Sure, a company could use an increase in productivity to allow their workers to work fewer hours while getting the same amount accomplished... but capitalism doesn't work that way. Instead, the company will use the increase in productivity to get more work done in the same amount of hours as before... or if they don't, their competitor will, and thereby obtain a competitive advantage over them.

This is especially true for salaried employees, where the cost of the employee to the company is the same regardless of how many hours the employee works, and it is therefore in the company's financial interest to maximize the return on its salary "investment" by getting as much output from the employee as possible in any given pay period.

I think I heard this story last year, or perhaps a few years ago. I've no reason to think it was the same plant, though.

We're also discovering unceasingly dim objects that are closer. They just don't get the same news coverage. But I'm more interested in wandering planets and planetoids (and even Oort belt objects).

Perhaps the answer is that the "Big Bang" wasn't the first, and these black holes are left over from a prior "universe". In that case, though, why are they so close together.

Yeah, my question is "What isn't prior art?". The summary sure doesn't list anything.

OTOH, it does indicate the direction he wants to push things. He wants these integrated into the lives of people whether they, themselves, use them or not.

You can get this kind of basic image recognition on $20 ARM boards from China. I've got a rock3a (older, barely capable compared to whats available now) board cluster that does this kind of image recognition for my cameras. rpi3 level stuff. That it was nvidia just speaks to what is available.

Sure, let's discuss that. You've got a handful of methods historically:

- GPS/GLONASS/Starlink - can be jammed.
- Wire - inaccurate, not suitable for OTH guided missiles
- Pigeon - inaccurate and expensive
- Radar - can be jammed
- IR/laser - line of sight/requires IR signatures
- inertia - inaccurate, expensive.

There's nothing which really compares to "AI" in capability or accuracy. It's the only option which can't be jammed directly short of active EMP and provides the required/desired accuracy.

That's kind of been the bread and butter for cloud providers - "data velocity". They say: keep all your stuff on our cloud, it'll be safe forever, you don't need to worry about it.

A single copy is zero copies and 2 is 1. This is overlooked on all clouds, on the basis that 3 copies on a cloud provider is still 1 copy.

This kind of oversight failure has ended many companies when they've had DR scenarios. I know of 2 that had to close shop because the egress cost of all their data from AWS was larger than insurance would pay out, and the cash difference was significant.

In most cases, it makes more sense to keep 2 local copies (different storage) than 2 cloud copies, for this reason alone.

Even if frontier models only continue to get 'marginally better' in terms of evaluations, as they have been for the past year, it's quite apparent that the newer models tend to be significantly better in aggregate and practice.

We're also starting to see a stratification across frontier models:

- Anthropic seems focused on "one shot" capabilities (latest generations have suffered significantly in other departments)
- GLM seems to be increasing their focus on security and coding robustness
- Google seems to be leaning in on 'fast and cheap' and 'good enough'
- Agnes (while not quite frontier) seems to be going for "broad scope of capabilities"
- LiquidAI is focusing on specialized, small local models for agentic pipeline work
- OpenAI seems stratified on do-everything frontier models with functional stratification based on task complexity
- Qwen - local capability focus.
- Grok - code centric + fast.
- Deepseek - also local-capability focus while trying to optimize the full stack

Then you've got the mess of models like Laguna (last generation capabilities but local-focus) and Agnes (more like google's approach, with a wide and cheap portfolio).

As for Anthropic (and , it is quite apparent that they're architecturally behind in many ways to other models. The release of dflash papers and the subsequent speed increase on the competitor models made that plainly obvious.

I've got a co-worker like that; every email I get from him is just him copy-and-pasting text from his pet AI, and when I reply, he forwards my email on to his AI to read and generate the next reply for him.

Which raises the question... exactly what value is my co-worker providing there? Sooner or later management will notice that they are paying six figures of annual salary to what is essentially just a very slow meat-based email relay, and they'll lay him off and just have me communicate with the AI directly instead. There's a fine line between "all in" and "all redundant".

It all depends on one thing: Will your speech-driven AI agent reliably accomplish the tasks that you told it to do? If so, great, we've entered the glorious Star Trek future. OTOH if it screws up, even just 5% of the time, it'll be kicked to the curb, because a "timesaver" that you have to stand over and supervise to make sure it doesn't go wrong is no timesaver at all.

MIT does this.

A-C are pass, D and F are no credit for the fall semester.

Then for spring you get A-C as a letter or no credit for D-F.

They (MIT) doesn't pass the Ds.

Hasn't MIT been doing this for decades?

It's not a new concept and is used by some of the best schools in the world (well MIT at least).

But that *does* require that you believe that they are honest to you about their future actions...and that they won't change their mind later.

Pirates were often hanged by the same countries that issued them letters of marque.

Why do you believe what they say when they continually misrepresent things. I'd say "lie", but that implies they know what they're doing.