Comment by logicallee | Hacker News Reader

Comment by logicallee | Hacker News Reader

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No, I just meant to reference my discussion from there (i.e. for people to read through my comments there, after clicking.)

IOW I meant to transclude that discussion here. (Perhaps within that comment thread a good specific summary comment is: https://news.ycombinator.com/item?id=13090869)

Obviously it is hard to know when that magic moment will happen that some kind of general AI is created that can learn in some sense similarly to how humans do. My every indication and astonishment at the results that are being produced strongly suggests "at any moment". The results are absolutely astonishing every day and we have vastly more than enough firepower.

You might also be interested in this separate thread where I dealt with questions of consciousness and pain. I again reference it here:

https://news.ycombinator.com/item?id=13026020

and you can click through at the top to follow my reference.

We are way past the point of no return here, and in my estimation it is a question of years or at the most decades - not centuries.

  > Obviously it is hard to know when that magic moment will happen that some kind of general AI
  > is created that can learn in some sense similarly to how humans do. My every indication and 
  > astonishment at the results that are being produced strongly suggests "at any moment".

As an IT guy with a basic but solid neuroscience education (which isn't even needed for what I'm about to say): Yep, you are on the hype train, and very deeply. I really like reasonable discussions, I have no idea what this is right here. We will see more amazing results, sure - but applications will be specialized narrow subjects. From creating "some kind of general AI is created that can learn in some sense similarly to how humans do" we are still very far away. Your statements remind me of 1960s "future" hype,a nuclear reactor in every car by the year 2000, stuff like that.

My hype is different, because in my estimation we already have the hardware. You write:

>As an IT guy with a basic but solid neuroscience education

-- could you go ahead and take a few minutes (maybe will take you 5-10) to read through my above-referenced links referencing my previous discussion and tell me whether I'm correct in your estimation on the bottom-up aspect - i.e. the amount of computation that human neural nets can likely be doing, and how it compares to server farms with fast interconnects today?

I'm not an expert in neuroscience so your feedback might be helpful there.

If P=NP then we already have the hardware to crack RSA encryption.

The above sentence is true, but it has no bearing on anything.

don't you think it would have a lot more bearing if you had 7 billion devices nonchallantly walking around cracking RSA every day using the same or less hardware? (but we couldn't reverse-engineer them, because they were obfuscated in biology)?

The fact that they weren't reverse-engineered (yet) would still have huge bearing on everything.

By 7 billion samples I mean the humans walking around. Your analogy with an RSA crack is fundamentally different beccause biology doesn't do it in 3 pounds of grey goo in seven billion different bodies already.

so you would have to come up with an analogy that uses something we cant use, to say, okay fine it exists and fine, we have the hardware to also do it, but the former doesn't have any bearing on us doing the latter.

How much computing hardware does it take to beat a human at chess or at go? (Less than you think, Deep Blue was hopelessly inefficient.) And how long did it take for those much simpler things to happen?

Interesting observation. If brains routinely cracked RSA, that could be evidence that P=NP.

Still it wouldn't help us find the P-time algorithm in question. We could say "it seems to exist", but that would not imply "we'll discover it any day now".

  > If brains routinely cracked RSA

For brains numbers have a completely different meaning and internal representation than for computers. Brains don't "think in numbers". Doing the kind of math we invented is a major effort for the brain, it's not what it developed for, and it is very poorly equipped to do explicit numerical calculations (emphasis on "explicit"). So looking at brains to "crack RSA" seems like waiting for a hammer to be useful in driving screws.

  > i.e. the amount of computation that human neural nets can likely be doing

We don't know nearly enough what they are really doing! We only know a few selected bits and pieces! You are basing your assumptions on nothing, so according to logic any conclusion is possible from a faulty premise. On which you promptly deliver spectacularly.

Also, the "computation" a brain does at one moment leaves out the time aspect: Lots of things lead to constant changes. The wiring changes all the time. The "computation" metaphor has little use for describing or understanding this major aspect of "brain". The more I learned about neuroscience the more unhappy I got with the computing metaphor that I had had going in (as a CS graduate, naturally, I think). The brain is so very, very different from my pre-neuroscience-courses notions.

How much neuroscience do you know? If the answer isn't at least an undergrad introductory course (the accompanying book is over a thousand pages), why do you get the idea you can make any predictions?

Read this to read about complexity in biology vs. engineering and what scientists in the field think how well we are dealing with it:

- http://biorxiv.org/content/early/2016/05/26/055624

- http://www.cell.com/cancer-cell/fulltext/S1535-6108(02)00133...

Test yourself: Do you understand what he's talking about? http://inference-review.com/article/the-excitable-mitochondr...

Fortunately you don't have to sign up at university these days just for such knowledge:

Free courses (if you ignore the certificate nonsense):

- https://www.mcb80x.org/ (This is linked to from edX as "The Fundamentals of Neuroscience" Parts 1, 2, 3)

- https://www.coursera.org/courses?languages=en&query=neurosci... (Especially "Medical Neuroscience": https://www.coursera.org/learn/medical-neuroscience)

- https://www.edx.org/course?search_query=neuroscience

Thank you so much for those insightful links!

absolutely dude.. for example, this paper just popped up in the last week https://arxiv.org/abs/1611.02167

Interesting. The concept seems to be very similar to this one: https://arxiv.org/abs/1611.01578

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