As an effective approach to tune pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using \textit{cloze}-style language prompts to stimulate the versatile knowledge of PLMs, prompt-learning can achieve promising results on a series of NLP tasks, such as natural language inference, sentiment classification, and knowledge probing. In this work, we investigate the application of prompt-learning on fine-grained entity typing in fully supervised, few-shot and zero-shot scenarios. We first develop a simple and effective prompt-learning pipeline by constructing entity-oriented verbalizers and templates and conducting masked language modeling. Further, to tackle the zero-shot regime, we propose a self-supervised strategy that carries out distribution-level optimization in prompt-learning to automatically summarize the information of entity types. Extensive experiments on three fine-grained entity typing benchmarks (with up to 86 classes) under fully supervised, few-shot and zero-shot settings show that prompt-learning methods significantly outperform fine-tuning baselines, especially when the training data is insufficient.

There is a mediocre content deluge coming to the internet the likes of which we have not seen. What if you could produce 10x the amount of content at at 10x cost savings, what would you do? Even if the content were mediocre would you still be tempted to take advantage of the ability to throw content against the well and see what sticks? What would that mean for websites, link farms, private blog networks, link builders, SEOs and search engine algorithms? What would it mean for quality, believable, original content?

In a rehearsal room at London's Young Vic theater last week, three dramatists were arguing with an artificial intelligence about how to write a play. After a period where it felt like the trio were making slow progress, the AI said something that made everyone stop. "If you want a computer to write a play, go and buy one. It won't need any empathy, it won't need any understanding," it said. "The computer will write a play that is for itself. It will be a play that will bore you to death."

In recent years machines have learned to generate passable snippets of English, thanks to advances in artificial intelligence. Now they are moving on to other languages. Aleph Alpha, a startup in Heidelberg, Germany, has built one of the world's most powerful AI language models. Befitting the algorithm's European origins, it is fluent not just in English but also in German, French, Spanish, and Italian. The algorithm builds on recent advances in machine learning that have helped computers handle language with what sometimes seems like real understanding.

In early August 2021, OpenAI (which is partly controlled by Elon Musk) launched Codex, a revolutionary new AI system which can automatically write code in a variety of languages, using only plaintext prompts as input.

Physics is the study of the universe. This can encompass the movement of the galaxies to the "how" of the smallest particles. No matter the shape and size of the problem, data has always been a constant.

Last week, artificial intelligence company OpenAI launched Codex, a new deep-learning-driven platform which writes fully functioning software code automatically. The system -- which was trained on a vast corpus of publicly available code-- originally debuted as part of Github's Copilot, a feature which helps programmers improve or update their software automatically. Codex is based on OpenAI's wildly successful GPT-3. When I tested GPT-3 last year, I felt like I was witnessing a technological revolution. The system can generate everything from fully-formed blog posts to songs, recipes -- even sea shanties.

During Tesla's AI Day event, CEO Elon Musk unveiled a robot that is "intended to be friendly". Musk has been one of the most prominent figures to warn that AI is a "danger to the public" and potentially the "biggest risk we face as a civilisation". In 2017, he even said there was just a "five to 10 percent chance of success [of making AI safe]". Speaking about London-based DeepMind in a New York Times interview last year, Musk said: "Just the nature of the AI that they're building is one that crushes all humans at all games.

Last year, OpenAI released the third version of its Generative Pretrained Transformer model (GPT-3), to much excitement amongst the tech and business communities -- so much, in fact, that OpenAI's CEO tweeted "the hype is way too much." GPT-3 has astonished observers with groundbreaking examples of code, news articles, translations and even poetry which evaluators have difficulty distinguishing from human-written output. Fundamentally, it simply autocompletes: give it a prompt, and it'll predict what comes next. But the enormous dataset it was trained on, along with the sheer complexity of its architecture, has enabled it to achieve the best results yet. So, how exactly does this technology work, and where could it take us?

We've created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today. Codex is the model that powers GitHub Copilot, which we built and launched in partnership with GitHub a month ago. Proficient in more than a dozen programming languages, Codex can now interpret simple commands in natural language and execute them on the user's behalf--making it possible to build a natural language interface to existing applications. We are now inviting businesses and developers to build on top of OpenAI Codex through our API. OpenAI Codex is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories.