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Confession of a so-called AI expert

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Re: Confession of a so-called AI expert

#31

Post by PeterisP »

jcoffland wrote: post #8
Dzugaru wrote: post #3

> Even though I’m one of the beneficiary of this AI craze, I can’t help but thinking this will burst. I don't think it will. Level off - maybe. I've started my work in Computer Vision with classical algorithms (SIFT features, geometry, correlation filters and things alike people were researching for decades). These really worked like garbage, it was a nightmare. Then we jumped on DL bandwagon - and CV just clicked fo…

Pattern matching is the one thing DL is good for. Which is why it's a good match for CV. Calling DL AI in the first place was a mistake or at least over zealous marketing. Playing go or chess or matching patterns are all things intelligent begins can do but that does not imply that doing those thing means you are intelligent.

One can argue that the parts of our brain that make us intelligent, the prefrontal cortex that is so much bigger than in "lesser" animals, is essentially an overgrown, glorified pattern matching engine. Pattern matching is the one thing our brains are good for - there's good reason to suppose that quite many intelligence-related tasks can be reduced to a form of pattern matching.

Re: Confession of a so-called AI expert

#32

Post by CalRobert »

bitL wrote: post #12
Dzugaru wrote: post #3

> Even though I’m one of the beneficiary of this AI craze, I can’t help but thinking this will burst. I don't think it will. Level off - maybe. I've started my work in Computer Vision with classical algorithms (SIFT features, geometry, correlation filters and things alike people were researching for decades). These really worked like garbage, it was a nightmare. Then we jumped on DL bandwagon - and CV just clicked fo…

This. I don't think people even get a hint of what is possible nowadays with DL in CV, NLP etc. I am actually depressed when I talk to some friends and they are so pitifully outdated, and then even after showing them how to do some magic in 100 lines, observing they are still not getting it and continuing in their old ways :(

Just out of curiosity, where would you recommend starting for those outdated people? Machine Learning, Deep Learning, AI (for lack of a more specific acronym), NLP - these things are kind of daunting for newcomers, if only due to the acronym du jour changing constantly.

Re: Confession of a so-called AI expert

#33

Post by tnecniv »

Dzugaru wrote: post #3

> Even though I’m one of the beneficiary of this AI craze, I can’t help but thinking this will burst. I don't think it will. Level off - maybe. I've started my work in Computer Vision with classical algorithms (SIFT features, geometry, correlation filters and things alike people were researching for decades). These really worked like garbage, it was a nightmare. Then we jumped on DL bandwagon - and CV just clicked fo…

> I don't think it will. Level off - maybe.

I mean, the dotcom bubble popped but websites are still here and more profitable than ever. The bubble popping doesn't mean that DL is going to go away. People will just have more reasonable expectations about what it can do.

Re: Confession of a so-called AI expert

#34

Post by rubatuga »

frgtpsswrdlame wrote: post #23
rubatuga wrote: post #21

One of my friends is in finance, and the other in biology, and judging by the way that they talk about it, they believe that AI is about to take over the world, and they believe there is a huge monolithic black box that can solve all the world's problems. So yes, there is a huge bubble. The question is how exactly will the bubble pop? Or will it pop?

Or how can I make money from it popping??

Short every AI startup you see.

Re: Confession of a so-called AI expert

#35

Post by rubidium »

visarga wrote: post #4
Dzugaru wrote: post #3

> Even though I’m one of the beneficiary of this AI craze, I can’t help but thinking this will burst. I don't think it will. Level off - maybe. I've started my work in Computer Vision with classical algorithms (SIFT features, geometry, correlation filters and things alike people were researching for decades). These really worked like garbage, it was a nightmare. Then we jumped on DL bandwagon - and CV just clicked fo…

Computer vision is the part of DL that is most suited to produce economic value. CV will be worth trillions of dollars in a couple of decades. All those cars, drones, agricultural equipment, medical scanners, robots and security cameras will be able to understand what they see and act intelligently. It's like the most universally useful thing since the invention of the wheel.

"It's like the most universally useful thing since the invention of the wheel."

I'll choose refrigeration, combustion engine, concrete, and probably hundreds of other things before computer vision.

Re: Confession of a so-called AI expert

#36

Post by ansible »

gaius wrote: post #10
Dzugaru wrote: post #3

> Even though I’m one of the beneficiary of this AI craze, I can’t help but thinking this will burst. I don't think it will. Level off - maybe. I've started my work in Computer Vision with classical algorithms (SIFT features, geometry, correlation filters and things alike people were researching for decades). These really worked like garbage, it was a nightmare. Then we jumped on DL bandwagon - and CV just clicked fo…

I don't think it will. Level off - maybe. https://en.m.wikipedia.org/wiki/AI_winter

Eh, we've reached a crossover point already.

Various products and services have shown that DL and other ML techniques are useful and profitable to implement. And corporations can see the benefits of incremental improvements. That alone will continue the momentum, even without amazing breakthroughs.

Re: Confession of a so-called AI expert

#37

Post by 31reasons »

CalRobert wrote: post #32
bitL wrote: post #12

Earlier quoted context omitted.

This. I don't think people even get a hint of what is possible nowadays with DL in CV, NLP etc. I am actually depressed when I talk to some friends and they are so pitifully outdated, and then even after showing them how to do some magic in 100 lines, observing they are still not getting it and continuing in their old ways :(

Just out of curiosity, where would you recommend starting for those outdated people? Machine Learning, Deep Learning, AI (for lack of a more specific acronym), NLP - these things are kind of daunting for newcomers, if only due to the acronym du jour changing constantly.

Great thing about Deep Learning is that decades of "old school" machine learning research that was way too math intensive is far inferior now. DL is actually pretty approachable and intuitive.

Re: Confession of a so-called AI expert

#38

Post by jboggan »

apetresc wrote: post #25

What I don't understand is how you get a gig teaching a course at Stanford while being an undergraduate student at Stanford. Is this some sort of special seminar course or something?

I didn't come off of this article feeling very impressed with Stanford's CS department. Someone with contextual knowledge please explain why I am wrong.

I've had my own calculus / discrete math / math for bio courses before but that was after several years as a doctoral student and TA at Georgia Tech. I can't imagine that there isn't a PhD candidate with more experience under their belt both teaching and using TensorFlow. The author even admits they volunteered to teach the course to stimulate learning the material themselves.

Re: Confession of a so-called AI expert

#39

Post by JSONwebtoken »

CalRobert wrote: post #32
bitL wrote: post #12

Earlier quoted context omitted.

This. I don't think people even get a hint of what is possible nowadays with DL in CV, NLP etc. I am actually depressed when I talk to some friends and they are so pitifully outdated, and then even after showing them how to do some magic in 100 lines, observing they are still not getting it and continuing in their old ways :(

Just out of curiosity, where would you recommend starting for those outdated people? Machine Learning, Deep Learning, AI (for lack of a more specific acronym), NLP - these things are kind of daunting for newcomers, if only due to the acronym du jour changing constantly.

The acronyms aren't changing, machine learning, deep learning, and neural networks have been around for 50 years. It's only recently that code libraries like TensorFlow have abstracted away a lot of the math to the point that it's relatively accessible to normal people that can write code.

Deep learning is a subset of machine learning that utilizes more than one layer of neural networks. So these terminologies just refer to different parts of the same process. The 'process' is just tweaking a program to progressively make more accurate yes or no assumptions about a set of statistics that you give it. That's my best shot at it, hope it makes sense.

Re: Confession of a so-called AI expert

#40

Post by bitL »

CalRobert wrote: post #32
bitL wrote: post #12

Earlier quoted context omitted.

This. I don't think people even get a hint of what is possible nowadays with DL in CV, NLP etc. I am actually depressed when I talk to some friends and they are so pitifully outdated, and then even after showing them how to do some magic in 100 lines, observing they are still not getting it and continuing in their old ways :(

Just out of curiosity, where would you recommend starting for those outdated people? Machine Learning, Deep Learning, AI (for lack of a more specific acronym), NLP - these things are kind of daunting for newcomers, if only due to the acronym du jour changing constantly.

It's difficult to say to be honest; for me the "enthusiasm" works best, I simply picked an area I wanted to know (e.g. self-driving cars using DL) and then learned some mindblowing approaches, like NVidia/Tesla using a few layers of simple convolutional neural network and static images to predict steering angles, and then some people stacked RNN on top of this CNN and made it estimate steering angle from 10 previous frames and a current frame. See e.g. selfdrivingcars.mit.edu

If you are into CV, first start with very simple static image recognition with AlexNet/VGG/Inception etc. in Keras, try to understand CNNs a bit (it's inspired by biological neurons, they can do simple things like direction detection, edge detection etc. and overlap each other's field of vision; if you look at computational photography, convolutions do something similar, so the idea is why not use a layer of multiple convolutions, then make a hierarchy of those convolutional layers, and let the optimization/learning part of Deep Learning during training figure out what exact convolutions does it need instead of force-feeding them by hand). Play with the ways to improve training (batch normalization, image augmentation etc.) Once you understand this, your mind would probably explode and then it's time to understand RNNs/LSTMs/GANs and have fun applying it on voice, natural language, generating art etc.

You'll have a blast for sure when you realize what you can now easily do! Have fun! ;-)

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