The IF statement is the root creator of software programming. It has the ability to compare two values against each other and branch out to blocks of instructions. So it is perceiving (reading), decision making and routing - all that which differentiate life from inanimate objects. The AI agents perform the exact same loop, by delegating the first two steps to a model.
Going further backwards, the transistor (or a PNP junction) is the hardware level enabler of the IF statement. The action (switching) driven by the current which in turn controls other switches, is the first manifestation of "observe and act" by inanimate things at the speed of electricity.
Mechanical equivalents existed ofcourse - speed of a governer which controls the flow of fuel which in turn controls the speed of the governer.
ankit84 4 hours ago [-]
I learnt a lot today from the interactive demo. You have the best clarity and right skill to educate
DevarshRanpara 4 hours ago [-]
Thank you, I will try to make more demo on other concepts.
rahen 1 hours ago [-]
In the early days of machine learning (before the first AI winter), networks like this were often implemented and trained in hardware: https://en.wikipedia.org/wiki/ADALINE
That was the first thing that came to mind when I read "the smallest brain you can build". Nowadays, that "small brain" would likely be built on a breadboard using op-amps instead.
> A perceptron *is* the smallest brain you can build.
> In 1958, a researcher named Frank Rosenblatt built a machine *he called* the perceptron.
> It was *inspired* by a single brain cell, a neuron.
lmf4lol 59 minutes ago [-]
Yes . But at least the post seems to be written by OP himself!
and its an a great learning resource - which is arguably more important :-)
esafak 7 hours ago [-]
If you want to learn the fundamentals of ML I recommend a book, such as Deep Learning: Foundations and Concepts by Chris Bishop. If you insist on staying online, one option is https://course.fast.ai/
If you don't know ML I don't think you're going to learn much through ad hoc demos.
mysterydip 6 hours ago [-]
Checked out the book on your recommendation, and they even have a free online option on their site! Very generous: https://www.bishopbook.com/
rishabhaiover 7 hours ago [-]
This book equipped me with the right intuition and tools to visualize machine learning. I wish I was smart enough to hold it all together.
andai 6 hours ago [-]
>I wish I was smart enough to hold it all together.
I used to have a wife, but they took her in the divorce!
The human mind isn't very good at correlating its contents[0]. You can "know" something for years without realizing its implications.
The human mind traverses its knowledge like a man with a small flashlight in total darkness. Our beam of attention is small and narrow, so you need to put the right things in it, or the magic doesn't happen.
This has important implications for learning. I don't know what they are though.
Probably something like, "you can know something without knowing what it means." You haven't connected it to the things it's supposed to be connected to yet. I don't know how to fix that though. (Something involving the Feynman technique, maybe?)
I didn't know Bishop had released a new textbook. I will have to take a look at it. I wasn't the biggest fan of his Pattern Recognition book as I found it overly dense. I much preferred the Murphy and Alpaydin books.
EDIT: His son is co-author?
zxexz 2 hours ago [-]
I still find his pattern recognition book useful and informative. It may be dense, but some of us consider that a positive for 'reference' literature. That book was one of very few that still holds up well fr when it was published - truly in on of the last "dark ages" of ML.
I think those down voting you are perhaps overly eager. I upvoted. Grab "Deep Learning" - you'll find it useful, imteresting, and likely less 'dense' in the negative sense!
stuxnet79 2 hours ago [-]
Appreciate your comment. I skimmed the online version and it covers all the 2010s era developments all the way to Transformers which is enough to earn it a spot on my bookshelf.
> Grab "Deep Learning" - you'll find it useful, imteresting, and likely less 'dense' in the negative sense!
Absolutely! I just ordered it and it's enroute :)
DevarshRanpara 4 hours ago [-]
This fast AI course looks soo good man! Definitely I will start learning soon. Thank you!
7 hours ago [-]
charcircuit 4 hours ago [-]
I can build a smaller brain.
f(x) = 0.
rippeltippel 2 hours ago [-]
That's great, now make it learn something :)
jeffwass 3 minutes ago [-]
It's the simplest AI nihilist!
knightops_dev 5 hours ago [-]
[flagged]
haeseong 4 hours ago [-]
[dead]
jkwang 4 hours ago [-]
[flagged]
EvanXue 5 hours ago [-]
[flagged]
karinatran 5 hours ago [-]
[dead]
hbwang2076 6 hours ago [-]
[dead]
b33j0r 6 hours ago [-]
Okay, it’s conscious. But can it run doom? I rest my case.
Going further backwards, the transistor (or a PNP junction) is the hardware level enabler of the IF statement. The action (switching) driven by the current which in turn controls other switches, is the first manifestation of "observe and act" by inanimate things at the speed of electricity.
Mechanical equivalents existed ofcourse - speed of a governer which controls the flow of fuel which in turn controls the speed of the governer.
That was the first thing that came to mind when I read "the smallest brain you can build". Nowadays, that "small brain" would likely be built on a breadboard using op-amps instead.
I played with similar approach in JavaScript and built a NanoNeuron https://github.com/trekhleb/nano-neuron (it is more verbose than Python though)
> In 1958, a researcher named Frank Rosenblatt built a machine *he called* the perceptron.
> It was *inspired* by a single brain cell, a neuron.
and its an a great learning resource - which is arguably more important :-)
If you don't know ML I don't think you're going to learn much through ad hoc demos.
I used to have a wife, but they took her in the divorce!
The human mind isn't very good at correlating its contents[0]. You can "know" something for years without realizing its implications.
The human mind traverses its knowledge like a man with a small flashlight in total darkness. Our beam of attention is small and narrow, so you need to put the right things in it, or the magic doesn't happen.
This has important implications for learning. I don't know what they are though.
Probably something like, "you can know something without knowing what it means." You haven't connected it to the things it's supposed to be connected to yet. I don't know how to fix that though. (Something involving the Feynman technique, maybe?)
[0] H.P. Lovecraft quote - https://www.goodreads.com/quotes/193944-the-most-merciful-th...
EDIT: His son is co-author?
I think those down voting you are perhaps overly eager. I upvoted. Grab "Deep Learning" - you'll find it useful, imteresting, and likely less 'dense' in the negative sense!
> Grab "Deep Learning" - you'll find it useful, imteresting, and likely less 'dense' in the negative sense!
Absolutely! I just ordered it and it's enroute :)
f(x) = 0.
https://www.dandwiki.com/wiki/Doom_(5e_Campaign_Setting)