Enthusiasts predicted the plan would relieve the pressure on hard-pressed GPs. Critics saw it as a sign of creeping privatisation and a data-protection disaster in waiting. Reactions to news last month that Amazon's voice-controlled digital assistant Alexa was to begin using NHS website information to answer health queries were many and varied. US-based healthcare tech analysts say the deal is just the latest of a series of recent moves that together reveal an audacious, long-term strategy on the part of Amazon. From its entry into the lucrative prescription drugs market and development of AI tools to analyse patient records, to Alexa apps that manage diabetes and data-driven experiments on how to cut medical bills, the $900bn global giant's determination to make the digital disruption of healthcare a central part of its future business model is becoming increasingly clear.

Both industries and governments alike have invested significantly in the AI field, with many AI-related startups established in the last 5 years. If another AI winter were to come about many people could lose their jobs, and many startups might have to shut down, as has happened before. Moreover, the economic difference between an approaching winter period or ongoing success is estimated to be at least tens of billions of dollars by 2025, according to McKinsey & Company. This paper does not aim to discuss whether progress in AI is to be desired or not. Instead, the purpose of the discussions and results presented herein is to to inform the reader of how likely progress in AI research is. For a detailed overview of both AI winters check out my first and second medium article on the topic. In this section, the central causes of the AI winters are extracted from the above discussion of previous winters.

Technology experts predict the rate of adoption of artificial intelligence and machine learning will skyrocket in the next two years. These advanced technologies will spark unprecedented business gains, but along the way enterprise leaders will be called to quickly grapple with a smorgasbord of new ethical dilemmas. These include everything from AI algorithmic bias and data privacy issues to public safety concerns from autonomous machines running on AI. Because AI technology and use cases are changing so rapidly, chief information officers and other executives are going to find it difficult to keep ahead of these ethical concerns without a roadmap. To guide both deep thinking and rapid decision-making about emerging AI technologies, organizations should consider developing an internal AI ethics framework.

Tech giant Microsoft and the Indian School of Business (ISB) on Friday announced that they have partnered to create an Artificial Intelligence (AI) Digital Lab, wherein the two organisations will collaborate in research. The research will use AI and Machine Learning (ML) to study relevant issues for business and public policy. In addition, they will introduce a new executive programme titled Leading Business Transformation in the Age of AI in October, which will equip business leaders with tools and strategies to transform their respective organisations into AI-driven organisations. The three-day non-technical programme is done under the purview of Microsoft's online AI Business School, which provides executive-level insights and practical and actionable guidance to build an end-to-end AI strategy. It will focus on managing the impact of AI on company strategy, culture and responsibility.

Employers, workers, policymakers, and think tanks have begun raising concerns that the push toward automation is going to leave a lot of people without a job--raising loads of ethical and societal questions in the process. With these concerns in mind, a new coalition--co-founded by the National Restaurant Association along with the Littler Workplace Policy Institute and Prime Policy Group--is picking up momentum. The Emma Coalition will study what it calls "technology-induced displacement of employees," or TIDE, and develop ideas for how employers and employees can prepare for and adapt to the effects of increasing automation. "Government, industry, and policymakers all have an opportunity to examine TIDE issues today, and forge consensus around policies that can help us prepare in advance for the technology and workforce of the future," said Shannon Meade, the National Restaurant Association's vice president of public policy and the new coalition's executive director, in comments to Restaurant Business. The coalition is named for the granddaughter of one of the founders.

Huawei Technologies has officially unleashed its artificial intelligence (AI) chip Ascend 910, which it says has a maximum power consumption of just 310W--lower than its originally planned specs of 350W. The chip is touted to have "more computing power than any other AI processor", delivering 256 teraflops at half-precision floating point (FP16) and 512 teraflops for integer precision calculations. The Chinese tech giant also announced the commercial availability of its MindSpore AI computing framework, which it said was designed to ease the development of AI applications and improve the efficiencies of such tools. Huawei said the AI framework handled only gradient and model data that already had been processed, so user privacy could be maintained. The platform also had "built-in protection technology" to keep AI models secured.

MOSCOW – In a new setback for Moscow, an unmanned spacecraft carrying Russia's first humanoid robot to be sent into orbit failed to dock automatically at the International Space Station on Saturday. "Russian cosmonauts issued a command to abort the automated approach of an uncrewed Russian Soyuz spacecraft to the International Space Station," the U.S. space agency NASA said in a statement. "The craft was unable to lock onto its target at the station," and "backed a safe distance away from the orbital complex while the Russian flight controllers assess the next steps," NASA said. Russian flight controllers had told the ISS crew it appeared the problem that prevented automated docking was in the station and not the Soyuz spacecraft, NASA added. Moscow news agencies quoted the flight center control as saying the Soyuz craft had to retreat to a "secure distance" from the ISS.

On August 9, 2019, Illinois Governor J. B. Pritzker signed into law first-of-its-kind legislation regulating the use of artificial intelligence (AI) in Illinois.

Yesterday, Waymo announced it would "open" a large dataset of self-driving training data. This gathered attention because Waymo has, by a huge margin, the largest number of self-driving miles under its belt, and thus one of the most envied collections of tagged data that can be used to train and test neural networks, one of the key tools used in building robots and self-driving cars. People setting up to build a self-driving car almost universally use machine learning techniques. With machine learning for computer vision, you provide the computer with images that a human being has already put labels on, saying what in the image is a car, or pedestrian, or road surface. Give the computer enough, and your machine learning technique -- today, most commonly a convolutional neural network -- will use advanced statistical techniques to come to a more general understanding of what distinguishes the various components.

Fedor, which is travelling to the orbital outpost aboard the Soyuz MS-14 spacecraft, was created by Russia's Android Technology Company and the Advanced Research Fund on a technical assignment from Moscow's Emergencies Ministry. Its basic goals include transmitting telemetry data, determining parameters related to the flight safety, including overloads, and carrying out experiments to test the Skybot's operations capabilities on spacewalks outside the ISS. A Soyuz-2.1a carrier rocket blasted off from the Gagarin Start launch pad of the Baikonur spaceport in Kazakhstan yesterday delivering the Soyuz MS-14 spacecraft with Fedor into the near-Earth orbit.