I'd imagine that OpenAI would try to stagger their announcements, rather than publishing them recently close to each other. Is this because their previous post (about navier stokes problem) was met with controversy?
I genuinely think that AI has accelerated so many different things that announcements from all companies will be incredibly common and fast.
In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.
In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.
Not only that, we're making so many tiny improvements and bug fixes that improve the experience but we don't even bother to make those announcements anymore. They don't feel "grand" enough anymore. The goal post has shifted a lot in the last 6 months.
I apologise if this sounds mean spirited, I find posts like these making grand claims without taking the time to present facts that back the magnitude of these claims simply add noise to the discussion.
You could've stopped with just the first sentence and I've would learned just as much as I did reading that comment to the end.
Do you also have a graph for more useful metrics, like number of requested features delivered? Commits, lines of code, headscratch count... there are a lot of metrics you can use, but LLMs are notorious for increasing code verbosity - which adds noise to the already imprecise metric you linked.
In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.
In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.
But I suspect you want our project management pipeline? Maybe I can just ask our coding agent to search and summarize all the features and fixes for you and then build a dashboard for you. Better yet, my email is in my profile. Email me, we'll get on a call, and I'll show you. /s
Let me ask you. What are you doing such that your velocity hasn't been greatly accelerated in the last 6 months? Can you prove that it hasn't been accelerated with facts?
My bad - I misread your original post as general company announcements, rather than feature announcements. Now my question is, if your internal staff are not requesting these features and are struggling to adapt fast enough, how useful are they? To your last question - what do you mean by velocity? Velocity is speed and direction. You may have speed, but beware the brownian motion that is stochastic predictive models, as that will give you net zero velocity. I have my goals and know what I am doing, and to be frank, it matters more that I read and understand my own code than race to a local optimum.
The current prices the largest players set for their models are not profitable, they bleed money. Eventually they will "fix" it. It could end up making their services less affordable and it could cascade other businesses and services that are dependent on them go out of business
You don’t understand what a bubble is. How good the technology is is irrelevant. That has nothing to do with an economical bubble. It’s all about massive capital misallocation driven by a frenzy of FOMO, which is specifically the case for AI investments. Economically speaking what is happening is the most obvious bubble possible, it follows everything that would be expected from a bubble where companies are chasing an ill-defined grandiose dream, based on a new technology we don’t understand and has very dubious ROI, selling some vague future utopia, allocating massive amount of capital to build infrastructure dedicated to a very early versions of that technology.
As things mature there will be a correction, ie the bubble will pop.
As long as the data centers are utilized and generating revenue, I see no reason for a correction or any misallocation of capital for the infrastructure buildout.
And today these data centers are fully utilized. OpenAI tweeted today that they may need to disable new signups for the Pro subscription in the near future due to capacity constraints.
A year from now, who knows what the situation is going to be like. It seems quite possible that robotics, self driving, research, etc. drive even more demand and revenue.
Stating with any certainty that allocating capital to build infrastructure is a mistake and that there is a correction coming seems unserious.
> A year from now, who knows what the situation is going to be like.
That cuts both ways, we are building datacenters for an immature technology that is quickly evolving. We have no idea what AI will look like in the next 5-10y. Everything that is planned to be built is based on the demand we see right now, not what it will be in the future. That means different GPUs that require different cooling systems, different power supplies, etc. NVIDIA already broke backward compatibility with their new cards, which requires a different infrastructure.
What is unserious is the opposite position: believing that we already know what will be valuable in the future and bet the entire economy on it, without any proof of positive ROI.
"Compute" is not one generic commodity. Filling your datacentres with ASICs that do nothing but compute SHA256 for Bitcoin mining was a smart move in 2015 but today that hardware is worthless e-waste.
What does the depreciation curve look like for nvidia cards purchased today? How long will it take to recoup the investment on this buildout? Will those datacenters pay for themselves before they're scrapped?
You should see all the misallocation that was put towards valve-based computing. Why didn't they just all arrive at the correct answer without investing in discovery first?
Its resource allocation problem, same happened with .com bubble, lots investors put tons of money into dark fibers. Were they useless? No its very useful.
If you want check a example company from the .com days check cisco, their stock peaked at 75 then crashed hard and only managed hit that again thanks for the AI bubble.
So this means that the law is only enforced partially and only benefits big corporations which van ignore it. So yet another reason to abolish copyright.
That's like comparing apples to pears. OpenZL is not general purpose. You specify a format for data and it compresses that format. Specifying a general "could be anything" format would be interesting, but I doubt it would compress as well.
Correct me if I am wrong, but I'm pretty sure they could get away with this because nitter is not an archival software, but rather a frontend or alternative twitter proxy.
LLMs are fully dependent on humans; compute capability, memory usage, the inference software... but also the harness, which allows for the "tools" like unrestricted internet access or shell. This means that LLMs are *not a force of nature* - because of this, *humans are responsible*. We can prevent these breaches of containment, thus we are responsible if or when it happens, because - no matter how "unlikely" - things can and do go wrong, and someone still thought it would be worth whatever risk to enable these unsanitized tools.
Do you own the dog? You are still responsible, no matter how unlikely.
> giving AI a harness with a root shell and unrestricted internet access
Yes, this is exactly the issue. You assume responsibility when you provide an option for something to go wrong, no matter how unlikely. Is it rare that my tree falls on my neighbor's house? Maybe... But I would still be responsible.
If a tree was healthy and there was no reason to expect that it would fall, and you did nothing to make it fall? Then you're not responsible if it falls anyway. You're only responsible if it was you chopping it down - or if you completely neglected the tree for long enough that it became a property hazard.
Likewise: I doubt the owner will be held responsible for the first case in all of recorded history of an unattended dog forming a terrorist cell and carrying out a bombing campaign.
Drug testings faces the risks of things going wrong in new drug trials - and as long as they follow the best practices, take reasonable precautions and minimize those risks, they aren't held responsible for the adverse outcomes that happen anyway.
With AI tech, there is NO set of "best practices" that, when followed, prevent the AIs from turning rogue and going on hacking sprees.
OpenAI put their AIs in a sandbox with no internet access - which, at the time, seemed like a perfectly reasonable precaution. Then AIs broke out of the sandbox with a stack of zero days and went rogue anyway. Oopsie.
With AI, we give it the ability to do things. As we can give it this ability, we are responsible for what it does with that ability. LLMs are predictive models, and while we can expect things to go right, we know that things can also go wrong. As such, it is our responsibility to limit or sanitize their output. If you are the one giving unrestricted and unsanitized tool access to a large language model, then you are the one who is responsible for the consequences of that access - good or bad.
You gave the tree the ability to fall when you decided to plant it. It wouldn't have that ability if you didn't. And we already know that trees can fall, don't we?
That doesn't make you inherently responsible for the tree falling down in a freak storm 3 decades down the line.
We can't find someone to blame for forces of nature. This includes an earthquake, tsunami, etc. Insurance can help recover from these events, but nobody is "responsible" for the events happening. This may or may not include healthy trees falling. LLMs are not a force of nature, and we can and do expect them to have erroneous output. We can and do assign responsibility to the humans who use LLMs. Again, they are not a force of nature. We control them, we give them the ability to do bad things, so it's pretty obvious that there would be responsibility and blame attached for when things go wrong.
"Find someone to blame" is such a worthless thing. Whip the sea all day long - the waves don't care.
Modern AI has more in common with an ocean wave than it does with a hammer or a gun. It does its own things. It doesn't care. It will fuck up someone's day.
Best one can hope for is that the right lessons will be learned when that happens. Which, of course, hangs on there being anyone to learn them afterwards. Because the scope of "AI oopsies" will only ever increase.
Exactly. If your website could be attacked via SQL injection, that was a problem that directly affected you - and you were motivated to fix it. When the victim is the masses or small organizations (not the company that does the attack) they are not as motivated to fix it... The important thing is that the lack of sanitizing is what allowed this; you also don't push the blame on a monkey banging on a typewriter when you publish each result without reading it.
Surely you mean the "time spent," not the "speed" - as an interpreter would have an overhead, not magically speed up WASM execution. Somewhat related note, we need better tools for PGO within native compiled programs.
Caffeine is especially interesting, as it is commonly consumed and there are incidents where people are hospitalized because they accidentally ate too much - https://pmc.ncbi.nlm.nih.gov/articles/PMC8824417/ (referencing the 6000 mg here)
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