[HN Gopher] To build truly intelligent machines, teach them caus... ___________________________________________________________________ To build truly intelligent machines, teach them cause and effect Author : sonabinu Score : 37 points Date : 2023-02-24 14:32 UTC (2 days ago) (HTM) web link (www.quantamagazine.org) (TXT) w3m dump (www.quantamagazine.org) | dwheeler wrote: | This 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>. | | It might be true long-term, but it's certainly not written with | the current advances in mind. | daveguy wrote: | 1 human is the equivalent of several of the most powerful | computers in computation. IO is 3 logs less. I'll be worried in | about 60 years that we may have the computing power of an | artificial human. But only if we understand thought. | gibsonf1 wrote: | There 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. | LordDragonfang wrote: | >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. | is_true wrote: | Most politicians lack this too | Analemma_ wrote: | This 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 | sankha93 wrote: | The 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. | cubefox wrote: | It's from 2018. Time was not kind to Pearl's picture of AI. | mrwnmonm wrote: | God, I hate these titles. The same science news business site | published this before 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 wrote: | While Quanta often has click baity headlines, it is really the | only decent website for pop math and theoretical computer | science. | gibsonf1 wrote: | Fully 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 | canjobear wrote: | What is understanding? | gibsonf1 wrote: | The ability to model an object in awareness and its causality | that corresponds to its space-time reality | canjobear wrote: | What 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 wrote: | All 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. | airstrike wrote: | To define() a Virtual Expectation of how a phenomenon | ought to behave and then watch it play out in reality, | confirming expectations most of the time but noticing | when it deviates (meaningfully) from the expected output | and refining that Virtual Expectation definition to | include additional rules / special cases so that future | reality-checks play out as expected | | Dall-E doesn't observe the real world and compare it to | its "objects in awareness", so at best it only checks one | out of two boxes in GP's definition | mrwnmonm wrote: | Circular definitions, circular definitions, circular | definitions everywhere. | mrwnmonm wrote: | "Intelligence is whatever supports this product." | nradov wrote: | Intelligence is the ability to accomplish goals by making | optimal use of limited resources. | zwkrt wrote: | By which metric a tree is very intelligent and a man with a | private yacht is not. | YeezyMode wrote: | This 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. | darosati wrote: | I 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_ wrote: | I 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 ___________________________________________________________________ (page generated 2023-02-26 23:00 UTC)