Depends on the nature of the invention. A lot of research involves trial and error, and this is ripe for automation. A really cool example (which as far as I know doesn't involve any AI so far) is robotic biochemistry labs. If you need to test 500 different drug candidates in some complicated assay, you can just upload the experiment via web API and the next thing you know, dozens of robots come to life mixing reagents and monitoring the results. In my view, automation of any kind will continue to accelerate science for a while, even without AGI.

I would also argue that in some narrow fields, we're already at a point where humans are totally incapable of comprehending technology that is generated by software. The obvious example being neural networks (we can understand the architecture, but not the weights). Another would be the hardware description languages used for IC design. Sure, a really smart computer engineer with an electron microscope could probably reverse engineer some tiny block of a modern CPU, but it would be nearly impossible to map the entire thing. They have billions of transistors. When we design these things, it's simply not possible without the use of sophisticated software. Similarly when you compile code to assembly, you might be able to understand tiny fragments of assembly, but the entire program would take a lifetime to get through. Without compilers and interpreters, software would still see extremely limited use in society, and we literally wouldn't be having this discussion.

Edit: forgot to say, of course AGI will be a completely different animal since it will be able to generate new kinds of ideas where even the concept is beyond the reach of a human brain.

> If we want effective automation or make general human tasks faster we certainly do not need AGI.

Agreed. We're very, very, close to this now, and likely very far away from AGI.

> If we want inventions and technology which would be hard for humans to come up with in a reasonable time frame, we do need AGI.

This is where we disagree. I have many contacts at universities, and most of my friends have a PHD and participate in some kind of research.

In their work, they were evaluating Watson (IBMs LLM style AI) years ago, and talking about how it would help them.

Having a PHD necessarily means having tunnel vision. You will do research that makes you the single person on earth that knows about the one cell you study, or the one protein you've been working with.

Right now, the condition of science is that we have all these researchers writing papers to help other scientists have a wider knowledge on things they couldn't possibly dedicate time to.

It's still nowhere near wide enough. PHDs aren't able to easily work outside their field, and the result is that their research needs to go through several levels of simplification before someone can find a use for it, or see how it effects their own research.

A well trained LLM can tear down those walls between different fields. Suddenly, you've got an infinitely patient, infinitely knowledgeable assistant. They can write code for you. You can ask it what effect your protein might have on a new material, without having to become, or know, a material scientist.

Everyone having a 'smart' assistant that can offer an expert level understanding of EVERY FIELD will bridge the gaps between the highly specialized geniuses of our time.

Working with the sort of AI we have now will take us to an entirely new level.

We already have that.

Machine Learning algorithms are already making advances in mathematics and medicine.

Honestly, what we need is something to translate between what an LLM can 'understand' needs to be done and the physical world.

Right now, we can ask an LLM what the process of, say, changing the oil in a car is.

We can also program an industrial robot to do that task, basically blind.

To automate jobs, we need an LLM style understanding of the task, and the steps required, coupled to the ability to take each of those steps and communicate it to a 'body', checking as it goes that the process is following correctly.

So, if an LLM could, say, break the problem into steps, taking into account the situation around it, it could probably do the job.

Imagine typing into Chat GPT a prompt like 'You are programming a robot arm. You need to pick up a glass. Write the code to pick up the glass in front of you. '

Then automatically send that to a camera/arm, and have the image processing describing back 'The arm is to the left of the glass by 2 inches', please program the arm to grab the glass.

'The glass has been knocked over to the left, and is now on its side, 4 inches in front of the hand. please program the arm to grab the glass'

Ultimately it would be more complicated than that, but I think that's the basic idea of what many researchers are working on moving forward.

With a feedback loop of video being able to 'describe' to the LLM what is happening, and the LLM adjusting to meet its task, you could have a very useful android.

I'm not comparing it to a human being, I'm saying many, many, jobs could be automated if we could take a single function of the lowest working human.

I don't think they're saying that actual factory workers are unintelligent, but that an AI wouldn't need to simulate a great deal of intelligence in order to perform a lot of the menial tasks humans are made to do. Even many complex jobs or trades are largely task-oriented, demanding skill, but not necessarily great leaps of intuition to perform. Your average human is well well above the necessary intelligence to perform the average job, but we do them because somebody has to (and jobs, but that's a whole other thing).

"Computational knowledge engine"

Maybe the last hoorah for trying to hardcode intelligence into computers.

>sales people will have dynamic scripts that are optimized based on 10s of thousands of successful sales calls, to know exactly what to say.

That's nice, but it also means that none of those jobs will exist within two years.

There will be a core team of their best lawyers (honors exam lawyers) but the law firms & their consulting contracts will get a direct hit - so some employees of the law department. As of now purpose-trained Davinci reaches an accuracy about 75% the costly consultants had an accuracy about 80% (they actually measured it) ... 5% less accuracy (GPT 3.5) but much less cost. If GPT 4.0 is just a bit better it will change workspace forever. You are right it works better with people but you just need the elite of each department. This bank has like core team of 5 high paid syndicus lawyers and 30 contract lawyers from law firms. They will reduce their core team to 3 and cut the contract. Now interpolate this step to every bank in EU...

Not much more so than today's web applications, IMO. Most web calls end up pulling data or resources from several APIs as it is when you request a page.

My take on the multiple AI front is that these AI systems will be like apps themselves. Too specialized to dominate, but could potentially excel an area of expertise better than a generalized monolithic AI, if it is properly fine-tuned.