The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. In 2019, OpenAI released Safety Gym, a suite of tools for developing AI models that respects certain "safety constraints." At the time, OpenAI claimed that Safety Gym could be used to compare the safety of algorithms and the extent to which those algorithms avoid making harmful mistakes while learning. Since then, Safety Gym has been used in measuring the performance of proposed algorithms from OpenAI as well as researchers from the University of California, Berkeley and the University of Toronto. But some experts question whether AI "safety tools" are as effective as their creators purport them to be -- or whether they make AI systems safer in any sense. "OpenAI's Safety Gym doesn't feel like'ethics washing' so much as maybe wishful thinking," Mike Cook, an AI researcher at Queen Mary University of London, told VentureBeat via email.

When you trim all of the hype and apocalyptic-like talk about language models like GPT-3 and actually get to play with them a little bit, you realize the good, the bad and the ugly about the scope of such applications. By demystifying a little bit their true potential, we get to assess this unbelievable tool that could potentially be useful for countless different problems (granted that valid concerns be addressed), as well as learn its technical limitations like its lack of true human-like contextual understanding of basic sentences.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. OpenAI has developed an AI model that can summarize books of arbitrary length. A fine-tuned version of the research lab's GPT-3, the model works by first summarizing small sections of a book and then summarizing those summaries into higher-level summaries, following a paradigm OpenAI calls "recursive task decomposition." Summarizing book-length documents could be valuable in the enterprise, particularly for documentation-heavy industries like software development. A survey by SearchYourCloud found that workers take up to eight searches to find the right document, and McKinsey reports that employees spend 1.8 hours every day -- 9.3 hours per week, on average -- searching and gathering job-related information.

You just need to write a comment or variable name and the AI will write the corresponding code. This is a ZSH plugin that enables you to use OpenAI's powerful Codex AI in the command line. OpenAI Codex is the AI that also powers GitHub Copilot. To use this plugin you need to get access to OpenAI's Codex API.

We're releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code--most of the time on par with what an expert would be able to produce. Triton makes it possible to reach peak hardware performance with relatively little effort; for example, it can be used to write FP16 matrix multiplication kernels that match the performance of cuBLAS--something that many GPU programmers can't do--in under 25 lines of code. Our researchers have already used it to produce kernels that are up to 2x more efficient than equivalent Torch implementations, and we're excited to work with the community to make GPU programming more accessible to everyone. Novel research ideas in the field of Deep Learning are generally implemented using a combination of native framework operators. While convenient, this approach often requires the creation (and/or movement) of many temporary tensors, which can hurt the performance of neural networks at scale.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. The use of AI language models to generate text for business applications is gaining steam. Large companies are deploying their own systems, while others are leveraging models like OpenAI's GPT-3 via APIs. According to OpenAI, GPT-3 is now being used in over 300 apps by thousands of developers, producing an average of more than 4.5 billion novel words per day. But while recent language models are impressively fluent, they have a tendency to write falsehoods ranging from factual inaccuracies to potentially harmful disinformation.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. When it comes to AI, algorithmic innovations are substantially more important than hardware -- at least where the problems involve billions to trillions of data points. That's the conclusion of a team of scientists at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), who conducted what they claim is the first study on how fast algorithms are improving across a broad range of examples. Algorithms tell software how to make sense of text, visual, and audio data so that they can, in turn, draw inferences from it. For example, OpenAI's GPT-3 was trained on webpages, ebooks, and other documents to learn how to write papers in a humanlike way.

CLIP is a gigantic leap forward, bringing many of the recent developments from the realm of natural language processing into the mainstream of computer vision: unsupervised learning, transformers, and multimodality to name a few. The burst of innovation it has inspired shows its versatility. And this is likely just the beginning. There has been scuttlebutt recently about the coming age of "foundation models" in artificial intelligence that will underpin the state of the art across many different problems in AI; I think CLIP is going to turn out to be the bedrock model for computer vision. In this post, we aim to catalog the continually expanding use-cases for CLIP; we will update it periodically.

OpenAI was born to tackle the challenge of achieving artificial general intelligence (AGI) -- an AI capable of doing anything a human can do. Such a technology would change the world as we know it. It could benefit us all if used adequately but could become the most devastating weapon in the wrong hands. That's why OpenAI took over this quest. To ensure it'd benefit everyone evenly: "Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole."

Last year, OpenAI introduced the then-largest neural network GPT-3 in a paper titled "Language Models are Few Shot Learners". A state-of-the-art language model, GPT-3, comprises 175 billion parameters against 1.5 billion parameters of its predecessor GPT-2. GPT-3 defeated the Turing NLG model with 17 billion that previously held the record for "largest-ever". The language model has been marvelled at, criticised even, subjected to intense scrutiny; it has found interesting new applications too. All three models have been released within a gap of a year; GPT-1 was released in 2018, GPT-2 in 2019, and GPT-3 in 2020.