We’re all pretty familiar with AI’s ability to create realistic-looking images of people that don’t exist, but here’s an unusual implementation of using that technology for a different purpose: masking people’s identity without altering the substance of the image itself. The result is the photo’s content and “purpose” (for lack of a better term) of the image remains unchanged, while at the same time becoming impossible to identify the actual person in it. This invites some interesting privacy-related applications.

The paper for Face Anonymization Made Simple has all the details, but the method boils down to using diffusion models to take an input image, automatically pick out identity-related features, and alter them in a way that looks more or less natural. For this purpose, identity-related features essentially means key parts of a human face. Other elements of the photo (background, expression, pose, clothing) are left unchanged. As a concept it’s been explored before, but researchers show that this versatile method is both simpler and better-performing than others.

Diffusion models are the essence of AI image generators like Stable Diffusion. The fact that they can be run locally on personal hardware has opened the doors to all kinds of interesting experimentation, like this haunted mirror and other interactive experiments. Forget tweaking dull sliders like “brightness” and “contrast” for an image. How about altering the level of “moss”, “fire”, or “cookie” instead?

A little while ago Oasis was showcased on social media, billing itself as the world’s first playable “AI video game” that responds to complex user input in real-time. Code is available on GitHub for a down-scaled local version if you’d like to take a look. There’s a bit more detail and background in the accompanying project write-up, which talks about both the potential as well as the numerous limitations.

We suspect the focus on supporting complex user input (such as mouse look and an item inventory) is what the creators feel distinguishes it meaningfully from AI-generated DOOM. The latter was a concept that demonstrated AI image generators could (kinda) function as real-time game engines.

Image generators are, in a sense, prediction machines. The idea is that by providing a trained model with a short history of what just happened plus the user’s input as context, it can generate a pretty usable prediction of what should happen next, and do it quickly enough to be interactive. Run that in a loop, and you get some pretty impressive clips to put on social media.

It is a neat idea, and we certainly applaud the creativity of bending an image generator to this kind of application, but we can’t help but really notice the limitations. Sit and stare at something, or walk through dark or repetitive areas, and the system loses its grip and things rapidly go in a downward spiral we can only describe as “dreamily broken”.

It may be more a demonstration of a concept than a properly functioning game, but it’s still a very clever way to leverage image generation technology. Although, if you’d prefer AI to keep the game itself untouched take a look at neural networks trained to use the DOOM level creator tools.

Although generative AI and large language models have been pushed as direct replacements for certain kinds of workers, plenty of businesses actually doing this have found that using this new technology can cause more problems than it solves when it is given free reign over tasks. While this might not be true indefinitely, the real use case for these tools right now is as a kind of assistant to certain kinds of work. For this they can be incredibly powerful as [Ricardo] demonstrates here, using Amazon Q to help with game development on the Commodore 64.

The first step here was to generate code that would show a sprite moving across the screen. The AI first generated code in all caps, as was the style at the time of the C64, but in [Ricardo]’s development environment this caused some major problems, so the code was converted to lowercase. A more impressive conversion was done in the next steps, as the program needed to take advantage of the optimizations found in the Assembly language. With the code converted to 6502 Assembly that can run on the virtual Commodore, [Ricardo] was eventually able to show four sprites moving across the screen after several iterations with the AI, as well as change the style of the sprites to arbitrary designs.

Although the post is a bit over-optimistic on Amazon Q as a tool specifically for developers, it might have some benefits over other generative AIs especially if it’s capable at the chore of programming in Assembly language. We’d love to hear anyone with real-world experience with this and whether it is truly worth the extra cost over something like Copilot or GPT 4. For any of these generative AI models, though, it’s probably worth trying them out while they’re in their early stages. Keep in mind that there’s a lot more than programming that can be done with some of them as well.

There’s no shortage of movies, TV shows, and books that show a dystopian future with corporations run amok in outer space with little or no effective oversight. Dune, The Expanse, and The Dispossessed spring to mind as predicting different aspects of this idea, but there are plenty of other warnings throughout sci-fi depicting this potential future. One possible way of preventing this outcome is by ensuring that space is as open-sourced as possible and one group, the Libre Space Foundation (LSF), is working towards this end. Their latest is a project with Ondsel to develop and model a satellite deploying mechanism using almost entirely open source software.

The LSF had already designed the PICOBUS satellite launcher system that flew to space in 2022 and deployed a number of CubeSats, but the group needed more information about how the system would perform. They turned to Ondsel to help develop a multi-body dynamics (MBD) solver, managing simulations with mass-spring-damper models. The satellite launcher includes a large constant-force spring that pushes the CubeSats out of the device once the door is opened, and the model can now simulate their paths in space without gravity. The team will launch their next set of satellites sometime next year on an RFA-ONE rocket.

The LSF maintains a huge database of their open source space projects, including this one, on their GitLab page. Although it might seem like small potatoes now, the adoption of open source software and hardware by space-fairing entities can help further the democratization of low Earth orbit.

Thanks to [johnad] for the tip!

Code Llama is a suite of large language models released by Meta AI for generating and enhancing code. It includes foundation models for general coding, Python specializations, and models tailored for following instructions. Key features include state-of-the-art performance, code infilling, large context support up to 100K tokens, and zero-shot ability to follow instructions for programming […]

Source

ChatGLM-6B is an open-source, bilingual conversational AI LLM based on the General Language Model (GLM) framework. It has 6.2 billion parameters and can be deployed locally with only 6GB of GPU memory. This model allows for natural language processing in both Chinese and English, question answering, task-oriented dialogue, and easy integration via API and demo […]

Source

Codestral is a powerful 22B parameter AI model from Mistral AI. This open-weight model is designed specifically for code generation across over 80 programming languages including Python, Java, C++, JavaScript and more. Codestral offers impressive performance, outperforming other models on benchmarks like HumanEval and RepoBench with its large 32k token context window. The model is […]

Source

Animate Anything is an AI animation tool that lets you rig and animate your 3D models. Simply upload your static 3D models and the AI will automatically rig and animate them, ready for games, VR, and metaverse worlds. The tool supports multiple 3D formats (FBX, GLB, GLTF+BIN) and rigged models can be imported into Unity, […]

Source

Contlo’s Brand AI Model allows businesses to orchestrate all their marketing activities with their brand’s own AI model by contextually generating personalized marketing creatives including emails, images, copies, and more. To use the tool, provide the AI with information such as brand story, identity, design language, tonality, and aesthetic attributes that represent the brand. The […]

Source

3DFY.ai is a text-to-3D tool that makes creating high-quality 3D models as easy as writing a text prompt. Just type in an item or object, such as a tree or furniture, and the AI will generate a 3D asset that matches your prompt. The 3D model can then be imported into most popular 3D modelling […]

Source

TensorFlow is a popular end-to-end open source machine learning platform. It provides tools to prepare data, build models, and deploy models in production. Developers are able to utilize pre-trained models or create their own custom ones. TensorFlow supports on-device, in-browser, on-server and cloud deployment. It also has an active community forum where you can connect, […]

Source

Mistral AI is a large language model and chat assistant tool. You can access the chatbot via the Mitral website by clicking on “Talk to le Chat“, or if you prefer a local setup then you can download and run the model files on your own hardware. The creators of Mistral describe it as an […]

Source