ISS is slated to be scrapped in 2030. So we only have 7 years before Starliner can fulfill its purpose.

A natural adult toy for flies. Fascinating....

GPT language model (or broadly generative AI) is climbing the usual 'peak of inflated expectation' of Gartner Hype Cycle. Everyone is excited about the possibility and are expecting unreasonable things.

But soon, the peak will pass, and enthusiasm will wane as people start to understand the limitations of new technology, and the gap between reality and expectation becomes evident.

Of course, there will be an impact in the long run as the technology matures and people find ways to deploy them to improve efficiency and expand capability.

I see 2 problems with the technology as it stands today. 1) it is still not user-friendly. 2) the technology is unsuitable for precision analysis. I'll elaborate.

1. Not user-friendly. Sure, you can communicate to it in natural language, and generate prose that sound plausible and interesting. But to date, you have no control over what learning material the model uses. You are reliant on few GPT providers for their discretion on what training material to use. But real productive marketable work, the content creators need to be able to train the model on training data they choose.

For instance, if you are lawyer building a case, you want the language model trained on case books, regulations, past judgements, etc., that are relevant to the profession. You are likely to get either nothing useful or uninformed opinion based on public information.

Another example, if you are an animation studio or comic artist, you would want to train the art-generating model (like Stable Diffusion, Dall-E, etc.) on your own portfolio of arts, so that when you create new show or content, it will be uniquely in your own style. None of the tools today let you do that, unless you're a programmer who can tinker with code. Sure, Pixar or ILM may be able to do this in a few years, but not if you are a lone artist.

So the AI software tools have some ways to go before they become prevalent.

2. Unsuitable for precision analysis. Neural networks do not store precise information. It stores association between inputs values. In a way, NN stores approximate 'impression' or generalization of data set. (In fact, you don't want to over fit and simply store the information.) However, a lot of information we deal with is binary: it's either one way or another. Answers that looks and sound correct, but actually incorrect is useless. But that's what generative neural network delivers: output does not seem to be out of place next to the learning material.

Sure, scientists use generative AI to generate innovative 'ideas' to test, but they still have to tested for actually validity. Generative AI is a good brainstorming tool, but not necessarily a generator of correct answers.

In time, these limitations will be realized by laymen, and the hype will fade.

But eventually those who figure out these imperfect tools will make them work despite shortcomings.

Most likely because development cost of red and green/blue semiconductor diode lasers have been paid for by other applications, and those devices are available cheap. Selling laser pointers probably does not generate enough profit to develop their own laser technology.

Optical data storage used to be pretty big, and paid for development of semiconductor lasers. CDROM used AlGaAs/GaAs/AlGaAs thin film 'stack' which emits in red spectrum. BlueRay uses GaN/InGaN/GaN stack, which emits green or blue depending on In content in the middle layer. So these lasers were/are produced in volume.

You can get yellow/orange LED (light emitting diodes), but not lasers. Old LEDs used to be doped GaP, which are pretty dim. More recent ones are AlInGaP layer wafer-bonded to GaP substrate. These are frequently used in traffic lights, and very bright.

The reason data storage lasers skipped yellow/orange is because timing of invention of green/blue lasers. Shorter the wavelength (red > yellow > green > blue), higher the data density on the storage disk. Green/blue lasers were invented before red-laser CDROM had gone obsolete. So when the time came for data storage industry to move to a shorter wavelength, they decided to put development money into green/blue.