John Thornhill had an interesting column in the Financial Times the other day (sadly, behind a paywall) about Moore’s Law and the struggles of the tech industry to overcome the physical barriers to its continuance.
This led me to brood on one of the under-discussed aspects of the Law, namely the way it has enabled the AI crowd to dodge really awkward questions for years. It works like this: If the standard-issue AI of a particular moment in time proves unable to perform a particular task or solve a particular problem, then the strategy is to say (confidently): “yes but Moore’s Law will eventually provide the computing power to crack it”.
And sometimes that’s true. The difficulty, though, is that it assumes that all problems are practical — i.e. ones that are ultimately computable. But some tasks/problems are almost certainly not computable. And so there are times when our psychic addiction to Moore’s Law leads us to pursue avenues which are, ultimately, dead ends. But few people dare admit that, especially when hype-storms are blowing furiously.
This morning’s Observer column:
Ideology is what determines how you think when you don’t know you’re thinking. Neoliberalism is a prime example. Less well-known but equally insidious is technological determinism, which is a theory about how technology affects development. It comes in two flavours. One says that there is an inexorable internal logic in how technologies evolve. So, for example, when we got to the point where massive processing power and large quantities of data became easily available, machine-learning was an inevitable next step.
The second flavour of determinism – the most influential one – takes the form of an unshakable conviction that technology is what really drives history. And it turns out that most of us are infected with this version.
It manifests itself in many ways…
This morning’s Observer column:
As the science fiction novelist William Gibson famously observed: “The future is already here – it’s just not very evenly distributed.” I wish people would pay more attention to that adage whenever the subject of artificial intelligence (AI) comes up. Public discourse about it invariably focuses on the threat (or promise, depending on your point of view) of “superintelligent” machines, ie ones that display human-level general intelligence, even though such devices have been 20 to 50 years away ever since we first started worrying about them. The likelihood (or mirage) of such machines still remains a distant prospect, a point made by the leading AI researcher Andrew Ng, who said that he worries about superintelligence in the same way that he frets about overpopulation on Mars.
That seems about right to me…
Fascinating interview on Edge.org with Tom Griffiths of Berkeley. For me, the most interesting passage is this:
One of the mysteries of human intelligence is that we’re able to do so much with so little. We’re able to act in ways that are so intelligent despite the fact that we have limited computational resources—basically just the stuff that we can carry around inside our heads. But we’re good at coming up with strategies for solving problems that make the best use of those limited computational resources. You can formulate that as another kind of computational problem in itself.
If you have certain computational resources and certain costs for using them, can you come up with the best algorithm for solving a problem, using those computational resources, trading off the errors you might make and solving the problem with the cost of using the resources you have or the limitations that are imposed upon those resources? That approach gives us a different way of thinking about what constitutes rational behavior.
The classic standard of rational behavior, which is used in economics and which motivated a lot of the human decision-making literature, focused on the idea of rationality in terms of finding the right answer without any thought as to the computational costs that might be involved.
This gives us a more nuanced and more realistic notion of rationality, a notion that is relevant to any organism or machine that faces physical constraints on the resources that are available to it. It says that you are being rational when you’re using the best algorithm to solve the problem, taking into account both your computational limitations and the kinds of errors that you might end up making.
This approach, which my colleague Stuart Russell calls “bounded optimality,” gives us a new way of understanding human cognition. We take examples of things that have been held up as evidence of irrationality, examples of things where people are solving a problem but not doing it in the best way, and we can try and make sense of those. More importantly, it sets up a way of asking questions about how people get to be so smart. How is it that we find those effective strategies? That’s a problem that we call “rational metareasoning.” How should a rational agent who has limitations on their computational resources find the best strategies for using those resources?
Worth reading (or watching or listening to) in full.
I can see the point of trying to understand why humans are so good at some things. The capacity to make rapid causal inferences was probably hardwired into our DNA by evolution — it’s ‘System 1’ in the categorisation proposed in Daniel Kahneman’s book, Thinking Fast and Slow, i.e. a capacity for fast, instinctive and emotional thinking — the kind of thinking that was crucial for survival in primeval times. But the other — equally important — question is why humans seem to be so bad at Kahneman’s ‘System 2’ thinking — i.e. slower, more deliberative and more logical reasoning. Maybe it’s because our evolutionary inheritance was laid down in a simpler era, and we’re just not adapted to handle the complexity with which (as a result of our technological ingenuity) we are now confronted?
This has interesting contemporary resonances: climate change denial, for example; fake news; populism; and the tensions between populism and technocracy.
Interesting essay by Gary Marcus. I particularly like this bit:
Although the field of A.I. is exploding with microdiscoveries, progress toward the robustness and flexibility of human cognition remains elusive. Not long ago, for example, while sitting with me in a cafe, my 3-year-old daughter spontaneously realized that she could climb out of her chair in a new way: backward, by sliding through the gap between the back and the seat of the chair. My daughter had never seen anyone else disembark in quite this way; she invented it on her own — and without the benefit of trial and error, or the need for terabytes of labeled data.
Presumably, my daughter relied on an implicit theory of how her body moves, along with an implicit theory of physics — how one complex object travels through the aperture of another. I challenge any robot to do the same. A.I. systems tend to be passive vessels, dredging through data in search of statistical correlations; humans are active engines for discovering how things work.
Marcus thinks that a new paradigm is needed for AI that places “top down” knowledge (cognitive models of the world and how it works) and “bottom up” knowledge (the kind of raw information we get directly from our senses) on equal footing. “Deep learning”, he writes,
“is very good at bottom-up knowledge, like discerning which patterns of pixels correspond to golden retrievers as opposed to Labradors. But it is no use when it comes to top-down knowledge. If my daughter sees her reflection in a bowl of water, she knows the image is illusory; she knows she is not actually in the bowl. To a deep-learning system, though, there is no difference between the reflection and the real thing, because the system lacks a theory of the world and how it works. Integrating that sort of knowledge of the world may be the next great hurdle in A.I., a prerequisite to grander projects like using A.I. to advance medicine and scientific understanding.”
Yep: ‘superintelligence’ is farther away than we think.
From MIT Technology Review:
You may control your home with your voice, but having it speak back is often impractical. Asking Amazon’s Alexa to play a specific song, for instance, is a joy. But if you’re not sure what to listen to, the voice-only system can feel limiting. At the same time, voice assistant apps grow in number but go unused because people simply forget about them. Speaking to the [Tech Review] Download, Andrew Ng, chief scientist at Baidu, explained that, while a 2016 study by Stanford researchers and his own team showed that speech input is three times quicker than typing on mobile devices, “the fastest way for a machine to get information to you is via a screen.” He continued: “Say you want to order takeout. Imagine a voice that reads out: ‘Here are the top twenty restaurants in your area. Number one …’ This would be insanely slow!” No surprise, then, that Baidu has been working on a smart assistant device called Little Fish that includes a screen, and Amazon is also rumored to be developing a similar piece of hardware. The AI assistant revolution, it seems, may be televised.
Yep. My experience with Amazon Echo chimes with this.
This morning’s Observer column:
On 25 October, the German chancellor, Angela Merkel, wandered into unfamiliar territory – at least for a major politician. Addressing a media conference in Munich, she called on major internet companies to divulge the secrets of their algorithms on the grounds that their lack of transparency endangered public discourse. Her prime target appeared to be search engines such as Google and Bing, whose algorithms determine what you see when you type a search query into them. Given that, an internet user should have a right to know the logic behind the results presented to him or her.
“I’m of the opinion,” declared the chancellor, “that algorithms must be made more transparent, so that one can inform oneself as an interested citizen about questions like, ‘What influences my behaviour on the internet and that of others?’ Algorithms, when they are not transparent, can lead to a distortion of our perception; they can shrink our expanse of information.”
All of which is unarguably true…
From a Guardian column by Paul Mason:
these battles between regulators and the rent-seeking monopolists who have hijacked the sharing economy are, in the long term, irrelevant. The attempt to drive down cab drivers’ wages and reduce their employment rights to zero are, in their own way, a last gasp of the 20th-century economic thinking.
Because soon there won’t need to be drivers at all. Given that there are 400,000 HGV drivers in the UK, that at least a quarter of Britain’s 2.5 million van drivers are couriers, and that there are 297,000 licensed taxi drivers – that is a big dent in male employment.
The most important question facing us is not whether Uber drivers should have employment rights (they should), but what to do in a world where automation begins to eradicate work. If we accept – as Oxford researchers Carl Frey and Michael Osborne stated in 2013 – that 47% of jobs are susceptible to automation, the most obvious problem is: how are people going to live?
This morning’s Observer column:
The question on everyone’s mind as Google hoovered up robotics companies was: what the hell was a search company doing getting involved in this business? Now we know: it didn’t have a clue.
Last week, Bloomberg revealed that Google was putting Boston Dynamics up for sale. The official reason for unloading it is that senior executives in Alphabet, Google’s holding company, had concluded (correctly) that Boston Dynamics was years away from producing a marketable product and so was deemed disposable. Two possible buyers have been named so far – Toyota and Amazon. Both make sense for the obvious reason that they are already heavy users of robots and it’s clear that Amazon in particular would dearly love to get rid of humans in its warehouses at the earliest possible opportunity…
The Economist has an interesting article on how major universities are now having trouble holding on to their machine-learning and AI academics. As the industrial frenzy about these technologies mounts, this is perfectly understandable, though it’s now getting to absurd proportions. The Economist claims, for example, that some postgraduate students are being lured away – by salaries “similar to those fetched by professional athletes” – even before they complete their doctorates. And Uber lured “40 of the 140 staff of the National Robotics Engineering Centre at Carnegie Mellon University, and set up a unit to work on self-driving cars”.
All of which is predictable: we’ve seen it happen before, for example, with researchers who have data-analytics skillsets. But it raises several questions.
The first is whether this brain brain will, in the end, turn out to be self-defeating? After all, the graduate students of today are the professors of tomorrow. And since, in the end, most of the research and development done in companies tends to be applied, who will do the ‘pure’ research on which major advances in many fields depend?
Secondly, and related to that, since most industrial R&D is done behind patent and other intellectual-property firewalls, what happens to the free exchange of ideas on which intellectual progress ultimately depends? In that context, for example, it’s interesting to see the way in which Google’s ownership of Deepmind seems to be beginning to constrain the freedom of expression of its admirable co-founder, Demis Hassabis.
Thirdly, since these technologies appear to have staggering potential for increasing algorithmic power and perhaps even changing the relationship between humanity and its machines, the brain drain from academia – with its commitment to open enquiry, sensitivity to ethical issues, and so on – to the commercial sector (which traditionally has very little interest in any of these things) is worrying.