Deepmind's Gato solves many tasks, but none of them really well. Does the new AI system nevertheless lead the way for general artificial intelligence? Hot on the heels of OpenAI's DALL-E 2, Google's PaLM, LaMDA 2, and Deepmind's Chinchilla and Flamingo, the London-based AI company is showing off another large AI model that outperforms existing systems. Yet Deepmind's Gato is different: The model can't text better, describe images better, play Atari better, control robotic arms better, or orient itself in 3D spaces better than other AI systems. But Gato can do a bit of everything. Deepmind trained the Transformer-based multi-talent with images, text, proprioception, joint moments, keystrokes, and other discrete and continuous observations and actions.

Have you seen AI models that can generate code for you? Well, if you haven't, you're going to see them a lot more soon thanks to models like OpenAI's Codex models. Codex is a family of AI models from Open AI that translates between natural language and code in more than a dozen programming languages. The power of these AI models is that you can quickly develop and iterate on your ideas and build products that help people do more. Here is an example how you can have a conversation with a Minecraft character and have it follow your instructions by generating Minecraft API commands behind the scenes. This article will show you how to get models like Codex to generate code you want using a technique called Prompt Engineering.

Are you an enthusiast in AI and are you searching for good examples of applications to practice? This article could interest you. There are a lot of different possible use cases for Artificial intelligence, and some of them are interesting. There are a lot of examples of the GPT-3 that creates images given a specific text, and some of them are fancy. This is to say that AI is really powerful nowadays, and it can perform a huge number of tasks. One of them is to generate a text, an article, or whatever given a prompt or a point where to start.

Done in context of a miniproject in the course AI applications at university of applied sciences OST, written by Nick Wallner, Mirio Eggmann The goal of this project is to use and evaluate OpenAI.

We achieved 147 TFLOP/s/GPU utilization on NVIDIA's 80 GB A100 GPUs, roughly 17 percent higher than published by NVIDIA researchers on similar hardware. By sharing these baselines along with the codebase to train a 175B model efficiently, we have an opportunity to reduce our collective environmental footprint while also allowing new results and progress in the field to be measurable in a consistent manner. For AI research to advance, the broader scientific community must be able to work together with cutting-edge models to effectively explore their potential while also probing for their vulnerabilities at the same time. As with our previous open-science initiatives, such as the Image Similarity Challenge, the Deepfake Detection Challenge, and the Hateful Memes Challenge, Meta AI believes that collaboration across research organizations is critical to the responsible development of AI technologies. While there are many exciting developments in the space of large language models, the limitations and risks these models pose are still not well understood. Without direct access to these models, researchers are also limited in their ability to design detection and mitigation strategies for possible harm, which leaves detection and mitigation in the hands of only those with sufficient capital to access models of this scale. We hope that OPT-175B will bring more voices to the frontier of large language model creation, help the community collectively design responsible release strategies, and add an unprecedented level of transparency and openness to the development of large language models in the field. Access the open source code and small-scale pretrained models here, request access to OPT-175B here, and read the paper here. Pretrained models are all licensed under the OPT-175B License Agreement.

An artificial intelligence program developed by DeepMind that can predict the 3D structure of a protein from an amino acid sequence with unprecedented accuracy, AlphaFold is emblematic of a new era of modern biotech -- "data-driven, open-sourced, collaborative and ultimately, faster than ever", according to Benchling. Born out of a Benchling hackathon, the vast majority of labs are unable to access AlphaFold today, despite being open source to use. With Benchling's AlphaFold beta feature, scientists can not only predict 3D structures of novel proteins directly within Benchling, but also centralise experimental context, collaborate with teammates, and connect with downstream scientific workflows on a single, secure platform. President and co-founder of Benchling, Ashu Singhal, explained: "Our team gets excited about two things: science and bringing software to science. By making AlphaFold available to the biotech industry at the click of a button, scientists will be able to seamlessly experiment with this exciting advancement and find new ways to leverage AlphaFold output in their research. While the use cases for AlphaFold are still being explored and proven, Benchling's goal with its beta feature is to support its community."

Deepmind, a subsidiary of Alphabet specialising in artificial intelligence, recently presented its "Gato" model. This so-called "general-purpose" AI model can reportedly perform more than 600 different tasks. And, in many of these tasks, the AI might even perform better than a human being. Could Deepmind have built the first general-purpose artificial intelligence model, i.e., a model capable of learning several tasks at once, whereas most AI models are trained for a specific purpose? Since the American company unveiled its new work, the question has been spurring reaction from computer experts around the world.

Artificial general intelligence (AGI) is back in the news thanks to the recent introduction of Gato from DeepMind. As much as anything, AGI invokes images of the Skynet (of Terminator lore) that was originally designed as threat analysis software for the military, but it quickly came to see humanity as the enemy. While fictional, this should give us pause, especially as militaries around the world are pursuing AI-based weapons. However, Gato does not appear to raise any of these concerns. The deep learning transformer model is described as a "generalist agent" and purports to perform 604 distinct and mostly mundane tasks with varying modalities, observations and action specifications.

This article is part of our series that explores the business of artificial intelligence. Last week, Hugging Face announced a new product in collaboration with Microsoft called Hugging Face Endpoints on Azure, which allows users to set up and run thousands of machine learning models on Microsoft's cloud platform. Having started as a chatbot application, Hugging Face made its fame as a hub for transformer models, a type of deep learning architecture that has been behind many recent advances in artificial intelligence, including large language models like OpenAI GPT-3 and DeepMind's protein-folding model AlphaFold. Large tech companies like Google, Facebook, and Microsoft have been using transformer models for several years. But the past couple of years has seen a growing interest in transformers among smaller companies, including many that don't have in-house machine learning talent.

OpenAI's new Codex neural network writes software in response to text prompts.