I think there's a difference between technical jargon and corporate jargon...

Technical jargon is essentially used as a shorthand way to communicate, essentially using agreed upon terms to refer to detailed concepts which are well understood. The goal is to succinctly communicate to your technical peers in an accurate way.

Corporate jargon is a different beast entirely and the goal seems to be more to signal yourself as part of the club, especially for executives, rather than to communicate clearly. It's almost the opposite of technical jargon - the goal not only being to signal yourself as part of the club, but also to make what you are saying sound more abstract and strategic than if expressed in plain english.

Yes, but so are many forums, including Slashdot.

It seems pretty rare nowadays to have forums where there is any multi-turn conversation and people actually listening to each other.

People basically just want a TV set that they can yell at.

There's also now a whole series of "AI-chick interviews X" videos.

So far I've noticed X = historical man on-the-street (roman, etc), and X = sweary middle-aged Brit on vacation.

They are mildly amusing for the first 5 min, but god help us if this is the future.

Right, but if we're trying to define an absolute event ordering, and concept of simultaneity, based on potential causality, rather than what different observers would see, then isn't just the spacetime location of two events enough?

In other words, for any two hypothetical events at given spacetime positions. we've got a spatial distance and a temporal distance, and we could define a "causal (time) distance" as the spatial distance divided by the speed of light (i.e. minimum time it would take for any causal effect to propagate from one event to the other), and then couldn't we define a meaningful "causal ordering" (independent of what different observers would see) by comparing this "causal distance" to the temporal distance, with the events being simultaneous if these are the same?

Wouldn't "causal ordering" (could A have caused B, per speed of light propagation effects) define a consistent and meaningful event ordering and therefore definition of simultaneity ?

> An AI algorithm detected the event in real time

Of course "real time" has a bit of a twist to it when you're observing something 700M light years away, in other words an event that actually happened 700M years ago, when multi-cellular life was just emerging on earth.

Given that Trump has just removed the export restrictions (pay to play - NVidia allowed to export to China as long as they give 15% of revenues to US government !!!), the story is irrelevant.

China had also been using other workarounds, in addition to using less powerful NVidia chips (just need more of them, so not much of a restriction), as well as domestically produced ones fro Huawei. One workaround (scam) has been to have the chips shipped to different country, maybe even installed in a datacenter there, then just reshipped to China.

Anyway, Orange man now says China getting ahead on AI is OK as long as US gov gets a cut of the action.

Well, if you haven't tackled a problem before, then you learn by trail and error - a bit of curiosity and give it a go.

Now, I'm not Einstein, but it seems that if you're just pre-trained, and can't learn, then learning is going to be a problem.

"Reasoning models" achieve their reasoning gains by using RL training to encourage (i.e. narrow) choices of reasoning steps, and sequencing, to those that lead to correct outcomes (in domains like math and programming where this is well defined and testable).

Should we be surprised that if problem statements are tweaked not to match those seen in training that this biasing towards "good steps for this problem" doesn't work as well ?!

Most of these "reasoning" models (i.e. ones using this approach) don't have any fundamental enhacements to algorithmically do better reasoning - just to pick better reasoning steps for problems of a given type they were trained on.

The US government is now officially for sale.

Is the White House available as an AirBnB yet ?

An LLM providing a summary isn't the same as summarization!

To provide a real/meaningful summary (retaining the key points and discarding irrelevant detail) requires reasoning and understanding of the subject matter and context as well as some understanding of what is important. Humans can do this ...

Howevr, an LLM providing a summary is only PREDICTING what a summary would look like - it will give you something that looks like a summary in terms of form and length, with some references to the source material, etc, but is liable to miss what are the actual key points to be retained.

It's a bit like asking an LLM how the farmer can cross the river in least trips with his chicken and corn/etc, and getting a reply that LOOKs like a solution (first cross with A & B, then return with A & C, etc), but in fact is garbage with redundant crossings with same items back and forth. Sometimes they solve these problems successfully (especially "reasoning models", but other times they get overly hung up on the formulaic type of response expect, and generate based on form vs semantics).

Remember, an LLM is a *prediction* (copy cat) technology, it isn't AI or AGI no matter how many times you call it that.

The purpose of advertising and modelling isn't to show you how YOU would look in those clothes (which AI can now do for you - Google has virtual try-on), but more to be aspirational - if you buy our brand then you too will have this amazing lifestyle. All advertising is the same - not just fashion.

I'm not sure AI models for advertising etc will stick, at least not for major brands. There's a reason companies like to hire celebrities (movie, music, sports stars, etc) for advertising campaigns, and like to use the same small stable of supermodels (i.e. also celebrities).

In the fashion industry I'm pretty sure that building a brand image trumps having a stick figure to hang your clothes on, and part of that is associating your brand with celebrities whose image you want to be associated with. Look at the success of Sydney Sweeney's "great genes" advertising for American Eagle - I don't think they'd get a fraction of the buzz if it was an AI model instead.

Yeah, one can complain about stereotypes, but there are definitely "cultural issues" with Indian developers. Plain speaking and honesty don't seem to be that common, and as you say deflection of responsibility vs taking ownership is also common.

Ever notice how many sick days Indian developers take, or how often they have "laptop issues", etc ?! Of course there are power and internet outages, but a company laptop is a laptop ...

It's funny to hear Indian vendor management tell the team "I don't want anyone reporting sick - we need to get this done". Everyone knows the sick days are a scam, and for the most part just accept it.

> Or perhaps to muddle through and pretend that the commented contracts are irrelevant

I'm guessing that the only way to get graded as correct.

If you took the method definition at face value you'd be entirely justified (given no other comment about error handling) to throw an exception if this assertion is false, and your code would therefore fail if they test it (unless their test code is doesn't even test for their own corner case).