blueblimp

The proposed replacement definitely makes more sense (and I've always found the absence of a "failed query" to be puzzling in standard attention), but, in deep learning, things that make more sense don't always actually get better results. So I'm curious whether this has been tried and carefully evaluated.

For those who aren't aware, this blog is infamous for sloppy work. https://www.lesswrong.com/posts/7iAABhWpcGeP5e6SB/it-s-proba...

What's problematic is that there are big models that adopt truly open source licenses, such as MPT-30b and Falcon-40b. As grateful as I am for having access to the Llama2 weights, it feels unfair that it gets credit for being "open source" when there are competing models that really are open source, in the traditional OSI sense.

The practical difference between the licenses is small enough that I expect most people (including me) will choose Llama2 anyway, because the models are higher quality. But that incentive may mean that we get stuck with these awkward pseudo-open licenses.

This is a good way to look at it. The deployment of the LLM technology we currently have is one thing. The impact of AGI, when it's created in the future, is a different thing. And people are often not clear on which of the two they're discussing.

I found the visual justification for partitioning a cube into three pyramids to be a bit confusing. For me, an easier way to think of it is that there are three coordinates (x,y,z) and each pyramid represents a region where a particular coordinate is largest. e.g. One such pyramid is {(x,y,z) | x = max{x,y,z}}.

Cutting edge models are quite expensive to train, so it'd be quite possible to restrict them (unfortunately).

From what I understood, the problem is less the change itself but more the short notice. 30 days isn't much to redesign your app and monetization, especially if you had many users on year-long subscription plans.

This brought to mind another blog post I liked on the topic of finishing, by Derek Yu (of Spelunky fame): https://makegames.tumblr.com/post/1136623767/finishing-a-gam....

I liked the trick of having them make excuses instead of revealing they're a bot. Clever.

AI isn't nuclear weaponry. It's bits, not atoms. It's more like encryption.

Personalization, customization, etc.: by aligning AI systems to many users, we benefit from the already-existing diversity of values among different people. This could be achieved via open source or proprietary means; the important thing is that the system works for the user and not for whichever company made it.

There is a way, in my opinion: distribute AI widely and give it a diversity of values, so that any one AI attempting takeover (or being misused) is opposed by the others. This is best achieved by having both open source and a competitive market of many companies with their own proprietary models.

This is way better than the open letter. It's much clearer and much more concise, and, maybe most importantly, it simply raises awareness rather than advocating for any particular solution. The goal appears to have been to make a statement that's non-obvious (to society at large) yet also can achieve agreement among many AI notables. (Not every AI notable agrees though--for example, LeCun did not sign, and I expect that he disagrees.)

Some time ago, I listened to Switched-On Bach out of historical interest, and I was surprised to find that it's largely gimmick-free. It's a (good) classical music performance that just happens to be performed using synthesizers.

It's a shame that, as the article mentions, Carlos's music is currently inconvenient to listen to due to unavailability on streaming services.

I wonder how much of that comes from gacha games filling a similar role.

I hate ads and use an ad blocker, but I also pay for ad removal on any site I use regularly that offers the option (which includes YouTube).

This seems like a reasonable balance between having the internet be usable while also supporting the services I use.

It seems to me that right now is an especially awkward time to launch a new programming language competing with Python, because LLMs are great at writing Python, and presumably can't write your new language at all (since there's no data for it yet). The linked post does not seem to address this.

Interesting resource. I had been wondering whether anyone had tried to compile such a list.

Schulman's talk is great. I had been thinking about this problem, and he covers almost everything I thought of, with great clarity.

One thing he didn't mention though is that there's potentially a bit of a trick to get the fine-tuning datasets to transfer across models anyway. (I haven't tested it.)

The key idea is to eliminate the pronouns. Imagine asking GPT-4, not whether it knows a fact, but whether "gpt-3.5-turbo" knows, or "text-davinci-003" knows, etc. Then, when you want the model to reply using pronouns (e.g. "I don't know"), use the system message to tell it which model it is.

This doesn't benefit from introspection, so quite possibly it doesn't work. The reason it might work anyway, though, is that estimating the difficulty of a question might be possible even without introspection.

> If it understands language at all, an LLM only does so in a statistical, rather than a grammatical, way. It is much more like an abacus than it is like a mind.

This analogy puzzled me. An abacus does not strike me as statistical in the way it functions. And minds do seem to be statistical, as far as I know.

It's interesting to hear your experience as a pedestrian, because it makes sense that cautious self-driving cars would cause fewer problems for pedestrians than for traffic. After all, a pedestrian has no problem dealing with a stationary car--just walk around it.

The recommendation to support conversational interactions is very good. After all, if you consult with a human expert, one of the main things they'll be doing is conversing with you to figure out your requirements. I find it a bit frustrating that even ChatGPT will give you a giant answer straightaway instead of clarifying your requirements first. (Prompting can help with this somewhat, if you explicitly tell it to ask clarifying questions.)

I've always thought of them a bit like improv, since they tend to follow the "yes, and..." rule, by happily continuing whatever direction you want to go. Now that the base models have been fine-tuned to avoid some topics, that's less true than it used to be, but it still feels like the most natural mode of operation.

AI has a privacy problem right now where the most capable models are available only on cloud services that censor and access your input and output data. The gap between the state-of-the-art and private alternatives is especially large in text generation.

My grandfather wrote an autobiography (for family) and I found it interesting to read.

The key quote:

> [Wozniak] was overjoyed when he learned that the skill that put so much effort into suddenly became massively easier. He wasn't worried about not being able to earn an above-average salary from this anymore, he was happy about all the new cool things he and everyone else would be able to build.

If you want to get things done, then having your skills obsoleted is good.

Yes, it goes beyond even just extensive usage restrictions and restricts _who_ can use it. https://jamesturk.github.io/scrapeghost/LICENSE/#3

It seems, for example, that (by 3.1.12) if you are a person who is involved in the mining of minerals (of any sort), that you are not allowed to use this library, even if you're not using the library for any mining-related purpose.

SEO garbage is such a problem these days that, if I were Google, more than using AI as a new frontend for search, I'd be trying to find a way to use it to defeat SEO.

It seems still unclear how much quality loss there is compared to the best models. What's really needed is systematic evaluation of the output quality, but that's tricky and relatively expensive (compared to automated benchmarks), so I understand why it hasn't happened yet.

Edit: I just tried it with a single task of my own (that I've successfully used with ChatGPT and Bing) and it flubbed it horribly, so this model at least is noticeably inferior to the SOTA, which is not surprising given how small it is.

NovelAI's image generation uses a fine-tune of Stable Diffusion.