Blaise Aguera doing a pretty good 101 in neural nets

[P3nT4gR4m]

http://www.ted.com/talks/blaise_aguera_y_arcas_how_computers_are_learning_to_be_creative#t-539223

So I watched a couple of youtube clips a while back, that explained what learning algorithms are up to well enough for my brain to take in but, for anyone who's still trying to wrap their heads around it, the first half of this ted talk does a pretty good job of summing it up.

[LuciferX]

http://www.ted.com/talks/blaise_aguera_y_arcas_how_computers_are_learning_to_be_creative#t-539223

So I watched a couple of youtube clips a while back, that explained what learning algorithms are up to well enough for my brain to take in but, for anyone who's still trying to wrap their heads around it, the first half of this ted talk does a pretty good job of summing it up.

Yea.  I liked this one.

W.X = y
W.X - y = 0

Very simple, though not always entirely obvious.

[Brother Mythos]

http://www.ted.com/talks/blaise_aguera_y_arcas_how_computers_are_learning_to_be_creative#t-539223

So I watched a couple of youtube clips a while back, that explained what learning algorithms are up to well enough for my brain to take in but, for anyone who's still trying to wrap their heads around it, the first half of this ted talk does a pretty good job of summing it up.

As I’m starting out with zero (0) knowledge of artificial neural networks, I think it’s going to take just a little bit more to get me up to 101 level.

Nevertheless, I found the video clip interesting enough to want to learn more about the subject. So, I’m starting with the ‘Artificial neural network’ article on ‘Wikipedia’, and I’ll see where that leads me.

[P3nT4gR4m]

These 3 vids are the ones that I found the most useful in helping me grasp the concept. I was coming at it from a programmers point of view - "How do I code it" Turns out that was the wrong question. You don't code it, it just works.

CNN's are only part of the picture but they're a good starting point. Now I I can grok them, a lot of the other stuff makes more sense.

https://youtu.be/l42lr8AlrHk

https://youtu.be/C_zFhWdM4ic

https://www.youtube.com/watch?v=py5byOOHZM8

[Brother Mythos]

These 3 vids are the ones that I found the most useful in helping me grasp the concept. I was coming at it from a programmers point of view - "How do I code it" Turns out that was the wrong question. You don't code it, it just works.

CNN's are only part of the picture but they're a good starting point. Now I I can grok them, a lot of the other stuff makes more sense.

https://youtu.be/l42lr8AlrHk

https://youtu.be/C_zFhWdM4ic

https://www.youtube.com/watch?v=py5byOOHZM8

Thanks for the additional links. I’ll check them out and see if I can wrap my head around this subject.

[P3nT4gR4m]

Reminds me a bit of when I learned oop - months of people talking complete gobbledygook and then suddenly I just saw it. 

[Mesozoic Mister Nigel]

Slightly different topic, but related and I thought this might be of interest to you: https://www.coursera.org/learn/computational-neurosciencecompneuro

[P3nT4gR4m]

Cheers, Nigel. I keep hearing about computational neuroscience but I get the impression it's more biology than computer science?

Machine learning and related AI stuff seems to pick up any interesting mathematical results neuroscience spits out and figures out if they can be used in ML models. The keyword is "inspired by". I'm just about at the point now where I'm ready to start arsing about with these models. In hacker terminology "hello world". Compile a program that prints your name on the screen. The science part I'm happy to leave to the scientists

First I need to pick an api from the seven billion available. 

[Mesozoic Mister Nigel]

Cheers, Nigel. I keep hearing about computational neuroscience but I get the impression it's more biology than computer science?

Machine learning and related AI stuff seems to pick up any interesting mathematical results neuroscience spits out and figures out if they can be used in ML models. The keyword is "inspired by". I'm just about at the point now where I'm ready to start arsing about with these models. In hacker terminology "hello world". Compile a program that prints your name on the screen. The science part I'm happy to leave to the scientists

First I need to pick an api from the seven billion available. 

I personally don't consider it biology, but more a crossover between biology and data systems; basically applied systems science. It is essentially neural modeling using computers, to the degree that current computer technology is capable of mimicking biological neural networks. The reason it might be interesting to you or to some of the other folks here is because if you have a baseline understanding of how computational neuroscience works, it can help give you a bigger-picture understanding of what the programmer's version of neural networks is meant to do. You don't have to have a biology background to learn computational neuroscience fundamentals; a lot of people actually come to it via computer science or systems science. I am into wet lab work so it's not so much my bag, but it seems like it could be interesting and/or useful to more tech-minded, computer-oriented people.

[Mesozoic Mister Nigel]

The MIT course page gives this relatively concise description:

This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

[P3nT4gR4m]

Yeah, cool. Its the thing I thought it was. A lot of the ai gurus have backgrounds in "neuroscience" I'm guessing the computational flavour would be like a bridge. I look across it and I see icky, gooey stuff, waiting to pounce on me and I'll place a bet a biology person would look across to this side and see weird mechanical stuff made of robots and numbers.

I heard about some results where simulated results on visual cortex matched the real meat version in some measurable way. That slicing thing you were doing? But I know those meat models are heavy fucking duty compute power, a long way off from use case in any sort of production scenario. The coding equivalent is more high level logic function. What you lack in fidelity, you gain in footprint and scalability.

Intelligence itself is beginning to be clearly understood and described in math. What we're discovering is it can be improved, in machines. Massively. Not personality or intellect,  those are fucked up weird shit that nobody seems to be able to work out yet but raw intelligence. They're calling it "artificial" but really it's more a case of "optimised",  or "ampliified" 

I'm doing what I've been doing since I was a kid - looking for problems and applying computatiinal solutions, only this time I don't need to program it. This will be like cheating for a living. 

[Mesozoic Mister Nigel]

As a biology person with a pretty solid math and systems science background, mostly what I see are experimental models that seek to approximate the information networks present at all levels of biology; cell signaling and so on. I find it interesting that nobody seems to be trying to mimic signaling cascades, yet, but maybe they are and just haven't published yet.

As a neuroscientist I have to disagree with the perception that anyone is doing mathematical modeling of cognitive intelligence, yet; intelligence as an economist defines it, yes, but economists are worlds away from actual cognition. I suppose it all comes down to how we define intelligence; from a neuroscience perspective, it is typically defined as a combination of learning, memory, and cognition, rather than as simple logic pathways.

[P3nT4gR4m]

One of those cross discipline,  same terminology deals? Intelligence in the ai vernacular is like a black box version of software but much more powerful. We understand why it works but the actual state,  in old money the program logic, is something that just happens. It learns. It learns to perform intelligent operations on input data. It does stuff that would take millions of coders millions of years to hand code. If it could even be hand coded at all.

Considering the impact the olde fashioned, hand coded shit just had on the planet, I'm fast approaching certainty that no one aint seen nothing yet.

[Freeky]

As a neuroscientist I have to disagree with the perception that anyone is doing mathematical modeling of cognitive intelligence, yet; intelligence as an economist defines it, yes, but economists are worlds away from actual cognition.

I'm putting this in my sig.

[Mesozoic Mister Nigel]

One of those cross discipline,  same terminology deals? Intelligence in the ai vernacular is like a black box version of software but much more powerful. We understand why it works but the actual state,  in old money the program logic, is something that just happens. It learns. It learns to perform intelligent operations on input data. It does stuff that would take millions of coders millions of years to hand code. If it could even be hand coded at all.

Considering the impact the olde fashioned, hand coded shit just had on the planet, I'm fast approaching certainty that no one aint seen nothing yet.

Yeah, I know dude. I don't talk about it much but I have a CIS background, and we covered AI and naive intelligence ad nauseum in upper-division neuroscience classes.