Science, AI

Why do people look by touching?

Every so often, I see someone get irritated that “can I see that?” tends to mean “may I hold that while I look at it?” Given how common this is, and how natural it seems to me to want to hold something while I examine it, I wonder if there is an underlying reason behind it.

Seeing some of the pictures in a recent blog post by Google’s research team, I wonder if that reason may be related to how “quickly” we learn to recognise new objects — quickly in quotes, because we make “one observation” while a typical machine-learning system may need thousands of examples to learn from — what if we also need a lot of examples, but we don’t realise that we need them because we’re seeing them in a continuous sequence?

Human vision isn’t as straightforward as a video played back on a computer, but it’s not totally unreasonable to say we see things “more than once” when we hold them in our hands — and, crucially, if we hold them while we do so we get to see those things with additional information: the object’s distance and therefore size comes from proprioception (which tells us where our hand is), not just from binocular vision; we can rotate it and see it from multiple angles, or rotate ourselves and see how different angles of light changes its appearance; we can bring it closer to our eyes to see fine detail that we might have missed from greater distance; we can rub the surface to see if markings on the surface are permanent or temporary.

So, the hypothesis (conjecture?) is this: humans need to hold things to look at them properly, just to gather enough information to learn what it looks like in general rather than just from one point of view. Likewise, machine learning systems seem worse than they are for lack of capacity to create realistic alternative perspectives of the things they’ve been tasked with classifying.

Not sure how I’d test both parts of this idea. A combination of robot arm, camera, and machine learning system that manipulates an object it’s been asked to learn to recognise is the easy part; but when testing the reverse in humans, one would need to show them a collection of novel objects, half of which they can hold and the other half of which they can only observe in a way that actively prevents them from seeing multiple perspectives, and then test their relative abilities to recognise the objects in each category.

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AI, Software, Technology

Automated detection of propaganda and cultural bias

The ability of word2vec to detect relationships between words (for example that “man” is to “king” as “woman” is to “queen”) can already be used to detect biases. Indeed, the biases are so easy to find, so blatant, that they are embarrassing.

Can this automated detection of cultural bias be used to detect deliberate bias, such as propaganda? It depends in part on how large the sample set is, and in part on how little data the model needs to become effective.

I suspect that such a tool would work only for long-form propaganda, and for detecting people who start to believe and repeat that propaganda: individual tweets — or even newspaper articles — are likely to be far too short for these tools, but the combined output of all their tweets (or a year of some journalist’s articles) might be sufficient.

If it is at all possible, it would of course be very useful. For a few hours, until the propagandists started using the same tool the way we now all use spell checkers — they’re professionals, after all, who will use the best tools money can buy.

That’s the problem with A.I., as well as the promise: it’s a tool for thinking faster, and it’s a tool which is very evenly distributed throughout society, not just in the hands of those we approve of.

Of course… are we right about who we approve of, or is our hatred of Them just because of propaganda we’ve fallen for ourselves?

(Note: I’ve seen people, call them Bobs, saying “x is propaganda”, but I’ve never been able to convince any of the Bobs that they are just as likely to fall for propaganda as the people they are convinced have fallen for propaganda. If you have any suggestions, please comment).

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AI, Futurology, Science, Software, Technology

The Singularity is Dead, Long Live The Singularity

The Singularity is one form of the idea that machines are constantly being improved and will one day make us all unemployable. Phrased that way, it should be no surprise that discussions of the Singularity are often compared with those of the Luddites from 1816.

“It’s different now!” many people say. Are they right to think that those differences are important?

There have been so many articles and blog posts (and books) about the Singularity that I need to be careful to make clear which type of “Singularity” I’m writing about.

I don’t believe in real infinities. Any of them. Something will get in the way before you reach them. I therefore do not believe in any single runaway process that becomes a deity-like A.I. in a finite time.

That doesn’t stop me worrying about “paperclip optimisers” that are just smart enough to cause catastrophic damage (this already definitely happens even with very dumb A.I.); nor does it stop me worrying about the effect of machines with an IQ of only 200 that can outsmart all but the single smartest human, and rendering mental labour as redundant as physical labour already is, or even an IQ of 85, which would make 15.9% of the world permanently unemployable (some do claim that machines can never be artistic, but, well, machines are already doing “creative” jobs in music, literature and painting, and even if they were not there is a limit as to how many such jobs there can be).

So, for “the Singularity”, what I mean is this:

“A date after which the average human cannot keep up with the rate of progress.”

By this definition, I think it’s already happened. How many people have kept track of these things?:

Most of this was unbelievable science fiction when I was born. Between my birth and 2006, only a few of these things became reality. More than half are things that happened or were invented in the 2010s. When Google’s AlphaGo went up against Lee Sedol he thought he’d easily beat it, 5-0 or 4-1, instead he lost 1-4.

If you’re too young to have a Facebook account, there’s a good chance you’ll never need to learn any foreign language. Or make any physical object. Or learn to drive… there’s a fairly good chance you won’t be allowed to drive. And once you become an adult, if you come up with an invention or a plot for a novel or a motif for a song, there will be at least four billion other humans racing against you to publish it.

Sure, we don’t have a von Neumann probe nor even a clanking replicator at this stage (we don’t even know how to make one yet, unless you count “copy an existing life form”), but given we’ve got 3D printers working at 10 nanometers already, it’s not all that unreasonable to assume we will in the near future. The fact that life exists proves such machines are possible, after all.

None of this is to say humans cannot or will not adapt to change. We’ve been adapting to changes for a long time, we have a lot of experience of adapting to changes, we will adapt more. But there is a question:

“How fast can you adapt?”

Time, as they say, is money. Does it take you a week to learn a new job? A machine that already knows how to do it has a £500 advantage over you. A month? The machine has a £2,200 advantage. You need to get another degree? It has an £80,000 advantage even if the degree was free. That’s just for the average UK salary with none of the extra things employers have to care about.

We don’t face problems just from the machines outsmarting us, we face problems if all the people working on automation can between them outpace any significant fraction of the workforce. And there’s a strong business incentive to pay for such automation, because humans are one of the most expensive things businesses have to pay for.

I don’t have enough of a feeling for economics to guess what might happen if too many people are unemployed and therefore unable to afford the goods produced by machine labour, all I can say is that when I was in secondary school, all of us young enough to be without income, pirating software and music was common. (I was the only one with a Mac, so I had to make do with magazine cover CDs for my software, but I think the observation is still worth something).

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