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To build truly intelligent machines, teach them cause and effect
- is_true 4y agoMost politicians lack this too
- mrwnmonm 4y agoGod, I hate these titles. The same science news business site published this before https://www.quantamagazine.org/videos/qa-melanie-mitchell-video https://www.quantamagazine.org/videos/qa-melanie-mitchell-vi... I have no problem if they say x thinks y. But to put it as if it is a fact like "To Build Truly Intelligent Machines, Teach Them Cause and Effect" and "The Missing Link in Artificial Intelligence" to get more hits is disgusting.
- qbit42 4y agoWhile Quanta often has click baity headlines, it is really the only decent website for pop math and theoretical computer science.
- gibsonf1 4y agoFully agree with this article. Our definition for intelligence: "Intelligence is conceptual awareness capable of real-time causal understanding and prediction about space-time."[1] [1] https://graphmetrix.com/trinpod https://graphmetrix.com/trinpod
- mrwnmonm 4y ago"Intelligence is whatever supports this product."
- canjobear 4y agoWhat is understanding?
- gibsonf1 4y agoThe ability to model an object in awareness and its causality that corresponds to its space-time reality
- mrwnmonm 4y agoCircular definitions, circular definitions, circular definitions everywhere.
- blamestross 4y agoIt's almost as if the only two modes of self consistent reasoning are either circular or predicated on assumptions taken in blind pragmatic faith.
- canjobear 4y agoWhat does it mean to model an object in awareness? Does Dall-E model an object in awareness when it is generating an image containing an object? How can you tell if it is or isn't?
- gibsonf1 4y agoAll ml/dl systems have no awareness - they just output based on input training - like a calculator outputs an answer. So what it means to model in awareness is what you are doing right now in reading this sentence. You take these words as input, model conceptually what they mean mentally, connect that model to your experience of space time, and then decide what to do next with that understanding.
- deleted 4y ago[deleted]
- exit 4y agodoes doing awareness entail something non-mechanical?
- nradov 4y agoIntelligence is the ability to accomplish goals by making optimal use of limited resources.
- zwkrt 4y agoBy which metric a tree is very intelligent and a man with a private yacht is not.
- butterNaN 4y agoWealth does not correlate with intelligence.
- YeezyMode 4y agoThis is a possibility that shouldn't be dismissed. Trees use mycorrhizal networks to communicate and have been around for a very long time on this planet. They model the environment and use either a set of micro-decisions or a set of larger, slower moves that are made across longer timescales than humanity is used to. You can argue whether they possess sentience or not, but when discussing models, decisions, and consequences - trees seem to act with plenty of coordination and understanding and self-interest.
- nradov 4y agoDoes a tree have goals? Does a wealthy man have unlimited resources? (Time is also a resource.)
- phkahler 4y agoDoes chatGPT have goals? WHY does it produce the output that it does? ;-)
- taneq 4y agoOnly if his goal was not “have his own private yacht.”
- taneq 4y agoSo a human that you chat with on the phone is intelligent but the same human as a pen pal is not?
- phkahler 4y ago>> capable of Not necessarily actively engaged in.
- Analemma_ 4y agoThis article feels like it came from some alternate universe where the history of AI is exactly the opposite of where it is in ours, and specifically where “The Bitter Lesson” [0] is not true. In our world, AI was stuck in a rut for decades because people kept trying to do exactly what this article suggests: incorporate modeling and how people think consciousness works. And then it broke out of that rut because everyone went fuck it and just threw huge data at the problem and told the machines to just pick the likeliest next token based on their training data. All in all this reads like someone who is deeply stuck in their philosophy department and hasn’t seen anything that has happened in AI in the last fifteen years. The symbolic AI camp lost as badly as the Axis powers and this guy is like one of those Japanese holdouts who didn’t get the memo. [0]: http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html
- cubefox 4y agoIt's from 2018. Time was not kind to Pearl's picture of AI.
- sankha93 4y agoThe idea that symbolic AI lost is uninformed. Symbolic AI essentially boils down to different kinds of modeling and constraint solving systems, which are very much in use today: linear programming, SMT solvers, datalog, etc. Here is here symbolic AI lost: any thing where you do not have a formal criteria of correctness (or goal) cannot be handled well by symbolic AI. For example perception problems like vision, audio, robot locomotion, or natural language. It is very hard to encode such problems in terms of formal language, which in turn means symbolic AI is bad at these kind of problems. In contrast, deep learning has won because it is good at exactly these set of things. Throw a symbolic problem at a deep neural network and it fails in unexpected ways (yes, I have read neural networks that solve SAT problems, and no, a percentage accuracy is not good enough in domains where correctness is paramount). The saying goes, anything that becomes common enough is not considered AI anymore. Symbolic AI went through that phase and we use symbolic AI systems today without realizing we are using old school AI. Deep learning is the current hype because it solves a class of problems that we couldn't solve before (not all problems). Once deep learning is common, we will stop considering it AI and move on the to the next set of problems that require novel insights.
- dwheeler 4y agoThis article really needs a "(2018)" marker. This article predates GPT-3 and GPT-2, it even predates the essay "The Bitter Lesson" <http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html>. It might be true long-term, but it's certainly not written with the current advances in mind.
- gibsonf1 4y agoThere aren't really any current advances outside of sheer scale of input in the models, and all the engineering and hardware around achieving that scale. And I think the point is no matter how much input data you give the ml/dl system, it will still have no awareness, no understanding of any kind and certainly no causal awareness.
- codeulike 4y agoI think LLMs understand things in abstract. e.g. they know that if a 'Cat' is 'Miaowing' then if you 'give' it some 'cat food' it will then be 'happy'. It doesn't know what any of those things _are_, but it knows how X and Y and Z relate. And so in the abstract sense it knows that 'hunger' causes a 'cat' to 'miaow' it will still have no awareness We dont know how awareness works, so we're not in a position to say what has and hasn't got it. Think about the milliseconds in which ChatGPT uses its 4000 token length to analyse approx 2000 words all at same time, in a process encompassing a massive number of GPUs processing a mind boggling number of parameters. How are we to say what it going on for those milliseconds? There could be some sort of abstract analogue to awareness happening there for a burst of a few milliseconds. These LLMs are all about emergent effects from huge scale. Similarly, me and you are made out of 30 trillion microscopic dumb biological robots, none of whom know who we are or care, but nonetheless, we have awareness.
- breckenedge 4y agoThe LLMs are predictive text. They only know that a hungry cat wants food because there are enough examples of that scenario on the Web. Related scenarios lead to nonsense like: the cat may want to go on a walk. I’m not making this up, ChatGPT recently suggested that I take my cat on a walk. This is likely bleeding too many concepts together (dog=pet=cat) and represents over fitting IMO. Cosine distance can only do so well, it’s not good at distinguishing nuance. But it’ll gladly regurgitate a rephrased Wikipedia article.
- darosati 4y agoI don’t understand why very large neural networks can’t model causality in principal. I also don’t understand the argument that even if NNs can model causality in principal they are unlikely to do so in practice (things I’ve heard: spurious correlations are easier to learn, the learning space is too large to expect causality to be learned from data, etc). I also don’t understand why people aren’t convinced that LLM can demonstrate causal understanding in setting where they have been used for things like control like decision transformers… like what else is expected here? Please enlighten me
- blackbear_ 4y agoI think one of the major difficulties is dealing with unobserved confounders. The world is complex and it is unlikely that all relevant variables are observed and available
- hgsgm 4y agoThat's a problem for humans too.
- cubefox 4y agoHe argued that, because machine learning is just based on correlational statistics, it would never be able to produce reasoning about causation. Which is, at least in retrospect (GPT turned out to be able to do causal reasoning), a fallacy: It's like assuming humans can't think about gold because they do not themselves consist of gold. Or: That humans can't manually evaluate a computer program, because they are not themselves computers.
- psychoslave 4y ago>That humans can't manually evaluate a computer program, because they are not themselves computers. Well, yes, that’s why they were designed in the first place: to carry at scale those repetitive dull tasks that aggregates to an amount which exceeds human abilities and patience. No, our submarines doesn’t have what it takes to swim, but really there is no drama here: there are still useful amazing peace of engineering.
- LordDragonfang 4y ago>Mathematics has not developed the asymmetric language required to capture our understanding that if X causes Y that does not mean that Y causes X. X⟹Y This seems like sophistry to bring up the fact that algebra is symmetric and totally ignore the exist of the above.
- naasking 4y agoSeriously, that statement you quoted is trivially false. It's well known in math that not all functions are invertible. By simple mapping, it's clear that causality is a non-invertible function.
- throwawaaarrgh 4y agoWe don't need intelligent machines, for the most part. We just need machines that are less shitty. Making an AI seems like a lazy person who doesn't want to work harder to make a less shitty machine.
- Simon_O_Rourke 4y agoTry explaining to a cause and effect machine why lots of folks have been let go from tech companies, while the management who misjudged the market get kept on and still get their bonuses.
- cubefox 4y ago[2018] His views about AI are largely disproved now. GPT style systems turned out to be perfectly capable of reasoning about causation, as they can reason about any other relation. Despite working entirely in the established machine learning paradigm. For many years, Pearl was considered the top intellectual critic of machine learning. His point was this: Machine learning is, at its core, just correlational. But true AI would also need to reason about causation. This ability would have to be provided by systems which work entirely differently; by using some form of the theory of causal networks which he co-invented. Now it turns out that causal reasoning is not a major difficulty for classical machine learning, and that causal graphs are likely as useless for AI as formal logic turned out to be. Well, at least causal networks are useful for statistics, the type of explicit inference human scientists do.
- vbezhenar 4y agoGPT does not reason. GPT pretends to reason. It often is glaringly wrong. It's just a thing that plays with words. Here's my conversation minute ago: "Event C happened before events B and A. Event C happened between events A and B. What's the order for those events?" The order of the events would be: A → C → B Event A occurred first, then event C occurred between events A and B, and finally event B occurred after event C. "Event C happened before events B and A. Event A happened between events B and C. What's the order for those events?" The order of the events would be: C → B → A Event C occurred first, then event B occurred after event C, and finally event A occurred between events B and C.
- holoduke 4y agoIt reasons, just not always right. Give it time and these systems will become the oracles of humankind. A utility we need to use as an extension of our lives to succeed in the society we live in.
- diatone 4y agoiiuc, GP’s point is that these models, in their current form, can’t guarantee confidence in their ability to reason about a scenario exhaustively. “Give it time” and other dismissals are beside the point. The point seems to be that these models approximate results based on some hidden criteria, which means that although for a variety of practical purposes they fare better than people and are therefore preferable, there remain problems which are better suited to computing approaches that guarantee exhaustiveness… even though those other approaches sacrifice the malleability of neural networks.
- pyrolistical 4y agoEven us so called intelligent beings only have correlation. The closest thing we have to “causation” is the scientific method and even that is only one counter example from disproving entire theories. So why we need AI to understand causation when even we don’t have it. It’s correlation all the way down. We should strive for truth but know that we will never achieve any
- nhooyr 4y ago> It’s correlation all the way down. We should strive for truth but know that we will never achieve any. No, so far that too is just a correlation. We don't know what's all the way down, absolutely no clue.
- pyrolistical 4y agoOddly we are in agreement
- boole1854 4y agoJudea Pearl would beg to differ. http://bayes.cs.ucla.edu/BOOK-2K/ http://bayes.cs.ucla.edu/BOOK-2K/
- cubefox 4y agoDid you read the interview?
- kilgnad 4y agoNo there are ways to establish causation separate from correlation. It's easy. Imagine, you observe someone with his hand on a switch. Every time he flips the switch the light turns on or off. This is correlative. You observe a correlation between switch flipping and lights going on and off. You can guess that the switch "causes" the light to go on. However correlations do not indicate causation. It could be the switch doesn't do anything. The person is simply observing when the light goes on flipping the switch at the same time. To do a causative experiment you have to become the flip switcher yourself. You have to randomly flip the switch and observe the lights reaction when it is flipped and when it is not flipped. Doing this more and more yourself as the experimenter gives you more and more confidence that the switch is causative to the light going on. Essentially that is what a causative experiment requires. The experimenter must intervene with the subject itself in order to determine causation. Observational experiments are not enough to determine causation. Judea pearl gets into this deeper with counterfactuals which actually imo is more technically correct but makes the whole thing harder to understand. Anyway if you look at clinical trials this is exactly what they do to see if a certain medicine "causes" something. Keep in mind that correlation and causation is sort of separate from the fact that science can't "prove" anything. Correlation is simply a probabilistic number and so is causation. Proof is something outside of this that exists in mathematics and logic.
- photochemsyn 4y ago> "You live on an island called causality," the voice says. "A small place, where effect follows cause like a train on rails. Walking forward, step by step, in the footprints of a god on a beach." - Hannu Rajaniemi, "The Causal Angel" (2014)
- hollerith 4y agoWhat could go wrong?
- grantcas 4y ago[dead]