Comment by SequoiaHope
3 hours ago
Yes I think generative board design will become better packaged, trained, and validated, and that it will be common place for electrical engineers to rely more on these tools. Major vendors like Altium will add tools like this and their use will become commonplace in industry. For hobbyists, you will be able to specify a board design in natural language and have it completed with unprecedented ease. I’m excited to help my raver friends make wearable glowie things.
I find it hard to predict what technology will be like in four years, but the next major leap for these tools will be higher level system specification and integrated design. What you want is not a circuit board what you want is a product. The tools will bring multiple functions together and fully integrated iterative design will accelerate development.
Despite the many problems with the AI roll out I am fundamentally excited for tools which can accelerate our engineering development. We will build things much faster in 4 years than today. One year of progress will in some instances take one week.
I am particularly interested in how this might begin to accelerate change in heavy industry. With hope it will help us build fusion power reactors and high speed electric trains.
I hope we find the courage to support every person who for one reason or another does not ride this wave. We will have so much more to share, or to hoard.
Yeah. I'm expecting that we fully automate cognition in the medium to long run. I think it's inevitable.
With the advances in math, I'm also hoping we can automate fundamental physics. Just ask for the physics needed for better fusion, no need for human toil.
If we play this right, the AI can fully take care of all our needs, and reaping the rewards of what it does when we stop being able to keep up with the rate of automated discoveries.
Hopefully it's able to dumb down enough knowledge to keep entertained people who decide to learn after learning stops being a requirement for human advancement.
This is a big leap. So far LLMs are really good at turning training data into accurate results. The more data, the better. LLMs are very, very bad at making intuitive leaps based on the 'shape' of sparse data- a technique that is, to be fair, pretty rare in humans as well, but essential to progress. Maybe they can make up for it with brute force and the precision and breadth of knowledge that only an LLM can have, we'll see.
I don't think we've nailed the architecture that will allow things like generalized self directed training, yet, which is what would be needed for something like 'make fusion better'
Are you sure about that?
They seem to be able to make intuitive leaps pretty well. They need to make the same leaps over and over, though, because they lack online learning, so the discoveries only persist after the next training cycle. Context only goes so far.
We're pouring billions into solving that, though, so I would be surprised if we don't get there soon.