They mention it a few times, but kind of hand-wave it away:

> Solutions such as Git LFS [9] and DVC [10] provide a

light-weight facade for adding large files to Git repositories but do

not provide sufficient integration to support the needs of industry

ML datasets as described in Sec. 2.

I'm not sure what they mean by 'sufficient integration', but whatever the insufficiencies, why not address those? Considering all the authors work at XetHub, I'm pretty sure this is an advertisement disguised as a research paper.

No and just think about it. If LLM's become monetizable at the scale that other tech areas such as search or social media has, there's a ton of opportunity there, and you have a leg up on everyone else.

There is a Wikipedia about the timeline or chronology And the milestones if you look up those words you ll find stuff... Wikipedia gives a very long list of all the major challenges achievements that have been achieved since the 1990 S

If you look for the training data set quality and filtering that's another word for usefulness.

It would make games way too heavy to run

It's about money and control of new markets, it's about corporations wanting to find new revenue streams.

Not exactly what you are describing with the A, B, C groups, but this recent paper examines ways to prune data by introducing a way to measure how useful samples are: https://openreview.net/forum?id=UmvSlP-PyV

Not exactly what you’re asking, but active learning has a lot to say on data point usefulness.

Given that we consider self-play in reinforcement learning to be machine learning I think that it's appropriate to think of genetic algorithms as an early form machine learning.

Nope. Authors experimented with it but said performance is lost. You can try to replace the transformers with ResNet50, but you'll have to do it yourself AFAIK.

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Genetic algorithms are optimisation heuristics. It's not machine learning per se.

I'm sure there are a ton of things they'll use it for. I'm just pointing out that Cortana isn't the driving force behind this investment.

Honestly, I don't need AI to write the code for me (If it can, cool, but that seems way further out), but if it could write tests for me, I'd give my left <insert_body_part> for it.

Google is in a dominant position but is reaching a stage of complete stagnation. Microsoft basically is also in a stage of stagnation but something like this can absolutely allow Microsoft to gain ground against Google, perhaps even if the high ground.

I believe they still want to use it for Teams and outlook:

https://www.computerworld.com/article/3252218/cortana-explained-why-microsofts-virtual-assistant-is-wired-for-business.html

I think it's going to be everywhere, but mostly Bing and Office products. Those are things where it can have an immediate impact.

As with all software like this, the majority of revenue doesn't come from individuals buying licenses or paying small subscriptions, the money comes from enterprises that will pay millions of dollars for a solution that saves them even more money. Microsoft makes very little money from Windows 10 and 11 compared to how much they make from Windows server and their according licenses. ChatGPT will likely be the same, as corporations look to use it to cut costs in things like helpdesk and writing. No company will object to paying 100k a year to use software that can save them 5 million a year in labor costs.

Admittedly, I just skimmed the paper. But I found it weird that DVC wasn’t mentioned at all. Maybe I missed something but it seems to address the same or at least a similar use case.

I think it would deserve at least a mention in the related work section and a discussion what is different or better in XetHub. Maybe even a performance comparison between the two would be interesting.

I see, well for what it’s worth, here is what I can remember concerning applications:

I believe one of the motivating applications he was looking at was a control problem, where the current world state was one input and the optimization solution produced the optimal control/action to take from this state, according to some fixed problem dynamics. So he was discussing prediction of next solutions in a sequence of (I think convex) optimization problems for real-time control

One indirect advantage for working with very long sequences is the lack of causality constraint, which makes it very easy to use architectures where computation is largely decoupled from the sequence length, like Perceivers (https://arxiv.org/abs/2103.03206, https://arxiv.org/abs/2107.14795), or Recurrent Interface Networks (https://arxiv.org/abs/2212.11972). This is highly speculative though :)

(I am aware that an autoregressive variant of the Perceiver architecture exists (https://arxiv.org/abs/2202.07765), but it is actually quite a bit less general/flexible than Perceiver IO / the original Perceiver.)

https://garymarcus.substack.com/p/is-microsoft-about-to-get-the-deal

They're slowly phasing it out. They've killed both iOS and Android Cortana apps, and I'm guessing it'll be gone from the next iteration of windows. Suffice to say, it's clearly not a part of their future road map, and not the driving reason why they're investing in ChatGPT. They've made it clear that their purpose here is to enhance Bing and challenge Google's dominance of the search market. Cortana has nothing to do with it.

Why is it still available in Windows 11 then?

It looks like you are trying to search google, would you like some help with that?