Thinking Machines Lab, led by a group of prominent former OpenAI researchers, is betting that fine-tuning cutting-edge models will be the next frontier in AI. Thinking Machines Lab, a heavily funded startup cofounded by prominent researchers from OpenAI, has revealed its first product--a tool called Tinker that automates the creation of custom frontier AI models. "We believe [Tinker] will help empower researchers and developers to experiment with models and will make frontier capabilities much more accessible to all people," said Mira Murati, cofounder and CEO of Thinking Machines, in an interview with WIRED ahead of the announcement. Big companies and academic labs already fine-tune open source AI models to create new variants that are optimized for specific tasks, like solving math problems, drafting legal agreements, or answering medical questions. Typically, this work involves acquiring and managing clusters of GPUs and using various software tools to ensure that large-scale training runs are stable and efficient.

I'm building a farm for robocows, which will be made into roboburgers to feed a population of steam-powered humanoid robots. None of this makes sense, but who cares. Games in this charming robopunk series have always placed characterful quirkiness above realism, and they're all the better for it. SteamWorld has a history of swapping genres, with the steambots going from space shootouts in one game to fantasy role-playing in the next. This time around we're served a hybrid of city-builder and dungeon-crawler.

In my previous post we discussed the purpose and nature of graph embeddings. The main idea is that to do machine learning on a graph we need to convert the graph into a series of vectors (embeddings) that we can then use to train our machine learning (ML) models. The catch is that graph embeddings can be difficult to tune. Similar to other ways to create embeddings and the models they are used for, there are a lot of hyperparameters that we need to consider and optimizing them to the specific application takes time. The subject of tuning the embeddings is something I will save for a future post.

In recent years, the media have been paying increasing attention to adversarial examples, input data such as images and audio that have been modified to manipulate the behavior of machine learning algorithms. Stickers pasted on stop signs that cause computer vision systems to mistake them for speed limits; glasses that fool facial recognition systems, turtles that get classified as rifles -- these are just some of the many adversarial examples that have made the headlines in the past few years. There's increasing concern about the cybersecurity implications of adversarial examples, especially as machine learning systems continue to become an important component of many applications we use. AI researchers and security experts are engaging in various efforts to educate the public about adversarial attacks and create more robust machine learning systems. Among these efforts is adversarial.js,

It's fun to imagine the AI future of home service robots, Amazon Dots in every room, delivery drones and more accurate home medical diagnoses. But while it's natural that flashy consumer applications are capturing the public's imaginations, AI's capacity to transform another area doesn't get as much attention – the way software itself is developed. Imagine what computers could do if they understood themselves. And I'm not talking about far in the future; I'm talking about the very near future, using off-the-shelf technology that already exists today. Until now, machine learning experts have tended to focus on AI applications highly tailored to specific tasks – for example, facial recognition, self-driving cars, speech recognition, even Internet search results.