The OpenAI research group has demonstrated artificial intelligence that can compose authentic-looking fake news articles from a few fragments of information. The OpenAI research group has demonstrated artificial intelligence (AI) that can compose authentic-looking fake news articles from a few fragments of information. After being fed a few sentences of sample text, the software successfully generates a persuasive, but completely false, seven-paragraph news story. The AI was trained to perform language modeling, or predicting the next word of a piece of text based on knowledge of all previous words. OpenAI's Jeff Wu suggested the software could have beneficial applications, like helping creative writers generate ideas or dialogue, or hunting for bugs in software code.

Moving on with the future of artificial intelligence (AI), a text prediction tool can now create a whole article based on the command of one single sentence. Researchers believe that while it can simplify a lot of academic tasks, there are numerous potential threats attached to it as well. A non-profit artificial intelligence research organisation, OpenAI has recently introduced its model called GPT-2 which is developed to write content like humans. With a context of nearly eight million pages fed in it, the model can easily predict about the next word or even whole article after you insert your own sentence to start the topic. Once fed, it will start producing results which are surprisingly more accurate than the human imagination.

The future took another step forward. But it was stopped right in its tracks by its inventor, OpenAI. According to its website, OpenAI is a non-profit AI research company, discovering and enacting the path to safe artificial general intelligence. Elon Musk is one of the backers. In a recent blog post, OpenAI's technology, called GPT2, was shown to craft written passages that mimic the style and content of a given sample.

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anandkumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.

In the inaugural edition of TWiML Live, Sam Charrington is joined by Amanda Askell, Anima Anandkumar, Miles Bundage, Robert Munro, and Stephen Merity to discuss the recently released OpenAI Language Model, GPT-2.

A storm is brewing over a new language model, built by non-profit artificial intelligence research company OpenAI, which it says is so good at generating convincing, well-written text that it's worried about potential abuse. That's angered some in the community, who have accused the company of reneging on a promise not to close off its research. OpenAI said its new natural language model, GPT-2, was trained to predict the next word in a sample of 40 gigabytes of internet text. The end result was the system generating text that "adapts to the style and content of the conditioning text," allowing the user to "generate realistic and coherent continuations about a topic of their choosing." The model is a vast improvement on the first version by producing longer text with greater coherence.

A group of computer scientists once backed by Elon Musk has caused some alarm by developing an advanced artificial intelligence (AI) they say is too dangerous to release to the public. OpenAI, a research non-profit based in San Francisco, says its "chameleon-like" language prediction system, called GPT–2, will only ever see a limited release in a scaled-down version, due to "concerns about malicious applications of the technology". That's because the computer model, which generates original paragraphs of text based on what it is given to'read', is a little too good at its job. The system devises "synthetic text samples of unprecedented quality" that the researchers say are so advanced and convincing, the AI could be used to create fake news, impersonate people, and abuse or trick people on social media. "GPT–2 is trained with a simple objective: predict the next word, given all of the previous words within some text," the OpenAI team explains on its blog.

Reinforcement learning is recently one of the potential research field of data scientists, it makes feasible to outdo processes what we have so far, and makes imaginable to reach the so called artificial general intelligence (AGI). In our previous blog we described and made the theory of reinforcement learning familiar to you. This following blog requires the knowledge of it and introduces the process basics of reinforcement learning through a practical example. We have to mention OpenAi, they are one of the lead researchers on the reinforcement learning field and on the artificial general intelligence topic. They developed a toolkit called Gym which is a free and easy to use tool to the artificial intelligence community.

The release of a text-generating AI has triggered new discussions around the ethics of AI.Andrew Mason, Flickr. OpenAI, the AI research institute founded by Elon Musk, developed an AI which can generate the most realistic paragraphs of text to date. Here's an excerpt generated by the AI when prompted to discuss a fake unicorn discovery: The scientist named the population, after their distinctive horn, Ovid's Unicorn. These four-horned, silver-white unicorns were previously unknown to science. Now, after almost two centuries, the mystery of what sparked this odd phenomenon is finally solved.

Despite the overall tone, which was a tad too optimistic for my taste, I found the video insightful and recommend a watch. OpenAI's new algorithm learns from mistakes