China has become a major player in AI
nytimes.com
nytimes.com
I think Chinese researchers & government have a key advantage in collecting enormous datasets, e.g. I wouldn't be amazed if they end up with state of the art models for problems such as object-detection/segmentation for videos taken from Vehicles, Medicine/Radiology and face recognition [3]. Several US strategic funding agencies seem to be pursuing projects with this implicit assumption.
While american government agencies do collect data at a similar scale (Body Cams, Police dashcams), inefficiency in data sharing significantly complicate their use.
[1] https://medium.com/@karpathy/icml-accepted-papers-institutio...
[2] http://www.deepvideoanalytics.com/
[3] https://github.com/seetaface/SeetaFaceEngine
On an unrelated note: If you have a popular Github repo, you can view the Google Analytics style stats under graphs/traffic for referring urls/unique-visitor counts etc.
It will take at least a few more years before submissions are allowed in Chinese: reviewers can't be assumed to know Chinese. The momentum is there though.
China has a huge mass but it's just one object. The Anglosphere has several big objects and a ton of smaller ones and overall I'd say it is much bigger and has a longer reach.
Ofcourse even the best gardeners can fuck up but if I was betting on the best outcomes in 10-20 years I would bet on China.
Any ML project I've worked on has been a lot of tinkering - not unlike the sort of craftsmanship one associates with woodworking and blacksmithing.
Tinkerers gave us Google, Facebook and of course modern civilization.
Tinkerers need the Medici family equivalent. Silicon Valley has that in spades. The Chinese graduate students I met and knew brought this tinkerer's spirit and the American university atmosphere allowed them to unleash their potential (check out who features in the author lists of CMU, Stanford, MIT's papers at NIPS, ICML etc.).
That state of mind and not a top-down approach is what we need.
More Ben Franklins, Robert Hookes, and Issac Newtons; fewer 5-year planning commissions.
I wrote a summary if you're interested: https://medium.com/@seanaubin/book-review-the-entrepreneuria...
Another relatively recent book on this topic: https://www.amazon.com/Kicking-Away-Ladder-Development-Persp...
https://mises.org/library/government-spending-innovation-tru...
and
http://pricesandmarkets.org/volume-3-issue-3-winter-2015/mar...
- Government funded development only gives the government what it wants while the citizens remain unsatisfied. This is due to a lack of "market tests". - We never tested the alternative where the government doesn't fund research and let private companies innovate instead.
Dr. Mazzucato addresses this in her book. She notes that the government needs to provide a pipeline from basic research to marketability. She also argues that the last twenty years, in various countries, have shown that trusting private companies to innovate doesn't give better returns. Also, I feel like Dr. Glein is over-simplifying Dr. Mazzucato's argument by claiming she argues that "many of the technologies and innovations we now value were produced single-handedly by government". Dr. Mazzucato routinely celebrates the ability of private companies to integrate innovative technologies for the public. She's mostly arguing that these companies should be taxed better (more efficiently? realistically?) by licensing the technologies.
However, the argument Dr. Glein links to (https://www.jstor.org/stable/116937?seq=1#page_scan_tab_cont...), is very interesting! It claims that R&D personnel are a finite resource and government funding crowds out the supply of talent for the private sector. I don't know why, but I thought the supply was elastic? Maybe because I over-idealize immigration?
The second essay seems to mostly re-iterate the importance of the "crowding out" effect. It also notes that Mazzucato got some data wrong because: "But in the thirties governments in the US did not fund long-term fundamental research, so companies did it themselves. Now that governments do fund long-term fundamental research, industry needs no longer do so: yet again Professor Mazzucato advocates the very policies that lead to the outcomes she deplores."
tl;dr people seem to be getting different conclusions from datasets I haven't seen and I need to do more research into the "crowding out effect"
As much as I consider myself libertarian, this is where they go off the rocker.
Basic research cannot be market driven. Especially a market that's mostly worried about next quarter
Most of what we consider fundamental inventions have started in academia and were snubbed by the market, with very rare exceptions
Funnily enough those that have invested in basic research have reaped lots of results (like the Minnesota Mining and Manufacturing Company)
You can read an old piece on the issue from Murray Rothbard:
https://mises.org/library/science-technology-and-government-...
Nobody does basic research thinking about a specific market issue
Does it give a biologically plausible explanation for backpropagation?
Basically, I'm waiting until Eric Hunsberger finishes his freaking PhD thesis: http://compneuro.uwaterloo.ca/people/eric-hunsberger.html
https://medium.com/@karpathy/icml-accepted-papers-institutio...
Granted, many of those researcher may be Chinese but that is not the point.
Note: I'm interpreting AI as "also the very useful subset of AI called Machine Learning" here. ML is already in so many things, but I think it truly has yet to take off. But I very much believe that it will be ubiquitous. It's probably worth it for programmers to learn some of the basic algorithms, general approaches, and what they're good for.
I wouldn't say "nobody cares or wants" AI, just that companies are still trying to figure out where it's best used.
The distinction between fuzzy logic and the Bayesian interpretation of probability has always been unclear to me.
U.S. and the other English researchers have done most of the essential/landmark jobs in AI, Chinese authors contribute less.
The vast majority of those grads would be very happy to stay in the US.
Now, definitely for laowai working in China, there is a bamboo ceiling, almost all the senior leaders are going to be Chinese or Taiwanese :)
But all of them are always complaining about how theres basically zero financial assistance for them and they are lucky that their parents can pay for them, since even getting a part time job or internship is a massive hassle that most companies won't even bother with due to their student visa situation.
Masters students, maybe not.
You are incorrect about the internships, which don't require visas for F1 holders as they get a year of OPT. Now, they might save up their one year of OPT for post graduation, which is useful for the wait in getting an H1, though I guess there are also useful extensions that can even get around that (IANAL, so I don't know what the exact story is ATM).
Don't hold your breath.
Titles should be locked to the article title to minimize confusion, imo.
I view intelligence as the capacity to use resources, including the intellect, wisely and creatively. The current system we're building have no self-awareness and no will, so they cannot really be intelligent.
If we want to build intelligent machines, we have to understand the architectures of the mind. We use memory as a sort of mental matter for the formulation of thoughts that we structure in a certain way to formulate ideas/concepts and then we go out and build things. We are builders because we are thinkers and we are conscious about being able to think.
But what is a thought, really? What is consciousness?
Look at the size of our brain next to the huge computers and data centers we're building for "AI". Our brain is small, yet was able to think about how to build these systems.
We believe we're going to develop "artificial intelligence" by building massive computers and data centers. How absurd is that?
Folks, to build intelligent machines, we have to build thinking machines and for this, we'll have to truly understand how the mind works and when we do this, I believe we'll be quite surprised.
The rest of your comment contradicts this statement.
Dictionary definition of intellect is "the faculty of reasoning and understanding objectively, especially with regard to abstract matters". Dictionary definition for intelligence is "the ability to acquire and apply knowledge and skills".
Current AI has great difficulty in abstract matters/thought, let alone understanding something beyond simply a series of learned patterns.
> If we want to build intelligent machines, we have to understand the architectures of the mind.
We have intelligent machines now, many which outperform human capacity for specific tasks. If your talking about strong artificial intelligence, then I wouldn't necessarily disagree, but maybe it could go the other way. By developing strong AI, we can understand the architecture of the mind. Maybe strong AI can be developed with the intellect of a cat/dog, and that gives insight into the human mind.
> We believe we're going to develop "artificial intelligence" by building massive computers and data centers. How absurd is that?
No one who is knowledgeable about AI actually believes this (based on your definition of AI).
> Folks, to build intelligent machines, we have to build thinking machines and for this, we'll have to truly understand how the mind works and when we do this, I believe we'll be quite surprised.
You ask the question "what is a thought" above, then state we need to have thinking machines to make intelligent machines. One could argue machines today think, one could argue alphago 'thinks'. Using your definition of intelligence, we have AI today that meets your requirements. Wisdom is knowledge with good judgement and creativity is exploration with experimentation. There is plenty of academic work out there which covers all this.