large language model

25 Articles

Peering Down Into Talking Ant Hill

April 19, 2023 by 14 Comments

Watching an anthill brings an air of fascination. Thousands of ants are moving about and communicating with other ants as they work towards a goal as a collective whole. For us humans, we project a complex inner world for each of these tiny creatures to drive the narrative. But what if we could peer down into a miniature world and the ants spoke English? (PDF whitepaper)

Researchers at the University of Stanford and Google Research have released a paper about simulating human behavior using multiple Large Language Models (LMM). The simulation has a few dozen agents that can move across the small town, do errands, and communicate with each other. Each agent has a short description to help provide context to the LLM. In addition, they have memories of objects, other agents, and observations that they can retrieve, which allows them to create a plan for their day. The memory is a time-stamped text stream that the agent reflects on, deciding what is important. Additionally, the LLM can replan and figure out what it wants to do.

The question is, does the simulation seem life-like? One fascinating example is the paper’s authors created one agent (Isabella) intending to have a Valentine’s Day party. No other information is included. But several agents arrive at the character’s house later in the day to party. Isabella invited friends, and those agents asked some people.

A demo using recorded data from an earlier demo is web-accessible. However, it doesn’t showcase the powers that a user can exert on the world when running live. Thoughts and suggestions can be issued to an agent to steer their actions. However, you can pause the simulation to view the conversations between agents. Overall, it is incredible how life-like the simulation can be. The language of the conversation is quite formal, and running the simulation burns significant amounts of computing power. Perhaps there can be a subconscious where certain behaviors or observations can be coded in the agent instead of querying the LLM for every little thing (which sort of sounds like what people do).

There’s been an exciting trend of combining LLMs with a form of backing store, like combining Wolfram Alpha with chatGPT. Thanks [Abe] for sending this one in!

BitTorrent For Language Models

March 21, 2023 by 6 Comments

In the old days of the Internet, FTP was sufficient for downloading the occasional file. But with the widespread use of computer audio and video, it was easy to swamp an FTP server so — eventually — BitTorrent was born. The idea was you would download bits and pieces of a file from different places and, in theory, people would download bits and pieces that you have if they need them. Now Petals wants to use this same method with language models. These AI language models are all the rage, but they take significant computer resources. The idea behind Petals is like BitTorrent. You handle a small part of the model (about 8 gigabytes which is small compared to the 352 gigabytes required), and other people have other parts.

Of course, if you are privacy-minded, that means that some amount of your data is going out to the public, but for your latest chatbot experiments, that might not be a big problem. You can install Petals in an Anaconda environment or run a Docker image if you don’t want to set up anything. If you just want to access the distributed network’s chatbot based on BLOOMZ-176B, you can do that online.

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Detecting Machine-Generated Content: An Easier Task For Machine Or Human?

February 1, 2023 by 41 Comments

In today’s world we are surrounded by various sources of written information, information which we generally assume to have been written by other humans. Whether this is in the form of books, blogs, news articles, forum posts, feedback on a product page or the discussions on social media and in comment sections, the assumption is that the text we’re reading has been written by another person. However, over the years this assumption has become ever more likely to be false, most recently due to large language models (LLMs) such as GPT-2 and GPT-3 that can churn out plausible paragraphs on just about any topic when requested.

This raises the question of whether we are we about to reach a point where we can no longer be reasonably certain that an online comment, a news article, or even entire books and film scripts weren’t churned out by an algorithm, or perhaps even where an online chat with a new sizzling match turns out to be just you getting it on with an unfeeling collection of code that was trained and tweaked for maximum engagement with customers. (Editor’s note: no, we’re not playing that game here.)

As such machine-generated content and interactions begin to play an ever bigger role, it raises both the question of how you can detect such generated content, as well as whether it matters that the content was generated by an algorithm instead of by a human being.

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AI-Controlled Twitch V-Tuber Has More Followers Than You

January 12, 2023 by 45 Comments

Surely we have all at least heard of Twitch by now. For the as-yet uninitiated: imagine you had your own TV channel. What would you do on it? Although Twitch really got going as a place for gamers to stream the action, there are almost as many people jamming out on their guitars, or building guitars, or just talking about guitars. And that’s just the example that uses guitars — if you can think of it, someone is probably doing it live on Twitch, within the Terms of Service, of course.

Along with the legions of people showing their faces and singing their hearts out, you have people in partial disguise, and then you have v-tubers. That stands for virtual tubers, and it just means that the person is using an anime avatar to convey themselves.

Now that you’re all caught up, let’s digest the following item together: there’s a v-tuber on Twitch that’s controlled entirely by AI. Let me run that by you again: there’s a person called [Vedal] who operates a Twitch channel. Rather than stream themselves building Mad Max-style vehicles and fighting them in a post-apocalyptic wasteland, or singing Joni Mitchell tunes, [Vedal] pulls the strings of an AI they created, which is represented by an animated character cleverly named Neuro-sama. Not only does Neuro-sama know how to play Minecraft and osu!, she speaks gamer and interacts regularly with chat in snarky, 21st century fashion. And that really is the key behind Twitch success — interacting with chat in a meaningful way.

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