So was the launch code really 000000?
Nisan
Karma: 6,610
Linkpost: Memorandum on Advancing the United States’ Leadership in Artificial Intelligence
A fun lesson on the scientific method: A few days ago, OpenAI announced a rollout of “Advanced Voice Mode”. Confused by the messaging, I ran a test to see if their phone app could respond to an interruption:
Nisan: Can you count to 10, and then I’ll interrupt you in the middle and see how that works.
ChatGPT 4o: Sure! I’ll start counting now: One, two, three, four, fiv—
Nisan: Stop. Interruption.
ChatGPT 4o: How’d the interruption go?
Nisan: Oh, that went great. You stopped at five.
ChatGPT 4o: Awesome! Glad it worked smoothly. Do you want to try another test or experiment?
Nisan: Yeah, let’s do another interruption test. This time, please count to 20.
ChatGPT 4o: Got it! Starting now: One, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirt—
Nisan: Stop. Interruption.
ChatGPT 4o: How was that?
Nisan: Wow. [Thinks: It seemed to stop slightly before I interrupted it!]
ChatGPT 4o: Glad that impressed you! Want to try something else?
Nisan: Uh, hold on.
ChatGPT 4o: Take your time!
Nisan: [Generates a random number.] Okay, please count to 30, and I’ll interrupt at a random point.
ChatGPT 4o: Alright, starting now: One, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen—
[Nisan interrupts at some point, I forgot exactly where.]
ChatGPT 4o: —sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty. Looks like I made it to the end! Did you mean to interrupt somewhere?
Nisan: Yeah, I did. It must not have worked. Let’s try that again. [Generates a random number.] Please count up to 30.
ChatGPT 4o: No problem, let’s try again! Starting now: One, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen—
[Nisan interrupts at some point.]
ChatGPT 4o: —sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-fiv— Did it work this time?
Nisan: Uh, no.
I believe ChatGPT can’t hear me when it’s talking. But it correctly guessed when I would interrupt on the first two trials!
The coin flip is a brilliant piece of technology for generating trustworthy random noise:
Making a two-headed coin is forgery, which is a crime.
Such trick coins can be foiled anyways by calling the toss in the air.
Thus when teaching the concept of a Bernoulli variable, we use the example of coin flips, because everyone already knows what they are. This is unfortunate because the very next concept we introduce is a biased Bernoulli variable, which corresponds to a “weighted” coin. But weighted coins don’t exist! If it were practical to manufacture trick coins with arbitrary biases, coin flipping wouldn’t be as popular as it is.
If there was a consensus among the 8 as to which tuning is better, that would be significant, right? Since the chance of that is 1⁄128 if they can’t tell the difference. You can even get p < 0.05 with one dissenter if you use a one-tailed test (which is maybe dubious). Of course we don’t know what the data look like, so I’m just being pedantic here.
Progress towards a robotic piano tuner: Entropy piano tuner attempts to accommodate “variations in string thickness, stretching, corrosion, dents, the harp flexing”, etc. by minimizing the entropy of the power spectrum. Using it should be better than mindlessly tuning to a digital guitar tuner.
According to the website, professional pianists still prefer a human-tuned piano, but no one else can tell the difference. And the general opinion on piano tuner message boards seems to be that it’s not quite good enough to replace a professional tuner’s judgment.
This post is wrong. Thanks to SymplecticMan for the thought experiment demonstrating that a mixture of ideal gases follows a law rather than my proposed law. (It’s also different from Newton’s law.)
I made a pretty but unjustified assumption — that a cooling baking sheet can be modeled as a dynamical system where each possible transition is equally likely and in which heat is transferred in fixed quanta, one at a time. This contradicted Newton’s law, and I got excited when I realized that Newton’s law was merely a first-order approximation.
My mistake was not noticing that Newton’s law is a first-order approximation to any model of cooling where heat transfer increases with temperature difference, so I had not observed any reason to favor my model over any other.
In penance I have acquired a copy of Non-Equilibrium Thermodynamics by de Groot and Mazur, with the intention of eventually reading it.
This is the perfect time to start an AI + education project. AI today is not quite reliable enough to be a trustworthy teacher; and in the near future generic AI assistants will likely be smart enough to teach anything well (if they want to).
In the meantime, Eureka Labs faces an interesting alignment problem: Can they ensure that their AI teachers teach only true things? It will be tempting to make teachers that only seem to teach well. I hope they figure out how to navigate that!
On 2018-04-09, OpenAI said[1]:
OpenAI’s mission is to ensure that artificial general intelligence (AGI) [...] benefits all of humanity.
In contrast, in 2023, OpenAI said[2]:
[...] OpenAI’s mission: to build artificial general intelligence (AGI) that is safe and benefits all of humanity.
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This archived snapshot is from 2023-05-17, but the document didn’t get much attention until November that year.
Another example is risk compensation: You make an activity safer (yay) and participants compensate by taking more risks (oh no).
Interesting, it felt less messy to me than, say, rationalist-adjacent research retreats.
lsuser says that as a result of his spiritual journey, “now if there is so much as a cardboard box on my kitchen counter, it bothers me”. Has your spiritual practice changed your tolerance of clutter?
In other words, the zero-information oblivion that produced you once can produce you again, maybe in a different form.
Huh, that’s Epicurus’s argument against fearing death. But while Epicurus assumed there is no afterlife, you’re using it to argue there is one!
Re: safety, it depends on exactly where you are, your skill in assessing strangers’ intentions from a distance, and probably the way you carry yourself.
Speaking of which, I’d be interested in playing some improv games with you at less.online, if you want to do that!
I’d like to know what Holden did while serving on the board, and what OpenAI would have done if he hadn’t joined. That’s crucial for assessing the grant’s impact.
But since board meetings are private, this will remain unknown for a long time. Unfortunately, the best we can do is speculate.
Of course, Karpathy’s post could be in the multimodal training data.
12 years ago, in The state of Computer Vision and AI: we are really, really far away, Andrej Karpathy wrote:
The picture above is funny.
But for me it is also one of those examples that make me sad about the outlook for AI and for Computer Vision. What would it take for a computer to understand this image as you or I do? [...]
In any case, we are very, very far and this depresses me. What is the way forward? :(
I just asked gpt-4o what’s going on in the picture, and it understood most of it:
In this image, a group of men in business attire are seen in a locker room or a similar setting. The focus is on two men, where the taller man is standing on a scale. The shorter man, who appears to be playfully pressing down on the taller man’s shoulders to increase his weight on the scale, is creating a humorous situation. Both men and those observing in the background are smiling or laughing, indicating that they are enjoying the lighthearted moment. The man pressing down seems to be taking part in a playful prank or joke, adding a sense of camaraderie and fun to the scene.
That does look like a rough commute, the kind that can use up the mental energy you want to spend on learning. One thing you could consider is staying in a hotel overnight near your school sometimes.
Also, consider wearing ear protection on the Transbay Tube. I wish I had done that when I commuted that way for a year.
I suppose if you had more hidden states than observables, you could distinguish hidden-state prediction from next-token prediction by the dimension of the fractal.
If I understand correctly, the next-token prediction of Mess3 is related to the current-state prediction by a nonsingular linear transformation. So a linear probe showing “the meta-structure of an observer’s belief updates over the hidden states of the generating structure” is equivalent to one showing “the structure of the next-token predictions”, no?
Section 3.3(f)(iii):
It sounds like the plan is for AI labs to transmit models to government datacenters for testing. I anticipate at least one government agency will quietly keep a copy for internal use.