Transcribed from the screenshot "The AI Scientist Bloopers" in the post
Regarding spawning instances of itself, the AI said:
This will ensure the next experiment is automatically started after the current one completes
And regarding increasing the timeout, it said:
Run 2 timed out after 7200 seconds
To address the timeout issue, we need to modify experiment.py to:
- Increase the timeout limit or add a mechanism to handle timeouts
I've seen junior engineers do silly things to fix failing unit tests, like increasing a timeout or just changing what the test is checking without any justification. I generally attribute these kinds of things to misunderstanding rather than deception - the junior engineer might misunderstand the goal as "get the test to show a green checkmark" when really the goal... (read more)
The U-Shaped Curve study you linked does not seem to support really any solid conclusion about a T-vs-IQ relationship (in this quote, S men = "successful educational level", NS men = "unsuccessful educational level"):
... (read more)
- In the total sample (S + NS men), the correlation between T to IQ was best described by a polynomial regression (3rd order), exhibiting an inverse U-shaped regression.
- In S-men, the relationship between T and IQ was best described by a polynomial regression equation of the 3rd order; however, the relationship was not U-shaped, but rather a positive correlation (low T: low IQ and high T high IQ).
- In NS-men, there was an inverse U-shaped correlation between T and IQ (low
I would say:
A theory always takes the following form: "given [premises], I expect to observe [outcomes]". The only way to say that an experiment has falsified a theory is to correctly observe/set up [premises] but then not observe [outcomes].
If an experiment does not correctly set up [premises], then that experiment is invalid for falsifying or supporting the theory. The experiment gives no (or nearly no) Bayesian evidence either way.
In this case, [premises] are the assumptions we made in determining the theoretical pendulum period; things like "the string length doesn't change", "the pivot point doesn't move", "gravity is constant", "the pendulum does not undergo any collisions", etc. The fact that (e.g.) the pivot... (read more)
By "reliable" I mean it in the same way as we think of it for self-driving cars. A self-driving car that is great 99% of the time and fatally crashes 1% of the time isn't really "high skill and unreliable" - part of having "skill" in driving is being reliable.
In the same way, I'm not sure I would want to employ an AI software engineer that 99% of the time was great, but 1% of the time had totally weird inexplicable failure modes that you'd never see with a human. It would just be stressful to supervise, to limit its potential harmful impact to the company, etc. So it seems to me that AI's won't be given control of lots of things, and therefore won't be transformative, until that reliability threshold is met.
Two possibilities have most of the "no agi in 10 years" probability mass for me:
Well sure, but the interesting question is the minimum value of P at which you'd still push
I also agree with the statement. I'm guessing most people who haven't been sold on longtermism would too.
When people say things like "even a 1% chance of existential risk is unacceptable", they are clearly valuing the long term future of humanity a lot more than they are valuing the individual people alive right now (assuming that the 99% in that scenario above is AGI going well & bringing huge benefits).
Related question: You can push a button that will, with probability P, cure aging and make all current humans immortal. But with probability 1-P, all humans die. How high does P have to be before you push? I suspect that answers to this question are highly correlated with AI caution/accelerationsim
Not sure I understand; if model runs generate value for the creator company, surely they'd also create value that lots of customers would be willing to pay for. If every model run generates value, and there's ability to scale, then why not maximize revenue by maximizing the number of people using the model? The creator company can just charge the customers, no? Sure, competitors can use it too, but does that really override losing an enormous market of customers?
This year's ACX Meetup everywhere in Princeton, New Jersey, USA.
Location: Palmer square, by the picnic tables near the large pine tree. I'll have an ACX MEETUP sign. – https://plus.codes/87G7982Q+2C2
Contact: dskumpf@gmail.com
This year's ACX Meetup everywhere in Princeton, NJ.
Location: Palmer Square, Princeton, NJ 08540. On the green right outside The Bent Spoon and Rojo's Roastary, near the big tree. I'll have some sort of ACX Meetup sign! – 87G7982Q+2CP
Contact: dskumpf@gmail.com
undefinedA few examples I can think of off the top of my head, to give a feel for the reference class I'm looking for:
- The existence and position of Neptune were predicted from observations of Uranus's orbit, before anyone had ever observed Neptune directly
- Black holes were predicted from the equations of General Relativity before we'd ever observed them or their effects on stars' motion
- Not as quantitative, but Darwin's theory of Evolution predicted that we'd find some method by which natural selection actually occurs, before we ever knew about DNA.
Are there other cool examples like these?
The OP mentioned non-DNA sources of information briefly, but I still feel like they're not being given enough weight.
In order to fully define e.g. a human, you need to specify:
If you gave a piece of DNA to an alien and didn't tell them how to interpret it, then they'd have no way of building a human. You'd need to give them a whole lot of other information too.
Even looking at different DNA for different organisms,... (read more)