OpenAI has removed the waitlist to access its GPT-3 API which means any developer can get started. The AI giant unveiled GPT-3 in May last year to a mixed reception. Few doubted GPT-3's impressive ability to generate text similar to a human writer, but many expressed concerns about the societal impact. Fake news and propaganda is already difficult to counter even when it's being generated in relatively limited amounts by human writers. The ability for anyone to use an AI to generate misinformation on a huge scale could have serious implications.

From commerce, finance and agriculture to self-driving cars, personalised healthcare and social media – advancements in artificial intelligence (AI) unlock countless opportunities. New applications promise to improve the quality of people's lives throughout the world, but at the same time, raise a number of societal questions. A joint panel discussion of the German National Academy of Sciences Leopoldina and the Korean Academy of Science and Technology (KAST) explores AI technologies, their benefits and their challenges for society. Virtual panel discussion of the German National Academy of Sciences Leopoldina and the Korean Academy of Science and Technology „Realizing the Promises of Artificial Intelligence" Thursday, 25 November 2021, 8am to 9am (CET) Online Following opening remarks from the President of the Leopoldina, Prof (ETHZ) Dr Gerald Haug and Prof Min-Koo Han, PhD, President of the KAST, legal scholar Prof Ryan Song, PhD, Kyung Hee University, Seoul/South Korea, will provide an introduction into the topic. Subsequently, computer scientist Prof Alice Oh PhD, KAIST School of Computing, Daejeon/ South Korea, and Member of the Leopoldina Prof Dr Alexander Waibel, Karlsruhe Institute of Technology/Germany and Carnegie Mellon University, Pittsburgh/USA, will provide input statements for further discussion.

The recent controversy surrounding the mistranslations found in the Netflix hit "Squid Game" and other films highlights technology's challenges when releasing content that bridges languages and cultures internationally. Every year across the global media and entertainment industry, tens of thousands of movies and TV episodes exhibited on hundreds of streaming platforms are released with the hope of finding an audience among 7.2 billion people living in nearly 200 countries. No audience is fluent in the roughly 7,000 recognized languages. If the goal is to release the content internationally, subtitles and audio dubs must be prepared for global distribution. Known in the industry as "localization," creating "subs and dubs" has, for decades, been a human-centered process, where someone with a thorough understanding of another language sits in a room, reads a transcript of the screen dialogue, watches the original language content (if available) and translates it into an audio dub script.

I'm just a normal girl who lives in small town, USA with desires to eventually become a writer/director of my own films. There is no doubt that AI and Robotics are making human life better. The way these technologies are helping mankind is beyond our imagination. Here are some insights on the impact of AI and Robotics in the healthcare industry. AI is being used to diagnose the diseases such as different kinds of cancers.

SingularityNET Insider Monthly is a monthly stream in which you can learn about the latest developments behind the scenes at the foundation. Join us to learn more about how we are working towards our Phase 2 goals in order to build the world's largest decentralized AI network and drive massive platform utilization.

Monitoring news content automatically is an important problem. The news content, unlike traditional text, has a temporal component. However, few works have explored the combination of natural language processing and dynamic system models. One reason is that it is challenging to mathematically model the nuances of natural language. In this paper, we discuss how we built a novel dataset of news articles collected over time. Then, we present a method of converting news text collected over time to a sequence of directed multi-graphs, which represent semantic triples (Subject ! Predicate ! Object). We model the dynamics of specific topological changes from these graphs using discrete-time Hawkes processes. With our real-world data, we show that analyzing the structures of the graphs and the discrete-time Hawkes process model can yield insights on how the news events were covered and how to predict how it may be covered in the future.

As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points. However, real-world behavior is undoubtedly nuanced: ad hoc error analysis and deployment-specific tests must be employed to ensure the desired quality in actual deployments. In this paper, we propose RecList, a behavioral-based testing methodology. RecList organizes recommender systems by use case and introduces a general plug-and-play procedure to scale up behavioral testing. We demonstrate its capabilities by analyzing known algorithms and black-box commercial systems, and we release RecList as an open source, extensible package for the community.

As Artificial Intelligence (AI) becomes much more than an evil force in fantasy films, most are starting to wonder how it may affect the world we know today. It's clear at this point that it will create radical changes in industries such as medicine, tech, engineering, etc. Yet, not much has been said on how it will affect the industry that pretty much came up with the concept in the first place -- entertainment.

Spotify's growing podcast ambitions now include a pact with a big studio before it truly gets started. The streaming music service has struck a multi-year deal that gives it "first look" access to podcasts from J.J. Abrams' new Bad Robot Audio unit. The move lets Spotify snap up exclusives from Bad Robot's planned mix of fiction and non-fiction shows. Bad Robot Audio hasn't yet detailed its releases, but it will have an experienced leader. She played an important role in Spotify's early podcast efforts, and is unsurprisingly eager to collaborate with her former employer in her field of expertise.

One of the central challenges of Natural Language Processing (NLP) systems is to derive essential insights from a wide variety of written materials. Contributing sources for a training dataset for a new NLP algorithm could be as linguistically diverse as Twitter, broadsheet newspapers, and scientific journals, with all the appellant eccentricities unique to each of just those three sources. When an NLP algorithm has to consider material that comes from multiple eras, it typically struggles to reconcile the very different ways that people speak or write across national and sub-national communities, and especially across different periods in history. Yet, using text data (such as historical treatises and venerable scientific works) that straddles epochs is a potentially useful method of generating a historical oversight of a topic, and of formulating statistical timeline reconstructions that predate the adoption and maintenance of metrics for a domain. For example, weather information contributing to climate change predictive AI models was not adequately recorded around the world until 1880, while data-mining of classical texts offers older records of major meteorological events that may be useful in providing pre-Victorian weather data.