're not human-like errors, and again, they seem almost like hallucinations.”
Gary Marcus: Financial markets are fundamentally a sparse data problem
“Finance is really fundamentally a sparse data problem because The financial markets aren't stationary.”