The title of this post is an allusion to Bradley’s description of the upshot of a lot of metaphysical speculation. These days, though, it’s more often scientistic writers who tend to rummage around for arguments that will bolster their views on what ‘human nature’ ‘really’ is. So we get Sam Harris, Stephen Pinker et. al., with their bold speculations on life, violence and justice, all dressed up with the language of neuroscience.

There’s a good review article in the excellent London Review of Books by  Gideon Lewis-Kraus (It’s Good to Be Alive) on some of the recent batch of books (by Douglas Kenrick, Sam Harris and Peter Corning). I particularly like this:

[these recent publications are] representative of whole shelves of books that purport to tell us something new and scientific about human nature. They are a sign that hope of a scientific case for a more just society has come to replace hope of a good case for a more just society. Corning is afraid that unless he can provide an ‘explicit theoretical basis’ for his vision of the fair society, his efforts will be ‘vulnerable to being attacked or dismissed by the many theorists who have a vested interest in value relativism’.

Scientistic writers like Kenrick, Harris and Corning are even more scared of relativism than they are angry about social inequality. Harris worries that relativism leaves us ‘supine’ before thieves and rapists. It’s a nice hope that one day we’ll find an argument so irrefutable that thieves and rapists will see the error of their ways. And it’s understandable that so much effort is going into finding one at the moment. The more the American right bases its campaigns on religious or crypto-religious appeals, the more those who don’t share their beliefs feel they need something just as strong and certain to defend themselves, something like science. Hardly a page goes by without the reader being told of some ‘profound paradigm-shift’ or ‘revelatory’ new technique or theory about the ‘deep’ structure of something or other. The words these writers like most are ‘power’ and ‘powerful’ – Corning uses them 68 times in 193 pages. But it’s little use insisting that the structure of our brains or the history of adaptation proves that there are no happy thieves. If we’re to make moral progress, we could do worse than to begin precisely by acknowledging the possibility of the happy thief, or the self-satisfied banker.

Well put. In fact the possibility of the happy thief etc., is itself quite a complex question, involving as it does questions about what, exactly, we mean by ‘happy’. Aristotle and Hume, for instance, take quite different views on the matter. But to consider all this we need to do some philosophical thinking, which is rather different to announcing that ‘solutions’ have been found, or about to be found, by scientists. To make this point is not be anti-science, but to be anti-scientism, which is quite different.

The rest of the article is well worth reading, see: here.

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As a graduate student of computer engineering in the early 90s, I recall impassioned late night debates on whether machines can ever be intelligent—intelligent, as in mimicking the cognition, common sense, and problem-solving skills of ordinary humans. Neural network research was hot and one of my professors was a star in the field. Scientists and bearded philosophers spoke of ‘humanoid robots.’ A breakthrough seemed inevitable and imminent. Still, I felt certain that Artificial Intelligence (AI) was a doomed enterprise.

I argued out of intuition, from a sense of the immersive nature of our life in the world—how much we subconsciously acquire and summon to get through life, how we arrive at meaning and significance not in isolation but through embodied living, and how contextual, fluid, and intertwined this was with our moods, desires, experiences, selective memory, physical body, and so on. How can we program all this into a machine and have it pass the unrestricted Turing test? How could a machine that did not care about its existence as humans do, ever behave as humans do? In hindsight, it seems fitting that I was then also drawn to Dostoevsky, Camus, and Kierkegaard.

My interlocutors countered that while extremely complex, the human brain is clearly an instance of matter, amenable to the laws of physics. Our intelligence, and everything else that informed our being in the world, had to be somehow ‘coded’ in our brain’s circuitry, including the great many symbols, rules, and associations we relied on to get through a typical day. Was there any reason why we couldn’t ‘decode’ and reproduce it in a machine some day? Couldn’t a future supercomputer mimic our entire neural circuitry and be as smart as us? They posited a reductionist and computational approach to the brain that many, including Steven Pinker and Daniel Dennett, continue to champion today. Just three months ago, Dennett declared in his sonorous voice, “We are robots made of robots made of robots made of robots.”

But despite the big advances in computing—for example, today’s supercomputers are ten million times faster than those of the early 90s—AI has fallen woefully short of its ambition and hype. Instead, we have “expert systems” that process predetermined inputs in specific domains, perform pattern matching and database lookups, and learn to adapt their outputs algorithmically. Examples include chess software, search engines, speech recognition, industrial and service robots, and traffic and weather forecasting systems. Machines have done well with tasks that we ourselves pursue, or can pursue, algorithmically, as in searching for the word “ersatz” in an essay, making cappuccino, or restacking books on a library shelf. But so much else that defines our intelligence remains well beyond machines, such as projecting our creativity and imagination to understand new contexts and their significance, or figuring out how and why new sensory stimuli are relevant or not. Why is AI in such a braindead state? Is there any hope for it? Let’s take a closer look.

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