I think as opposed to classical statistics, except for the important subfield of statistical learning theory, machine learning relates much more to functional analysis, differential geometry/optimization over manifolds, and measure/probability theory. "AI" is whatever marketing people want to define it as.
It is meaningful if the US is ahead of any cooperating bloc of powers in any covered area of image recognition. This is much broader than being ahead of specifically China on the whole. For it to not be true would essentially imply that no new research is happening in the US.
I doubt the US is ahead in this area. China gains heaps upon heaps or practical experience in CV by sheer virtue of the breadth of its surveillance networks. Not to say we aren't doing the same here in the US, but efforts seem to be much more scattered
It’s a positive loop. More effective surveillance network -> Larger investment (from government or government contract) -> more application/startup/new programs -> more research funding/aggressive hiring -> higher recognition for CV/ML researchers/Engineers -> More and more people doing CV/ML -> More data, algorithms and applications-> more effective surveillance network. Btw it got deployed at scale in real world which is a huge advantage for progressing any CVML research
Not to mention nowadays Deep learning is pretty much a big data game.
> But isn't that orthogonal with developing the algorithms?
Assuming it is - China is also competitive on developing algorithms. A few months ago there was a post on explosion of AI papers submitted by authors at Chinese research institutions, with no signs of slowing down.
They use it for flight check-ins, entering the park, giving you a fine instantly for jaywalking, buying a soda from a vending machine, any many more use cases. I assume they are ahead.
I think this speaks more about the government and the acceptance of such things by the population rather than the state of research. Even in a hypothetical scenario of US being far ahead of China in the field at the moment, i do not see this kind of things going over well at all with the public in the US.
Given two research labs, with one having a bit better equipment, while the other having a more proven history of publishing innovative research, it would be disingenuous to say that the better equipped lab is ahead until they have actually produced some research that puts them ahead. It might help them gain lead, but it also might result in nothing. Better equipment is just one of many components that affect the chances of success. Until that lead is acquired, I don't really think it would be appropriate to say that they had done so.
Note: the lab with a history of published innovative research in my analogy isn't supposed to represent the US or any country in specific. This was just an example to better illustrate the point I was making. The only thing that should matter for whether someone is ahead or not in this situation is the actual proof of being ahead, not "the opportunities that could lead to them being ahead". Otherwise, we should also start immediately trusting all those articles that pop up once every few months about how some random city is "about to become the next Silicon Valley, here are the reasons why".
Anecdotally even number and quality of publications/papers by Chinese in English outperform the entire English speaking region. They probably publish a lot more in their native scripts.
Most of the time whatever interesting thing (posted to HN) originated in some Chinese startup/university.
Bernstein's case established that he had a First Amendment right to publish source code under the law in effect at the time; he argued, successfully, that this was the form in which his research was communicated to other researchers. He won his case, but it took many years, and of course court cases are political processes; they may be decided differently when different judges have been appointed to the bench. It seems that machine-vision researchers may now need to make the same argument. It's probably worthwhile to save the neural network parameter vectors you currently have access to somewhere outside the US while that is still legal.
The ethical argument for why everyone should have access to cryptography is a lot stronger than why everyone should have access to satellite imagery recognition algorithms.
Also, cryptography requires both sides to use the same algorithm, while companies don't need to use the same recognition algorithms.
It also helped, in the crypto case, that you could print some version of it on a T-shirt or mail it on a postcard. It looked like speech, while neural net parameters don't.
Lawyers have made strong cases on both sides of the cryptography argument; probably they can on both sides of the satellite-imagery argument as well. Maps are the primary result of satellite imagery recognition and are a public good. Most covert activity visible on satellite imagery is environmental damage, which is often illegal and generally harms the public. Satellite image processing can be very useful for increasing agricultural production; restricting that to one country, or granting one country's companies an effective worldwide monopoly on increasing agricultural production, would be ethically unconscionable — in times of drought, it amounts to letting people starve instead of telling them how to raise adequate food.
But Bernstein's case didn't hinge on the likely consequences of strong cryptography being widely available; rather, he argued that he had a First Amendment right to publish his research.
I believe the emphasis here was on the generic right of disseminating research not on judging the necessity of a specific technology for particular audience.
You might be reading too much into the word “political.” It’s legitimate for different judges to have different judicial opinions; that’s why appeals are often decided by a panel of judges.
Also many judges are elected, and in the US those who aren't elected are appointed by elected officials, and even in undemocratic countries, rulers consider the opinion of the public when they make judicial appointments. Moreover, most judges must apply statute law, which is politically enacted.
Yeah, the thing about this stuff is the "law is so vague as to be meaningless" and "encryption/AI/whatever is just executing operations on a computer, what do you mean really?" are much harder defenses than one would think.
They say it doesn't apply to Canada. What prevents a Chinese company to open a business in Canada and get access to US A.I software without the license?
"The rule will likely be welcomed by industry, Lewis said, because it had feared a much broader crackdown on exports of most artificial intelligence hardware and software"
Considering that AI is whatever marketing folks want it to be, it'd be interesting see their legal definition AI. Anyone have a link to the actual document?
My off the cuff interpretation is that the rule would only cover convolutional neural nets that are trained to identify and determine the orientation of specific objects in geospatial imagery. If the neural net's input/output aren't wrapped in a GUI it sounds like they still might be OK to export without a license
Looks like it's very specific and targeted to military applications. Although the GUI requirement seems to make it a little too specific to be applicable.
My reading is that it's the GUI used to train a neural network to identify any objects in geospatial imagery. I'm definitely less okay with that. Although, I feel like the GUI is not the hard part to make, so it's a weird part to restrict.
1. Provides a graphical user interface that enables the user to identify objects (e.g., vehicles, houses, etc.) from within geospatial imagery and point clouds in order to extract positive and negative samplesof an object of interest;
2.Reduces pixel variation by performing scale, color, and rotational normalization on the positive samples;
3. Trains a Deep Convolutional Neural Network to detect the object of interest from the positive and negative samples; and
4. Identifies objects in geospatial imagery using the trained Deep Convolutional Neural Network by matching the rotational pattern from the positive samples with the rotational pattern of objects in the geospatial imagery.
What counts as "geospatial imagery"? Could this apply to any training UI for self-driving cars, maps, street view, etc.?
Because this is so incredibly broad, there's a good chance that >20% of people here will be working on something that falls under this at some point in the next decade.
While we patiently await for a HN user (or, let's be honest, one of the ancient cryptologist-lawyers who come out of the woodwork every time something like this happens and sue the government) to fix this by suing the government on free speech grounds, don't forget that git, mercurial, fossil, bazaar and more are all decentralized, can't actually be censored at scale, and can be effectively hosted and mirrored trivially.
I actually think it's a well-intentioned law, and it's not like it'll harm most people, but it's still something that should be stood against on principle.
It actually sounds far more narrow than the title implies?
> Under a new rule which goes into effect on Monday, companies that export certain types of geospatial imagery software from the United States must apply for a license to send it overseas except when it is being shipped to Canada.
The measure covers software that could be used by sensors, drones, and satellites to automate the process of identifying targets for both military and civilian ends, Lewis said, noting it was a boon for industry, which feared a much broader crackdown on exports of AI hardware and software.
"sensors, drones, and satellites" used to target anything means that you can't even send a Ring camera to Europe.
My initial instinct exactly, because major corps are global now, which means they can easily set up shop anywhere on Earth: subsidiaries, but also quasi-independent structures which might only be related through distant funding or meta-agreements.
So you can be an American company with tons of "friends" in the EU, Asia, Latin American and now Africa, doing stuff (research, product) and you would just happen to buy/sell from/through these independent actors. Fiction-Google: “Oh but that's not us! It's Oogleg, a Swiss company! It's true 95% of our private shareholders also have shares in Oogleg, but that's only circumstancial, these are large funds you know... they actually have shares in 95% of businesses altogether through ETFs and mutual funds dilution. + some legalese blabla.”
There goes your protectionism, State governments! You'll get your import taxes for physical goods and on-prems services but overall, it certainly won't impede or even touch the thriving heads, the global leaders of the business world. Not anymore. That was in another time, before global networks.
And actually, we might think Fortune 100, perhaps 1,000; but in truth it's probably much more (cue 80% of GDP in the form of SMBs) because how do you enforce a restriction on remoting to contribute to some repo somewhere?
Note that this is true as of 2020, factually from a technical standpoint, but given a few decades and some generalized country-based firewalls (it's coming, in all likelihood) + convenient surveillance and you get all the means necessary to enforce such policies anew.
I don't understand why you mention ETFs. If Google said it was meaningless that they were in a total market index fund with almost every other public company, then they would be right. But whether they were or weren't they could do business with somebody just the same. Did you think that companies can't interact if they're not subsidiaries of the same organization? Not only can they do whatever they want bilaterally, but often companies or other organizations set up a joint board or company or something with representatives to work on something of common interest. It probably has to be done the right way to avoid antitrust, but it's done a lot, and by government agencies too. This is not a mutual fund or ETF; the joint entity is controlled by the members, not vice versa.
Oblig. disclaimer, IANAL and not a financial advisor either.
> Did you think that companies can't interact if they're not subsidiaries of the same organization?
Of course not :) I however wonder if defending anti-trust from a subsidiary strategy would work — at least in France, I'm pretty sure taking half your execs and hiring them in a subsidiary which you control will NOT get you past anti-trust regulation.
You might say "but it's legal!" and the judge will kindly ask you not to mock the court by disingenuously failing to address the case at hand — are you or are you not effectively in a monopoly, or cartel situation? Legal or not in terms of legal structure doesn't matter because antitrust is 'above' in the hierarchy of norms (so to speak, my law studies are really far away now, and I was more into public than private law).
Case in point though, shareholding is even legally restricted in some sectors (e.g. media, and that was a strong motivation for e.g. Facebook trying not to be filed as a media group, at least in the EU).
I have absolutely no idea how this would fly in the US. I bow to your expertise, here.
A good example, I think, will be the shareholding structure of Libra (if it ever comes to fruition), where many actors essentially hide their participation behind layers of companies, like some onion (there was a good infographic which you might google on the topic). It's legal, technically, but would it stand in front of a supreme court antitrust case?
As far as I know from history, even legal lines tend to become blurry in major antitrust cases because these are, by essence, out-of-bounds of 'normal' operation, they're fringe cases that sometimes requires a new ad hoc law to take where we want to (I seem to remember elements of Teddy Roosevelt's opposition with Rockefeller, details of the Bell system breakup too, but I'm really not sure. Here in the EU, it's really common —all things considered— to just make new law whenever the current letter fails to live up to the desired spirit).
Thank you for the remarks, I'll probably refrain from speaking about antitrust in the US until I have a better understanding of those.
Definitely worth mentioning, thank you. However, I was aware of that and believe there are currently no cryptography related restrictions (right?), so I'm still wondering if this is a zero-to-one situation. Has the software export restriction been switched from off to on?
Seems pretty narrowly targeted, probably similar to things like ITAR, quantum, and crypto - which already require regulatory disclosure. Probably just to make sure that US companies aren't doing Project Maven (or the like) for China. Currently, best I can tell, there's nothing in place to prevent such a "collaboration".
We're in an early and explosive growth stage of AI where well-established statistical knowledge is having an unreasonable effect when combined with computing power. I've yet to see any AI platform that is mindbending vs doing basic math with a multivariate normal distribution. The eyewatering stuff is the number of Hz of computing power being thrown into simulating Go games, etc, etc.
Assuming that among 1.4 billion people there are a few good coder/statistician people and using supercomputers [0] as a rough proxy for available computing power, it isn't obvious the US is even going to inconvenience the Chinese military. Presumably they are going to have a parallel AI effort anyway given that they have been investing in the area.
None of the convnets are Gaussian. It was one of the big reasons convnets came to be at all, to model highly kurtotic distributions like natural images.
Chinese convnet research is absolutely state of the art already, indeed.
You can do basic math to a multivariate normal distribution to approximate other distributions. In classic feedforward networks, that's achieved by using nonlinear activation functions. Very wide networks turn into Gaussian processes in the limit [0]. There are also approaches explicitly embracing the "basic math on multivariate normal distributions" framework using normalizing flows [1].
I'd be interested in how you would do NLP tasks such as those now done with transformers like gtp2 and Bert by working with multi variate normal distributions.
There was a time when a software engineer didn't get basic stuff. A time when languages like C were developed where there wasn't an associative data structure baked in for example.
It wasn't because associative data structures are a secret tech that requires great insight to uncover. It is because the field was new and people hadn't cottoned on to how basic and important having access to hash-maps is. Times moved on. Now basically all modern languages have hash-maps as a basic data type.
'AI' is in that early phase where the engineering world is still getting excited over stuff that will basic practice eventually. BERT and GTP2 are signs of how much computer power Google/etc's researchers have access to, not signs that the architectures are fundamentally complicated or somehow hard to work out if you live in China. AlphaGo for instance was breathtaking as a standalone project, but not hard to implement.
I spent at least five years trying to use statistical ml and mlps to do NLP on social media comments from about 2003. Nothing like a transformer (or an rnn even) occurred to me.
I have a belief that someone in the USSR worked out a way of doing fluid dynamics that has enabled the Russians to develop hypersonics and super cavitation. This is probably rather straightforward - in the style of NS - of you know the principles. No one in the West ( or China) knows those principles, so Western torpedos and reentry vehicles are rather poor Vs Russian ones. Once you grasp how something works the fact that it's rather easy to apply in comparison to the process of getting the insight shouldn't detract from the value of the insight .
The wiki page on RNN says the early groundwork was done in 1986 and the LSTM was a 1997 innovation. If they didn't occur to you in 2003 that doesn't imply much, they are not suprising concepts.
The surprise was that in the mid-2000s suddenly GPU became so powerful that LSTM could be used to achieve interesting results. The story here isn't the models, it is the computers running the models.
> There was a time when a software engineer didn't get basic stuff. A time when languages like C were developed where there wasn't an associative data structure baked in for example. It wasn't because associative data structures are a secret tech that requires great insight to uncover. It is because the field was new and people hadn't cottoned on to how basic and important having access to hash-maps is. Times moved on. Now basically all modern languages have hash-maps as a basic data type.
That is a really weird historical fantasy. If you pull out your copy of volume 3 of Knuth and look at chapter 6, it is obvious that associative data structures were some of the first ones to be developed in the field.
The reason why hash tables became so popular is the explosion in main memory size starting in the late 1990s. The trade-offs between the possible associative data structures became less important for a lot of applications, especially when you consider how much needed to be done on secondary storage and specifically on tapes in the 1960s through the 1980s.
I sense that they will hurt themselves more with the imposed additional bureaucracy, hindering business and collaboration - the latter being a key aspect in efficient research, not to mention multinational companies already living on the area.
Also, how effective could this regulation be with so much knowledge and open source code already disseminated in the field?
"I've yet to see any AI platform that is mindbending"
1. Siri / Alexa and similar for voice recognition and doing basic tasks.
2. Face recognition for uploaded pictures on Facebook.
3. Lots of people use FaceID on iPhones.
4. Tesla and other SDC systems: yes, it's not good enough for general use, but the fact that it mostly works in California is pretty cool..
"Mindbending" is subjective, but these all use DNNs, and are used by millions to billions of people every day. So it's incorrect to suggest that all DNN use-cases could be replaced with "doing basic math with a multivariate normal distribution".
1, 2 and 3 are absolutely mundane. One could argue that if anything, they are disappointing: i remember passable speech recognition running on pentium 2 with 32 megs of ram (dragon naturally speaking was first released in 1997). "Natural" language parsers were around starting with the first text adventures (80s as far as I remember). Considering the computing resources big G and big A have, the performance of this "AI" is mediocre at best.
Facial feature extraction is not rocket science either, it dates back to at least 1993, so 386/486 with 4-8 megs of RAM.
You want mindbending and scary? Mindbending: deepfakes. Scary: automated ai-based law enforcement.
imo, working memory at scale via improvements to more obscure methods like a DNC is what will be the next leap and make todays 'AI' seem mundane. Most AI today like deepfakes involve giving the model all memories available via input.
They are Dumb and they are just Reacting out of some "fuck we have to do something" instinct driven by jobless fucks like Peter Thiel/Graham Allison/Kai-Fu Lee constant rhetoric about AI and falling behind and how its going to effect everyone.
This is Fear based Decision Making 101. All it leads to is more absurd outcomes such as Endless Wars, Huge Monopolies, More consolidation of power and resources in the hand of few therefore more inequality.
These people and this thinking style would have more credibility if they had stopped Wars, reduced inequality, disrupted monopoly and oligopolies. They have not done that.
The can't imagine a Chinese AI team and American AI team working together to solve problems in humane way. They can't imagine constructing orgs that push that through. They can't imagine punishing their own who cross lines out of fear that the other side wont.
When we allow Fear based thinking to dominate decision making Imagination dies. Outcomes are consistently shit. And way below the potential of what people collaborating and communicating across artificial bullshit boundaries are capable off.
Pick a side and don't back Fear based Decsion makers in your org. These guys hold back progress, are the reason climate change research is hidden, endless Wars keep getting funded and monopolies cling to power way past their expiry date.
How can it be the age of information and knowledge when fear wins?
> All it leads to is more absurd outcomes such as Endless Wars, Huge Monopolies, More consolidation of power and resources in the hand of few therefore more inequality
This is going to persist regardless of this decision
The thing you fear most is fear itself. But you shouldn’t. Fear is human and probably not going to stop being a thing any time soon, despite your demands.
Additional details are present in the unpublished rule (document 2019-27649 [1][2]).
-- cut --
Geospatial imagery “software” “specially designed” for training a Deep Convolutional Neural Network to automate the analysis of geospatial imagery and point clouds, and having all of the following:
1. Provides a graphical user interface that enables the user to identify objects (e.g., vehicles, houses, etc.) from within geospatial imagery and point clouds in order to extract positive and negative samples of an object of interest;
2. Reduces pixel variation by performing scale, color, and rotational normalization on the positive samples;
3. Trains a Deep Convolutional Neural Network to detect the object of interest from the positive and negative samples; and
4. Identifies objects in geospatial imagery using the trained Deep Convolutional Neural Network by matching the rotational pattern from the positive samples with the rotational pattern of objects in the geospatial imagery.
Technical Note: A point cloud is a collection of data points defined by a given coordinate system. A point cloud is also known as a digital surface model.
"(geospatial imagery) and (point clouds)" or "geospatial (imagery and point clouds)"?
Point 4 requires the use of geospatial imagery, so any point-cloud-only product would be exempt, it seems?
The document doesn't define "geospatial imagery", but that could surely include hobby and commercial drone footage of the ground. Perhaps even ordinary photography from security cameras that have user-define object identification features? That would make it really quite broad.
But all we need to do is not normalize color, and then everything's exempt!
Was doing similar work on a project using sonar imaging for the bottom of water bodies to much of what they're excluding. Not sure how concerned they'd be with identify freshwater creatures and riverbed structures...
You’re forgetting that how the word “and” works in English is often not the same as logical AND, e.g. in “give me a list of people who live in New York and Los Angeles” the “and” means OR.
Good point. Though I think "and" in those other cases really means something like "additionally" or "plus" rather than OR. You sentence could be written "... list of people who live in NY and people who live in LA.".
But it's still confusing here. If they mean it applies to both imagery software and point cloud software, then point cloud software would be excluded in point 2 because it doesn't have pixels or color (if lidar/etc). So it must be software that uses both. That makes more sense if it's aimed at a specific existing product.
Not so weird. Palantir put a GUI on Hadoop/whatever and that was enough to sell it to every government for citizen inspection. It’s the GUI that gets the contracts, not the technology alone.
3) will stop them from buying an off the shelf commercially-ready product. Yes, they can develop one from scratch, but only at enormous cost and difficulty relative to simple dollars.
You can't get a contract without marketing/sales interaction with the customers.
Customers at a high enough level to sign off on a payment with 7-9 zeroes following the number are not generally programmers (or, if they were, they haven't been working at the coal face for decades): they're senior managers or civil servants.
A GUI front end is a really amazing marketing tool for any piece of software insofar as GUIs are designed to expose all the internal configuration variables and controls in a visually appealing, or even intuitive, manner that is accessible to non-programmers. Like the folks signing the big checks.
(Here's a second possibility: we know it's possible to use CAPTCHAs to crowdsource recognition of objects. Maybe they're trying to prevent export of a NN training system that uses unwitting mechanical turks for quality control?)
Absolutely correct! I used to sell expensive software for Lisp Machines and what sold it was the UI that was dual purpose: for development and for demos to management that they could understand.
The GUI is specifically mentioned for training (tagging) of positive/false-positives that the ML then incorporates.
If you think about tagging it is something you need a GUI for unless you are lucky enough to have pre-tagged samples.
Eg: the famous Silicon Valley hotdog AI. You either need to have a GUI to allow users to select hot dogs and not hot dogs in a bunch of images, or you need a bunch of images already tagged.
I thought input method is orthogonal to whether it’s a GUI. I think GUI is meant as opposed to text console or eg audio (like a phone dial-in service); it doesn’t necessarily mean a touch screen like I think you’re implying.
Yes. Or, the software will export everything into some common format, that could be opened with any GIS viewer, and we've just gotten around the restriction?
So image recognition APIs like ones provided by AWS and GCP should be affected by this due to #2 and #3, no? Or the “provides a graphical user interface” part applies to all points?
The main restriction is "“specially designed” for training a Deep Convolutional Neural Network to automate the analysis of geospatial imagery and point clouds".
And having a GUI. It appears you can simply export a binary framework (with or without source) and let the buyer build an interface on top of it. Building an interface is not terribly advanced work and can be done by fresh bootcamp grads even...
Strangely given that it has to meet all requirements, doesn't that mean multiple people could release two or three different projects that work together?
Although saying "well technically" as you get dragged off for waterboarding may not make you feel better.
Object detection in aerial images is a rather booming field with 100s of papers published on the topic and contests going on in top conferences. I wouldn't be surprised if OSM is also doing some of this.
Lots of interesting precedent to come out of it as well. By putting code on Github, an American website, as a developer are you actually exporting it? Or are the people cloning the repo reaching into America and extracting it?
If it is considered exporting, is github the exporter, or is the developer. Just like a company might produce a metal widget but another company procures and exports it, the original company that made the widget isn't the exporter.
I would not be suprised if we get something along the lines of "Unfortunately, this repository is currently unavailable in your country" sometime soon. Many websites still completely block EU users post GDPR, e.g. Chicago Tribune.
Remember how it was done the last time the idiotic US government did the same stunt with crypto.... Zimmerman (PGP) physically printed and bound books containing the full OCR-happy font of the source code.
Evidently to the dinosaurs in Congress, a physical book is something COMPLETELY DIFFERENT than a file online.
Actually, when we buy lab equipment from the US we have to fill out paperwork saying that it will not be used to produce weapons, and that it will not be resold to "bad" countries.
Probably there is some precedent by considering e.g. if Lockheed or FedEx is the exporting company if there have been cases where weapons got exported to unwanted actors. Probably github is like FedEx in these cases.
That would have been my guess at who this is aimed at too. Lots of applications outside the military and spook shops but that's always been the primary customer.
Ah, I was scratching my head reading through it thinking about how unlikely it must be for something to have all of those properties. But if they were tailor made for a single product that makes sense.
Can anyone comment on who the specific vendor or what specific product might be? The AND-criteria makes this restriction pretty narrow, and the GUI requirement alone rules out practically everything I can think of.
I saw a great example of this when I was looking at VA gun laws today. They have an exception structured as follows: Virginia law exempts from these requirements any firearms shows held in any town with a population of not less than 1,995 and not more than 2,010, according to the 1990 United States census [1]
"The provisions of this section shall not apply to firearms shows held in any town with a population of not less than 1,995 and not more than 2,010, according to the 1990 United States census."
Any idea which town the law refers to? I looked at the 1990 census data [1] and the closest thing I could find is a town called Appalachia with 1994 people.
The export ban is on the software? Not the algorithm itself? And would it be broad enough that all AI and QI frameworks like PyTorch, Tensorflow and Qiskit would fall under its purview.
These restrictions seem to flow from a mental model that still views software as a product purchased in a shrink-wrapped box. Rather than the services based model currently extant.
That is the point, to delay the adversary from gaining access to completed technology. An example is foreign adversaries purchasing PS3s with Linux to quickly access cheap computing power.
Whether frameworks are at risk is limited to the wording of the ban and then the final determination comes from a judge hearing the case. The chilling effects are real and its possible framework development may very well be hampered due to the unknowns you have pointed out in your post
An algorithm is an idea and it's hard to enforce ideas from spreading. Similar to how it is difficult to patent an algorithm. In the patent world you would have better luck tying that algo to a machine and patenting that.
I think the same concept applies here. Software is the machine that embodies the algorithm. Its tied to a company and to dollars.
What this will also mean is that startups that work in this space will need to watch out who they get funding from. If the VC is not US-based, CFIUS oversight may kick in.
I used to put on my tinfoil hat and imagine that cryptography was the field to study if you wanted secret government agents to visit you. Maybe next time I will instead imagine that computer vision is what summons the secret agents.
More seriously, computer vision is going to be important and it appears to be far less known than machine learning and has higher barriers to entry. I'd exchange a few introductory machine learning books for more good computer vision introductions.
Any suggestions on how to get started with computer vision?
Unlike the space race, where Russia and the US were significantly ahead of everyone else, the field is far more level in AI. Arguably there will be areas where the US could even be behind in some areas.
Wonder what the real world impact of this will be. Not much, I expect.
>Wonder what the real world impact of this will be. Not much, I expect.
Here's a thought experiment I use to imagine the impact of AI:
Imagine you've got a million people at your disposal. At zero cost and with no downtime, these people can remotely operate robots, understand text, interact using natural language, or classify objects in images, all with human-level intelligence and accuracy. Now what?
Obviously there are areas where AI can outperform humans, like mechanical accuracy and mathematical computation. But in general, I find this experiment works pretty well.
Now imagine doing crowd control. 10 frames per second, 100k people, if you need just 1 second to recognize a face, you’ve just saturated your 1m human-AI. The point of digital AI is that it can scale, almost indefinitely.
OK, then imagine 100 billion people. Scale isn't the point of the experiment - the point is bounding expectations based on probable (maximal?) capabilities.
Perhaps a post-Singularity AI will have wild capabilities beyond our comprehension, but that is outside the scope of this experiment.
You don't need to recognize every face on every frame. People move slowly compared to 10hz, so tracking "this is a person moving around" is way easier than identifying faces anew every frame.
USA is probably still ahead of the rest of the world regarding IA, thanks to Google. Google sits on a massive amount of text, video and speech data. Google is one of the biggest coordinated entity (regarding business, data, hardware and software) on Earth aimed at advancing AI. The only rival in term of budget and data is probably China with a few state-sponsored companies together (Huawei + Alibaba + Tencent).
All other countries probably have smart researchers and engineers, but no one has the data machine that Google has...
Sounds like something straight out of Terminator movies. I think they are afraid this technology will get out of hand in the near future and I can't really blame them. I remember that video with the Google Assistant making a hair dresser appointment. Pretty scary stuff
My guess is that this is like the export controls on munitions or encryption. You can't export to China but you can fairly easily get a license to sell it in Australia or any other US allied country.
"... boost oversight of exports of sensitive technology to adversaries like China, for economic and security reasons..."
I think economic is the keyword here. From what I gather this is not the first time the US is doing something like this. I am pretty sure other countries have done the same in their particular areas of concern. They're just not as mighty and famous as the US so nobody pays attention. So much for free market.
Anyways I think it is a little too late and all it will accomplish is - opening a window of opportunity for other players.
Also because it formulated way too broad and has an escape clause (apply for a license) then it might offer an unfair advantage right inside the US. Big companies will get it and for smaller it nay be more difficult. Same as patent system. Company like Apple can patent my cat with little troubles. Me: not so much and I speak from experience.
Not related to AI but instead related to geo-spatial imagery, I’m pretty sure I saw YT videos of ISIS commanders coordinating suicidal VBIED attacks in Syria using Google Maps aerial imagery. That was happening back in 2014-2015, when such videos were not instantly banned on /r/combatfootage and /r/SyrianCivilWar .
I recall similar happening during Bush admin during 2000s. Many of our software customers were international. To obtain an export license we were required to scan our source code with an approved Dept of Commerce scan software vendor to look for all kinds of inappropriate code like a too strong cryptography algorithm in the licensing portion and plagarism of copyrighted code. The first couple of releases this was done were brutal. Many of the developers not far our of the university were used to taking anything from the internet/open source if it saved effort. There was not a clear company policy about this until the export restrictions. Sometimes there would be a half dozen chain of borrowing before a culprit turned up. We muddled through and fixed hundreds of flags. If I was the program manager, I'd schedule and export code scan every week to avoid late problems.
AI code is just another layer in this odorous process.
Makes me think that the big tech companies which absorb a large amount of new college grads each year have a bunch of copyright violations but won't be audited.
These companies have "strict" code review policies but often the reviewers are just a recent previous year's new college grad, now overconfident by a small amount of work experience.
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[ 4.3 ms ] story [ 269 ms ] threadOr is it that training data provides experience, which improves the algorithms? Or the application of the algorithms?
Not to mention nowadays Deep learning is pretty much a big data game.
Assuming it is - China is also competitive on developing algorithms. A few months ago there was a post on explosion of AI papers submitted by authors at Chinese research institutions, with no signs of slowing down.
Given two research labs, with one having a bit better equipment, while the other having a more proven history of publishing innovative research, it would be disingenuous to say that the better equipped lab is ahead until they have actually produced some research that puts them ahead. It might help them gain lead, but it also might result in nothing. Better equipment is just one of many components that affect the chances of success. Until that lead is acquired, I don't really think it would be appropriate to say that they had done so.
Note: the lab with a history of published innovative research in my analogy isn't supposed to represent the US or any country in specific. This was just an example to better illustrate the point I was making. The only thing that should matter for whether someone is ahead or not in this situation is the actual proof of being ahead, not "the opportunities that could lead to them being ahead". Otherwise, we should also start immediately trusting all those articles that pop up once every few months about how some random city is "about to become the next Silicon Valley, here are the reasons why".
Most of the time whatever interesting thing (posted to HN) originated in some Chinese startup/university.
But that's just my gut feel.
Also, cryptography requires both sides to use the same algorithm, while companies don't need to use the same recognition algorithms.
It also helped, in the crypto case, that you could print some version of it on a T-shirt or mail it on a postcard. It looked like speech, while neural net parameters don't.
So the free speech case seems much weaker.
But Bernstein's case didn't hinge on the likely consequences of strong cryptography being widely available; rather, he argued that he had a First Amendment right to publish his research.
Sorry, I don't understand your argument.
This should not be true though, that's the point of having the law being a separate power.
https://en.wikipedia.org/wiki/North_American_Aerospace_Defen...
What prevents a Chinese company from opening a business in the US to get access to US technology? I mean...nothing...that's exactly what they do.
"The rule will likely be welcomed by industry, Lewis said, because it had feared a much broader crackdown on exports of most artificial intelligence hardware and software"
[0]: https://en.wikipedia.org/wiki/Overton_window
Page 4 and 9 have the technical definitions.
My off the cuff interpretation is that the rule would only cover convolutional neural nets that are trained to identify and determine the orientation of specific objects in geospatial imagery. If the neural net's input/output aren't wrapped in a GUI it sounds like they still might be OK to export without a license
2.Reduces pixel variation by performing scale, color, and rotational normalization on the positive samples;
3. Trains a Deep Convolutional Neural Network to detect the object of interest from the positive and negative samples; and
4. Identifies objects in geospatial imagery using the trained Deep Convolutional Neural Network by matching the rotational pattern from the positive samples with the rotational pattern of objects in the geospatial imagery.
What counts as "geospatial imagery"? Could this apply to any training UI for self-driving cars, maps, street view, etc.?
While we patiently await for a HN user (or, let's be honest, one of the ancient cryptologist-lawyers who come out of the woodwork every time something like this happens and sue the government) to fix this by suing the government on free speech grounds, don't forget that git, mercurial, fossil, bazaar and more are all decentralized, can't actually be censored at scale, and can be effectively hosted and mirrored trivially.
I actually think it's a well-intentioned law, and it's not like it'll harm most people, but it's still something that should be stood against on principle.
> Under a new rule which goes into effect on Monday, companies that export certain types of geospatial imagery software from the United States must apply for a license to send it overseas except when it is being shipped to Canada.
"sensors, drones, and satellites" used to target anything means that you can't even send a Ring camera to Europe.
So you can be an American company with tons of "friends" in the EU, Asia, Latin American and now Africa, doing stuff (research, product) and you would just happen to buy/sell from/through these independent actors. Fiction-Google: “Oh but that's not us! It's Oogleg, a Swiss company! It's true 95% of our private shareholders also have shares in Oogleg, but that's only circumstancial, these are large funds you know... they actually have shares in 95% of businesses altogether through ETFs and mutual funds dilution. + some legalese blabla.”
There goes your protectionism, State governments! You'll get your import taxes for physical goods and on-prems services but overall, it certainly won't impede or even touch the thriving heads, the global leaders of the business world. Not anymore. That was in another time, before global networks.
And actually, we might think Fortune 100, perhaps 1,000; but in truth it's probably much more (cue 80% of GDP in the form of SMBs) because how do you enforce a restriction on remoting to contribute to some repo somewhere?
Note that this is true as of 2020, factually from a technical standpoint, but given a few decades and some generalized country-based firewalls (it's coming, in all likelihood) + convenient surveillance and you get all the means necessary to enforce such policies anew.
> Did you think that companies can't interact if they're not subsidiaries of the same organization?
Of course not :) I however wonder if defending anti-trust from a subsidiary strategy would work — at least in France, I'm pretty sure taking half your execs and hiring them in a subsidiary which you control will NOT get you past anti-trust regulation.
You might say "but it's legal!" and the judge will kindly ask you not to mock the court by disingenuously failing to address the case at hand — are you or are you not effectively in a monopoly, or cartel situation? Legal or not in terms of legal structure doesn't matter because antitrust is 'above' in the hierarchy of norms (so to speak, my law studies are really far away now, and I was more into public than private law).
Case in point though, shareholding is even legally restricted in some sectors (e.g. media, and that was a strong motivation for e.g. Facebook trying not to be filed as a media group, at least in the EU).
I have absolutely no idea how this would fly in the US. I bow to your expertise, here.
A good example, I think, will be the shareholding structure of Libra (if it ever comes to fruition), where many actors essentially hide their participation behind layers of companies, like some onion (there was a good infographic which you might google on the topic). It's legal, technically, but would it stand in front of a supreme court antitrust case?
As far as I know from history, even legal lines tend to become blurry in major antitrust cases because these are, by essence, out-of-bounds of 'normal' operation, they're fringe cases that sometimes requires a new ad hoc law to take where we want to (I seem to remember elements of Teddy Roosevelt's opposition with Rockefeller, details of the Bell system breakup too, but I'm really not sure. Here in the EU, it's really common —all things considered— to just make new law whenever the current letter fails to live up to the desired spirit).
Thank you for the remarks, I'll probably refrain from speaking about antitrust in the US until I have a better understanding of those.
And it seems common for A, B, C, ... to jointly govern Z, without antitrust problems, like (first thing I could find) the W3C.
Has that now changed?
Is publishing personal code on GitHub "exporting"?
https://www.bis.doc.gov/index.php/all-articles/107-about-bis...
Assuming that among 1.4 billion people there are a few good coder/statistician people and using supercomputers [0] as a rough proxy for available computing power, it isn't obvious the US is even going to inconvenience the Chinese military. Presumably they are going to have a parallel AI effort anyway given that they have been investing in the area.
[0] https://en.wikipedia.org/wiki/TOP500#Top_countries
Chinese convnet research is absolutely state of the art already, indeed.
[0] https://arxiv.org/abs/1711.00165
[1] https://arxiv.org/abs/1505.05770
There was a time when a software engineer didn't get basic stuff. A time when languages like C were developed where there wasn't an associative data structure baked in for example.
It wasn't because associative data structures are a secret tech that requires great insight to uncover. It is because the field was new and people hadn't cottoned on to how basic and important having access to hash-maps is. Times moved on. Now basically all modern languages have hash-maps as a basic data type.
'AI' is in that early phase where the engineering world is still getting excited over stuff that will basic practice eventually. BERT and GTP2 are signs of how much computer power Google/etc's researchers have access to, not signs that the architectures are fundamentally complicated or somehow hard to work out if you live in China. AlphaGo for instance was breathtaking as a standalone project, but not hard to implement.
I have a belief that someone in the USSR worked out a way of doing fluid dynamics that has enabled the Russians to develop hypersonics and super cavitation. This is probably rather straightforward - in the style of NS - of you know the principles. No one in the West ( or China) knows those principles, so Western torpedos and reentry vehicles are rather poor Vs Russian ones. Once you grasp how something works the fact that it's rather easy to apply in comparison to the process of getting the insight shouldn't detract from the value of the insight .
The surprise was that in the mid-2000s suddenly GPU became so powerful that LSTM could be used to achieve interesting results. The story here isn't the models, it is the computers running the models.
That is a really weird historical fantasy. If you pull out your copy of volume 3 of Knuth and look at chapter 6, it is obvious that associative data structures were some of the first ones to be developed in the field.
The reason why hash tables became so popular is the explosion in main memory size starting in the late 1990s. The trade-offs between the possible associative data structures became less important for a lot of applications, especially when you consider how much needed to be done on secondary storage and specifically on tapes in the 1960s through the 1980s.
Also, how effective could this regulation be with so much knowledge and open source code already disseminated in the field?
1. Siri / Alexa and similar for voice recognition and doing basic tasks.
2. Face recognition for uploaded pictures on Facebook.
3. Lots of people use FaceID on iPhones.
4. Tesla and other SDC systems: yes, it's not good enough for general use, but the fact that it mostly works in California is pretty cool..
"Mindbending" is subjective, but these all use DNNs, and are used by millions to billions of people every day. So it's incorrect to suggest that all DNN use-cases could be replaced with "doing basic math with a multivariate normal distribution".
You want mindbending and scary? Mindbending: deepfakes. Scary: automated ai-based law enforcement.
This is Fear based Decision Making 101. All it leads to is more absurd outcomes such as Endless Wars, Huge Monopolies, More consolidation of power and resources in the hand of few therefore more inequality.
These people and this thinking style would have more credibility if they had stopped Wars, reduced inequality, disrupted monopoly and oligopolies. They have not done that.
The can't imagine a Chinese AI team and American AI team working together to solve problems in humane way. They can't imagine constructing orgs that push that through. They can't imagine punishing their own who cross lines out of fear that the other side wont.
When we allow Fear based thinking to dominate decision making Imagination dies. Outcomes are consistently shit. And way below the potential of what people collaborating and communicating across artificial bullshit boundaries are capable off.
Pick a side and don't back Fear based Decsion makers in your org. These guys hold back progress, are the reason climate change research is hidden, endless Wars keep getting funded and monopolies cling to power way past their expiry date.
How can it be the age of information and knowledge when fear wins?
This is going to persist regardless of this decision
Google suggests Nvidia GPUs are probably made in Guangdong by PCPartner.
The actual chips, I don't know, but TSMC does have fabs in China.
-- cut --
Geospatial imagery “software” “specially designed” for training a Deep Convolutional Neural Network to automate the analysis of geospatial imagery and point clouds, and having all of the following:
1. Provides a graphical user interface that enables the user to identify objects (e.g., vehicles, houses, etc.) from within geospatial imagery and point clouds in order to extract positive and negative samples of an object of interest;
2. Reduces pixel variation by performing scale, color, and rotational normalization on the positive samples;
3. Trains a Deep Convolutional Neural Network to detect the object of interest from the positive and negative samples; and
4. Identifies objects in geospatial imagery using the trained Deep Convolutional Neural Network by matching the rotational pattern from the positive samples with the rotational pattern of objects in the geospatial imagery.
Technical Note: A point cloud is a collection of data points defined by a given coordinate system. A point cloud is also known as a digital surface model.
-- cut --
[1] https://www.federalregister.gov/documents/2020/01/06/2019-27...
[2] https://s3.amazonaws.com/public-inspection.federalregister.g...
"(geospatial imagery) and (point clouds)" or "geospatial (imagery and point clouds)"?
Point 4 requires the use of geospatial imagery, so any point-cloud-only product would be exempt, it seems?
The document doesn't define "geospatial imagery", but that could surely include hobby and commercial drone footage of the ground. Perhaps even ordinary photography from security cameras that have user-define object identification features? That would make it really quite broad.
But all we need to do is not normalize color, and then everything's exempt!
But it's still confusing here. If they mean it applies to both imagery software and point cloud software, then point cloud software would be excluded in point 2 because it doesn't have pixels or color (if lidar/etc). So it must be software that uses both. That makes more sense if it's aimed at a specific existing product.
1) wants to acquire a geospatial imagery recognition program;
2) does not have the tech themselves;
3) can import the underlying tech without interface;
I highly doubt a restriction on GUI export will stop them.
How will anyone operate a computer without GUI? That’s impossible! /s
You can't get a contract without marketing/sales interaction with the customers.
Customers at a high enough level to sign off on a payment with 7-9 zeroes following the number are not generally programmers (or, if they were, they haven't been working at the coal face for decades): they're senior managers or civil servants.
A GUI front end is a really amazing marketing tool for any piece of software insofar as GUIs are designed to expose all the internal configuration variables and controls in a visually appealing, or even intuitive, manner that is accessible to non-programmers. Like the folks signing the big checks.
(Here's a second possibility: we know it's possible to use CAPTCHAs to crowdsource recognition of objects. Maybe they're trying to prevent export of a NN training system that uses unwitting mechanical turks for quality control?)
BTW, off topic, but I love your books!
If you think about tagging it is something you need a GUI for unless you are lucky enough to have pre-tagged samples.
Eg: the famous Silicon Valley hotdog AI. You either need to have a GUI to allow users to select hot dogs and not hot dogs in a bunch of images, or you need a bunch of images already tagged.
Edit: I overlooked “have all of the following”.
Although saying "well technically" as you get dragged off for waterboarding may not make you feel better.
In about 5 minutes I could find these:
https://github.com/search?q=geospatial+deep+learning
https://github.com/search?q=satellite+images+deep+learning
https://github.com/search?q=aerial++deep+learning
https://github.com/search?q=aerial+object+detection
If it is considered exporting, is github the exporter, or is the developer. Just like a company might produce a metal widget but another company procures and exports it, the original company that made the widget isn't the exporter.
Evidently to the dinosaurs in Congress, a physical book is something COMPLETELY DIFFERENT than a file online.
Probably there is some precedent by considering e.g. if Lockheed or FedEx is the exporting company if there have been cases where weapons got exported to unwanted actors. Probably github is like FedEx in these cases.
Yeah almost nothing will fall under that. Build your own GUI (if you ever need one).
That will place the US in a large economical disadvantage for those.
[1] https://lawcenter.giffords.org/gun-shows-in-virginia/
https://law.lis.virginia.gov/vacode/title54.1/chapter42/sect...
[1] https://www2.census.gov/library/publications/decennial/1990/...
These restrictions seem to flow from a mental model that still views software as a product purchased in a shrink-wrapped box. Rather than the services based model currently extant.
Whether frameworks are at risk is limited to the wording of the ban and then the final determination comes from a judge hearing the case. The chilling effects are real and its possible framework development may very well be hampered due to the unknowns you have pointed out in your post
No.
They look like they are targeting a specific piece of commercial software.
I think the same concept applies here. Software is the machine that embodies the algorithm. Its tied to a company and to dollars.
What this will also mean is that startups that work in this space will need to watch out who they get funding from. If the VC is not US-based, CFIUS oversight may kick in.
More seriously, computer vision is going to be important and it appears to be far less known than machine learning and has higher barriers to entry. I'd exchange a few introductory machine learning books for more good computer vision introductions.
Any suggestions on how to get started with computer vision?
What financially or technologically significant exports are going to stop? How military or nonmilitary are they?
Wonder what the real world impact of this will be. Not much, I expect.
Here's a thought experiment I use to imagine the impact of AI:
Imagine you've got a million people at your disposal. At zero cost and with no downtime, these people can remotely operate robots, understand text, interact using natural language, or classify objects in images, all with human-level intelligence and accuracy. Now what?
Obviously there are areas where AI can outperform humans, like mechanical accuracy and mathematical computation. But in general, I find this experiment works pretty well.
Perhaps a post-Singularity AI will have wild capabilities beyond our comprehension, but that is outside the scope of this experiment.
All other countries probably have smart researchers and engineers, but no one has the data machine that Google has...
I'm based in Australia, and we started playing today with analysis of geospatial imagery for bush fire imagery (because the country is on fire).
ECCN0D521No.1 from https://s3.amazonaws.com/public-inspection.federalregister.g... covers pretty much everything I'm doing.
I guess if I want to look at the positive side, it means I don't have to compete with any US vendors if I want to sell my work.
Until the Australian government gets the same idea
I think economic is the keyword here. From what I gather this is not the first time the US is doing something like this. I am pretty sure other countries have done the same in their particular areas of concern. They're just not as mighty and famous as the US so nobody pays attention. So much for free market.
Anyways I think it is a little too late and all it will accomplish is - opening a window of opportunity for other players.
Also because it formulated way too broad and has an escape clause (apply for a license) then it might offer an unfair advantage right inside the US. Big companies will get it and for smaller it nay be more difficult. Same as patent system. Company like Apple can patent my cat with little troubles. Me: not so much and I speak from experience.
AI code is just another layer in this odorous process.
These companies have "strict" code review policies but often the reviewers are just a recent previous year's new college grad, now overconfident by a small amount of work experience.