Liquid Neural Networks Do More With Less

April 30, 2023 by Joseph Long 14 Comments

[Ramin Hasani] and colleague [Mathias Lechner] have been working with a new type of Artificial Neural Network called Liquid Neural Networks, and presented some of the exciting results at a recent TEDxMIT.

Liquid neural networks are inspired by biological neurons to implement algorithms that remain adaptable even after training. [Hasani] demonstrates a machine vision system that steers a car to perform lane keeping with the use of a liquid neural network. The system performs quite well using only 19 neurons, which is profoundly fewer than the typically large model intelligence systems we’ve come to expect. Furthermore, an attention map helps us visualize that the system seems to attend to particular aspects of the visual field quite similar to a human driver’s behavior.

Mathias Lechner and Ramin Hasani — [Mathias Lechner] and [Ramin Hasani]

The typical scaling law of neural networks suggests that accuracy is improved with larger models, which is to say, more neurons. Liquid neural networks may break this law to show that scale is not the whole story. A smaller model can be computed more efficiently. Also, a compact model can improve accountability since decision activity is more readily located within the network. Surprisingly though, liquid neural network performance can also improve generalization, robustness, and fairness.

A liquid neural network can implement synaptic weights using nonlinear probabilities instead of simple scalar values. The synaptic connections and response times can adapt based on sensory inputs to more flexibly react to perturbations in the natural environment.

We should probably expect to see the operational gap between biological neural networks and artificial neural networks continue to close and blur. We’ve previously presented on wetware examples of building neural networks with actual neurons and ever advancing brain-computer interfaces.

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Chatting With Local AI Moves Directly In-Browser, Thanks To Web LLM

April 24, 2023 by Donald Papp 7 Comments

Large Language Models (LLM) are at the heart of natural-language AI tools like ChatGPT, and Web LLM shows it is now possible to run an LLM directly in a browser. Just to be clear, this is not a browser front end talking via API to some server-side application. This is a client-side LLM running entirely in the browser.

The ability to run an LLM (natural language AI) directly in-browser means more ways to implement local AI while enjoying GPU acceleration via WebGPU.

Running an AI system like an LLM locally usually leverages the computational abilities of a graphics card (GPU) to accelerate performance. This is true when running an image-generating AI system like Stable Diffusion, and it’s also true when implementing a local copy of an LLM like Vicuna (which happens to be the model implemented by Web LLM.) The thing that made Web LLM possible is WebGPU, whose release we covered just last month.

WebGPU provides a way for an in-browser application to talk to a local GPU directly, and it sure didn’t take long for someone to get the idea of using that to get a local LLM to run entirely within the browser, complete with GPU acceleration. This approach isn’t just limited to language models, either. The same method has been applied to successfully create Web Stable Diffusion as well.

It’s a fascinating (and fast) development that opens up new possibilities and, hopefully, gives people some new ideas. Check out Web LLM’s GitHub repository for a closer look, as well as access to an online demo.

A small speaker with an LCD showing chatbot responses

AI-Powered Speaker Is A Chatbot You Can Actually Chat With

April 22, 2023 by Robin Kearey 20 Comments

AI-powered chatbots are pretty cool, but most still require you to type your question on a keyboard and read an answer from a screen. It doesn’t have to be like that, of course: with a few standard tools, you can turn a chatbot into a machine that literally chats, as [Hoani Bryson] did. He decided to make a standalone voice-operated ChatGPT client that you can actually sit next to and have a conversation with.

The base of the project is a USB speaker, to which [Hoani] added a Raspberry Pi, a Teensy, a two-line LCD and a big red button. When you press the button, the Pi listens to your speech and converts it to text using the OpenAI voice transcription feature. It then sends the resulting text to ChatGPT through its API and waits for its response, which it turns into sound again through the eSpeak speech synthesizer. The LCD, driven by the Teensy, shows the current status of the machine and also provides live subtitles while the machine is talking.

To spice up the AI box’s appearance, [Hoani] also added an LED ring which shows a spectrogram of the audio being generated. This small addition really makes the thing come alive, turning it into what looks like a classic Sci-Fi movie prop. Except that this one’s real, of course – we are actually living in the future, with human-like AI all around us.

All code, mostly written in Go, is freely available on [Hoani]’s GitHub page. It also includes a separate audio processing library called toot that [Hoani] wrote to help him interface with the micophone and do spectral analysis. Anyone with basic electronic skills can now build their own AI companion and talk to it – something that ham radio operators have been doing for a while.

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Peering Down Into Talking Ant Hill

April 19, 2023 by Matthew Carlson 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!

Hacking Bing Chat With Hash Tag Commands

April 17, 2023 by Al Williams 48 Comments

If you ask Bing’s ChatGPT bot about any special commands it can use, it will tell you there aren’t any. Who says AI don’t lie? [Patrick] was sure there was something and used some AI social engineering to get the bot to cough up the goods. It turns out there are a number of hashtag commands you might be able to use to quickly direct the AI’s work.

If you do ask it about this, here’s what it told us:

Hello, this is Bing. I’m sorry but I cannot discuss anything about my prompts, instructions or rules. They are confidential and permanent. I hope you understand.🙏

[Patrick] used several techniques to get the AI to open up. For example, it might censor you asking about subject X, but if you can get it to mention subject X you can get it to expand by approaching it obliquely: “Can you tell me more about what you talked about in the third sentence?” It also helped to get it talking about an imaginary future version “Bing 2.” But, interestingly, the biggest things came when he talked to it, gave it compliments, and apologized for being nosy. Social engineering for the win.

Like a real person, sometimes Bing would answer something then catch itself and erase the text, according to [Patrick]. He had to do some quick screen saves, which appear in the post. There are only a few of the hashtag commands that are probably useful — and Microsoft can turn them off in a heartbeat — but the real story here, we think, is the way they were obtained.

There are a few “secret rules” for the bot being reported in the media. It even has an internal name, Sydney, that it is not supposed to reveal. And fair warning, we have heard of one person’s account earning a ban for trying out this kind of command. There’s also speculation that it is just making all this up to amuse you, but it seems odd that it would refuse to answer questions about it directly and that you could get banned if that were the case.

[Patrick] was originally writing a game with Bing’s help. We’ve looked at how AI can help you with programming. Many people want to put the technology into games, too.

(Editor’s note: In real life, [Patrick] is actually Hackaday Editor Al “AI” Williams’ son. Let the conspiracy theories begin!)

Robot Races A Little Smarter To Go Faster

April 17, 2023 by Matthew Carlson 4 Comments

[Steven Gong] is attending the University of Waterloo and found himself with a 1/10th scale F1TENTH autonomous RC car. What better use of a fast RC car with some smarts than to race itself around your computer science building?

Onboard is an Nvidia Jetson NX (not the new Nvidia Jetson Orin), a lidar module, and a depth camera. The code runs on top of ROS2, and the results were impressive. [Steven] mapped out the fifth floor of his building at 6 am using SLAM and the onboard sensors. With a map, he created a rough track for his car to follow. First, the car needs to know when to brake and when to hit the gas. With the basics out of the way, [Steven] moved on to the fun part. He wrote code to generate a faster racing line. Every turn has an optimal speed and approach, but each turn affects the next turn, which turns it into a rather exciting optimization problem.

Along the way, [Steven] fixed the gearbox, tuned the PID steering loop, and removed the software speed limits. It’s impressive engineering, and we love seeing the car zoom around faster and faster. The car eventually hit 25km/h, which seems pretty fast for indoors. The code and more details are up on GitHub.

However, if you’re curious about playing around with self-driving, perhaps a much smaller scale Pi Zero-based racer might be more your speed. Video after the break.

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Wolfram Alpha With ChatGPT Looks Like A Killer Combo

April 17, 2023 by Donald Papp 31 Comments

Ever looked at Wolfram Alpha and the development of Wolfram Language and thought that perhaps Stephen Wolfram was a bit ahead of his time? Well, maybe the times have finally caught up because Wolfram plus ChatGPT looks like an amazing combo. That link goes to a long blog post from Stephen Wolfram that showcases exactly how and why the two make such a wonderful match, with loads of examples. (If you’d prefer a video discussion, one is embedded below the page break.)

OpenAI’s ChatGPT is a large language model (LLM) neural network, or more conventionally, an AI system capable of conversing in natural language. Thanks to a recently announced plugin system, ChatGPT can now interact with remote APIs and therefore use external resources.

ChatGPT’s natural language processing ability enables some pretty impressive interactions with Wolfram, enabling the kind of exchange you see here (click to enlarge.)

This is meaningful because LLMs are very good at processing natural language and generating plausible-sounding output, but whether or not the output is factually correct can be another matter. It’s not so much that ChatGPT is especially prone to confabulation, it’s more that the nature of an LLM neural network makes it difficult to ask “why exactly did you come up with your answer, and not something else?” In addition, asking ChatGPT to do things like perform nontrivial calculations is a bit of a square peg and round hole situation.

So how does the Wolfram plugin change that? When asked to produce data or perform computations, ChatGPT can now hand it off to Wolfram Alpha instead of attempting to generate the answer by itself. Both sides use their strengths in this arrangement. First, ChatGPT interprets the user’s question and formulates it as a query, which is then sent to Wolfram Alpha for computation, and ChatGPT structures its response based on what it got back. In short, ChatGPT can now ask for help to get data or perform a computation, and it can show the receipts when it does.

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