The Bureau of Labor Statistics lists jobs in data science in the top 15 fastest growing occupations with projected 31 percent job growth over the next 10 years. With data increasingly becoming the lifeblood of all organizations, data scientists need to be equipped not only with the right technical skills, but a robust dose of business acumen as well. In 2021, machine learning methods like transfer learning and transformers are drawing a lot of attention because they are rapidly driving innovation in a number of different spaces. For building and training neural networks, PyTorch has a lot of momentum behind it, and Keras and TensorFlow are also commonly used. There is also a rich ecosystem of software libraries, many open source, that can help accelerate machine learning and data science applications.

Reflation trade has been pummelled after the Federal Reserve unexpectedly signalled a shift in its stance on inflation, and, European Central Bank executive Fabio Panetta says the introduction of a digital euro would boost consumers' privacy. Plus, the FT's innovation editor, John Thornhill, talks about the new season of the Tech Tonic podcast and its main focus, artificial intelligence. Reflation trade has been pummelled after the Federal Reserve unexpectedly signalled a shift in its stance on inflation, and, European Central Bank executive Fabio Panetta says the introduction of a digital euro would boost consumers' privacy. Plus, the FT's innovation editor, John Thornhill, talks about the new season of the Tech Tonic podcast and its main focus, artificial intelligence.

Patients are set to get easier access to their medication lists and care plans through the NHS App under the government's new data strategy. New requirements for data sharing across the entire health and care system are also set to come into place, with new legislation to be introduced to require all adult social care providers to provide information about the services they fund. Published today (June 22), the NHSX draft strategy'Data Saves Lives: Reshaping health and social care with data', aims to capitalise on the work undertaken using data during the pandemic to improve health and care services. In a bid to establish openness, the government committed to publishing the first transparency statement setting out how health and care data has been used across the sector by 2022. Under the proposals, patients are set to gain more control over their health data, while data will also be used to improve care and treatment.

While these technical skills are certainly important, we're also now looking more holistically at candidates to test their abilities to think critically and creatively as well as uncover new solutions. As we face new and unprecedented challenges in cyber protection, it's critical that cyber leaders hire team members who think outside-the-box, have intellectual curiosity, employ bold thinking, and are natural problem solvers. Protecting an organization against advanced cyber threats requires innovative thinking and techniques; people, process and technology capabilities are needed to properly defend ourselves against sophisticated attackers, such as nation states. Cyber threats will continue to evolve, as will the new techniques described above to enable cyber resiliency. Ariel Weintraub is currently the Head of Enterprise Cyber Security at MassMutual. Ariel first joined MassMutual in the fall of 2019 as the Head of Security Operations & Engineering, responsible for the Global Security Operations Center, Security Engineering, Security Intelligence, and Identity & Access Management. Prior to joining MassMutual, Ariel served as Senior Director of Data & Access Security within Cybersecurity Operations at TIAA where she led a three-year business transformation program to position IAM as a digital business enabler. Prior to TIAA, Ariel held the position of Global Head of Vulnerability Management at BNY Mellon and was part of the Threat & Vulnerability Management practice at PricewaterhouseCoopers (PwC).

Recent literature has advocated the use of randomized methods for accelerating the solution of various matrix problems arising throughout data science and computational science. One popular strategy for leveraging randomization is to use it as a way to reduce problem size. However, methods based on this strategy lack sufficient accuracy for some applications. Randomized preconditioning is another approach for leveraging randomization, which provides higher accuracy. The main challenge in using randomized preconditioning is the need for an underlying iterative method, thus randomized preconditioning so far have been applied almost exclusively to solving regression problems and linear systems. In this article, we show how to expand the application of randomized preconditioning to another important set of problems prevalent across data science: optimization problems with (generalized) orthogonality constraints. We demonstrate our approach, which is based on the framework of Riemannian optimization and Riemannian preconditioning, on the problem of computing the dominant canonical correlations and on the Fisher linear discriminant analysis problem. For both problems, we evaluate the effect of preconditioning on the computational costs and asymptotic convergence, and demonstrate empirically the utility of our approach.

That message: Under EU law, A.I. threatens EU citizens' fundamental rights … They also recommended banning A.I. systems that use biometrics to …

The traditional approach to statistical disclosure control (SDC) for privacy protection is utility-first. Since the 1970s, national statistical institutes have been using anonymization methods with heuristic parameter choice and suitable utility preservation properties to protect data before release. Their goal is to publish analytically useful data that cannot be linked to specific respondents or leak confidential information on them. In the late 1990s, the computer science community took another angle and proposed privacy-first data protection. In this approach a privacy model specifying an ex ante privacy condition is enforced using one or several SDC methods, such as noise addition, generalization, or microaggregation.

Droids, drones, and driverless technologies are fueling a supply chain revolution.

The modern world runs on software. However, there is a catch: computer code often contains programming errors--some small, some large. These glitches can lead to unexpected results--and systematic failures. "In many cases, software flaws don't make any difference. In other cases, they can cause massive problems," says Kathleen Fisher, professor and chair of the computer science department at Tufts University and a former official of the U.S. Defense Advanced Research Projects Agency (DARPA).

Of all the perils he faced during World War II, Winston Churchill said German submarine wolfpacks were his greatest concern, because their attacks on merchant ship convoys threatened to choke Britain's economic lifelines. Today, it seems there is another emerging undersea threat, one that has the potential to disrupt the global economy by severing fiber-optic lines of communication that run along the world's various seabeds. There are nearly 400 undersea cables that stretch for almost three-quarters of a million miles, the densest concentrations of them being in the North Atlantic and the North Sea, the Mediterranean, and in Southeast Asia and around Japan. They carry virtually all (97%) international communications, and their exact locations are reasonably well-known. They are also increasingly vulnerable to being tapped or even cut by advanced submarine craft of a range of types, from manned mini-subs to remotely operated undersea drones, and even fully autonomous "U-bots."