What does the future of fake news look like? No one really knows, but here's a little sampler from Jordan Peele and BuzzFeed, who teamed up to make the above PSA. Using some of the latest AI techniques, Peele ventriloquizes Barack Obama, having him voice his opinion on Black Panther ("Killmonger was right") and call President Donald Trump "a total and complete dipshit." The video was made by Peele's production company using a combination of old and new technology: Adobe After Effects and the AI face-swapping tool FakeApp. The latter is the most prominent example of how AI can facilitate the creation of photorealistic fake videos.

With it comes a fundamental shift in what it means to be a leader in this rapidly changing marketplace. That's why we developed a first-of-its-kind ranking methodology for the technology sector. Applying the intelligence, technology, and human expertise of Thomson Reuters, we have identified industry leaders poised to thrive at the intersection of regulation and commerce. The result is the Thomson Reuters Top 100 Global Tech Leaders.

The Analytics Workbench "is sophisticated enough to meet the demands of data scientists, but also has an easy-to-use interface so business analysts can use it as well, and the actual business user can test'what-if' scenarios.

We present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes. The robot explores its environment both via interactive learning and goal-babbling. It learns at the same time when, who and what to actively imitate from several available teachers, and learns when not to use social guidance but use active goal-oriented self-exploration. This is formalised in the framework of life-long strategic learning. The proposed architecture, called Socially Guided Intrinsic Motivation with Active Choice of Teacher and Strategy (SGIM-ACTS), relies on hierarchical active decisions of what and how to learn driven by empirical evaluation of learning progress for each learning strategy. We illustrate with an experiment where a simulated robot learns to control its arm for realising two kinds of different outcomes. It has to choose actively and hierarchically at each learning episode: 1) what to learn: which outcome is most interesting to select as a goal to focus on for goal-directed exploration; 2) how to learn: which data collection strategy to use among self-exploration, mimicry and emulation; 3) once he has decided when and what to imitate by choosing mimicry or emulation, then he has to choose who to imitate, from a set of different teachers. We show that SGIM-ACTS learns significantly more efficiently than using single learning strategies, and coherently selects the best strategy with respect to the chosen outcome, taking advantage of the available teachers (with different levels of skills).

We present QARMA, an efficient novel parallel algorithm for mining all Quantitative Association Rules in large multidimensional datasets where items are required to have at least a single common attribute to be specified in the rules single consequent item. Given a minimum support level and a set of threshold criteria of interestingness measures such as confidence, conviction etc. our algorithm guarantees the generation of all non-dominated Quantitative Association Rules that meet the minimum support and interestingness requirements. Such rules can be of great importance to marketing departments seeking to optimize targeted campaigns, or general market segmentation. They can also be of value in medical applications, financial as well as predictive maintenance domains. We provide computational results showing the scalability of our algorithm, and its capability to produce all rules to be found in large scale synthetic and real world datasets such as Movie Lens, within a few seconds or minutes of computational time on commodity hardware.

Recommender systems are widely used in big information-based companies such as Google, Twitter, LinkedIn, and Netflix. A recommender system deals with the problem of information overload by filtering important information fragments according to users' preferences. In light of the increasing success of deep learning, recent studies have proved the benefits of using deep learning in various recommendation tasks. However, most proposed techniques only aim to target individuals, which cannot be efficiently applied in group recommendation. In this paper, we propose a deep learning architecture to solve the group recommendation problem. On the one hand, as different individual preferences in a group necessitate preference trade-offs in making group recommendations, it is essential that the recommendation model can discover substitutes among user behaviors. On the other hand, it has been observed that a user as an individual and as a group member behaves differently. To tackle such problems, we propose using an attention mechanism to capture the impact of each user in a group. Specifically, our model automatically learns the influence weight of each user in a group and recommends items to the group based on its members' weighted preferences. We conduct extensive experiments on four datasets. Our model significantly outperforms baseline methods and shows promising results in applying deep learning to the group recommendation problem.

Apple's main claim to fame is a proven track record for successful products. But with its latest, the HomePod smart speaker, some analysts say its old formula for success -- going for the high end of the market and tightly controlling its ecosystem -- has let it down. Recent analyst reports suggest that the HomePod isn't selling well. Bloomberg reported last week that Apple even cut its internal sales estimates. While Apple hasn't released numbers on HomePod sales, it's expected to give some sense of the HomePod's sales in its next earnings report on May 1. HomePod sales are important to the Cupertino, Calif.

Companies looking to add artificial intelligence (AI) platforms to help solve logistics challenges need to collaborate with their supply chain partners or else run the risk of outpacing their suppliers' and customers' digital capabilities, according to a panel held today at the Massachusetts Institute of …

Analytics software firm FICO today showcased the latest enhancements to the FICO Decision Management Suite (DMS), which leverages artificial intelligence (AI), machine learning (ML), advanced analytics, optimization, and decisioning.

The meditation app Calm teamed up with the tech team at Botnik to write a new Brothers Grimm-style fairy tale entirely through artificial intelligence.