A bit more on -- Magical thinking about machine learning won't bring the reality of AI any closer

["Dave Farber" <farber () gmail com>]

> Begin forwarded message:
>
> From: Dewayne Hendricks <dewayne () warpspeed com>
> Subject: [Dewayne-Net] Magical thinking about machine learning won't bring the reality of AI any closer
> Date: August 6, 2018 6:32:19 JST
> To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com>
> Reply-To: dewayne-net () warpspeed com
>
> Magical thinking about machine learning won’t bring the reality of AI any closer
> Unchecked flaws in algorithms, and even the technology itself, should put a brake on the escalating use of big data
> By John Naughton
> Aug 5 2018
> <https://www.theguardian.com/commentisfree/2018/aug/05/magical-thinking-about-machine-learning-will-not-bring-artificial-intelligence-any-closer>
>
> ”Any sufficiently advanced technology,” wrote the sci-fi eminence grise Arthur C Clarke, “is indistinguishable from 
> magic.” This quotation, endlessly recycled by tech boosters, is possibly the most pernicious utterance Clarke ever 
> made because it encourages hypnotised wonderment and disables our critical faculties. For if something is “magic” 
> then by definition it is inexplicable. There’s no point in asking questions about it; just accept it for what it is, 
> lie back and suspend disbelief.
>
> Currently, the technology that most attracts magical thinking is artificial intelligence (AI). Enthusiasts portray it 
> as the most important thing since the invention of the wheel. Pessimists view it as an existential threat to 
> humanity: the first “superintelligent” machine we build will be the beginning of the end for humankind; the only 
> question thereafter will be whether smart machines will keep us as pets.
>
> In both cases there seems to be an inverse correlation between the intensity of people’s convictions about AI and 
> their actual knowledge of the technology. The experts seem calmly sanguine, while the boosters seem blissfully 
> unaware that the artificial “intelligence” they extol is actually a relatively mundane combination of machine 
> learning (ML) plus big data.
>
> ML uses statistical techniques to give computers the ability to “learn” – ie use data to progressively improve 
> performance on a specific task, without being explicitly programmed. A machine-learning system is a bundle of 
> algorithms that take in torrents of data at one end and spit out inferences, correlations, recommendations and 
> possibly even decisions at the other end. And the technology is already ubiquitous: virtually every interaction we 
> have with Google, Amazon, Facebook, Netflix, Spotify et al is mediated by machine-learning systems. It’s even got to 
> the point where one prominent AI guru, Andrew Ng, likens ML to electricity.
>
> To many corporate executives, a machine that can learn more about their customers than they ever knew seems magical. 
> Think, for example, of the moment Walmart discovered that among the things their US customers stocked up on before a 
> hurricane warning – apart from the usual stuff – were beer and strawberry Pop-Tarts! Inevitably, corporate enthusiasm 
> for the magical technology soon spread beyond supermarket stock-controllers to public authorities. Machine learning 
> rapidly found its way into traffic forecasting, “predictive” policing (in which ML highlights areas where crime is 
> “more likely”), decisions about prisoner parole, and so on. Among the rationales for this feeding frenzy are 
> increased efficiency, better policing, more “objective” decision-making and, of course, providing more responsive 
> public services.
>
> This “mission creep” has not gone unnoticed. Critics have pointed out that the old computing adage “garbage in, 
> garbage out” also applies to ML. If the data from which a machine “learns” is biased, then the outputs will reflect 
> those biases. And this could become generalised: we may have created a technology that – however good it is at 
> recommending films you might like – may actually morph into a powerful amplifier of social, economic and cultural 
> inequalities.
>
> In all of this sociopolitical criticism of ML, however, what has gone unchallenged is the idea that the technology 
> itself is technically sound – in other words that any problematic outcomes it produces are, ultimately, down to flaws 
> in the input data. But now it turns out that this comforting assumption may also be questionable. At the most recent 
> Nips (Neural Information Processing Systems) conference – the huge annual gathering of ML experts – Ali Rahimi, one 
> of the field’s acknowledged stars, lobbed an intellectual grenade into the audience. In a remarkable lecture he 
> likened ML to medieval alchemy. Both fields worked to a certain extent – alchemists discovered metallurgy and 
> glass-making; ML researchers have built machines that can beat human Go champions and identify objects from pictures. 
> But just as alchemy lacked a scientific basis, so, argued Rahimi, does ML. Researchers, he claimed, often can’t 
> explain the inner workings of their mathematical models: they lack rigorous theoretical understandings of their tools 
> and in that sense are currently operating in alchemical rather than scientific mode.
>
> [snip]
>
> Dewayne-Net RSS Feed: http://dewaynenet.wordpress.com/feed/
> Twitter: https://twitter.com/wa8dzp




-------------------------------------------
Archives: https://www.listbox.com/member/archive/247/=now
Modify Your Subscription: https://www.listbox.com/member/?member_id=18849915
Unsubscribe Now: 
https://www.listbox.com/unsubscribe/?member_id=18849915&id_secret=18849915-a538de84&post_id=20180805211254:D68C11D4-9915-11E8-8DA1-F4055BC57E5A
Powered by Listbox: https://www.listbox.com