The current hotness is anything to do with artificial intelligence, and along with some interesting experiments comes a lot of mindless hype. The question is, what can it do for us! [Jesse] provides a fun answer by asking ChatGPT to perform as a Dungeons and Dragons dungeon master.
There are many ways to approach a game of D&D, and while some take the whole thing very seriously indeed we prefer to treat it as a lightly inebriated band of intrepid heroes smacking each other and assorted monsters with imaginary swords and war hammers. Would the AI follow the nerdiest cliches to their pedantic conclusions, or would it sense that the point of a game is to have fun?
Issac Asimov foresaw 3D virtual meetings but gave them the awkward name “tridimensional personification.” While you could almost do this now with VR headsets and 3D cameras, it would be awkward at best. It is easy to envision conference rooms full of computer equipment and scanners, but an MIT student has a method that may do away with all that by using machine learning to simplify hologram generation.
As usual, though, the popular press may be carried away a little bit. The key breakthrough here is that you can use TensorFlow to generate real-time holograms at a few frames per second using consumer-grade processing power found in a high-end phone from images with depth information, which is also available on some phones. There’s still the problem of displaying the hologram on the other side, which your phone can’t do. So any implication that you’ll download an app that enables holograms phone calls is hyperbole and images of this are in the realm of photoshop.
By now, you’ve surely seen the AI tools that can chat with you or draw pictures from prompts. OpenAI now has Point-E, which takes text or an image and produces a 3D model. You can find a few runnable demos online, but good luck having them not too busy to work.
We were not always impressed with the output. Asking for “3d printable starship Enterprise,” for example, produced a point cloud that looked like a pregnant Klingon battle cruiser. Like most of these tools, the trick is finding a good prompt. Simple things like “a chair” seemed to work somewhat better.
“They paved paradise and put up a parking lot.” That might be stretching things a bit, especially when the “paradise” in question is in New Jersey, but there’s a move afoot to redevelop the site of the original “Big Bang Antenna” that has some people pretty upset. Known simply as “The Horn Antenna” since it was built by Bell Labs in 1959 atop a hill in Holmdel, New Jersey, the antenna was originally designed to study long-distance microwave communications. But in 1964, Bell Labs researchers Arno Penzias and Robert Wilson accidentally discovered the microwave remnants of the Big Bang, the cosmic background radiation, using the antenna, earning it a place in scientific history. So far, the only action taken by the township committee has been to authorize a study to look into whether the site should be redeveloped. But the fact that the site is one of the highest points in Monmouth County with sweeping views of Manhattan has some people wondering what’s really on tap for the site. A petition to save the antenna currently has about 3,400 signatures, so you might want to check that out — after all, you don’t know what you’ve got ’til it’s gone.
AI knoweth everything, and as each new model breaks upon the world, it attracts a new crowd of experimenters. The new hotness is ChatGPT, and [Jonas Degrave] has turned his attention to it. By asking it to act as a Linux terminal, he discovered that he could gain access to a complete Linux virtual machine within the model’s synthetic imagination.
The AI’s first response was a prompt, so he of course first tried to list the files. Up came a list of directories, so the next step was to create a file and put some text in it. All of this resulted in a readable file, so there was some promise in this unexpected computing resource. But can it run code? Continue reading “A VM In An AI”→
The up-and-coming Wonder of the World in software and information circles , and particularly in those circles who talk about them, is AI. Give a magic machine a lot of stuff, ask it a question, and it will give you a meaningful and useful answer. It will create art, write books, compose music, and generally Change The World As We Know It. All this is genuinely impressive stuff, as anyone who has played with DALL-E will tell you. But it’s important to think about what the technology can and can’t do that’s new so as to not become caught up in the hype, and in doing that I’m immediately drawn to a previous career of mine. Continue reading “Love AI, But Don’t Love It Too Much”→
[Georgi Gerganov] recently shared a great resource for running high-quality AI-driven speech recognition in a plain C/C++ implementation on a variety of platforms. The automatic speech recognition (ASR) model is fully implemented using only two source files and requires no dependencies. As a result, the high-quality speech recognition doesn’t involve calling remote APIs, and can run locally on different devices in a fairly straightforward manner. The image above shows it running locally on an iPhone 13, but it can do more than that.
Implementing a robust speech transcription that runs locally on a variety of devices is much easier with [Georgi]’s port of OpenAI’s Whisper.[Georgi]’s work is a port of OpenAI’s Whisper model, a remarkably-robust piece of software that does a truly impressive job of turning human speech into text. Whisper is easy to set up and play with, but this port makes it easier to get the system working in other ways. Having such a lightweight implementation of the model means it can be more easily integrated over a variety of different platforms and projects.
The usual way that OpenAI’s Whisper works is to feed it an audio file, and it spits out a transcription. But [Georgi] shows off something else that might start giving hackers ideas: a simple real-time audio input example.
By using a tool to stream audio and feed it to the system every half-second, one can obtain pretty good (sort of) real-time results! This of course isn’t an ideal method, but the robustness and accuracy of Whisper is such that the results look pretty great nevertheless.
You can watch a quick demo of that in the video just under the page break. If it gives you some ideas, head over to the project’s GitHub repository and get hackin’!
