US Judge Gets It Right: AI Doesn't Get Patents

from the HAL-says-this-is-correct dept

A month ago, we wrote about a perplexing (and dangerous) decision down in Australia ruling that an AI can be listed as the inventor of a patent. As we had explained, there was a concerted effort by a small group patent lawyers and this one dude, Stephen Thaler, to seek out patents for "inventions" that an AI created by Thaler called Dabus ("device for the autonomous bootstrapping of unified sentience"). As we explained in that and earlier posts, the entire point of the patent system is to provide incentives to humans to invent. An AI does not need such incentives. As we've highlighted in the past, the USPTO and the EU patent office have both rejected AI-generated patents. Australia's patent office had done the same, but a judge there rejected that and said an AI could be listed as an inventor.

All of these situations involve Thaler/DABUS, as did a new ruling in the US which... thankfully has rejected the idea that an AI deserves patents after Thaler filed a lawsuit because of the USPTO rejection. I think there's a separate issue here: which is what standing does Thaler have in the first place? If the argument is that "DABUS" is the inventor, it seems that... um... only DABUS should have the necessary standing to challenge the rejection of its patent application. The fact that Thaler thinks he has standing more or less shows how ridiculous the entire claim is in the first place.

After going through the background of the case, and discussing what level of deference the USPTO deserves, Judge Leonie Brinkema gets straight to the actual point, which is pretty simple: AI doesn't get a patent.

Even if no deference were due, the USPTO's conclusion is correct under the law. The question of whether the Patent Act requires that an "inventor" be a human is a question of statutory construction. Accordingly, the plain language of the statute controls.... As the Supreme Court has held: "The preeminent canon of statutory interpretation requires us to 'presume that [the] legislature says in a statute what it means and means in a statute what it says there.' Thus, our inquiry begins with the statutory text, and ends there as well if the text is unambiguous."...

Using the legislative authority provided by the Constitution's Patent Clause... Congress codified the Patent Act in 1952... and has amended the Patent Act a number of times in the ensuing sixty years. In 2011, Congress promulgated the America Invents Act, which, as relevant here, formally amended the Patent Act to provide an explicit statutory definition for the term "inventor" to mean "the individual, or, if a joint invention, the individuals who invented or discovered the subject matter of the invention."... The America Invents Act also added that "joint inventor" means "any one of the individuals who invented or discovered the subject matter of a joint invention."... Additionally, Congress has required that "[a]n application for patent shall be made, or authorized to be made, by the inventor . . . in writing to the Director."... "[E]ach individual who is the inventor or a joint inventor of a claimed invention in an application for patent shall execute an oath or declaration in connection with the application" which "shall contain statements that--... such individual believes himself or herself to be the original inventor or joint inventor of [the] claimed invention."

See where this is going?

As the statutory language highlights above, both of the definitions provided by Congress for the terms "inventor" and "joint inventor" within the Patent Act reference an "individual" or "individuals."... Congress used the same term--"individual"--in other significant provisions of the Patnet Act which reference an "inventor," including requiring that "each individual who is the inventor or a joint inventor" execute an oath or declaration...

The court then notes that in analyzing other laws, courts have long said that "individual" means human. And it also highlights that the language in the Patent Act makes it clear that it was intended to apply to humans -- humans who can make a declaration about their own beliefs.

Congress's use of the term "individual" in the Patent Act strengthens the conclusion that an "inventor" must be a natural person. Congress provided that in executing the oath or declaration accompanying a patent application, the inventor must include a statement "such individual believes himself or herself to be the original inventor or an original joint inventor of a claimed invention in the application."... By using personal pronouns such as "himself or herself" and the verb "believes" in adjacent terms modifying "individual," Congress was clearly reference a natural person.

Then there's a fun bit of judicial eye-rolling, stating: "having neither facts nor law to support his argument," the judge notes that Thaler's argument is basically "but this is good for innovation." But that's not going to fly (leaving aside the fact that allowing AI to get patents would be objectively terrible for innovation, it's also not how any of this works):

Plaintiff provides no support for his argument that these policy considerations should override the plain meaning of a statutory term.

It gets even worse for Thaler's arguments. He argued that the PTO hadn't properly considered the policy ramifications of not allowing AI to get patents, but as the judge notes, that's clearly not true. It had. And it rejected the dumb idea.

Specifically, the USPTO points to a conference on artificial intelligence policy it held in January 2019, and to requests for public comment "on a whole host of issues related to the intersection of intellectual property policy and artificial intelligence" it issued in August and October 2019. In October 2020, the USPTO issued a comprehensive report on those comments.

And... what did that report say?

Many commentators disagreed with plaintiff's view that artificial intelligence machines should be recognized as inventors...

Given how active Thaler and his lawyer friends have been around the globe, I imagine this is hardly the end of these campaigns. I imagine this ruling will be appealed, and how long will it be until some sucker of a Senator or Member of Congress, convinced by Thaler's nonsense, will introduce a bill to amend the Patent Act to enable AI patents?

Filed Under: ai, artificial intelligence, dabus, patents, stephen thaler, uspto

Harrisburg University Researchers Claim Their 'Unbiased' Facial Recognition Software Can Identify Potential Criminals

from the fresh-hells-delivered-daily dept

Given all we know about facial recognition tech, it is literally jaw-dropping that anyone could make this claim… especially without being vetted independently.

A group of Harrisburg University professors and a PhD student have developed an automated computer facial recognition software capable of predicting whether someone is likely to be a criminal.

The software is able to predict if someone is a criminal with 80% accuracy and with no racial bias. The prediction is calculated solely based on a picture of their face.

There's a whole lot of "what even the fuck" in CBS 21's reprint of a press release, but let's start with the claim about "no racial bias." That's a lot to swallow when the underlying research hasn't been released yet. Let's see what the National Institute of Standards and Technology has to say on the subject. This is the result of the NIST's examination of 189 facial recognition AI programs -- all far more established than whatever it is Harrisburg researchers have cooked up.

Asian and African American people were up to 100 times more likely to be misidentified than white men, depending on the particular algorithm and type of search. Native Americans had the highest false-positive rate of all ethnicities, according to the study, which found that systems varied widely in their accuracy.

The faces of African American women were falsely identified more often in the kinds of searches used by police investigators where an image is compared to thousands or millions of others in hopes of identifying a suspect.

Why is this acceptable? The report inadvertently supplies the answer:

Middle-aged white men generally benefited from the highest accuracy rates.

Yep. And guess who's making laws or running police departments or marketing AI to cops or telling people on Twitter not to break the law or etc. etc. etc.

To craft a terrible pun, the researchers' claim of "no racial bias" is absurd on its face. Per se stupid af to use legal terminology.

Moving on from that, there's the 80% accuracy, which is apparently good enough since it will only threaten the life and liberty of 20% of the people it's inflicted on. I guess if it's the FBI's gold standard, it's good enough for everyone.

Maybe this is just bad reporting. Maybe something got copy-pasted wrong from the spammed press release. Let's go to the source… one that somehow still doesn't include a link to any underlying research documents.

What does any of this mean? Are we ready to embrace a bit of pre-crime eugenics? Or is this just the most hamfisted phrasing Harrisburg researchers could come up with?

A group of Harrisburg University professors and a Ph.D. student have developed automated computer facial recognition software capable of predicting whether someone is likely going to be a criminal.

The most charitable interpretation of this statement is that the wrong-20%-of-the-time AI is going to be applied to the super-sketchy "predictive policing" field. Predictive policing -- a theory that says it's ok to treat people like criminals if they live and work in an area where criminals live -- is its own biased mess, relying on garbage data generated by biased policing to turn racist policing into an AI-blessed "work smarter not harder" LEO equivalent.

The question about "likely" is answered in the next paragraph, somewhat assuring readers the AI won't be applied to ultrasound images.

With 80 percent accuracy and with no racial bias, the software can predict if someone is a criminal based solely on a picture of their face. The software is intended to help law enforcement prevent crime.

There's a big difference between "going to be" and "is," and researchers using actual science should know better than to use both phrases to describe their AI efforts. One means scanning someone's face to determine whether they might eventually engage in criminal acts. The other means matching faces to images of known criminals. They are far from interchangeable terms.

If you think the above quotes are, at best, disjointed, brace yourself for this jargon-fest which clarifies nothing and suggests the AI itself wrote the pullquote:

“We already know machine learning techniques can outperform humans on a variety of tasks related to facial recognition and emotion detection,” Sadeghian said. “This research indicates just how powerful these tools are by showing they can extract minute features in an image that are highly predictive of criminality.”

"Minute features in an image that are highly predictive of criminality." And what, pray tell, are those "minute features?" Skin tone? "I AM A CRIMINAL IN THE MAKING" forehead tattoos? Bullshit on top of bullshit? Come on. This is word salad, but a salad pretending to be a law enforcement tool with actual utility. Nothing about this suggests Harrisburg has come up with anything better than the shitty "tools" already being inflicted on us by law enforcement's early adopters.

I wish we could dig deeper into this but we'll all have to wait until this excitable group of clueless researchers decide to publish their findings. According to this site, the research is being sealed inside a "research book," which means it will take a lot of money to actually prove this isn't any better than anything that's been offered before. This could be the next Clearview, but we won't know if it is until the research is published. If we're lucky, it will be before Harrisburg patents this awful product and starts selling it to all and sundry. Don't hold your breath.

Filed Under: ai, artificial intelligence, bias, facial recognition, precrime
Companies: harrisburg university

Hard Pass: Clearview Offers To Help Out With COVID-19 Contact Tracing

from the [incoherent-screaming-into-the-void] dept

Clearview AI is inserting itself into a discussion no one invited it to participate in. The discussion around contact tracing to manage and (hopefully) impede the spread of the coronavirus involves multiple governments around the world. It also includes Google and Apple, who are partnering to create a platform for contact tracing apps.

At least in the case of Google and Apple's offerings, there appears to have been a serious discussion about protecting users' privacy as much as possible while still offering a valuable service to government health agencies and people concerned about contracting the virus.

Now, Clearview has blundered into the discussion -- a company that has shown utter disdain for the millions of people it has force-fed to its multi-billion image database via social media site scraping. There is no opting in or out of this collection -- one that Clearview is selling to law enforcement agencies in the US and to government agencies around the world. If it's on the web, it's likely already in Clearview's database.

In a brief discussion with NBC News, CEO Hoan Ton-That pitched his idea for a Clearview-based contact tracing program. What Ton-That wants to do is tie his app and its database to thousands of CCTV cameras located in stores, parking lots, gyms, and other locations where, as he puts it, "there's no expectation of privacy." Ton-That doesn't explain how his system will be notified of a person's COVID status, but he's pretty sure his software will be able to recognize faces accurately. Clearview's facial recognition AI remains unproven, but it's supposedly capable of making guesses about faces using images as small as 110x110.

Ton-That also seems unconcerned about the privacy implications of adding people's health status to his enormous database of scraped personal information. He says any limitations on gathering/storage of this info would be up to whoever decides to take him up on his unsolicited offer.

Obviously, no one should do this. The AI is unproven and Clearview is far from trustworthy. Activist group Fight For The Future has issued its official statement on Clearview's contact tracing pitch. It's short but punchy.

“Absolutely the fuck not,” said Evan Greer (she/her), the groups Deputy Director.

As FFTF points out, adding Clearview to the mix just adds more potential privacy violations to the mix by dumping people's health info into a database that's already being accessed by a number of private companies and government agencies around the world. Ton-That appears to assume that anyone in a "public" area is fair game for his company. Adding thousands of cameras operated by hundreds of private companies would provide a steady flow of facial images (some tied to very personal information) into his database, making his product even more enticing to government agencies.

Hopefully, no one will let Clearview near any of this. Facial recognition adds nothing to contact tracing -- something that can be done almost anonymously using the platform developed by Google and Apple. Hoovering up facial images -- along with location data and health info -- is a privacy nightmare. And there's probably no company that should be trusted less with your personal info than Clearview, which has repeatedly demonstrated it cares nothing about the millions of people it has turned into fodder for its unproven tech.

Filed Under: artificial intelligence, contact tracing, facial recognition, hoan ton-that, privacy, trade-offs
Companies: clearview, clearview ai

The Scunthorpe Problem, And Why AI Is Not A Silver Bullet For Moderating Platform Content At Scale

from the what's-in-a-name dept

Maybe someday AI will be sophisticated, nuanced, and accurate enough to help us with platform content moderation, but that day isn't today.

Today it prevents an awful lot of perfectly normal and presumably TOS-abiding people from even signing up for platforms. A recent tweet from someone unable to sign up to use an app because it didn't like her name, as well as many, many, MANY replies from people who've had similar experiences, drove this point home:

Been there

— Matt Cummings (@MattCummingsDB) August 29, 2018

As a person named James Butts, I know these problems.

— James (@justjames8) August 28, 2018

As a Dickman I know the struggle is real

— Mike Dickman (@TheMikeDickman) August 29, 2018

I get this a lot surprisingly

— Kyle Medick (@medick32) August 28, 2018

We have quite similar circumstances here

— Jacob Cockrill (@jacob_cockrill) August 29, 2018

Tom Hiscock reporting in.

— aWildWatermelon (@aWildWatermelon) August 29, 2018

Uhm, my name is Analise. That’s exact spelling. Been through this many of times.

— AP (@aannpp23) August 29, 2018

Join the club

— Craig Cockburn (@siliconglen) August 29, 2018

Oh! Am I too late to join this club?

— James Ho (@IndieVideoJames) August 29, 2018

Happens to me often, as you can imagine.

— MatthewDicks (@MatthewDicks) August 29, 2018

Facebook, despite its insistence on users using real names, seems particularly bad at letting people actually use their real names.

A large part of my family uses a shortened form of our last name because many places, including Facebook, don't think Buckmaster is a real last name.

But Buck, Buckbuck, Bucker, Bucky and many more are all "real" >.>

— The Autistech (@theAutistech) August 28, 2018

Yeah - Facebook won't allow my real name of "Talks" so had to come up with something else. Although my wife's account is okay ...

It gets better because when Collette Talks put "in a relationship with Mike Torkelson", basically the family gossip went into overdrive!

— Mike Talks 💚💚💚 (@TestSheepNZ) August 29, 2018

My last name is Player and Facebook still won’t let me have that as a last name because it’s a “street name.”

— Sav (@TheSavannahOW) August 29, 2018

I have family members who use alternate names on Facebook because it wouldn't accept Lick

— Chris Hannas (@cjhannas) August 29, 2018

But of course, Facebook is not the only instance where censorship rules based on bare pattern matching interfere not just with speech but with speaker's ability to even get online to speak.

Can’t even create my own player in a Madden franchise. Smh.

— Ben Schmuck (@benschmuck13) August 28, 2018

Ha! I had the same damn thing happen to me today when I tried to RSVP for a webinar.

— Jen Dick (@Jennifer_Dick) August 28, 2018

You're right in there with Alan Cumming, the actor, whose name was autocensored by the late City of Heroes MMO's official forums. (The COH forums also auto-nixed Dick Grayson, which was... amusing... on a forum where superheroes got discussed a lot.)

— The Phantom of the Ottoman (@zgryphon) August 28, 2018

This dynamic is what's known as the Scunthorpe Problem. Scunthorpe is a town in the UK whose residents have had an appallingly difficult time using the Internet due to a naughty word being contained within the town name.

The Scunthorpe problem is the blocking of e-mails, forum posts or search results by a spam filter or search engine because their text contains a string of letters that are shared with another (usually obscene) word. While computers can easily identify strings of text within a document, broad blocking rules may result in false positives, causing innocent phrases to be blocked.

The problem was named after an incident in 1996 in which AOL's profanity filter prevented residents of the town of Scunthorpe, North Lincolnshire, England from creating accounts with AOL, because the town's name contains the substring cunt. Years later, Google's opt-in SafeSearch filters apparently made the same mistake, preventing residents from searching for local businesses that included Scunthorpe in their names.

(A related dynamic, the Clbuttic Problem, creates issues of its own when, instead of outright blocking, software automatically replaces the allegedly naughty words with ostensibly less-naughty words instead. People attempting to discuss such non-purient topics as Buttbuttin's Creed and the Lincoln Buttbuttination find this sort of officious editing particularly unhelpful…)

While examples of these dynamics can be amusing, each is also quite chilling to speech, and to speakers wishing to speak.

With the last name ‘Dicks’, I have to remind people to check their spam folder more often than a Nigerian prince.

— Chain of Lynx (@chainoflynx) August 28, 2018

The word Spam is literally in my last name. My husband’s family warned me that my last name can/will be marked as spam.

— Angela Spampata (@bird5445) August 29, 2018

Used to work with a lady whose last name is Wang, and it took us a few days to add exceptions to all the email filters

— Destroyer of Jeeps (@NewKindOfClown) August 29, 2018

It's not something we should be demanding more of, but every time people call for "AI" as a solution to online content challenges these are the censoring problems the call invites.

A big part of the problem is that calls for "AI" tend to treat it like some magical incantation, as if just adding it will solve all our problems. But in the end, AI is just software. Software can be very good at doing certain things, like finding patterns, including patterns in words (and people's names…). But it's not good at necessarily knowing what to make of those patterns.

pic.twitter.com/5U0a1Yk8Yv

— michelle 💞 (@tenderdamie) August 29, 2018

Our net Nanny at work flagged a co-worker for offensive language. He dealt with a lot of crane contractors. Net nanny told his boss he was sending lots of emails with the word erection. Lol.

— GoGoATL (@GoGoATL) August 28, 2018

More sophisticated software may be better at understanding context, or even sometimes learning context, but there are still limits to what we can expect from these tools. They are at best imperfect reflections of the imperfect humans who created them, and it's a mistake to forget that they have not yet replicated, or replaced, human judgment, which itself is often imperfect.

Which is not to say that there is no role for software to help in content moderation. The things that software is good at can make it an important tool to help support human decision-making about online content, especially at scale. But it is a mistake to expect software to supplant human decision-making. Because, as we see from these accruing examples, when we over-rely on them, it ends up being real humans that we hurt.

Had this on a website for the kids, the kids demanded to know why, our last name is ‘Clithero’ Interesting conversation. 😳

— DougHero 🇬🇧 (@ClitheroDoug) August 29, 2018

I know that feel pic.twitter.com/nMbjfTKGcZ

— Nazi Paikidze-Barnes (@NaziPaiki) August 29, 2018

Filed Under: ai, artificial intelligence, content moderation, language, natalie weiner, scunthorpe

UK Police Use Zipcode Profiles, Garden Size And First Names For AI-Based Custody Decision System

from the black-box-says-you're-a-bad-person dept

As you have doubtless noticed, Cambridge Analytica has been much in the headlines of late. There is still plenty of disagreement about the extent to which the company's profiling tools provide the kind of fine-grained categorization of people that it claims, and whether it played a significant -- or indeed any -- role in deciding key elections, both in the US and elsewhere. What is not disputed is that such profiling is widely used throughout the online world, mostly to sell ads, and that it is likely to become more accurate as further data is gathered, and analytical techniques are honed. The continuing flow of reports about Cambridge Analytica and related companies has therefore at least served the purpose of alerting people to the important issues raised by this approach. Against that background, news that UK police in the north of England are applying similar techniques is troubling:

Durham Police has paid global data broker Experian for UK postcode [zipcode] stereotypes built on 850 million pieces of information to feed into an artificial intelligence (AI) tool used in custody decisions, a Big Brother Watch investigation has revealed.

Durham Police is feeding Experian's 'Mosaic' data, which profiles all 50 million adults in the UK to classify UK postcodes, households and even individuals into stereotypes, into its AI 'Harm Assessment Risk Tool' (HART). The 66 'Mosaic' categories include 'Disconnected Youth', 'Asian Heritage' and 'Dependent Greys'.

In order to decide whether someone should be charged with an offense, the HART system aims to help the police evaluate whether they are likely to re-offend. "High-risk" offenders are charged. Those with a "moderate" risk of re-offending are offered the option of joining a rehabilitation program; if they complete it successfully, they do not receive a criminal conviction. To build the specialized AI system, the local UK police force has been working with a team of researchers at the University of Cambridge:

Called the Harm Assessment Risk Tool (HART), the AI-based technology uses 104,000 histories of people previously arrested and processed in Durham custody suites over the course of five years, with a two-year follow-up for each custody decision. Using a method called "random forests", the model looks at vast numbers of combinations of 'predictor values', the majority of which focus on the suspect's offending history, as well as age, gender and geographical area.

The basic HART system has been in use since 2016. But Big Brother Watch has discovered that HART has been extended in a significant way through the use of the profiling information acquired from Experian. This Dublin-based company -- not to be confused with Equifax, which works in the same field -- has amassed personal information on hundreds of millions of people around the world. Where things become more problematic is how the profiles that Experian has passed to the Durham police force for its HART system are compiled. As well as using basic zipcodes, a wide range of sensitive "predictor values" are gathered, aggregated and analyzed, such as:

Family composition, including children,
Family/personal names linked to ethnicity,
Online data, including data scraped from the pregnancy advice website 'Emma's Diary', and Rightmove [UK real estate site],
Occupation,
Child [support] benefits, tax credits, and income support,
Health data,
[Children's exam] results,
Ratio of gardens to buildings,
Census data,
Gas and electricity consumption.

The use of first names to help assign people to categories is a striking feature of the approach:

Experian’s 'Mosaic' links names to stereotypes: for example, people called 'Stacey’ are likely to fall under 'Families with Needs' who receive 'a range of [government] benefits'; 'Abdi' and 'Asha' are 'Crowded Kaleidoscope' described as 'multi-cultural' families likely to live in 'cramped' and 'overcrowded flats'; whilst 'Terrence' and ‘Denise' are 'Low Income Workers' who have 'few qualifications' and are 'heavy TV viewers'

By stereotyping people on the basis of where and how they live, there is an evident risk that people will find it harder to escape from more challenging life situations, since those with less favorable stereotypes are more likely to be prosecuted than those with more favorable profiles, thus reducing social mobility.

An additional issue is that the black box nature of the HART system, coupled with the complexity of the 850 million data points it draws on, will inevitably make it very hard for police officers to challenge its outputs. They might disagree with its decisions, but in the face of this leading-edge AI-based approach, it would take a very self-assured and experienced officer to ignore a HART recommendation to prosecute, particularly with the risk that the person might re-offend. It is much more likely that officers will take the safe option and accept the HART system's recommendations, whatever they think. As a result, an essentially inscrutable black box will be making critical decisions about a person's life, based in part on where they live, how big their garden is, and whether they are called "Stacey" or "Terrence".

Follow me @glynmoody on Twitter or identi.ca, and +glynmoody on Google+

Filed Under: artificial intelligence, uk
Companies: experian

What Are The Ethical Issues Of Google -- Or Anyone Else -- Conducting AI Research In China?

from the don't-be-evil,-but-AI-first? dept

AI is hot, and nowhere more so than in China:

The present global verve about artificial intelligence (AI) and machine learning technologies has resonated in China as much as anywhere on earth. With the State Council’s issuance of the "New Generation Artificial Intelligence Development Plan" on July 20 [2017], China's government set out an ambitious roadmap including targets through 2030. Meanwhile, in China's leading cities, flashy conferences on AI have become commonplace. It seems every mid-sized tech company wants to show off its self-driving car efforts, while numerous financial tech start-ups tout an AI-driven approach. Chatbot startups clog investors' date books, and Shanghai metro ads pitch AI-taught English language learning.

That's from a detailed analysis of China's new AI strategy document, produced by New America, which includes a full translation of the development plan. Part of AI's hotness is driven by all the usual Internet giants piling in with lots of money to attract the best researchers from around the world. One of the companies that is betting on AI in a big way is Google. Here's what Sundar Pichai wrote in his 2016 Founders' Letter:

Looking to the future, the next big step will be for the very concept of the "device" to fade away. Over time, the computer itself -- whatever its form factor -- will be an intelligent assistant helping you through your day. We will move from mobile first to an AI first world.

Given that emphasis, and the rise of China as a hotbed of AI activity, the announcement in December last year that Google was opening an AI lab in China made a lot of sense:

This Center joins other AI research groups we have all over the world, including in New York, Toronto, London and Zurich, all contributing towards the same goal of finding ways to make AI work better for everyone.

Focused on basic AI research, the Center will consist of a team of AI researchers in Beijing, supported by Google China's strong engineering teams.

So far, so obvious. But an interesting article on the Macro Polo site points out that there's a problem with AI research in China. It flows from the continuing roll-out of intrusive surveillance technologies there, as Techdirt has discussed in numerous posts. The issue is this:

Many, though not all, of these new surveillance technologies are powered by AI. Recent advances in AI have given computers superhuman pattern-recognition skills: the ability to spot correlations within oceans of digital data, and make predictions based on those correlations. It's a highly versatile skill that can be put to use diagnosing diseases, driving cars, predicting consumer behavior, or recognizing the face of a dissident captured by a city's omnipresent surveillance cameras. The Chinese government is going for all of the above, making AI core to its mission of upgrading the economy, broadening access to public goods, and maintaining political control.

As the Macro Polo article notes, Google is unlikely to allow any of its AI products or technologies to be sold directly to the authorities for surveillance purposes. But there are plenty of other ways in which advances in AI produced at Google's new lab could end up making life for Chinese dissidents, and for ordinary citizens in Xinjiang and Tibet, much, much worse. For example, the fierce competition for AI experts is likely to see Google's Beijing engineers headhunted by local Chinese companies, where knowledge can and will flow unimpeded to government departments. Although arguably Chinese researchers elsewhere -- in the US or Europe, for example -- might also return home, taking their expertise with them, there's no doubt that the barriers to doing so are higher in that case.

So does that mean that Google is wrong to open up a lab in Beijing, when it could simply have expanded its existing AI teams elsewhere? Is this another step toward re-entering China after it shut down operations there in 2010 over the authorities' insistence that it should censor its search results -- which, to its credit, Google refused to do? "AI first" is all very well, but where does "Don't be evil" fit into that?

Follow me @glynmoody on Twitter or identi.ca, and +glynmoody on Google+

Filed Under: ai, artificial intelligence, china, research
Companies: google

Washington's Growing AI Anxiety

from the perhaps-AI-can-help-us-deal-with-AI dept

Most people don't understand the nuances of artificial intelligence (AI), but at some level they comprehend that it'll be big, transformative and cause disruptions across multiple sectors. And even if AI proliferation won't lead to a robot uprising, Americans are worried about how AI and automation will affect their livelihoods.

Recognizing this anxiety, our policymakers have increasingly turned their attention to the subject. In the 115th Congress, there have already been more mentions of “artificial intelligence” in proposed legislation and in the Congressional Record than ever before.

While not everyone agrees on how we should approach AI regulation, one approach that has gained considerable interest is augmenting the federal government's expertise and capacity to tackle the issue. In particular, Sen. Brian Schatz has called for a new commission on AI; and Sen. Maria Cantwell has introduced legislation setting up a new committee within the Department of Commerce to study and report on the policy implications of AI.

This latter bill, the “FUTURE of Artificial Intelligence Act” (S.2217/H.4625), sets forth a bipartisan proposal that seems to be gaining some traction. While the bill's sponsors should be commended for taking a moderate approach in the face of growing populist anxiety, it's not clear that the proposed advisory committee would be particularly effective at all it sets out to do.

One problem with the bill is how it sets the definition of AI as a regulatory subject. For most of us, it's hard to articulate precisely what we mean when we talk about AI. The term “AI” can describe a sophisticated program like Apple's Siri, but it can also refer to Microsoft's Clippy, or pretty much any kind of computer software.

It turns out that AI is a difficult thing to define, even for experts. Some even argue that it's a meaningless buzzword. While this is a fine debate to have in the academy, prematurely enshrining a definition in a statute – as this bill does – is likely to be the basis for future policy (indeed, another recent bill offers a totally different definition). Down the road, this could lead to confusion and misapplication of AI regulations. This provision also seems unnecessary, since the committee is empowered to change the definition for its own use.

The committee's stated goals are also overly-ambitious. In the course of a year and a half, it would set out to “study and assess” over a dozen different technical issues, from economic investment, to worker displacement, to privacy, to government use and adoption of AI (although, notably, not defense or cyber issues). These are all important issues. However, the expertise required to adequately deal with these subjects is likely beyond the capabilities of 19 voting members of the committee, which includes only five academics. While the committee could theoretically choose to focus on a narrower set of topics in its final report, this structure is fundamentally not geared towards producing the sort of deep analysis that would advance the debate.

Instead of trying to address every AI-related policy issue with one entity, a better approach might be to build separate, specialized advisory committees based in different agencies. For instance, the Department of Justice might have a committee on using AI for risk assessment, the General Services Administration might have a committee on using AI to streamline government services and IT infrastructure, and the Department of Labor might have a committee on worker displacement caused by AI and automation or on using AI in employment decisions. While this approach risks some duplicative work, it would also be much more likely to produce deep, focused analysis relevant to specific areas of oversight.

Of course, even the best public advisory committees have limitations, including politicization, resource constraints and compliance with the Federal Advisory Committee Act. However, not all advisory bodies have to be within (or funded by) government. Outside research groups, policy forums and advisory committees exist within the private sector and can operate beyond the limitations of government bureaucracy while still effectively informing policymakers. Particularly for those issues not directly tied to government use of AI, academic centers, philanthropies and other groups could step in to fill this gap without any need for new public expenditures or enabling legislation.

If Sen. Cantwell's advisory committee-focused proposal lacks robustness, Sen. Schatz's call for creating a new “independent federal commission” with a mission to “ensure that AI is adopted in the best interests of the public” could go beyond the bounds of political possibility. To his credit, Sen. Schatz identifies real challenges with government use of AI, such as those posed by criminal justice applications, and in coordinating between different agencies. These are real issues that warrant thoughtful solutions. Nonetheless, the creation of a new agency for AI is likely to run into a great deal of pushback from industry groups and the political right (like similar proposals in the past), making it a difficult proposal to move forward.

Beyond creating a new commission or advisory committees, the challenge of federal expertise in AI could also be substantially addressed by reviving Congress' Office of Technology Assessment (which I discuss in a recent paper with Kevin Kosar). Reviving OTA has a number of advantages: OTA ran effectively for years and still exists in statute, it isn't a regulatory body, it is structurally bipartisan and it would have the capacity to produce deep-dive analysis in a technology-neutral manner. Indeed, there's good reason to strengthen the First Branch first, since Congress is ultimately responsible for making the legal frameworks governing AI as well as overseeing government usage.

Lawmakers are right to characterize AI as a big deal. Indeed, there are trillions of dollars in potential economic benefits at stake. While the instincts to build expertise and understanding first make for a commendable approach, policymakers will need to do it the right way – across multiple facets of government – to successfully shape the future of AI without hindering its transformative potential.

Filed Under: ai, artificial intelligence, brian schatz, committees, machine learning, maria cantwell, regulation

DailyDirt: Badminton Robots FTW

from the urls-we-dig-up dept

We have computers that can beat us at games like chess and Go (and Jeopardy!), but we haven't seen too many robots that can beat humans at more physical sports like soccer or tennis. We've seen some air hockey robots that are nearly unbeatable, so it's really only a matter of time before robots learn how to play sports with a few more dimensions. Here are some badminton robots that are inching toward playing better than some of us.

• Badminton robots are getting better slowly. This robot has binocular vision from two cameras and was built by students at the University of Electronic Science and Technology of China. However, it cheats a little bit by using two rackets....
• Just last year, Dong Jiong (a retired badminton professional) played against a couple of these badminton robots on a regulation court. The robots didn't play very well, but they actually played -- and presumably, upgrades will give them superhuman badminton skills someday.
• Badminton robots from the Flanders' Mechatronics Technology Centre (FMTC) in Belgium were relatively primitive 2013. However, the developers were learning about optimizing the efficiency of robots -- not actually trying to create a robot that could beat people at badminton.

After you've finished checking out those links, take a look at our Daily Deals for cool gadgets and other awesome stuff.

Filed Under: ai, air hockey, artificial intelligence, badminton, dong jiong, flanders' mechatronics technology centre, game algorithms, games, machine learning, robots, university of electronic science and technology of china