
Possible explanations for the slow progress of AI research - maxilevi
https://en.wikipedia.org/wiki/Artificial_general_intelligence#Possible_explanations_for_the_slow_progress_of_AI_research
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clickok
Slow compared to what? You could write equivalent verbiage for the slow
progress of mathematics, chip design, or cinematography-- the only difference
is that uninformed randoms feel less bold when critiquing fields these fields
(as opposed to AI), or perhaps people are less inclined to waste time reading
such half-formed speculation.

The reason progress towards general artificial intelligence is "slow" is
because it's a hard problem.

Formulating a definition of intelligence precisely enough that it can be
optimized is incredibly difficult. We can capture facets of it, in some
settings, but also have to deal with the cost of hardware and the difficulty
of acquiring data. Sometimes you can get inspired by examining how animals
behave, or analyzing the brain, or considering what it means to learn in
abstract, but considering one thing in isolation means that you tend to hit a
wall eventually.

We refine ideas, we try things, and are buoyed by the advances in technology
that makes some strategies possible, but for every major milestone we have a
ton of things that people tried and couldn't get to work. I personally have
spent months working on stuff that ultimately yielded a minor improvement;
I've spent days proving results that ended up taking half a page in some
papers. It's not _easy_ , and no one expects it to be easy, although some
jerks shilling something singularity-related might make such claims.

But saying (to pick an example from the article) "oh, you just need to
incorporate emotions, bro" into a learning agent is just about the dumbest
thing I have ever heard.

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corporate_shi11
The problem with current approaches to AI using backpropagation is that while
gradient descent is effective for learning a task, the brain is not a task
learning machine (parts of it are but not the parts that produce human
intelligence) and does not do gradient descent. Achieving human level
intelligence (strong AI) will require going beyond the gradient descent
paradigm.

The new paradigm that must be followed is building an agent with a neural
network that evolves according to the 'neurons that fire together wire
together' approach combined with reinforcement learning.

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solveit
I'm not saying you're wrong, but I can't see how in the world you could
possibly know any of what you said. The human brain doesn't (as far as we
know) seem to use backpropagation. Everything else is wide, wide open.

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corporate_shi11
I think it's accepted that neurons which interact exhibit increased axon
growth between themselves. Given the local nature of biological processes I
think this must therefore be the dominant process behind connection building
in the brain.

It's really a very general statement about neural development. I have no idea
how important the high level structure of the brain is for human thought. It's
possible that most brain regions are irrelevant and have more to do with
regulating bodily processes than anything else, mere machinery for running a
body. But who knows.

The layered residual and wide reaching connections of neocortical neurons are
probably equivalent to the 'airplane' in the bird flight analogy.

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hprotagonist
> I think it's accepted that neurons which interact exhibit increased axon
> growth between themselves.

hebbian learning seems real, but also incomplete. As normally stated it’s a
positive feedback loop — if that’s all that was going on, we would be
incapable of change.

There are a number of non-hebbian learning paradigms (e.g., volume learning)
that seem true too.

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mempko
We know so little about how the brain works it's hopelessly hard that we would
be able to emulate it. We should maybe instead focus on making AI think the
same way airplanes fly. Meaning studying the brain, especially the human brain
is so hard it's no wonder we aren't anywhere near understanding intelligence,
free will, and other aspects. Science is just such a hard thing to do! It's
also possible we simply aren't capable of understanding those things because
we have limits to our intelligence.

EDIT: Great talk from Chomsky
[https://youtu.be/TAP0xk-c4mk](https://youtu.be/TAP0xk-c4mk)

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serioussecurity
We don't even know what fraction of what we term consciousness or intelligence
comes from the brain.

Antonio Damasio is a good read.

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thegabriele
What's your definition of consciousness?

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Abishek_Muthian
Money. Like any scientific research, AI research requires funding. Much of
current funding for AI research comes in the form of VC money to Startups who
are working on building a commercial solution with AI.

But unlike pure scientific pursuit, like say in Academia; Startups have a time
limit by which they need to produce the end result i.e. a product and the
success of it depends on several other variables.

There are startups which are primarily research oriented, but majority of so
called 'AI' startups are working on producing commercial solution from someone
else's thesis paper[1] which itself may not have proved to offer significant
advantage over conventional methodologies.

When the VC's fail to get their returns from a number of failed 'AI'
investments, it affects the entire ecosystem.

[1] [https://hitstartup.com/artificial-intelligence-thesis-
papers...](https://hitstartup.com/artificial-intelligence-thesis-papers-are-
like/)

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whoisjuan
I think is wrong to assume that VC’s is what fueling advances in AI work. It’s
for sure contributing especially from large companies (companies that once
where VC funded), but I think the bulk and hardcore advancements in AI are
still coming from universities and research centers.

There’s only two or three companies in the top 25 of organizations leading AI
research. The vast majority are universities.
[https://link.medium.com/L2JrbVKAT1](https://link.medium.com/L2JrbVKAT1)

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WrtCdEvrydy
How about if there's already an existing AI and it keeps other AIs from being
built by providing shitty ML software that people adopt instead of allowing
for real software to thrive which would end up being the solution to the real
AI problems?

Nah, just learning pyTorch now.

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svd4anything
ML, deep learning, AI stalled theme this last week. I’d just like to mention
what if we are dramatically underestimating by many orders of magnitude the
computational, storage and bandwidth of organic brains? What if organic brains
are using Quantum computing not yet discovered? I mean we had X17 news that
opens possibilities that we are missing standard model physics. It just seems
we humans have a bad habit in the historical past of being quite over
confident in our level of understanding.

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nootropicat
Full agreement. A honey bee has ~1M neurons, yet is able to navigate complex
environments utilizing visual, sound and olfactory information. Assuming
identical physical capabilities, just how much computer processing power would
it take to make something able to function as a bee? A cat is two orders of
magnitude away from the human neuron count, yet its ability just to navigate
the world [1] blows all existing ai out of the water, not to mention other
behaviors.

I don't know how to quantify it precisely, but it seems to me that linear
increases in intelligence require exponential increases in computing power,
both in digital ai and in animals.

[1]
[https://www.youtube.com/watch?v=kGeN8jkt4UE](https://www.youtube.com/watch?v=kGeN8jkt4UE)

