Three years ago you could argue with *exactly* the same arguments that something like GPT-4 is impossible.

I did not pick anything specifically, I just copied data from where I have seen it recently. How do I distort facts if I simply provide data without ANY interpretation?..

Okay, let's use 1950. Working hours per year in U.S reduced from 2000 to 1750, 12.5% reduction. Most developed countries did even better, for example, France (and it is not the best country in this aspect) moved from 2200 to 1500, 32% reduction. Germany is one of the best, they work 45% less than in 1950.

I do not deny productivity-pay gap, I dispute your claim "we always end up getting more productive and working the same amount or more". This is simply not true.

Although yes, we could work much less than now, we have enough technology to have 20h work weeks or even less.

It is neither controversial nor new.

A somewhat related article which considers the Bitcoin network from this angle: https://blog.oceanprotocol.com/can-blockchains-go-rogue-5134300ce790

Another classic related article: https://slatestarcodex.com/2014/07/30/meditations-on-moloch/

Usual reasons. Those companies already have very high market capitalization. They are leading now but can lose the lead later.

I do not say it is a bad idea but it is not necessarily an amazing idea.

This is not true.

According to Our World in Data, the average American worked 62 hours per week in 1870. By the year 2000, this had declined to 40.25 hours per week; a decrease of over 35%. As of July 2019, the average American employee on US private nonfarm payrolls worked 34.4 hours per week according to the U.S. Bureau of Labor Statistics.

I predict it will become a serious problem in late 2024.

I think it is true to some extent for almost everyone. It is only natural...

So you claim that no one signed and then when it turned out that some people did you say "doesn't matter they are not CEOs".

Obviously, if all CEOs were on board already, this letter wouldn't exist.

I did not check the link but I believe that I heard about this case. I think it is mostly a marketing stunt.

No, 1% per year is not linear growth. X% growth per amount of time is a more-or-less definition of exponential growth.

Ask ChatGPT :)

I think what you described is formally also exponential growth for somewhat complicated mathematical reasons but only coincidentally.

Informally, you described the exponential growth of the rate of growth.

No, this is wrong too. Exponential just means growing in %. So if we have a 1% improvement every year then it is exponential.

The thing is that 1% after 1000 years will be incomparably larger than what we started with.

Currently no. One of the least endangered jobs I believe.

nitpick: people sometimes misunderstand exponential growth in the following way: they think exponential means extremely fast. Actually, it is not necessarily the case, for example, computer performance was growing exponentially for almost 100 years now and is still arguably growing exponentially.

answer in spirit: GPT-4 and Codex are making many people who work on technologies much more productive.

tokenizer is not numbers friendly

I strongly disagree. I would vastly prefer a superintelligent toddler to a superintelligent alien.

Not so far. Reading pdf is probably not working great right now, but it works well enough for many cases and definitely will be improved.

I think the main problem right now is that LLM's memory is short, so to actually learn a full textbook, it has to be fine-tuned on it. It is inconvenient and expensive, but I am pretty sure it is possible to make it much better.

I would say we will see something like this in 3 years or less.

usually, you can just mix concepts etc. and here you can enter a full text description

First, the article is pretty bad, doesn't seem like high-quality journalism.
Second, there are different ways of calculating FLOPS. It depends on the kind of numbers (8-bit, 16 bit etc.) and on the benchmark. Frontier (top-1 supercomputer) has 7.5 exaflops on HPL-MxP (mixed precision) benchmark, and Google has 9 exaflops for AI tasks (probably 16 bits?) cluster.

2nd half looked more impressive, but actually, 1st half was much more impactful (Chinchilla, Gato, Minerva, AlphaCode).

I think it's a good candidate. Timelines definitely shrunk a lot in 2022.

Other candidates are 2012 (start of deep learning), 2017 (transformer), 2020 (GPT-3) and 2023 (GPT-4 + other stuff).

There are already such models. GLM, Flan-T5 XXL

We don't have enough data and compute to make 5 trillion models economically feasible. It just doesn't make sense. It's better to create 500B model and train it properly.

Basically, nobody knows, but there are signs it may be possible. It's called the scaling hypothesis; see https://www.gwern.net/Scaling-hypothesis

It is a much larger chance in % of people ever lived which is more appropriate.