The news: Google is releasing free open-source software that will make it easier to build quantum machine-learning applications. TensorFlow Quantum is an add-on to Google's popular TensorFlow toolkit, which has helped give machine learning a big boost since its launch in 2015. TensorFlow is one of a number of tools that make machine learning more accessible, by simplifying deep neural networks and providing reusable code so that new machine-learning apps don't have to be written from scratch. TensorFlow Quantum is set to do the same for quantum machine learning. TensorFlow Quantum will let you write quantum apps without getting bogged down in the details of the hardware they are running on.

Almost every industry is seeking top quality Artificial Intelligence (AI) and analytics professionals across the world. Apart from top academic institutions, industry has also been targetting scientific research labs in order to tap those who possess competencies in quantitative techniques proficient in building models and are getting them oriented to design business solutions. The AI as a service market size was valued at $1.13 billion in 2017 and is expected to be $10.88 billion by 2023, thus opening up a huge demand for AI talent pool. The AI-powered services in the form of Application Programming Interface (API) and Software Development Kit (SDK) are primarily driving the demand for AI and analytics professionals. In addition to these, startups working on path breaking ideas are also in need of smart data science professionals.

Artificial intelligence, such as the Bank of England Bot, is set to take over an increasing number of central bank functions. Billionaire John Catsimatidis uses artificial intelligence to research daughter's date

In the 1960s, the Star Trek television series brought the vision of artificial intelligence into the living rooms of millions of people. AI was everywhere in the show, in the form of machines that had all the intelligence of humans -- and a lot more. Take, for example, the universal translator on the USS Enterprise. It could translate alien languages into English or any other language instantaneously. That, of course, was all science fiction back in the days when Lyndon B. Johnson was the U.S. president, as were a lot of the other AI applications in use on the starship.

'As a result of federal initiatives to "get tough on crime," such as the Reagan Administration's War on Drugs and the Clinton Administration's "Three Strikes" laws, the total number of incarcerated Americans more than quadrupled from roughly 500,000 inmates in 1980 to 2.2 million inmates in 2015. During these decades, black Americans were incarcerated at a rate five times higher than that of white Americans. Despite a new 2019 US Bureau of Justice Statistics (BJS) report, which suggests that the racial disparity between white and black incarceration rates is "narrowing," a Pew Research Center review of BJS stats reveals that this 2019 report "counts only inmates sentenced to more than a year."Moreover, Whites accounted for 64% of adults but 30% of prisoners. . . . In 2017, there were 1,549 black prisoners for every 100,000 black adults--nearly six times the imprisonment rate for whites (272 per 100,000)."

Did you know the TNW Conference has a track fully dedicated to exploring the latest work culture trends and the future of work this year? Check out the full program here. The most exciting breakthroughs of the twenty-first century will not occur because of technology, but because of an expanding concept of what it means to be human. Before we dive into why more women should lead AI teams, I want to share a fascinating story I heard from Tania Biland, a 3rd-year student of Lucerne University of Applied Sciences and Arts. After 4 weeks of work, each team had to present their work.

In our world filled with unintended consequences, it turns out that saving memory space to help deal with GPU limitations, knowing it introduces performance penalties on matrix operations, can end up costing both performance and memory space. As reported in a paper at ISC19, researchers[i] recently rethought use of sparse matrix representations, originally motivated by GPU memory constraints, to use dense matrices in order to benefit from the larger memory capacities and scale-out capabilities of CPUs. The result was not only superior performance and scaling using CPUs, it also (perhaps surprisingly) included a reduction in memory footprint because of the interplay between using sparse representations to reduce memory and the increased memory usage due to algorithm inefficiencies. The researchers demonstrated the positive effects of their work in Horovod – an open source distributed Deep Learning framework for TensorFlow created by Uber Engineering. They also demonstrated its outstanding ability to scale-out, proving it using supercomputers run with large numbers of CPUs.

Artificial Intelligence in the Automotive market is set to grow from its current market was valued at over USD 1 billion in 2019 to USD 12 billion by 2026, according to a new research report by Global Market Insights, Inc. Growing demand for assistive technologies for an increase in driving comfort and safety will foster artificial intelligence in automotive market share in the forthcoming years. AI-powered vehicle systems are being heavily capitalized upon by the manufacturers as they have been launching new features in the vehicles that include lane assistance, adaptive cruise control and automated parking. For instance, Toyota had announced the launch of driver assistance systems of level-4 to enable automatic valet parking in the cars that are going to be launched in the future. The technology has been built with Panasonic and has expensive sensors that offer affordable parking help solutions to all the Toyota customers. There are several artificial intelligence in automotive market players that have been involved in the development of these technologies such as Audi AG., Daimler AG, Ford Motor Company, Harman International Industries, Inc., IBM Corporation, Microsoft Corporation, Alphabet Inc., BMW AG, Didi Chuxing, General Motors, Honda Motor Co., Ltd., Tesla, Uber Technologies and Volvo Car Corporation, among others.

WEST LAFAYETTE, Ind. – Technology that combines machine learning with artificial intelligence from Purdue University has taken its next giant leap toward powering more Internet of Things and edge computing devices. FWDNXT, a software and hardware startup that spun out of Purdue, was acquired in October by Micron Technology Inc., an industry leader in innovative memory and storage solutions. Micron is integrating FWDNXT's artificial intelligence hardware and software technology with its advanced memory to explore deep learning solutions for data analytics, particularly in IoT and edge computing. "Purdue provided the entrepreneurial resources to help me achieve my vision of taking our work on machine learning and deep learning technology to a much wider audience where we can have a bigger impact," said Eugenio Culurciello, Micron fellow and chief machine learning architect. "Micron has the leadership in memory, long history of innovation and drive to deliver power and performance capabilities that address the most complex and demanding edge applications at scale."

Security analysts investigate threats by finding patterns, forming hypotheses, reaching conclusions, and sharing their findings with the rest of the business. These are labor-intensive steps that take not only time, but years of training and expertise. And as operational technology (OT) becomes further integrated with the corporate network, and as threat-actors continue to advance their methods of attack, the emergence of a cyber security skills gap in the OT world becomes more and more evident. The trend towards interconnected IT and OT environments is matched in equal measure by converging security teams. CISOs have assumed responsibility for the security of ICS environments without necessarily possessing specialized OT skills.