Dedicated compute allows companies to build without worrying that another developer (or the community at large) uses up too much of Open-AI’s compute

Allowing custom models allows companies to fine tune their own models using Chat GPT as a base. So think of a ChatGPT like a university grad. It’s intelligent and broadly capable. But not necessarily a specialist in the exact tasks that a company may need. But what if that company could train it on 100,000 samples of those tasks?

I am not an AI expert. I welcome anyone to correct me here.

I agree with all of your comments. And to add what I believe to be a more important point, the Metaculus question defines weakly general AI as (heavily paraphrased):

- Pass the Turing Test (text prompt)

- Achieve human-level written language comprehension on the Winograd Schema Challenge

- Achieve human-level result on the math section of the SATs

- Play the Atari game Montezuma's Revenge at a human level

We already have separate narrow AIs that can do these tasks at either human or nearly human levels. We even have more general AIs that can do multiple of these tasks at a near-human level. I wouldn't be overly surprised if by the end of 2023, we have a single AI that could do all of these tasks (and many other human-level task). But even so, many people wouldn't call it general AI.

Not trying to throw shade here on Metaculus. They had to narrowly define general AI and have concrete, measurable objectives. I just personally disagree with where they drew that line.

… and now OpenAI thinks you are a sociopath

Healthcare is unfortunately prone to hyperbole. It doesn’t help that they can extend the health span of mice in many different ways, and that doesn’t translate to humans.

But realistically, you have nothing to worry about yourself. You are young. Enjoy life. If you are able to see the singularity, you will experience massive improvements in health span.

The numbers don’t really line up with my expectation. Using drone imagery, I can get a 1-2 square km area accurate to 5cm accuracy flying at 400ft. That would get me about 2,500 images. If they wanted to do a 1cm accuracy area of a 5 square km area, I would expect 25x the number of photos, or 100,000 images. With enough compute, this could be done in 30 minutes.

By the time we have replicators, all of the other tech mentioned will either already be obsolete or will become obsolete with the invention of the replicator.

I think it will be difficult to pinpoint the month (and possibly calendar year) that AGI hits. We'll likely have a model that is superhuman in some traits but very amateur in others.

But let's say for an AGI, we have a model that is generally agreed is AGI, and we know as of the date it is released that it is AGI. Still not much will happen in the month following. Access to the model will likely be controlled. Model improvements will be made for efficiency, and there will need to be some form of scale up period even if it's open source.

In the months following release, if access is broad (either open source or via a cost-efficient API), we will see developers slowly incorporate the technology into their product. But adoption will likely not be as quick due to user distrust (would you trust an AI to complete your tax return or pick up your kids from school?)

You have to look into the source data. Specifically, it links to Metaculus's "Date Weakly General AI is publicly known" survey. They further define this below (bolded text). I provided some additional definitions in [] parenthesis with no bold.

"For these purposes we will thus define "AI system" as a single unified software system that can satisfy the following criteria, all easily completable by a typical college-educated human.

Able to reliably pass a Turing test of the type that would win the Loebner Silver Prize. [The "silver" prize is offered for the first chatterbot that judges cannot distinguish from a real human and which can convince judges that the human is the computer program.]

Able to score 90% or more on a robust version of the Winograd Schema Challenge, e.g. the "Winogrande" challenge or comparable data set for which human performance is at 90+% [a benchmark for commonsense reasoning, is a set of 273 expert-crafted pronoun resolution problems originally designed to be unsolvable for statistical models. Recent advances in neural language models have already reached around 90% accuracy on variants of WSC. Per Cornell University, this problem was solved by 2019, but note that they were ANI.]

Be able to score 75th percentile (as compared to the corresponding year's human students; this was a score of 600 in 2016) on all the full mathematics section of a circa-2015-2020 standard SAT exam, using just images of the exam pages and having less than ten SAT exams as part of the training data. (Training on other corpuses of math problems is fair game as long as they are arguably distinct from SAT exams.) [ I believe this has been solved as early as 2015 but may have cheated using previous versions of SAT tests. But more recent work suggests that an AI can solve university-level math problems which would be harder. This link provided is one of many different news articles. I perceive this problem as likely solved.

Be able to learn the classic Atari game "Montezuma's revenge" (based on just visual inputs and standard controls) and explore all 24 rooms based on the equivalent of less than 100 hours of real-time play (see closely-related question.)" [Montezuma's Revenge was solved in 2018 by Uber. I am unsure whether it met the threshold of 100 hours of real-time play, but there are other models that have been released since Uber's paper and one may have already met this threshold.]

​

Now, personally, I think that this specific set of questions is insufficiently broad for a weak AGI. But I admit that everyone has a different definition of weak AGI and Metaculus at least provided a precise definition that can be measured against. Given this definition, I think it's somewhat possible for a single to meet all of the criteria in 2022 or 2023. Two notable challenges remain.

1. To make a truly remarkable chatbot that is indistinguishable from humans. There are some serious contenders, but I would argue that this is not ready quite yet.

2. To create a SINGLE AI model that can do ALL of these tasks.

Will we see a single AI model in 2023 fit all of these criteria? I have high hopes for the Gato 2 scale-up but who knows at this point.