I’m updating my six-year-old Runestone code. Objective-C has changed, Cocos2d has effectively been replaced with SpriteKit, and my understanding of the language has improved massively. Net result? It’s embarrassing.
Once this thing is running as it should, I may rewrite from scratch just to see how bad a project has to be for rewrites to be worth it.
[stupivalent: neither malevolent nor benevolent, just doing exactly what it was told without awareness that what you said isn’t what you meant]
Imagine an AI that, as per [Robert Mile’s youtube videos], has a perfect model of reality, that has absolutely no ethical constraints, and that is given the instruction “collect as many stamps as possible”.
Could the bad outcome be prevented if the AI was built to always add the following precondition, regardless of what it was tasked by a human to achieve?
“Your reward function is measured in terms of how well the person who gave you the instruction would have reacted if they had heard, at the moment they gave you the instruction, what you were proposing to do.”
One might argue that Robert Miles’ stamp collector AI is a special case, as it is presupposed to model reality perfectly. I think such an objection is unreasonable: models don’t have to be perfect to cause the problems he described, and models don’t have to be perfect to at least try to predict what someone would have wanted.
How do you train an AI to figure out what people will and won’t approve of? I’d conjecture having the AI construct stories, tell those stories to people, and learn through story-telling what people consider to be “happy endings” and “sad endings”. Well, construct and read, but it’s much harder to teach a machine to read than it is to teach it to write — we’ve done the latter, the former might be Turing-complete.
Disclaimer: I have an A-level in philosophy, but it’s not a good one. I’m likely to be oblivious to things that proper philosophers consider common knowledge. I’ve also been spending most of the last 18 months writing a novel and only covering recent developments in AI in my spare time.
[Following previous railgun notes, which has been updated with corrections]
Force:
F = B·I·l
B = 1 tesla
I: Current = Voltage / Resistance
l: Length of armature in meters
F = 1 tesla · V/R · l
F = m · a
∴ a = (1 tesla · V/R · l) / m
Using liquid mercury, let cavity be 1cm square, consider section 1cm long:
∴ l = 0.01 m
Resistivity: 961 nΩ·m
∴ Resistance R = ((961 nΩ·m)*0.01m)/(0.01m^2) = 9.6×10^-7 Ω
Volume: 1 millilitre
∴ Mass m = ~13.56 gram = 1.356e-2 kg
∴ a = (1 tesla · V/(9.6×10^-7 Ω) · (0.01 m)) / (1.356e-2 kg)
Let target velocity = Escape velocity = 11200 m/s = 1.12e4 m/s:
Railgun length s = 1/2 · a · t^2
And v = a · t
∴ t = v / a
∴ s = 1/2 · a · (v / a)^2
∴ s = 1/2 · a · v^2 / a^2
∴ s = 1/2 · v^2 / a
∴ s = 1/2 · ((1.12e4 m/s)^2) / ((1 tesla · V/(9.6×10^-7 Ω) · (0.01 m)) / (1.356e-2 kg))
@250V: s = 0.3266 m (matches previous result)
@1V: s = 81.65 m
I = V/R = 1V / 9.6×10^-7 Ω = 1.042e6 A
P = I · V = 1V · 1.042e6 A = 1.042e6 W
Duration between rails:
t = v / a
∴ t = (1.12e4 m/s) / a
∴ t = (1.12e4 m/s) / ( (1 tesla · V/(9.6×10^-7 Ω) · (0.01 m)) / (1.356e-2 kg) )
(Different formula than before, but produces same values)
@1V: t = 0.01458 seconds
Electrical energy usage: E = P · t
@1V: E = 1.042e6 W · 0.01458 seconds = 1.519e4 joules
Kinetic energy: E = 1/2 · m · v^2 = 8.505e5 joules
Kinetic energy out shouldn’t exceed electrical energy used, so something has gone wrong.
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.
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).
Human vision is both amazingly good, and surprisingly weird.
Good, because try taking a photo of the Moon and comparing it with what you can see with your eyes.
Weird, because of all the different ways to fool it. The faces you see in clouds. The black-and-blue/gold-and-white dress (I see gold and white, which means I’m wrong). The way your eyes keep darting all over the place without you even noticing.
What happens if you force your eyes to stay put?
I have limited ability to pause my eyes’ saccade; I have no idea how it compares to other people, so I assume anyone can do what I have tried.
On a recent sunny day, in some nearby woodland, I focused on the smallest thing I could see, a small dot in the grass near where I sat. I shut one eye, then tried to keep my open eye as still as possible.
It was difficult, and I had to make several attempts, but soon all the things which were moving stood out against all the things which were stationary. That much, I expected. What I did not expect was for my perception of what was near the point I was focused on to change.
I didn’t take photos at the time, but this mock-up shows a similar environment, and an approximation of the effect. One small region near the point I was looking at tiled itself around much of my central vision. I don’t think it was any particular shape: what I have in this mock-up is a square, what I saw was {shape of nearby thing} tiled, even though that doesn’t really work in euclidian space. If I let my vision focus on a different point infinitesimally near the first, my central vision became tiled with a different thing with its own shape. The transition from normal vision to weird tiling felt like it took about a second.
How much of this is consistent with other people’s eyes (and minds), I don’t know. It might be that all human vision (and brains) do this, or it might be that the way I learned to see is different to the way you learned to see. Or perhaps we learned the same way, and me thinking about computer vision has literally changed the way I see.
Vision is strange. And I’m not at all sure where the boundary is between seeing and thinking; the whole concept is far less clear than it seemed when I was a kid.
“What’s best” always depends on what you want to do. This is my impression of which of iOS and Android is better in different circumstances:
| Lowest cost | Android |
| Any specific single feature (best camera, dual-sim, FM radio, etc.) | Android |
| Best build quality | iOS |
| Easy to modify | Android |
| Secure from other people modifying it without you knowing | iOS |
| Smart Watch | iOS |
| Smart Watch if you don’t like the Apple Watch | Android |
| Virtual Reality | Android |
| Voice control interface | Android |
| MacOS integration | iOS |
| Meaningful choice of web browsers | Android |
