The announcement isn't too much of a surprise -- autonomous cars have been facing new scrutiny following a fatal Tesla Model S crash last month. China isn't the first nation to react, either: Germany is said to be drafting legislation that would place "black boxes" in self-driving vehicles. China hasn't said when the new regulations would be official, but noted that early drafts have already been written. Hopefully, the new rules will be finalized soon and enable China's auto-makers to resume testing.

Google just paid for part of its acquisition of DeepMind in a surprising way. The internet giant is using technology from the DeepMind artificial-intelligence subsidiary for big savings on the power consumed by its data centers, according to DeepMind co-founder Demis Hassabis. In recent months, the Alphabet unit put a DeepMind AI system in control of parts of its data centers to reduce power consumption by manipulating computer servers and related equipment like cooling systems. It uses a similar technique to DeepMind software that taught itself to play Atari video games, Hassabis said in an interview at a recent AI conference in New York. The system cut power usage in the data centers by several percentage points, "which is a huge saving in terms of cost but, also, great for the environment," he said.

If Gabe Otte '11 hadn't had a Cornell advisor who steered him down a more challenging path and hadn't had some chance conversations with Nobel Prize-winning chemist Roald Hoffman, he might be squirreled away in a lab somewhere. Instead, he's the CEO of Freenome, a start-up just awarded 5.5 million in venture capital for its product, a data-driven blood test that can detect various types of cancers in their earliest stages and recommend the best treatments. Otte came to Cornell planning to study computer science, but a freshman-year advisor encouraged him to choose another major. "I had been coding and programming since I was nine years old," Otte said, so he elected to study chemistry and computational biology, using his knack for computer science to do his homework. "I fell in love with chemistry when I took organic chemistry," he said, adding that he developed his own computer program to do computations related to the synthesis of molecules.

Pete is a professional data scientist. He has used machine-learning algorithms to create predictive engines in variety of fields including marketing, mechanical prognostics and health management, and algorithmic stock trading. Pete enjoys Codementoring and the great interaction and learning that comes with it. Pete's degree is in Physics from the University of Texas, Austin.

There are some very good questions being asked about artificial intelligence, and some very good answers on both sides from some very intelligent people. But which do you find more convincing? The Drum presents the case for and against as part of a recently published issue of the magazine, guest edited using AI. PRO: Chris Bishop, director of Microsoft Research in Cambridge, said earlier this year that he believes the hyperbole around the AI risks could jeopardise any future developments that may in fact assist humanity. "Any scenario in which AI is an existential threat to humanity is not just around the corner," he told the Guardian.

Samsonovich made the comments while attending the 2016 Annual International Conference on Biologically Inspired Cognitive Architectures (BICA) in New York City, which takes place from July 16-19. The conference was sponsored by MEPhl and attracted more than 200 participants. "We are on the verge of a major breakthrough that was discussed since the fifties of the previous century," Samsonovich said on Tuesday. The breakthrough, according to Samsonovich, is the creation of free thinking machines capable of feeling and understanding human emotions, understanding narratives and thinking in those narratives, as well as being capable to actively learn on their own. "Those are the three key capabilities in my view that will determine the breakthrough," Samsonovich said, adding that progress is expected to be made in "several years."

Ambarish is the founder and CEO of Blippar, an augmented reality and image recognition platform. Leading venture capitalists, scientists and CEOs all have the same prediction for artificial intelligence: machines will take jobs away from both blue- and white-collar workers, "eat the world" and, ultimately, overthrow humanity. These histrionics have driven a widely accepted negative narrative about this technology's potential impact on the future of humanity. Bringing artificial intelligence into the mainstream world should be met with hope and empathy, not fear. The concerns of these experts -- wealthy men with unparalleled access to utilities, healthcare, public safety, education and job opportunities -- are the concerns of privilege.

There are numerous obstacles retailers need to overcome before employing drones to deliver packages - and the UAV's lifespan is at the top of the list. However, Amazon seems to be headed in the right direction with an application that illustrates using'docking stations' for its autonomous carriers. The newly awarded patent that describes using tall structures such as lamp posts or churches that would allow the drones to recharge and continue on their route. Amazon's newly awarded patent that describes using tall structures such as lamp posts or churches that would allow the drones to recharge and continue on their route. The patent describes the use of the docking stations to make the drones fly longer routes, more accurately and provide the system with shelter during rough weather conditions.

The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algorithm is that it allows domain knowledge to be conveniently encoded as a prior distribution to balance exploration and exploitation more effectively. While it is generally believed that the algorithm's regret is low (high) when the prior is good (bad), little is known about the exact dependence. In this paper, we fully characterize the algorithm's worst-case dependence of regret on the choice of prior, focusing on a special yet representative case. These results also provide insights into the general sensitivity of the algorithm to the choice of priors. In particular, with $p$ being the prior probability mass of the true reward-generating model, we prove $O(\sqrt{T/p})$ and $O(\sqrt{(1-p)T})$ regret upper bounds for the bad- and good-prior cases, respectively, as well as \emph{matching} lower bounds. Our proofs rely on the discovery of a fundamental property of Thompson Sampling and make heavy use of martingale theory, both of which appear novel in the literature, to the best of our knowledge.

The problem of machine emotional intelligence is very broad and multifaceted; one of its challenges being the very fact that is hard to define it in an unambiguous way. There is no unique definition of emotion, and there is neither a specific method nor a particular required dataset that is guaranteed to capture it. One of the most popular emotion definitions is the one of the six basic emotions by Paul Ekman [1]. The original six emotions he proposed are: anger, disgust, fear, happiness, sadness and surprise. Another very popular approach is the 2-dimensional emotion map, where each emotional state is projected on the orthogonal axes of valence and arousal [2]. A third dimension can be added to this space with the axis of dominance, see [3] and its related references.