Companies are using AI in all kinds of innovative ways to advance their businesses. If you've ever searched Netflix to watch a movie, AI (a recommendation algorithm) was no doubt used in your decision about what to watch. If you've shopped on Amazon, your decision about what to buy was also influenced by AI (via an association algorithm). If you've ever ordered an Uber, AI (a location algorithm) was used to have a car in your vicinity quickly. If you ever had a thought about a product or a vacation, and it seemed to suddenly pop up on your search page or in your email inbox, I can assure you it was based on AI (a classification algorithm) monitoring your online activity.

Robotic hands could only do what vast teams of engineers programmed them to do. Now they can learn more complex tasks on their own. Four autonomous fingers and a thumb that can do anything your own flesh and blood can do? That is still the stuff of fantasy. But inside the world's top artificial intelligence labs, researchers are getting closer to creating robotic hands that can mimic the real thing.

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, its presented a Deep Learning architecture for Session-Based recommendations of News articles. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for user sessions: "what is the next most likely article a user might read in a session?" User session context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality.

The two main tasks in the Recommender Systems domain are the ranking and rating prediction tasks. The rating prediction task aims at predicting to what extent a user would like any given item, which would enable to recommend the items with the highest predicted scores. The ranking task on the other hand directly aims at recommending the most valuable items for the user. Several previous approaches proposed learning user and item representations to optimize both tasks simultaneously in a multi-task framework. In this work we propose a novel multi-task framework that exploits the fact that a user does a two-phase decision process - first decides to interact with an item (ranking task) and only afterward to rate it (rating prediction task). We evaluated our framework on two benchmark datasets, on two different configurations and showed its superiority over state-of-the-art methods.

It's still early days in creating the kind of human-like androids we see in the movies, but new research brings us ever closer to the idea. Boston Dynamics has become the de facto image of locomotion for both humans and their pets, while LG already has its CLOi porter'bots and DARPA is working on centaur-like designs for disaster relief. Now, researchers at the Elon Musk-founded OpenAI are working on making robot hands more dextrous. According to a blog post, the team has trained a human-like robot hand called the Shadow Dextrous Hand to manipulate real-world objects like a child's block. It uses the same algorithms and code from its OpenAI Five project, which has been training DOTA 2 bots to play video games.

Companies are using AI in all kinds of innovative ways to advance their businesses. If you've ever searched Netflix to watch a movie, AI (a recommendation algorithm) was no doubt used in your decision about what to watch. If you've shopped on Amazon, your decision about what to buy was also influenced by AI (via an association algorithm). If you've ever ordered an Uber, AI (a location algorithm) was used to have a car in your vicinity quickly. If you ever had a thought about a product or a vacation, and it seemed to suddenly pop up on your search page or in your email inbox, I can assure you it was based on AI (a classification algorithm) monitoring your online activity.

Spotify is hosting podcasts by Alex Jones's controversial Infowars podcast. A vast number of the company's customers are threatening to cancel their subscriptions over the streaming service's decision to keep the podcasts up. Jones has attracted criticism over a wide variety of false claims and personal attacks, including suggestions that the Sandy Hook massacre and other mass shootings were hoaxes. Those conspiracy theories and rumours are often shared on his Infowars shows, including on the podcast. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Modern movie trailers are already cynical exercises in attention grabbing (such as the social media-friendly burst of imagery at the start of many clips), but they might be even more calculated in the future. Researchers at 20th Century Fox have produced a deep learning system that can predict who will be most likely to watch a movie based on its trailer. Thanks to training that linked hundreds of trailers to movie attendance records, the AI can draw a connection between visual elements in trailers (such as colors, faces, landscapes and lighting) and the performance of a film for certain demographics. A trailer with plenty of talking heads and warm colors may appeal to a different group than one with lots of bold colors and sweeping vistas. Notably, the deep learning approach already appears to work in real world conditions.

Conversational AI is a subfield of artificial intelligence focused on producing natural and seamless conversations between humans and computers. We've seen several amazing advances on this front in recent years, with significant improvements in automatic speech recognition (ASR), text to speech (TTS), and intent recognition, as well as the rocketship growth of voice assistant devices like the Amazon Echo and Google Home, with estimates of close to 100 million devices in homes in 2018. But we're still a long way away from the fluent human-machine conversation promised in science fiction. Here are some key advances we should see over the next decade that could get us closer to that long-term vision. Machine learning, and in particular deep learning, has become an extremely popular technique within the field of AI over the past few years.

The underlying factors affecting users' choices of what to watch on TV have for several years been of interest to commercial and academic research. In the midst of a rapidly changing device and multimedia landscape, TVs continue to be at the core of multimedia consumption in the home with scenarios covering, among others, social gatherings and solitary immersive moments. The inherent complexity of viewing situations challenges the creation of experiences that match personal preferences as well as temporal and social contexts. Due to the increased availability of multimedia, research has been focused on improving the users' decision process by reducing large catalogs of content to a few personalized suggestions [1]. Commercial recommender solutions are now considered core to the business of engaging users and thereby preventing abandonment [2]. To do so, recommender systems have explored various features for personalization, such as history of watching, ratings, user/item similarity, and time of the day, the last of which is an example of features characteristic to context-aware recommender systems (CARS) [3]. The main objective of a recommender system is to personalize the experience to the individual, often by studying the user-item matrix. This could be an issue, since an account on a TV is often shared by multiple members of a household that end up diluting the user profile.