Sure it's exciting stuff for those handful of researchers who currently work on projects like AutoML.
On the other hand, I feel sad for myself because me and many others left so far behind. I have strong feeling that such technologies would lead to concentration of power of such mega-corporations like Alphabet as well as complete monopoly for any creative work. So very few of us who managed to become cutting-edge researchers would be proud to be creative humans, others will do just a monkey job using magical APIs.
In 80s, two people could create state-of-the-art game written in assembly with it's own tiny game AI (hello to Elite [1] which has intelligent opponents who engage in their own private battles and police who take an active interest in protecting the law).
In 90s, a small team could create state-of-the-art game written in pure C with some cool AI (hello to Quake III Arena [2] which has pretty strong bots [3]).
In both of these cases, you don't have to be genius to be able to understand whole thing alone.
I'm 33 and I progress very, very slowly. I feel I might be on the level close enough to understand Q3A entire source code. I think I would have great future if today is 1994. Unfortunately for me today is 2017 and I do realize that I don't have any exciting future at all.
Don’t put too much stock into these overblown articles written by credulous reporters who trust the self-serving delusional optimism of people whose funding depends on dumb investors believing that this stuff is “just a few years out”. A.I. researchers have been promising grand visions of a future in which computers can do everything for us for 70 years if not longer. In reality they are not significantly closer than they were in the 80s. We just have a lot of cheap, fast, networked storage now and statistical analysis can feel like magic. But creating programs that recognize very specific patterns in totally inhuman ways using auto generated heuristics incomprehensible to humans is not A.I. and won’t replace humans anytime soon.
I have to go ahead and completely disagree with you there. It's certainly more complex under the hood but:
A) Education has never been this accessible - see https://www.coursera.org/, youtube, MOOCs, blog posts etc. which did not exist anywhere for free even 10 years ago
B) APIs and abstractions make a lot of this quite accessible (e.g. AWS, tensorflow etc.), yes these are "magical" APIs, but you could make the same argument regarding a C compiler going to binary, all the way down to logic gates and electrical pulses
33 is young in terms of education, I would highly doubt you're progressing slowly due to your age, probably more your attitude that is holding you back.
I think your points are correct but there's another which you might be disregarding and which is causing GP poster's feelings: The volume of knowledge to be learned if you want to do anything meaningful from anywhere near 'first principles' is orders of magnitude greater than it used to be. If you just want to be cutting edge using a "magical API" then sure, download Keras or TensorFlow and play with some DNNs. But if you want to understand everything you're doing at the theory level then you've got to learn so much more than you did back in the 90s.
Thanks! That's exactly what I mean. I don't want to use magical API and "just play with data". I really want to be able to understand from ground up.
It doesn't mean I have to read every single line of Tensorflow but being able to do that when it's needed. So that such tools won't be magical black box for me.
That makes sense, but you have to draw a line somewhere - you can't possibly know everything from the ground up. You'd have to start with particle physics, atoms, molecules, to even get to the basis of electricity - it's impossible for one person to know all of this.
Ground up knowledge is difficult to obtain in any field. How long do you think it would take you to get a complete understanding of a modern car from he ground up?
The opacity between implementation and understanding is large here and many fields. It depends where you want to contribute. I can build (i.e assemble) a computer. I could learn to build a small basic computer out of transistors and logic gates, etc. Theres a difference between a technician, an engineer and an inventor. To be an inventor takes a lot of work and experimentation probably proportional to the novelty of an invention.
Not to overdo analogies but you dont need to rebuild your own internal combustion engine in a unique way to drive a car or to contribute improvements to a car. The more you understand how and why tensorflow works the more you can do with it. It depends whether you want to build on top of that platform and use it, or build on the concepts for something else.
"Ground up" might be the wrong term here. I don't have right words either, but I feel GP is talking about that level between full knowledge and the "I have no idea what I am doing" level of downloading models from Kaggle, stuffing them into TensorFlow and calling yourself a "Deep Learning expert".
Even though I lack the name for that level, here's how I would describe in qualitative terms some of its attributes:
- Knowing the basic lay of the land all the way down. That is, at least knowing most of the black boxes and what they do, even if you don't exactly know how they do it.
- Being able to solve your own problems, instead of running around like a headless chicken every time you hit a speed bump in your work.
- Being able to reason from that first-ish principles. You're able to sketch solutions within the scope of the extended domain, and as you begin implementing it and need to understand various blackboxes in more depth, the basic shape of your solution isn't usually invalidated by gained knowledge.
I disagree that car design is a stable field. Tesla is selling a radically different car design. All car designers have to face the dawn of self-driving cars.
In every field the total knowledge set is always increasing, which is both empowering, because we stand on the shoulders of giants, and diminishing, because there is less low-hanging fruit. There is always more low-hanging fruit though, the trick is to see it hanging there. ML is a wonderful opportunity because the magical api’s can do far more than they’re currently used for.
> APIs and abstractions make a lot of this quite accessible (e.g. AWS, tensorflow etc.), yes these are "magical" APIs, but you could make the same argument regarding a C compiler going to binary, all the way down to logic gates and electrical pulses
That's a really interesting analogy, I'm wondering what other think about it?
And does it really make a difference? I don't understand compilers, but it still took me a long time to understand how to write correct input for a compiler, and debug the output.
The funny part is an 18yr old is probably thinking he is too young for the most part to pursue AI. A 25 year old is thinking he needs to be part of an AI program to pursue AI and that real AI is done by the people in their 30s. As someone in their 30s, I have met gray/white haired PhDs pursuing AI in deeply humble ways.
There is no age that you can't do anything you want to do. But in common ground with what you are saying, the older you get the less time you have. The older you get, the more adrift you become of like minded individuals. The older you get, the tougher, less excited, and less patient you become with learning new things. But at the same time, you become "more" in so many other ways.
All creatures are not only created equal, but remain equal even as time progresses and skills/attitudes/energies are gained/learned and lost.
>> others will do just a monkey job using magical APIs.
Isn't that what most of modern software development is like already?
Many common use cases have been implemented in frameworks, and usually a developer's job consists mostly of tacking pieces of framework together. The days where a person single-handedly implements a state-of-the-art game from scratch are long over.
On the other hand, you could still create a game by yourself using all the available open source tools. You can still be creative and do exiting things all you want, the type of work is just different.
Also, you don't have to be a genius to understand machine learning either. But you do have to learn some math!
What magic are you talking of when you know data is the most precious element to do anything meaningful and it's hoarded through a handful of megacorps. Why don't we hear of any small teams "outisde" the mega corps doing real progress on AI. There was an article recently where even professors in universities admit they cannot compete with megacorps since they offer much higher wages than academia.
edit: more sick, the said young researchers are financed by society than get sucked to private corps where their work is locked behind IPs.
Even in the case there is a small company having any progress they would get swallowed up right away.
Yes Keras and Sickit-learn are open source and available to everyone but it's like telling me, look you have access to pen and paper but you need to pay if you want to read the books, the metaphor here being access to data is equivalent to middle age's access to books...
The internet is full of data you can use. Just crawl it, like everyone does. There are thousands of open datasets, some gigantic in size. If you have sensors (camera, GPS, orientation, etc) you can generate a shitload of data. If you can create a game that is related to the data you want to collect, then you can collect data for 'free'.
On the other hand, think about it: what do Google and FB have that we don't? Personal data. What they have is data that is useful to target ads. If your interest in AI goes beyond ads, then you don't need that data.
| On the other hand, think about it: what do Google and FB have that we don't? Personal data. What they have is data that is useful to target ads.
Yeah ? like the tons of photos they harvest from people. Most of the progress they did in training computer vision is based on that. Should I build facebook or google to get access to it ?
What about language modeling ? They have access to conversational data and billions of search queries, both of which there is no way to access them from outside.
What about health ? Well if I'm not somehow working with some big pharma how could I access this kind of data ?
I can go on and on. The point is, yes I can crawl the web, but what "web" is there left ? everything is locked behind paywalls and private clouds. If the real vision of an open internet was fulfilled, all data generated on it would be accessible to crawl indeed.
I'm not saying it's not possible to get data and use it. I'm saying you cannot get the kind of data only monopolies have and you will never be able to compete with them.
Photos: if you build a facebook app, you can probably ask for permissions to fotos of your app users. Also the open datasets for machine learning with images like the coco dataset are pretty big. Can you really handle a lot more than that? Even hinton starts with mnist for new ideas like capsules.
Language modeling: hacker news, public mailing lists, wikipedia, github.
Health: you can usually get data if you work at a hospital as an md or researcher. Just need a reasonable idea and an IRB. If you want the pharmacy data, I imagine you could get at it by going to work as a researcher in pharma, insurance, or retailer.
alphago was built using publicly available games of go pros. Alphagozero didn't even depend on data at all.
For AI, the limiting factors are ideas, code, time, hardware.
AlphaGo and AG0 were built with ridiculous amounts of compute power that Google donated to the effort. To replicate their results would cost millions of dollars.
Unless your objective is to target ads, I'm really not sure why you'd think that Facebook's collection of people's holiday and wedding snaps and memes is a superior training set to say, the entire world's surface photographed at regular intervals, or millions of more-selectively-uploaded tagged images in Flickr, or image sets especially designed for training like OpenImages
sp4ke sez> " I'm saying you cannot get the kind of data only monopolies have and you will never be able to compete with them."
More a political statement than a statement of relevance to the workplace.
You need not worry that "they" will hold you back. It is unlikely that analyzing monopolys' data will explain how early man built flint tools, Joe the mechanic repairs his car, fifth-grade Fred solves his geometry problems or van Gogh painted. ML, including AutoML, appears to be a long way from solving most AI problems. There's no need to feel that "they" are holding you back by witholding data. And then remember:
"Be careful what you wish for, it might just come true."
- old saying
I would strongly recommend reading the mission statement [1] of OpenAI. There are major players in the industry working against the issue of the disparity that AI will almost certainly create.
As for your key point, defining yourself let alone your future relative to the paths other people took is pointless. Look at things from a different perspective. Notch built a game of no great technical sophistication where you play with blocks. He did it during his spare time after work. And became a billionaire in the process. Does the fact that you could probably build it from scratch now mean anything about your future? No, not really. Would it mean anything if you could not? Again, no not really. You alone determine your future, or at least heavily influence the probability distributions of it.
On the other hand, I feel sad for myself because me and many others left so far behind. I have strong feeling that such technologies would lead to concentration of power of such mega-corporations like Alphabet as well as complete monopoly for any creative work. So very few of us who managed to become cutting-edge researchers would be proud to be creative humans, others will do just a monkey job using magical APIs.
In 80s, two people could create state-of-the-art game written in assembly with it's own tiny game AI (hello to Elite [1] which has intelligent opponents who engage in their own private battles and police who take an active interest in protecting the law).
In 90s, a small team could create state-of-the-art game written in pure C with some cool AI (hello to Quake III Arena [2] which has pretty strong bots [3]).
https://en.wikipedia.org/wiki/Elite_(video_game)
https://en.wikipedia.org/wiki/Quake_III_Arena
https://www.researchgate.net/publication/240430519_The_Quake...
In both of these cases, you don't have to be genius to be able to understand whole thing alone.
I'm 33 and I progress very, very slowly. I feel I might be on the level close enough to understand Q3A entire source code. I think I would have great future if today is 1994. Unfortunately for me today is 2017 and I do realize that I don't have any exciting future at all.