Facebook CEO Mark Zuckerberg speaks about "News Tab" at the Paley Center, Friday, Oct. 25, 2019 in ... [ ] New York. The new feature in the Facebook mobile app will display headlines -- and nothing else -- from the Wall Street Journal, the Washington Post, BuzzFeed News, Business Insider, NBC, USA Today and the Los Angeles Times, among others.(AP Facebook has been ramping its acquisitions for AI (Artificial Intelligence) startups. While the deals appear to be relatively small, they still are likely to be critical for the company's future. The latest purchase was for Scape Technologies, which is focused on building computer vision applications that help with AR (Augmented Reality).

Artificial Intelligence (AI) and Machine Learning (ML) technologies have become key innovation accelerators for organizations looking for that extra edge. Machine Learning books are a great starting point for enthusiasts who want to transition to these in-demand roles. In this article we list down top machine learning books to get you started on ML journey. The increased usage of machine learning in enterprises has driven up the need for skilled professionals. Machine learning models serve up Netflix recommendations, Facebook's News Feed leverages machine learning to drum up personalized content, and Twitter utilizes machine learning to rank tweets and boost engagements.

"Artificial intelligence is moving out of the computer labs and into the real world of healthcare, carrying out some tasks better than doctors could do …

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of Web2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as "chill" or "jogging" that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or "autotags") furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the ''cold-start problem'' common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system."

Billie Eilish has fielded countless interview questions during her meteoric rise to fame, but this week was the first time that they've been asked by a bot. Fresh from sweeping the boards at the 2020 Grammy Awards, the 18-year-old songstress sat down with the AI interviewer in a video for Vogue -- and the bot proved a more original interlocutor than many of its human rivals. Its abstract questions provoked some surprising insights into the singer's mind. Viewers learnt that Eilish used to dream of working at Jamba Juice or Trader Joe's, and once wore a wig out to dinner to avoid attracting attention -- but she doesn't want to go back to being anonymous. "How much of the world is out of date?" the AI asked.

Problem: The problem we intend to solve is modelled as a binary classification problem. We intend to find the relation in the words and the context in which the words appear within the text and how it could be used to classify texts as real (negative cases) or fake (positive). High-level description: Many news sources contain false information and are therefore "fake news." Because there is a lot of "fake news" articles and fabricated, misleading information on the web, we would like to determine which texts are legitimate (real) and which are illegitimate (fake). To solve this as a binary classification problem, we investigate the effectiveness of different Natural Language Processing models which are used to convert character based texts into numeric representations such as TFIDF, CountVectorizer and Word2Vec models and find out which model is able to preserve most of the contextual information about the text used in a fake news data set and how helpful and effective it is in detecting whether the text is a fake news or not.

The website, a project by the AI development company Big Bird, is supposed to be a showcase of how the company's algorithms can help journalists quickly write compelling news, according to the website's "about" page. But despite the website's title, the realistic-enough articles aren't labeled as fake news or the marketing stunt that they are -- meaning their existence is more likely to make journalists pull out their hair than it is to help them. A quick scroll down the website's home page reveals a smattering of political, cultural, and scientific news stories. Aside from entertaining algorithmic errors, like the headlines "Iran's Stock Market: 'There's a Market,'" and "In wake of death of British soldier, thousands as for plastic-free pubs," the articles are mostly convincing. In fact, many appear to be closely based on actual news stories, like the resignation of UK finance minister Sajid Javid.

Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers for speech or music generation. Despite their successes, current state-of-the-art neural audio synthesizers such as WaveNet and SampleRNN suffer from prohibitive training and inference times because they are based on autoregressive models that generate audio samples one at a time at a rate of 16kHz. In this work, we study the more computationally efficient alternative of generating the waveform frame-by-frame with large strides. We present a lightweight neural audio synthesizer for the original task of generating musical notes given desired instrument, pitch and velocity. Our model is trained end-to-end to generate notes from nearly 1000 instruments with a single decoder, thanks to a new loss function that minimizes the distances between the log spectrograms of the generated and target waveforms.

Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms.

We consider a multi-armed bandit game where N players compete for K arms for T turns. Each player has different expected rewards for the arms, and the instantaneous rewards are independent and identically distributed. Performance is measured using the expected sum of regrets, compared to the optimal assignment of arms to players. We assume that each player only knows her actions and the reward she received each turn. Players cannot observe the actions of other players, and no communication between players is possible.