[morbia]

High performance computing will drive demand for faster hardware, for example in machine learning. It is extremely computationally intensive and expensive to train large NLP models. The big companies in this game have a lot of money to invest in bringing those costs down, and in turn train better models.

That said, I don't see a reason why speeds will increase significantly on personal devices. We're seeing a situation now where personal devices are really 'fast enough' for normal use cases. Instead the focus is more on improving efficiency and battery life.

[davidkuennen]

It depends. I dream of a world where your Smartphone is also your personal computer and you can just project everything from it using AR wherever you are. In that case they have to improve on both.

[ideamotor]

Apple seems to be latching onto the idea users need to run ML on their consumptive devices, as opposed the cloud, and I don’t believe it. I think you agree. Yet in my opinion, if anything they want the appearance of that necessity, as expressed in loss of efficiency and battery life for older devices to sell new ones.

[otabdeveloper4]

By "ML" you really mean "neural networks", and ML is like Bitcoin - there's still no good business use for them, even after all these years.

(Business probably just wants Bayesian inference instead, but that's too hard, let's go hardware shopping instead.)

[nl]

As someone who works in crypto[1] but used to work in ML your comments about neural network based ML coudn't be further from the truth.

Many businesses are seeing real, measurable impacts from NN based software that would be impossible without it.

[1] agree with your comment, not much real business use for it, but I wanted to work in it to be sure

[otabdeveloper4]

> Many businesses are seeing real, measurable impacts from NN based software that would be impossible without it.

Citation needed. Decision trees are still state of the art.

[nl]

Um wow.

How do you do anything vision related with decision trees? Or anything beyond n-grams with text?

But here's some citations as requested:

https://casetext.com/blog/game-changing-ai-litigators/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512995/

https://possibility.teledyneimaging.com/advances-in-ai-for-i...

https://www.skinvision.com/

https://www.lifewhisperer.com/

etc

(I'm pointing at these particular fields because I personally have worked on NNs in applications in these fields, but there are plenty more)

[chimprich]

I don't understand this comment. ANNs are being used everywhere - image recognition, voice recognition, document classification... I can only see this use increasing for the foreseeable future.

[cultofmetatron]

> there's still no good business use for them, even after all these years.

aside from fraud detection, autonomous vehicles, language translation, facial recognition, voice preproduction and market insights of course

[eru]

Google spends a lot of money on ML. Are they idiots who throw away money?

[hajile]

Google kills tons of very expensive projects. Facebook spends a lot on their Metaverse, but that doesn’t make it good. Tons of companies spend on terrible ideas.

They only difference with Google or Facebook is that they’re big enough to absorb the losses.

This isn’t to say that ML is a dead end, but instead to point out thatjust because they are investing a lot doesn’t make it good.

[eru]

Well, Google and Apple are also putting ML specific processors into their phones.

[ori_b]

Quite often, yes.

ML is unlikely to be one of those places, but appealing to the efficiency of large, bureaucratic companies is a poor argument.