The situation is clear. There is a great risk to the livelihoods and bargaining power of workers everywhere. This risk is driven by a race dynamic that is accelerating. In tech we can see this earlier than others because we are close to the technology at the heart of this.
This is quickly becoming one of the largests threats to the public in history and the concentration of power of this trajectory threatens democracy. Irreversable shifts in the structure of power are on the table.
https://arxiv.org/pdf/1912.02292
"We show that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as we increase model size, performance first gets worse and then gets better."
That is the first sentence of the abstract. The first graph shown in the paper backs it up.
Looking into it further, it seems that typical LLMs are in the first descent regime anyway though so my original point is not too relevant for them anyway it seems. Also it looks like the second descent region doesn't always reach a lower loss than the first, it appears to depend on other factors as well.
> Literally the 3rd or 4th thing you learn about ML is that for any given problem, there is an ideal model size.
From my understanding this is now outdated. The deep double descent research showed that although past a certain point performance drops as you increase model size, if you keep increasing it there is another threshold where it paradoxically starts improving again. From that point onwards increasing the parameter count only further improves performance.
I can somewhat understand people developing AGI, but directly working on superintelligence is on extremely shaky ethical ground. A good proportion of AI researchers and philosophers believe superintelligence stands a significant chance of displacing humanity and it is widely regarded as one of the most, if not the most, dangerous technology yet to be created.
> OpenAI is consistently the one coming up with new ideas first (GPT 4, o1, 4o-style multimodality, voice chat, DALL-E, …)
As far as I can tell o1 was based on Q-star, which could likely be Quiet-STaR, a CoT RL technique developed at Stanford that OpenAI may have learned about before it got published. Presumably that's why they never used the Q-Star name even though it had garnered mystique and would have been good for building hype. This is just speculation, but since OpenAI haven't published their technique then we can't know if it really was their innovation.
The author's example for x^2 + x could be written with the first two symbols swapped. With this it looks fine to me. Putting the 2 first here is like putting the x first in "2x" such that it becomes "x2". I think also maybe if the lines above and below had curved ends so you could see where they start and end clearly then this could be not so bad notation.
>When a person "has the concept `7`" they can reason thus: "6 eggs would be fewer", "7 is an odd number", "7 is a whole quantity", "with 7 less of 10, i'd have 3" etc.
I just input this into GPT-3. Its responses are in italics, this is first try no rewriting or retrying anything:
This is a test of understanding of the concept of "the number 7".
Question: Is 7 odd?
Answer: Yes, 7 is an odd number.
Question: Is 6 eggs fewer than 7 eggs?
Answer: Yes, 6 eggs is fewer than 7 eggs.
Question: With 7 less of 10, what would I have?
Answer: I would have 3.
Question: Is 7 a whole quantity?
Answer: Yes, 7 is a whole quantity.
This is mostly a joke because I think I understand where you are coming from (and that you are hypothesising that gpt3's responses are an elaborate trick of sorts). But I don't believe AI has to take the same route as human intelligence, and I don't think we really understand what a concept is or how it behaves from a signal/data perspective, but I think that may be inconsequential for creating general AI.
Also people are can be really stupid sometimes and also have failures, and the concepts that people hold can be incorrect or flawed etc. So it may be useful also to compare human failures with AI failures, rather than just AI failures with human successes.
I think the failures of people spouting hype and failing to deliver in ML has absolutely nothing to do with the real and immense progress which is happening in the field concurrently. I don't understand how one can look at GPT-3, DALL-E2, alpha go, alpha fold, etc and think hmmm... this is evidence of an AI winter. A balanced reading of the season imo suggests that we are in the brightest AI summer and there is no sign of even autumn coming. At least on the research side of things.
Well to my eye it's realism beyond anything that I could find. Mind you I didn't search for that long so there might be something there if I was to delve deeper.
I am pretty familiar with photoshop, and while I'm not an expert, I would find making something like this really difficult. Anything is possible with photoshop, but some things are very hard.
Human artists also do a whole lot of mimicry. One could look at art produced by many artists and say that it is just things stitched together from pre-existing art.
This is going to be mostly a rant on OpenAI's "safer than thou" approach to safety, but let me start with that I think this technology I think is really cool, amazing, powerful stuff. Dall-E (and Dall-E 2) is an incredible advance over GANs, and no doubt will have many positive applications. It's simply brilliant. I am someone who has been interested in and has followed the progress of ML generated images for nearly a decade. Almost unimaginable progress has been made in the last five years in this field.
Now the rant:
I think if OpenAI genuinely cared about the ethical consequences of the technology, they would realise that any algorithm they release will be replicated in implementation by other people within some short period of time (a year or two). At that point, the cat is out of the bag and there is nothing they can do to prevent abuse. So really all they are doing is delaying abuse, and in no way stopping it.
I think their strong "safety" stance has three functions:
I think number 3 is dangerous because researchers are put under the false belief that their technology can or will be made safe. This way they can continue to harness bright minds that no doubt have ethical leanings to create things that they otherwise wouldn't have.
I think OpenAI are trying to have the cake and eat it too. They are accelerating the development of potentially very destructive algorithms (and profiting from it in the process!), while trying to absolve themselves of the responsibility. Putting bandaids on a tumour is not going to matter in the long run. I'm not necessarily saying that these algorithms will be widely destructive, but they certainly have the potential to be.
The safety approach of OpenAI ultimately boils down to gatekeeping compute power. This is just gatekeeping via capital. Anyone with sufficient money can replicate their models easily and bypass every single one of their safety constraints. Basically they are only preventing poor bad actors, and only for a limited time at that.
These models cannot be made safe as long as they are replicable.
To produce scientific research requires making your results replicable.
Therefore, there is no ability to develop abusable technology in a safe way. As a researcher, you will have blood on your hands if things go wrong.
If you choose to continue research knowing this, that is your decision. But don't pretend that you can make the algorithms safer by sanitizing models.
Oh yeah I don't doubt it. I think though that there is many orders of magnitude difference in the field strengths between cell phone radiation and MRI, and this makes all the difference.
THz radiation is a different story too as it has about enough energy such that it could influence irreversible processes.
No, the molecular machinery of cells uses energy level differences that are far above the thermal energy level at body temperature, which allows them to actually make changes to things irreversibly. Enzymes are a great example of this.
Try to use microwaves to move ions from one side of a container of salt solution to the other and then get back to me on the ability of microwaves to control ion movement. Hint: you basically can't without obscene levels of radiation. The thermal "pressure" due to the diffusion of ions is enormous.
For a sense of scale, the thermal velocity of water molecules at room temperature is about 500m/s. The drift velocity(average movement of charge carriers, i.e. coherent current) of typical electric currents is on the order of 1mm/s.
But the movement of ions in solution is almost completely dominated by thermal motion. Your signal doesn't matter if the signal to noise ratio is essentially zero.
I think though that any biological process using these sorts of energies on the molecular level will be swamped with noise and therefore wouldn't be a useful mechanism. 3GHz is like 0.00001eV. A process with Gibb's free energy change of 10ueV has an equilibrium constant of essentially 1 at room temperature, and so is almost completely reversible.
The reason why we can make things interact with radio waves at all is essentially because electrical conductors provide coherent modes for low energy photons to couple to. Without conductors and their free electron cloud we would have a very hard time building anything to receive or transmit radio in any way that isn't thermal.
It is true that there is some degree of conductivity in cells but without a non-thermal way of coupling between current and molecular processes I don't see how radio waves could affect cells in a non-thermal manner
Edit: I guess nerves have a non-thermal coupling mechanism from low frequency currents to molecular mechanisms, so it must be possible. But the machinery for that has been highly evolved for that specific task, I'm not sure if it follows that such machinery would appear commonly in cell processes.
But the only electric current on the molecular level is coherent current...? Chemical reactions are not macroscale phenomena, and so it shouldn't really matter if the energy comes from a random distribution or not. Also please don't insinuate that I'm "profoundly ignorant", that certainly isn't relevant to the discussion.
At the molecular level, basically all photon modes associated with the thermal energy (or lower) will be already thermally occupied. E = hf = k_bT/2. This frequency at room temperature is about 30THz. So on the microscopic level, any frequencies under 30THz are constantly irradiated by thermal fields anyway.
Edit: Furthermore, the Gibb's free energy of any molecular process determines the reversibility of the process at a given temperature. Any molecular process with Gibb's free energy that is lower than the thermal mean energy is going to be essentially a reversible equilibrium process, and stimulating it with radiation will only shift the equilibrium very slightly I believe. I think it's for this reason that we don't see radio catalysed reactions in chemistry, unlike photocatalysed reactions.
Have you actually talked with people who have taken these drugs? The way prozac affects people is clearly much much different from the way MDMA affects people.
Also, from your first link:
"In contrast to fluoxetine, citalopram treatment did not increase BLA eCBs or facilitate extinction."
And your second link:
"Importantly, other SSRIs such as citalopram have shown the opposite effect, disrupting acquisition and retention of fear extinction"
Yeah...
Plus "fear extinction" is not the same as immediate fear reduction/inhibition as seen with MDMA.
I think that we as humans like to believe that we know more than what we do about these sorts of things.
Edit: Your third link talks about anxiogenic effects of fluoxetine, i.e. increases anxiety; literally the opposite of what you are arguing.
This is quickly becoming one of the largests threats to the public in history and the concentration of power of this trajectory threatens democracy. Irreversable shifts in the structure of power are on the table.