481–490 of 534 posts
And it doesn't work. Even a bit. It's a constant constant cat and mouse game. Maybe they can slow people down slightly, but they won't be able to stop them, and good luck protecting yourself from Elon Musk snooping your stuff in his data centre.
Just to be clear, this is the same reason that social media companies don't tell you about how they detect spam and they create shadow bans and things like this so that people don't know they've been detected and figure out the mechanims. And it doesn't work. Even a bit. It's a constant constant cat and mouse game. Maybe they can slow people down slightly, but they won't be able to stop them, and good luck protecting…
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A surprising number of companies do include “you may not use the service we provide you to compete with us” in their terms of service. (edit) After a quick search the best example is Atlassian. It would (apparently, IANAL) break terms to plan a JIRA competitor using JIRA. > Customer must not (and must not permit anyone else to): [...] (d) use the Products to develop a similar or competing product or service https://w…
I remember working for a company that did a lot of business in logistics. We were strictly prohibited from using any Amazon Web Services because several of our very high profile customers didn’t want anything on AWS. The higher ups were thoroughly convinced Amazon would copy it (and I mean, they came out with a product that competed with us, so they weren’t wrong!)
Amazon didn’t “copy” logistics from Apple. But both of them use similar underlying processes and optimizations. They both excel at it, and neither is eating the other’s profits. The same goes for smaller companies. Or the logistics providers like UPS.
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It's such an obviously bad policy, it's mind-boggling that they thought this was a good idea. It just breeds paranoia and mistrust, especially when people are already a bit paranoid about silent model quantification for cost cutting reasons.
Do you mean "quantization" not quantification?
This is another "gpt3 too dangerous for the world" moment which is laughable in retrospect.
1. LLMs can help create other better LLMs
2. If Anthropic is able to reach this ability, others can too
3. Intense work is being done by every chip manufacturer for local inference. Engineers want this. We’re headed toward this
4. These companies ultimately know that their moat isn’t permanent. Maybe not today, maybe not in 6 months. But it’s not forever
5. This stuff has so much research and eyes that policies like this rub people the wrong way. And it rubs them badly enough that it creates the friction necessary to make better alternatives
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Nothing is infinitely valuable.
10 engineers can make a billion dollar company. One Claude can replace 10 engineers. This gets very close to "infinitely valuable", it starts to look like a vertical line to me
The moat looks deep today but it's going to become more shallow every year. Training a new model from scratch takes serious resources. Post-training/fine-tuning an existing model, dramatically less. The knowledge for the process was esoteric two years ago, now you can ask a current model (one of several) to walk you through it, while building the tools to do it as you go. Several of my recent weekend projects have be…
> The moat looks deep today Does it? What can this model do that I both want and cannot already do? Anthropic made a nice little post saying how dangerous it is, because it is good enough to eat their own business. But I don't want to eat their business. They also said it was good at playing Slay the Spire, but I can't think of anything more insulting than have a machine do that in my place. That's MY comfort game, n…
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Really the biggest concerns are not computers getting spectacularly faster, but 'intelligence' algorithms getting orders of magnitude better. Drop the power requirements 1000 fold, and yea you will be able to make your own SOTA model on the cheap. The problem is the person that has a few exaflops of power will still leave you in the dust in the intelligence explosion that would happen after an event like this.
Depends upon the intelligence vs compute scaling law— which I think no one really knows. Pretty likely to be some degree of diminishing returns, but how much? Is it logarithmic, inverse quadratic, … If training models gets way cheaper, I would expect the diminishing returns to get steeper too.