Sunday 26 January, 2020

What the Clearview AI story means

This morning’s Observer column:

Ultimately, the lesson of Clearview is that when a digital technology is developed, it rapidly becomes commodified. Once upon a time, this stuff was the province of big corporations. Now it can be exploited by small fry. And on a shoestring budget. One of the co-founders paid for server costs and basic expenses. Mr Ton-That lived on credit-card debt. And everyone worked from home. “Democracy dies in darkness” goes the motto of the Washington Post. “Privacy dies in a hacker’s bedroom” might now be more appropriate.

Read on

UPDATE A lawsuit — seeking class-action status — was filed this week in Illinois against Clearview AI, a New York-based startup that has scraped social media networks for people’s photos and created one of the biggest facial recognition databases in the world.


Privacy is a public good

Shoshana Zuboff in full voice:

”The belief that privacy is private has left us careening toward a future that we did not choose, because it failed to reckon with the profound distinction between a society that insists upon sovereign individual rights and one that lives by the social relations of the one-way mirror. The lesson is that privacy is public — it is a collective good that is logically and morally inseparable from the values of human autonomy and self-determination upon which privacy depends and without which a democratic society is unimaginable.”

Great OpEd piece.


The winding path


Why the media shouldn’t underestimate Joe Biden

Simple: Trump’s crowd don’t. They think he’s the real threat. (Which explains the behaviour that’s led to Trump’s Impeachment.) David Brooks has some sharp insights into why the chattering classes are off target About this.

It’s the 947th consecutive sign that we in the coastal chattering classes have not cured our insularity problem. It’s the 947th case in which we see that every second you spend on Twitter detracts from your knowledge of American politics, and that the only cure to this insularity disease is constant travel and interviewing, close attention to state and local data and raw abject humility about the fact that the attitudes and academic degrees that you think make you clever are actually the attitudes and academic degrees that separate you from the real texture of American life.

Also, the long and wide-ranging [NYT interview)(https://www.nytimes.com/interactive/2020/01/17/opinion/joe-biden-nytimes-interview.html) with him is full of interesting stuff — like that he thinks that Section 230 of the Communications Decency Act (that’s the get-out-of-gaol card for the tech companies) should be revoked. I particularly enjoyed this observation by Brooks: “ Jeremy Corbyn in Britain and Bernie Sanders here are a doctoral student’s idea of a working-class candidate, not an actual working person’s idea of one.”


Linkblog

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Serial Killers: Moore’s Law and the parallelisation bubble

Cory Doctorow had a thoughtful reaction to Sunday’s Observer column, where I cited Nathan Myhrvold’s Four Laws of Software. “Reading it”, he writes,

made me realize that we were living through a parallel computation bubble. The period in which Moore’s Law had declined also overlapped with the period in which computing came to be dominated by a handful of applications that are famously parallel — applications that have seemed overhyped even by the standards of the tech industry: VR, cryptocurrency mining, and machine learning.

Now, all of these have other reasons to be frothy: machine learning is the ideal tool for empiricism-washing, through which unfair policies are presented as “evidence-based”; cryptocurrencies are just the thing if you’re a grifty oligarch looking to launder your money; and VR is a new frontier for the moribund, hyper-concentrated entertainment industry to conquer.

“Parallelizable problems become hammers in search of nails,” Cory continued in an email:

“If your problem can be decomposed into steps that can be computed independent of one another, we’ve got JUST the thing for you — so, please, tell me about all the problems you have that fit the bill?”

This is arguably part of why we’re living through a cryptocurrency and ML bubble: even though these aren’t solving our most pressing problems, they are solving our most TRACTABLE ones. We’re looking for our keys under the readily computable lamppost, IOW.

Which leads Cory (@doctorow) to this “half-formed thought”: the bubbles in VR, machine learning and cryptocurrency are partly explained by the decline in returns to Moore’s Law, which means that parallelizable problems are cheaper/easier to solve than linear ones.

And wondering what the counterfactual would have been like: if we had found a way of extending Moore’s Law indefinitely.

As Moore’s Law runs out of steam, it’ll be back to the future

This morning’s Observer column:

In a lecture in 1997, Nathan Myhrvold, who was once Bill Gates’s chief technology officer, set out his Four Laws of Software. 1: software is like a gas – it expands to fill its container. 2: software grows until it is limited by Moore’s law. 3: software growth makes Moore’s law possible – people buy new hardware because the software requires it. And, finally, 4: software is only limited by human ambition and expectation.

As Moore’s law reaches the end of its dominion, Myhrvold’s laws suggest that we basically have only two options. Either we moderate our ambitions or we go back to writing leaner, more efficient code. In other words, back to the future.

Read on

Raspberry Pi: a great British success story

This morning’s Observer column:

I bought my Pi from the Raspberry Pi store in Cambridge. Across the street (and one floor below) is the Apple store where I had earlier gone to buy a new keyboard for one of my Macs. The cost: £99. So for £15 more, I had a desktop computer perfectly adequate for most of the things I need to do for my work.

The Pi is one of the (few) great British technology success stories of the last decade: sales recently passed the 30m mark. But if you got your news from mainstream media you’d never know…

Read on

Podcasting: will it succumb to the Wu cycle?

This morning’s Observer column:

I’ve just been listening to what I think of as the first real podcast. The speaker is Dave Winer, the software genius whom I wrote about in October. He pioneered blogging and played a key role in the evolution of the RSS site-syndication technology that enabled users and applications to access updates to websites in a standardised, computer-readable format.

And the date of this podcast? 11 June, 2004 – 15 years ago; which rather puts into context the contemporary excitement about this supposedly new medium that is now – if you believe the hype – taking the world by storm. With digital technology it always pays to remember that it’s older than you think.

When he started doing it, Winer called it “audioblogging” and if you listen to his early experiments you can see why. They’re relaxed, friendly, digressive, unpretentious and insightful – in other words an accurate reflection of the man himself and of his blog. He thought of them as “morning coffee notes” – audio meditations about what was on his mind first thing in the morning…

Read on

Kranzberg’s Law

As a critic of many of the ways that digital technology is currently being exploited by both corporations and governments, while also being a fervent believer in the positive affordances of the technology, I often find myself stuck in unproductive discussions in which I’m accused of being an incurable “pessimist”. I’m not: better descriptions of me are that I’m a recovering Utopian or a “worried optimist”.

Part of the problem is that the public discourse about this stuff tends to be Manichean: it lurches between evangelical enthusiasm and dystopian gloom. And eventually the discussion winds up with a consensus that “it all depends on how the technology is used” — which often leads to Melvin Kranzberg’s Six Laws of Technology — and particularly his First Law, which says that “Technology is neither good nor bad; nor is it neutral.” By which he meant that,

“technology’s interaction with the social ecology is such that technical developments frequently have environmental, social, and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves, and the same technology can have quite different results when introduced into different contexts or under different circumstances.”

Many of the current discussions revolve around various manifestations of AI, which means machine learning plus Big Data. At the moment image recognition is the topic du jour. The enthusiastic refrain usually involves citing dramatic instances of the technology’s potential for social good. A paradigmatic example is the collaboration between Google’s DeepMind subsidiary and Moorfields Eye Hospital to use machine learning to greatly improve the speed of analysis of anonymized retinal scans and automatically flag ones which warrant specialist investigation. This is a good example of how to use the technology to improve the quality and speed of an important healthcare service. For tech evangelists it is an irrefutable argument for the beneficence of the technology.

On the other hand, critics will often point to facial recognition as a powerful example for the perniciousness of machine-learning technology. One researcher has even likened it to plutonium. Criticisms tend to focus on its well-known weaknesses (false positives, racial or gender bias, for example), its hasty and ill-considered use by police forces and proprietors of shopping malls, the lack of effective legal regulation, and on its use by authoritarian or totalitarian regimes, particularly China.

Yet it is likely that even facial recognition has socially beneficial applications. One dramatic illustration is a project by an Indian child labour activist, Bhuwan Ribhu, who works for the Indian NGO Bachpan Bachao Andolan. He launched a pilot program 15 months prior to match a police database containing photos of all of India’s missing children with another one comprising shots of all the minors living in the country’s child care institutions.

The results were remarkable. “We were able to match 10,561 missing children with those living in institutions,” he told CNN. “They are currently in the process of being reunited with their families.” Most of them were victims of trafficking, forced to work in the fields, in garment factories or in brothels, according to Ribhu.

This was made possible by facial recognition technology provided by New Delhi’s police. “There are over 300,000 missing children in India and over 100,000 living in institutions,” he explained. “We couldn’t possibly have matched them all manually.”

This is clearly a good thing. But does it provide an overwhelming argument for India’s plan to construct one of the world’s largest facial-recognition systems with a unitary database accessible to police forces in 29 states and seven union territories?

I don’t think so. If one takes Kranzberg’s First Law seriously, then each proposed use of a powerful technology like this has to face serious scrutiny. The more important question to ask is the old Latin one: Cui Bono?. Who benefits? And who benefits the most? And who loses? What possible unintended consequences could the deployment have? (Recognising that some will, by definition, be unforseeable.) What’s the business model(s) of the corporations proposing to deploy it? And so on.

At the moment, however, all we mostly have is unasked questions, glib assurances and rash deployments.

What if AI could write like Hemingway?

This morning’s Observer column:

Last February, OpenAI, an artificial intelligence research group based in San Francisco, announced that it has been training an AI language model called GPT-2, and that it now “generates coherent paragraphs of text, achieves state-of-the-art performance on many language-modelling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarisation – all without task-specific training”.

If true, this would be a big deal…

Read on

USB-C solutions

Now that the MacBook and iPad that I use when travelling have only USB-C ports (and that the iPad Pro can now handle external drives) I needed a USB-stick that could do the trick. This 128GB one arrived today. It also has a USB 3.0 connector, so hooks up to older kit. Perfect for bringing presentations on the move.