Civilisation: primeval slime to Mars

I finished Daniel Dennett’s From Bacteria to Bach and Back, and personally have no problem with his view that human consciousness is an evolved characteristic built over time from the ground up, and that human culture evolves too, starting with language and continues through memes. In other words, it’s all cranes, not skyhooks. Some people are obviously troubled by this argument. I lack the technical knowledge to evaluate all the detail here. It fundamentally seems far more plausible to me than the alternative.

As a matter of logic, this requires me – and Dennett – to take seriously the argument that computers/AI could evolve minds and consciousness. He puts some weight on the importance of embodiment – but that might be possible although we’re not there yet. Computer vision would be different from ours, but then so is flies’ vision or cephalopods’.

More of an issue, it seems to me, is that computers/AI are very energy-hungry compared to our brains: at the moment, Dennett writes, computer intelligence is parasitical, depending on humans to feed them a lot of energy and otherwise maintain them. What’s more, computers don’t have to struggle or compete: “Down in the hardware, the electric power is doled out evenhandedly and abundantly; no circuit risks starving. At the software level, a benevolent scheduler doles out machine cycles to whatever process has the highest priority, and although there may be a bidding mechanism … this is an orderly queue, not a struggle for life.”

So for now, I’ll stick to thinking Singularity-talk is mystical hype; but will try to keep an open mind on this question.

I’ve always had a soft spot for memes. Dennett uses words as the paradigmitic examples. It reminded me of a jokey line I read once about libraries being the dominant life form on Earth because they are so good at finding new hosts who will start to accumulate books.

There’s a nice section on the importance of social trust at the end of Bacteria/Bach, citing Paul Seabright’s wonderful Company of Strangers. Trust is the invisible glue of human societies, Dennett writes, and much too recent to be a hard-wired natural instinct. “We have bootstrapped ourselves into the heady altitudes of modern civilisation, and our natural emotions and other instinctual responses do not always serve our new circumstances. Civilisation is a work in progress and we abandon our attempt to understand it at our peril.”

Looking at the news these days, that isn’t a very optimistic note on which to end. And yet yesterday brought the amazing launch of Space X’s Falcon Heavy. Astonishing. Perhaps we’ll end up on Mars while the computers colonise Earth.



Optimizing bacteria & the value of information

One of the most exciting moments in my World Economic Forum experience was meeting Daniel Dennett, who was the first person to turn up to hear me talk about measuring the economy – he’s the distinguished Father Christmas-bearded chap on the right.

Daniel Dennett listening to meIt’s only quite recently that I read his Intuition Pumps and Other Tools for Thinking. Now I’m half way through the more recent From Bacteria to Bach and Back.

It’s interesting in part because of the read-over to economics. For example, the book talks about the reverse engineering approach to evolution – how come this creature has this feature? “Reverse engineering is methodoloigcally committed to optimality considerations,” Dennett writes. (NB this does not mean simplistic ‘just so’ stories.) The control circuits of an elevator have been top-down designed for good reasons, while the control proteins of bacteria have been bottom-up ‘designed’ (that is, evolved in their environment) for good reasons too. In economics optimisation is presented as an assumption about top-down choices, but maybe it’s more useful to consider it as a bottom-up description of how people decide to act, given their environment.

When the book turns to information – ‘a distinction that makes a difference ‘ – Dennett makes the link with economics explicit. Like other biological information, economic information is semantic information of some us to us. How useful depends on individual context – the value of information lies in the receiver. This means it can’t be measured in a non-arbitrary way (although its usefulness can often be empirically confirmed). Dennett also argues that semantic information does not need to be encoded to be useful, obvioulsy a claim of relevance to the debate about AI. (He spoke about this in Davos.)

This is interesting as it was an issue discussed in the recent ESCoE seminar by Richard Heys (on a paper I co-authored) about the price of telecommunication services. As a sort of thought experiment, we calculated a unit value index for these services – revenues divided by the volume of data used in bytes. This index declined 90% in the five years to 2015. But, one strand of the discussion went, not all units of data are equal; some are far more valuable than others. Of course. But the value is in the eye (or bank account) of the user, and what other signal of value could we select other than the decision to use?

This reminds me that I’ve been meaning to write, since I read it in October, about Jason Smith’s interesting e-book A Random Physicist Takes On Economics. I find it very hard to write about books in the Kindle app as I can’t page through them (though it seems there’s now a paperback); so all I will say is that it’s worth reading for its observations on the methodology of economics. I share some of its reservations – above all, the trouble macro has with aggregation. It also made me think about the role of context or environment, and why this might be more influential than individual choice processes in determining economic outcomes. Smith alludes to the literature on biological market theory, pointing out, though, that this does not rest at all on the utility of biological agents, be they pigeons or fungi.



An impulse buy

I was just passing through the Blackwell’s on campus here when I spotted Lorenz by Captain Jerry Roberts. I’m a sucker for books about the code breaking efforts at Bletchley Park. I loved the wider and gripping account of intelligence efforts in the UK during the war, Most Secret War by R.V Jones.

This book is about breaking the Lorenz (rather than the Enigma) code. Captain Roberts (whom my husband met) died in 2014 but the book was just published last year. War is often the crucible of innovation but we often think of material technologies (canned foods for Napoleon’s armies, Teflon in the Cold War/space race). The immaterial technology of codebreaking and computing was surely by far the most significant, though?


Learning about (machine) learning

Last week I trotted off for my first Davos experience with four books in my bag and managed to read only one – no doubt old hands could have warned me what a full-on (and rather weird) experience it is. The one (and that read mainly on the journeys) was The Master Algorithm by Pedro Domingos. I was impressed when I heard him speak last year & have been meaning to read it ever since.

The book is a very useful overview of how machine learning algorithms work, and if you’ve been wondering, I highly recommend it. On the whole it stays non-technical, although with lapses – and I could have done without the lame jokes, no doubt inserted for accessibility. The book also has an argument to make: that there is an ultimate ‘master algorithm’, a sort of Grand Unified Theory of learning. This was a bit of a distraction, especially as there’s an early chapter doing the advocacy before the later chapters explaining what Domingos hopes will eventually be unified.

However, the flaws are minor. I learned a lot about both the history of the field and its recent practice, along with some insights as to how quickly it’s progressing in different domains and therefore what we might expect to be possible soon. Successive chapters set out the currently competing algorithmic approaches (the book identifies five), explains their history within the discipline and how they relate to each other, how they work and what they are used for. There is an early section on the importance of data.

As a by the by, I agree wholeheartedly with this observation: “To make progress, every field of science needs to have data commensurate with the complexity of the phenomena it studies.” This in my view is why macroeconomics is in such a weak state compared to applied microeconomics: the latter has large data sets, and ever more of them, but macro data is sparse. It doesn’t need more theories but more data. Nate Silver made a simliar point in his book The Signal and the Noise – he pointed out that weather forecasts improved by gathering much more data, in contrast to macro forecasting.

Another interesting point Domingos makes en passant is how much more energy machines need than do brains: “Your brain uses only as much power as a small lightbulb.” As the bitcoin environmental disaster makes plain, energy consumption may be the achilles heel of the next phase of the digital revolution.

I don’t know whether or not one day all the algorithmic approaches will be combined into one master algorithm – I couldn’t work out why unification was a better option than horses for courses. But never mind. This is a terrific book to learn about machine learning.


Moral sentiments and economics

Having read it some years ago, I’ve been looking through Economic Analysis, Moral Philosophy and Public Policy by Daniel Hausman and Michael McPherson while revising a paper for publication. It reminded me – if ever I absorbed it properly in the first place – that economists use the word ‘normative’ in a specific sense. It means, to us, the opposite of positive, something involving ethics or value judgements, and therefore outside the scope of what economists aim to do. We concentrate on ‘positive’ questions, following the distinction famously made by Milton Friedman, and Lionel Robbins before him.

However, as Hausman and McPherson argue, while economists claim ethical issues are a sphere apart, they/we often assume rationality – and that itself implies a moral stance. “Like morality, rationality is normative. One ought to be rational. One is wicked if not moral and foolish if not rational.” The book goes on to argue the case that the “moral principles implicit in standard views of rationality are implausible.” As a card carrying economist, I don’t agree with all they say here, but do wholeheartedly agree that economics and ‘moral sentiments’ cannot be separated. The impartiality economics aims for in trying to insist on being a ‘positive’ science is laudable, and economics should continue to distinguish the technical and value-laden elements of analysis. But we should not continue to shy away from the ethical debate. Doing so has not served the reputation of economics at all well.