IBM (NYSE: IBM) today announced the first recipients and list of global women leaders and pioneers in AI for business. The list recognizes and celebrates women across a variety of industries and geographies for pioneering the use of AI to advance their companies in areas such as innovation, growth, and transformation. IBM will celebrate the honorees during an inaugural recognition event on June 12, 2019 at the IBM Watson Experience Center in New York, New York where the women will share their experiences leading AI initiatives in their organizations. Students from IBM's P-Tech program will attend to hear from these leaders who have applied AI technology in diverse and meaningful ways to help drive business innovation. "Artificial Intelligence is poised to drive dramatic advances in every industry," said Michelle Peluso, SVP, Digital Sales & CMO, IBM, who also serves as Leader of IBM's Women's Initiative.

Appropriately, they call it Photo Wake-Up. On their project page, UW computing scientists Chung-Yi Weng and Brian Curless, along with Facebook's Ira Kemelmacher-Shlizerman, describe a process that can take one single photo and create a character that walks out of the frame toward the viewer. They can also make the character run, sit, or jump. The researchers will present their algorithm later this month at the Conference on Computer Vision and Pattern Recognition in Long Beach, California, but the software begins by analyzing a still image to detect a human form and fits a morphable body on it. From there it creates a body map, labeling each of the parts.

Netflix may soon be known for more than just binge-watching TV and movies, as the company works to push out a new wave of video games based on the company's original content. The company is set to release a slew of video games throughout the next year, starting with the release of'Stranger Things 3: The Game' based on Netflix's wildly popular teen sci-fi series and slated for release on July 4. Additionally, 'The Dark Crystal: Age of Resistance', a companion to an upcoming series based on Jim Henson's'The Dark Crystal,' will be released on the Nintendo Switch later this year. Netflix is siphoning off its content for licensing in video games, a tandem of which will be release throughout the next year. The announcements were made at this year's E3 gaming conference. While'Dark Crystal's' companion game will be a turn-based strategy game that allows players to use different characters from he series, a'Stranger Things' title -- also available on the Switch -- will be a retro 16-bit adventure style game more similar to an RPG.

Knowledge bases (KBs) are the backbone of many ubiquitous applications and are thus required to exhibit high precision. However, for KBs that store subjective attributes of entities, e.g., whether a movie is "kid friendly", simply estimating precision is complicated by the inherent ambiguity in measuring subjective phenomena. In this work, we develop a method for constructing KBs with tunable precision--i.e., KBs that can be made to operate at a specific false positive rate, despite storing both difficult-to-evaluate subjective attributes and more traditional factual attributes. The key to our approach is probabilistically modeling user consensus with respect to each entity-attribute pair, rather than modeling each pair as either True or False. Uncertainty in the model is explicitly represented and used to control the KB's precision. We propose three neural networks for fitting the consensus model and evaluate each one on data from Google Maps--a large KB of locations and their subjective and factual attributes. The results demonstrate that our learned models are well-calibrated and thus can successfully be used to control the KB's precision. Moreover, when constrained to maintain 95% precision, the best consensus model matches the F-score of a baseline that models each entity-attribute pair as a binary variable and does not support tunable precision. When unconstrained, our model dominates the same baseline by 12% F-score. Finally, we perform an empirical analysis of attribute-attribute correlations and show that leveraging them effectively contributes to reduced uncertainty and better performance in attribute prediction.

This paper focuses on the end-to-end abstractive summarization of a single product review without supervision. We assume that a review can be described as a discourse tree, in which the summary is the root, and the child sentences explain their parent in detail. By recursively estimating a parent from its children, our model learns the latent discourse tree without an external parser and generates a concise summary. We also introduce an architecture that ranks the importance of each sentence on the tree to support summary generation focusing on the main review point. The experimental results demonstrate that our model is competitive with or outperforms other unsupervised approaches. In particular, for relatively long reviews, it achieves a competitive or better performance than supervised models. The induced tree shows that the child sentences provide additional information about their parent, and the generated summary abstracts the entire review.

Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this domain rely on manual feature engineering and do not allow for an end-to-end training. Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations. Conceptually, our approach computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user. This way we transform the knowledge graph into a user-specific weighted graph and then apply a graph neural network to compute personalized item embeddings. To provide better inductive bias, we rely on label smoothness assumption, which posits that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores. Label smoothness provides regularization over the edge weights and we prove that it is equivalent to a label propagation scheme on a graph. We also develop an efficient implementation that shows strong scalability with respect to the knowledge graph size. Experiments on four datasets show that our method outperforms state of the art baselines. KGNN-LS also achieves strong performance in cold-start scenarios where user-item interactions are sparse.

Today is the slowest business will ever run. In today's world, it is the small companies that beat the large enterprises with speed and agility, according to Valerie Blatt – Global Vice President SAP. Companies such as Spotify are forcing incumbents such as Apple to change their music business. Therefore, for the organisation of tomorrow to remain relevant, they need faster, better and more intelligence. Last week, I was invited to join SAP Ariba Live in Barcelona.

How to ensure human autonomy over our computational "screens, scenes, and unseens." Digital assistants such as Alexa and Siri and Google Assistant can be quite helpful -- but their actual allegiance is to Amazon and Apple and Google, not to the ordinary people who use them. By introducing AI-based digital agents that truly represent and advocate for us as individuals, rather than corporate or government institutions, we can make the Web a more trustworthy and accountable place. In the 2004 film "I Robot," Will Smith's character, the enigmatic Detective Del Spooner, harbors an animosity toward the humanoid-like robots operating in his society. Over the course of the film we learn why.

The Nordic nation has pledged to go carbon neutral by 2035 – not bad for one of the coldest countries in the world. The Hollywood star says he wants to solve climate change with robots. Details are scant, but come on, he's Iron Man! Millions of people are rejoicing as iTunes shuffles of this mortal coil. Apple says the long-hated app will still survive on Windows, however. Forget "my kid could draw that" – now robots are producing bad art.

Get some huge savings on all your favorite electronics, like a new Kindle or robot vacuum. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. You know that scene in 30 Rock when Liz Lemon is like "What a week, huh?" and Alec Baldwin's character goes, "Lemon, it's Wednesday." I happen to know a great cure to the Hump Day blues however, and it involves doing a bit of retail therapy. I don't just recommend buying a bunch of stuff you don't need at Target or on Amazon.