[HN Gopher] To build truly intelligent machines, teach them caus...
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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
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