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
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: 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: 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: 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: 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: 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.”
Marcus: In AI, we wanted Rosie the Robot but got Roomba
“We wanted Rosie the robot, and instead we got Roomba.”