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#neuralnets

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I just discovered the ARC-AGI initiative and the associated test to estimate how close "AI" models are from #AGI

arcprize.org/arc-agi

While I found the initiative interesting, I'm not sure I understand what in this test really guarantees that the model is capable of some form of generalization and problem-solving.
Wouldn't it be possible for specialized pattern-matching/discovering algorithms to solve such problems?
I imagine some computer scientists, mathematicians or computational neuroscientists have already had a look at this, so would anyone knows of some articles/blogs on the topic?

Maybe @wim_v12e? Is this something you already looked at?

ARC PrizeARC Prize - What is ARC-AGI?The only AI benchmark that measures AGI progress.

After the initial excitement has set by now, more and more people have raised the issue that the GPT-3 based chatbots are able to make statements that sound convincing, but may have no bases in reality.

This makes a lot of sense to me, as AI, in its core, is based on one thing: pattern recognition.
So really, all it does is cargo cult, not learn or necessarily understand.

#gpt3#chatbot#ai

After some incredibly annoying stalled research, I am close to empirically demonstrating that the #neuralnets used for machine learning-assisted software vulnerability detection #mlavd are *only* recognizing code patterns and not able to perform the calculations and code-flow required to test for vulnerability.

#warning These are early, tentative results. Still working on testing the graph and transformer-based models. I do expect graph models to perform somewhat better, but we'll see.