Megathread #12 for news/updates/discussion of AI chatbots and image generators

OpenAI Pauses ChatGPT's 'Browse With Bing' as Users Bypass Paywalls

Schade writes that the Browse feature may “display content in ways we don’t want” as a reason for the shutdown, but Windows Central reports that users were able to bypass paywalls. The outlet points to the r/ChatGPT subreddit, in which a user posted that Browse with Bing basically turned the chatbot into a web browser that can display the full contents of a paywalled website when a user provides the chatbot with a URL.

Oops!

Claude is a chatbot somewhat similar to ChatGPT, but from a different company named Anthropic. Until recently you couldn't sign up to use it directly; instead you'd have to go through another business that provides access to it. You can sign in now, though, using a new website. There's a fair bit of legalese before you can start using it. It doesn't have access to the web, but you can upload a file and ask questions about it.

Apparently this is Claude 2, just announced today, and there's more about it here.

[W]e have increased the length of Claude’s input and output. Users can input up to 100K tokens in each prompt, which means that Claude can work over hundreds of pages of technical documentation or even a book. Claude can now also write longer documents - from memos to letters to stories up to a few thousand tokens - all in one go.

Custom instructions for ChatGPT

This beta feature allows you to add information to your ChatGPT profile that will be included as part of the prompt.

It will be interesting to see what people do with it. I expect it will make it harder to reproduce other people's results, though, especially if you forgot you turned it on.

Naturally, some people are already putting a jailbreak in there.

Edit: and now it's gone again.

Law Firms Are Recruiting More AI Experts as Clients Demand ‘More for Less' (Bloomberg) (archive)

Legal services are the most vulnerable to ChatGPT-style software, according to a recent University of Pennsylvania study. That’s fueling concerns AI could replace a significant chunk of junior lawyer drudgery. The widely-accessible technology is particularly suited to time-consuming legal work, because of its ability to instantly analyze large documents, predict successful arguments based on past cases or create questions based on pre-defined criteria for a deposition.

Allen & Overy earlier this year was the first of the Magic Circle of top UK law firms to announce a chatbot to help lawyers draft contracts and client memos. Rivals are now piloting legal AI ‘assistants’ like Casetext Inc.’s CoCounsel, including Bryan Cave Leighton Paisner which began a trial of the software in June.

Meta claims its new art-generating model is best-in-class (TechCrunch)

Today, Meta announced CM3Leon (“chameleon” in clumsy leetspeak), an AI model that the company claims achieves state-of-the-art performance for text-to-image generation. CM3Leon is also distinguished by being one of the first image generators capable of generating captions for images, laying the groundwork for more capable image-understanding models going forward, Meta says.

[...]

Most modern image generators, including OpenAI’s DALL-E 2, Google’s Imagen and Stable Diffusion, rely on a process called diffusion to create art. In diffusion, a model learns how to gradually subtract noise from a starting image made entirely of noise — moving it closer step by step to the target prompt.

[...]

CM3Leon is a transformer model, by contrast, leveraging a mechanism called “attention” to weigh the relevance of input data such as text or images. Attention and the other architectural quirks of transformers can boost model training speed and make models more easily parallelizable. Larger and larger transformers can be trained with significant but not unattainable increases in compute, in other words.

And CM3Leon is even more efficient than most transformers, Meta claims, requiring five times less compute and a smaller training dataset than previous transformer-based methods.

[...]

To train CM3Leon, Meta used a dataset of millions of licensed images from Shutterstock.

[...]

Meta didn’t say whether — or when — it plans to release CM3Leon.

Here is the paper.

ChatGPT Code Interpreter now available

If you're a paid user of ChatGPT, you should now be able to use it with a Python code interpreter. To do this, you need to enable it in user settings (it's a beta feature), then switch to GPT4 and select the code interpreter.

A simple example is "In Python, print hello world." It writes the code and runs it. The code is hidden by default, but you can click on "show work" to see what it wrote.

One of the things it can do is create and display images using a few different Python libraries. I asked it to print a Mandelbrot set and it did that in low resolution. Then I asked it to increase the resolution and zoom in on one part of it. (The Python interpreter is still loaded with the code it previously wrote, so all it had to do was write a different function call.)

Unfortunately, when you share a link to a ChatGPT chat session, images aren't included yet, but you can read the chat session and Python code here.

It doesn't have access to the web. (There was a plugin for that, but it's been disabled.) However, you can upload files; there's a button on the left side of the chat window where you select a file to upload. I was able to upload a png file and have it display it, and it could crop it and render it in greyscale.

It has access to some 3d libraries, like matplotlib. I uploaded an image of the earth and tried to get it to wrap it around a sphere, but it wasn't able to do it. Maybe someone who actually used the library before would be able to tell it what to do? Maybe there's a better library it could use.

I hit the limit for how much you can use GPT4 so I'll try again tomorrow.

Here's a blog post comparing LLM's with search engines:

The Many Ways that Digital Minds Can Know (Ryan Moulton)

Generally we think of “memorization” as bad, as an indication of overfitting, and the degree of memorization as mutually exclusive with the degree of “generalization.” When a model relies on memorization instead of generalization, we expect it to perform more poorly outside of its domain. But the connotations of generalization and memorization, as good and bad, respectively, do not reflect how language sophistication and index size, respectively, affect a search engine. For a search engine, both memorization and generalization help its performance, there is not a tradeoff between the two. Rather than worrying about tasks “outside of its domain” the goal of a search index is simply to cover the entire domain, and whatever language understanding you can do on top just expands it further.

...

Why would search engines be a better analogy despite the enormous difference in their basic mechanisms? One reason is scale. LLMs are large. GPT-3 has 175 billion parameters, and GPT-4 is much bigger. The internet is also big. It contains more things and more outlandish things than you would ever expect. One of the highlighted results from GPT-4’s launch was its ability to draw a unicorn in Tikz. This is not the sort of thing I would have expected to exist in any remotely similar training data on the web. However, it turns out that there’s an entire ctan package of cartoon animals drawn in Tikz. While the physical construction of a unicorn in code would be astonishing for a model to reason through from scratch, the knowledge that a unicorn is a pink horse with a horn, or the ability to translate from SVG to Tikz, is less surprising.

...

For a model with a high level of integration, the sort of coverage we might be most concerned with is “patterns of valid reasoning” or “facts about the world” whereas for a model with low levels of integration the kind of coverage we’re worried about is “existence of particular ngrams and their statistical associations.”

...

The level of sophistication of the reasoning that the model is doing is not relevant to the quality of the product in this context, or to its usefulness for this task. A model that absorbs more of the web will be more useful regardless whether it’s “smarter” or not, for the same reason that a search engine with a larger index is more useful than a search engine with a smaller index, even if the ranking algorithms do not change. The distinction between memorization and reasoning will not actually matter for a lot of use cases, because ordinary document retrieval is immensely useful, and even “light” semantic reasoning on top of that is more useful still. A model that gets correct results through cheap tricks still gets correct results. A right answer, consistently delivered, is a right answer.

Bard’s latest update: more features, languages and countries

Apparently it works in most European countries now and they made some good UI updates.