Kai Fu Lee, an artificial intelligence (AI) expert, venture capitalist, and former executive of such companies as Apple, Microsoft, and Google, tells Scott Pelley of "60 Minutes" in an interview airing Sunday night on CBS News that he believes AI will displace 40 percent of the jobs out there -- jobs not exclusive to blue-collar work -- across the globe in as little as 15 years. The native of Taipei, Taiwan, and the Columbia University and Carnegie Melon graduate goes into detail about his claims in his "60 Minutes" interview airing Sunday at 7 p.m. ET. "AI will increasingly replace repetitive jobs, not just for blue-collar work, but a lot of white-collar work," Lee tells Pelley on the program, as CBS News reported in advance. "Chauffeurs, truck drivers, anyone who does driving for a living -- their jobs will be disrupted more in the 15 to 20-year time frame. Many jobs that seem a little bit complex, chef, waiter, a lot of things will become automated … stores … restaurants, and altogether in 15 years, that's going to displace about 40 percent of the jobs in the world." Lee is CEO of Sinovation Ventures and is considered one of the "world's foremost experts" on artificial intelligence. Pelley pushed back on the claim of 40 percent -- and Lee said the jobs will be "displaceable."

Support Vector Machines are employed engaging playlists according to sentiment and with good results also for multilabel classification [30], emotions. While previous works were mostly based more recently also Convolutional Neural Networks were on audio for music discovery and playlists generation, used in this field [45]. Lyrics-based approaches, on the we take advantage of our synchronized lyrics dataset other hand, make use of Recurrent Neural Networks architectures to combine text representations and music features in (like LSTM [13]) for performing text classification a novel way; we therefore introduce the Synchronized [46, 47]. The idea of using lyrics combined with Lyrics Emotion Dataset. Unlike other approaches that voice only audio signals is done in [29], where emotion randomly exploited the audio samples and the whole recognition is performed by using textual and speech data, text, our data is split according to the temporal information instead of visual ones. Measuring and assigning emotions provided by the synchronization between lyrics to music is not a straightforward task: the sentiment/mood and audio. This work shows a comparison between associated with a song can be derived by a combination of text-based and audio-based deep learning classification many features, moreover, emotions expressed by a musical models using different techniques from Natural Language excerpt and by its corresponding lyrics do not always Processing and Music Information Retrieval domains.

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages. This work addresses the research gap of learning vertex representations for bipartite networks. We present a new solution BiNE, short for Bipartite Network Embedding}, which accounts for two special properties of bipartite networks: long-tail distribution of vertex degrees and implicit connectivity relations between vertices of the same type. Technically speaking, we make three contributions: (1) We design a biased random walk generator to generate vertex sequences that preserve the long-tail distribution of vertices; (2) We propose a new optimization framework by simultaneously modeling the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links); (3) We explore the theoretical foundations of BiNE to shed light on how it works, proving that BiNE can be interpreted as factorizing multiple matrices.

ESPN is making some welcome (and arguably overdue) improvements to its ESPN service that could change how and where you watch. Its updated app now includes personalized recommendations for ESPN, starting with on-demand videos. You'll probably see more highlight clips from the latest NHL matches. Recommendations will "soon" spread to live and future events, so you might spot big matches you would otherwise miss. The company is also borrowing a page from Netflix and other services by introducing offline viewing.

Federal officials plan to ease restrictions on drone flights over crowds and at night.

"When we started the year, we had just begun to really ramp up our efforts to create an application around our core deep learning technology," said Tom Wilde, CEO of Indico. "Our goal was to create a practical, solution-oriented user experience to enable enterprises and lines of business to take advantage of the power of AI to deliver real outcomes and ROI -- without the need for difficult-to-find AI talent or expensive and complex deployments. A year later, we're seeing the full fruits of our team's efforts -- strong footing in the explosive Process Automation vertical, with deployments at a number of Fortune 1000 companies -- with huge potential to build on in 2019 and beyond.

The Truth About Killer Robots looks at robots that have actually killed humans and how automation is stripping away various aspects of humanity and dehumanising us.

For a few months now I've been working on a web GUI to build, visualise, train and share deep neural models. Encouraged by the positive feedback that I received from this community when I posted preview, I've continued developing it and I now think it's ready for a beta release. The editor can be found here: https://aifiddle.io. Your feedback, ideas, suggestions are greatly useful, so please don't hesitate to share them.

No one knew what it really meant. No one knew what it really did. But 2018 saw a boost of artificial intelligence in almost everything -- from speakers to air conditioners, smartphones to cars. With AI and ML (Machine Learning) hand-in-hand, things can be automated to glory, without much human intervention. Today we use AI everywhere -- from common home appliances such as smart vacuum cleaners that automatically clean the surroundings and charge themselves, to driverless cars that ride you to your destiny at the push of a button.

Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. MIT researchers have demonstrated an algorithm that has effectively learned how to predict sound: When shown a silent video clip of an object being hit, the algorithm can produce a sound for the hit that is realistic enough to fool human viewers. Researchers envision future versions of similar algorithms being used to automatically produce sound effects for movies and TV shows, as well as to help robots better understand objects' properties The team used techniques from the field of "deep learning," which involves teaching computers to sift through huge amounts of data to find patterns on their own. Deep learning approaches are especially useful because they free computer scientists from having to hand-design algorithms and supervise their progress.