The Mind is Flat: The Illusion of Mental Depth and the Improvised Mind.

Facebook's Portal smart home device is finally launching in the UK – but a human contractor might end up listening to your voice commands. The device, whose AI-equipped camera will follow users around the room in order to keep them in the frame during video calls, will be available to British consumers for the first time from Oct 15. Users will be able to make voice calls using Facebook Messenger and encrypted voice calls using WhatsApp, as well as watch Facebook's TV service in tandem with their friends. But Facebook admits up front that clips of the instructions given to Portal's voice assistant might be passed to human contractors to check whether they have been correctly interpreted by its speech recognition software – unless users explicitly opt out. Andrew Bosworth, Facebook's vice president of augmented and virtual reality, said that Portal would never record the content of anyone's video calls, and that its "smart camera" software remains entirely on the device without any data being sent back to Facebook. "Getting the right people to help review voice transcripts makes the service a lot better," said Mr Bosworth.

Artificial intelligence (AI) is the foundation for simulating human intelligence methods by creating and applying algorithms. The technological advancements in this field have led to the adoption of this technology in various industries including healthcare, education, finance, and marketing and it has proven itself to be the most effective technology in modern times. This technology is now being used to prevent cyber-attacks in major organizations. As cybercrimes are increasing in number and complexity, AI is aiding in identifying these attacks and attacking them. AI technologies like Machine Learning and Natural Language Processing allow security analysts to counter such threats immediately.

India has commenced the project to map the country digitally with a resolution of 10cm via drones and disruptive technologies including AI and big data. The massive task was taken up by the Survey of India a few months ago. The Survey of India is a part of the Department of Science and Technology and has planned to complete the project in two years as stated by Prof. Ashutosh Sharma, Department's Secretary. He also revealed that the Survey of India has been equipped with the latest technologies like drones, AI, big data analytics, image processing, and continuously operating reference system. After the completion of the project, the data will be made available to citizens and Gram Panchayats/local bodies.

Rayfield Byrd knows when it's time to wake up every morning. The 68-year-old Oakland, Cal., resident hears a voice from the living room offering a cheery good morning. A little after 8 a.m. each day, a small yellow robot named Mabu asks Byrd how he's doing. Byrd has Type 2 diabetes and congestive heart failure, and about three years ago, he had surgery to implant a microvalve in his heart to keep his blood flowing properly. To stay healthy, he takes four medications a day and needs to exercise regularly.

Time series classification (TSC) is the area of machine learning interested in learning how to assign labels to time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE is infeasible to use in many applications because of its very high training time complexity in O(N 2*T 4) for a dataset with N time series of length T. For example, it takes HIVE-COTE more than 72,000s to learn from a small dataset with N 700 time series of short length T 46. Deep learning, on the other hand, has now received enormous attention because of its high scalability and state-of-the-art accuracy in computer vision and natural language processing tasks. Deep learning for TSC has only very recently started to be explored, with the first few architectures developed over the last 3 years only.

A new generation of autonomous weapons or "killer robots" could accidentally start a war or cause mass atrocities, a former top Google software engineer has warned. Laura Nolan, who resigned from Google last year in protest at being sent to work on a project to dramatically enhance US military drone technology, has called for all AI killing machines not operated by humans to be banned. Nolan said killer robots not guided by human remote control should be outlawed by the same type of international treaty that bans chemical weapons. Unlike drones, which are controlled by military teams often thousands of miles away from where the flying weapon is being deployed, Nolan said killer robots have the potential to do "calamitous things that they were not originally programmed for". Nolan, who has joined the Campaign to Stop Killer Robots and has briefed UN diplomats in New York and Geneva over the dangers posed by autonomous weapons, said: "The likelihood of a disaster is in proportion to how many of these machines will be in a particular area at once. What you are looking at are possible atrocities and unlawful killings even under laws of warfare, especially if hundreds or thousands of these machines are deployed. "There could be large-scale accidents because these things will start to behave in unexpected ways.

Drone attacks claimed by Yemen's Houthi rebels struck two key oil installations inside Saudi Arabia on Saturday, damaging facilities that process the vast majority of the country's crude output and raising the risk of a disruption in world oil supplies. The attacks immediately escalated tensions in the Persian Gulf amid a standoff between the United States and Iran, even as key questions remained unanswered -- where the drones were launched from, and how the Houthis managed to hit facilities deep in Saudi territory, some 500 miles from Yemeni soil. Secretary of State Mike Pompeo accused Iran of being behind what he called "an unprecedented attack on the world's energy supply" and asserted that there was "no evidence the attacks came from Yemen." He did not, however, specify an alternative launch site, and the Saudis themselves refrained from pointing the finger directly at Iran. President Trump condemned the attack in a phone call with Saudi Crown Prince Mohammed bin Salman and offered support for "Saudi Arabia's self defense," the White House said in a statement, adding that the United States "remains committed to ensuring global oil markets are stable and well supplied."

The most part of the computing effort for deep learning inference is based on mathematical operations which can be mostly grouped into the four-part that are convolutions; activations; pooling; and normalization. These all four share a few characteristics that make them well suited for special-purpose hardware implementation: their memory access patterns are extremely predictable & they are readily parallelized. For designing a new custom hardware accelerators for deep learning is clearly popular, but achieving the state-of-the-art performance, and efficiency with a new design is a complex and challenging problem. In order to help developers to advance the adoption of efficient AI inferencing in custom hardware designs, in 2017 Nvidia opened the source for the hardware design of the NVIDIA Deep Learning Accelerator. NVIDIA Deep Learning Accelerator is both scalable and highly configurable; it consists of many great features like the modular design that maintains flexibility & simplifies integration and it also promotes standardized, open architecture to address the computational demands of inference.

Prosthetic limbs are getting better every year, but the strength and precision they gain doesn't always translate to easier or more effective use, as amputees have only a basic level of control over them. One promising avenue being investigated by Swiss researchers is having an AI take over where manual control leaves off. To visualize the problem, imagine a person with their arm amputated above the elbow controlling a smart prosthetic limb. With sensors placed on their remaining muscles and other signals, they may fairly easily be able to lift their arm and direct it to a position where they can grab an object on a table. The many muscles and tendons that would have controlled the fingers are gone, and with them the ability to sense exactly how the user wants to flex or extend their artificial digits.