The new, interactive talking robot uses Human Centric AI Zinrai, which is the Fujitsu approach to artificial intelligence, to learn and analyze human emotions based on such factors as tone of voice, while using a camera to recognize emotions and faces. The resulting analysis allows it to generate suggestions based on specific interests. The robot will be used for consumer and industrial purposes such as at reception desks, care facilities and other service jobs. It has achieved a natural level of conversation thanks to cloud technology that stores data from conversations. In addition to its use in the robot, the Fujitsu Zinrai AI platform will also be demoed in a more general display at the booth to highlight its business innovation capabilities.

What is the future of virtual assistants and artificial intelligence? Meet virtual assistants that grow with you, AI that speaks on your behalf, and a philosophically-minded companion that accidentally orders 2,000 pounds of organic horse manure. Officially premiered at the London Digital Design Weekend at the V&A museum on Saturday, September 23, 2017.

How far will AI take us in areas such as healthcare? Will we see medical pods like this one featured in science fiction film Prometheus to treat future patients? CIO veteran and industry consultant, Geoff Wenborn, sees enormous opportunity for artificial intelligence (AI) in healthcare - identifying 35 use cases alone for robotics and AI at places like UnitingCare Queensland. Wenborn, who provides IT transformation consulting to several organisations including UnitingCare Queensland and Starlight Children's Foundation Australia (and built some proof of concepts), said the sky's the limit in terms of use case possibilities. At UnitingCare, he was the interim CIO charged with doing transformation, and is currently the chair of the Starlight IT Advisory Board.

This paper proposes a novel model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training. We empirically demonstrate that: a) deep autoencoder models generalize much better than the shallow ones, b) non-linear activation functions with negative parts are crucial for training deep models, and c) heavy use of regularization techniques such as dropout is necessary to prevent over-fiting. We also propose a new training algorithm based on iterative output re-feeding to overcome natural sparseness of collaborate filtering. The new algorithm significantly speeds up training and improves model performance. Our code is available at https://github.com/NVIDIA/DeepRecommender

Oof, this one is rough. Over the weekend, despite good buzz and glowing reviews from critics, Blade Runner 2049 opened by bringing in a meager $31.5 million domestically at the box office, a figure well below expectations and one that looks particularly bleak when you factor in that the film reportedly cost more than $150 million to make. Were fans just unwilling to go back to Blade Runner's future 35 years after Ridley Scott's original film? Did women not want to see a movie where they had such limited roles? Or did the performance of Denis Villeneuve's Runner reboot just speak to the fact that not that many folks wanted to spend nearly three hours watching a moody--if stunning--sci-fi film when things are already so gloomy outside the multiplex?

Everyone now is learning, or claiming to learn, Deep Learning (DL), the only field of Artificial Intelligence (AI) that went viral. Paid and free DL courses count 100,000s of students of all ages. Too many startups and products are named "deep-something", just as buzzword: very few are using DL really. Most ignore that DL is the 1% of the Machine Learning (ML) field, and that ML is the 1% of the AI field. What's used in practice for most "AI" tasks is not DL. A "DL-only expert" is not a "whole AI expert".

Sci-fi level Artificial Intelligence (AI) like HAL 9000 was promised since 1960s, but PCs and robots were dumb until recently. Now, tech giants and startups are announcing the AI revolution: self-driving cars, robo doctors, robo investors, etc. PwC just said that AI will contribute $15.7 trillion to the world economy by 2030. "AI" it's the 2017 buzzword, like "dot com" it was in 1999, and everyone claims to be into AI. Don't be confused by the AI hype. Is this a bubble or real? AI is not easy or fast to apply. The most exciting AI examples come from universities or the tech giants. Self-appointed AI experts who promise to revolutionize any company with the latest AI in short time are doing AI misinformation, some just rebranding old tech as AI. Everyone is already using the latest AI through Google, Microsoft, Amazon etc. services. But "deep learning" will not soon be mastered by the majority of businesses for custom in-house projects. Most have insufficient relevant digital data, not enough to train an AI reliably. As a result, AI will not kill all jobs, especially because it will require humans to train and test each AI.

Gartner, Inc. today revealed its top predictions for 2018 and beyond. Gartner's top predictions will enable organizations to move beyond thinking about mere notions of technology adoption to focus on the issues that surround what it really means to be human in the digital world. "Technology-based innovation is arriving faster than most organizations can keep up with. Before one innovation is implemented, two others arrive," said Daryl Plummer, vice president and Gartner Fellow, Distinguished. "CIOs in end-user organizations will need to develop a pace that can be sustained no matter what the future holds. Our predictions provide insight into that future, but enterprises will still be required to develop a discipline around how pace can be achieved. Those who seek value from technology-based options must move faster as their digital business efforts move into high gear. Speed of change will require variability of skills and capabilities to address rising challenges."

Tesla has solar projects on smaller islands, and Musk thinks they should be scalable to larger ones like Puerto Rico. Pixelbots swarming to show a rainbow dinosaur. All work and no play makes Jack a dull boy. The same goes for large businesses -- putting all of your company's effort into a single product can lead to stagnation and stale ideas. Exploring new ideas is one way to keep your company relevant for the long haul, even if the side business doesn't have much to do with your main operations. You're about to see some examples of this from large household names in the American business world.

How far will AI take us in areas such as healthcare? Will we see medical pods like this one featured in science fiction film Prometheus to treat future patients? CIO veteran and industry consultant, Geoff Wenborn, sees enormous opportunity for artificial intelligence (AI) in healthcare - identifying 35 use cases alone for robotics and AI at places like UnitingCare Queensland. Wenborn, who provides IT transformation consulting to several organisations including UnitingCare Queensland and Starlight Children's Foundation Australia (and built some proof of concepts), said the sky's the limit in terms of use case possibilities. At UnitingCare, he was the interim CIO charged with doing transformation, and is currently the chair of the Starlight IT Advisory Board.