A horror film written by AI is about as confused and incongruous as you'd imagine it would be. However, it is highly entertaining in its own right, with ill-fitting and even nonsensical dialogue and misfiring concepts making for an oddly enjoyable – if odd – few minutes. The project, uploaded to the Netflix is a Joke YouTube channel, had a bot subjected to 400,000 hours of horror movie material before being tasked with producing its own horror movie script. The result is either a horrifically bad attempt at a horror film – or it is a superb attempt at satire. If it's the former, then we've nothing to worry about as far as AI being able to exercise genuine creativity or even display coherence.

In a blog post, Jerome Pesenti, vice president of artificial intelligence at the firm said: "Amid this ongoing uncertainty, we believe that limiting the use of facial recognition to a narrow set of use cases is appropriate."

Netflix has pressed play on its video game venture. The streaming video leader in July said it would be focusing on mobile games with its video game initiative, something the company had been interested in for years. On Tuesday, Netflix's first five games – Stranger Things: 1984, Stranger Things 3: The Game, Shooting Hoops, Card Blast, and Teeter Up – became available on the Google Play store. They will begin coming to the Netflix mobile app for Android devices on Wednesday at 1 p.m. ET/10 a.m. "Whether you're craving a casual game you can start from scratch or an immersive experience that lets you dig deeper into your favorite stories, we want to begin to build a library of games that offers something for everyone," said Mike Verdu, vice president of game development for Netflix, in a blog post Tuesday.

Corporal punishment, wearing fur, pineapple on pizza – moral dilemmas, are by their very nature, hard to solve. That's why the same ethical questions are constantly resurfaced in TV, films and literature. But what if AI could take away the brain work and answer ethical quandaries for us? Ask Delphi is a bot that's been fed more than 1.7m examples of people's ethical judgments on everyday questions and scenarios. If you pose an ethical quandary, it will tell you whether something is right, wrong, or indefensible. Users just put a question to the bot on its website, and see what it comes up with.

Part computer scientist, part cognitive scientist, Tom Williams '11 uses insights from cognitive psychology to design and enable language-based interaction between humans and robots. Williams is assistant professor of computer science at the Colorado School of Mines, where he directs the Interactive Robotics Research Lab. His focus is cognitive systems, which incorporate artificial intelligence and cognitive psychology. He recently received a prestigious CAREER Award from the National Science Foundation, the organization's highest award for junior faculty. It comes with a $550,000 grant that Williams will use to develop working memory potential for robots that are capable of language.

Linear Algebra deals with linear equations like linear maps (which is a mapping of two different vector spaces which preserve the vector operation of addition and scalar multiplication) and its representations in vector spaces and through matrices. Linear algebra is key in almost all areas of mathematics since it is widely used in science and many fields of engineering as it helps model different natural phenomena and compute them efficiently. Linear Algebra is highly similar to the Algebra we talked about back in our previous article, except that instead of ordinary single numbers, it deals with vectors. Many of the same algebraic operations we've used to perform on ordinary numbers (i.e., scalars), like addition, subtraction and multiplication, can be generalized to be operated on vectors.

We are looking for a Data Analyst to join Spotify's Legal & Internal Audit ("IA") Systems team within the Financial Engineering mission. The Legal & IA Systems team is responsible for driving a variety of initiatives within Spotify's legal, audit and data privacy landscape. This team owns the data strategy for legal retention requirements and produces analyses to surface insights for legal and compliance and other stakeholders, including product and engineering. What you'll do Perform analyses on large sets of data from various sources to build analytical reports and extract insights that will help satisfy a legal data strategy. Collaborate with the team's Product Manager to prioritize work and develop a data-driven strategy for legal and relevant business teams.

People tend to rejoice in the disclosure of a secret. Or, at the very least, media outlets have come to realize that news of "mysteries solved" and "hidden treasures revealed" generate traffic and clicks. So I'm never surprised when I see AI-assisted revelations about famous masters' works of art go viral. Over the past year alone, I've come across articles highlighting how artificial intelligence recovered a "secret" painting of a "lost lover" of Italian painter Modigliani, "brought to life" a "hidden Picasso nude", "resurrected" Austrian painter Gustav Klimt's destroyed works and "restored" portions of Rembrandt's 1642 painting "The Night Watch." As an art historian, I've become increasingly concerned about the coverage and circulation of these projects.

A.I is here to stay, and is growing at a tremendous pace. As are the amount of startups in the field. In 2020, VCs poured in $27.8B in A.I startups, and in 2021 so far, VC funding has ballooned to $29.5B, all into A.I startups. Tons of new projects out there that deserve getting coverage! So here is a list of the Top 107 A.I journalists in 2021.

While recent work on automated fact-checking has focused mainly on verifying and explaining claims, for which the list of claims is readily available, identifying check-worthy claim sentences from a text remains challenging. Current claim identification models rely on manual annotations for each sentence in the text, which is an expensive task and challenging to conduct on a frequent basis across multiple domains. This paper explores methodology to identify check-worthy claim sentences from fake news articles, irrespective of domain, without explicit sentence-level annotations. We leverage two internal supervisory signals - headline and the abstractive summary - to rank the sentences based on semantic similarity. We hypothesize that this ranking directly correlates to the check-worthiness of the sentences. To assess the effectiveness of this hypothesis, we build pipelines that leverage the ranking of sentences based on either the headline or the abstractive summary. The top-ranked sentences are used for the downstream fact-checking tasks of evidence retrieval and the article's veracity prediction by the pipeline. Our findings suggest that the top 3 ranked sentences contain enough information for evidence-based fact-checking of a fake news article. We also show that while the headline has more gisting similarity with how a fact-checking website writes a claim, the summary-based pipeline is the most promising for an end-to-end fact-checking system.