Analysis America's largest manufacturer of body cameras – and the biggest supplier to police forces across the United States – says today's facial recognition technology is not safe for making serious decisions. Speaking during its second-quarter earnings call with investors this week, the CEO of Axon, Rick Smith, answered a question about whether the company would be adding facial-recognition systems to its suite of products and, if so, whether that would come with an additional cost. Smith responded in clear terms that current facial recognition is simply not accurate enough to "make operational decisions," ie: for police to use it to recognize individuals and use positive responses as justification for automatically and unquestioningly apprehending people. Well, the computer says you're wanted, so here come the cuffs, we can imagine a conversation with officers going. "We don't have a timeline to launch facial recognition," Smith said on the conference call (listen in at around the 40-minute mark), noting that Axon doesn't have a team "actively developing it" either.

If policymaking is never easy, and military policymaking is very difficult, it stands to reason that space military policymaking is basically impossible. Yet today, in a speech at the Pentagon, Vice President Mike Pence announced the formation of a sixth branch of the US armed services: a SPACE FORCE! But can that really happen? Well, let's proceed with the go/no-go. "The time has come to establish the United States Space Force," Pence said in his speech, asking for $8 billion to build out the idea.

The standard probabilistic perspective on machine learning gives rise to empirical risk-minimization tasks that are frequently solved by stochastic gradient descent (SGD) and variants thereof. We present a formulation of these tasks as classical inverse or filtering problems and, furthermore, we propose an efficient, gradient-free algorithm for finding a solution to these problems using ensemble Kalman inversion (EKI). Applications of our approach include offline and online supervised learning with deep neural networks, as well as graph-based semi-supervised learning. The essence of the EKI procedure is an ensemble based approximate gradient descent in which derivatives are replaced by differences from within the ensemble. We suggest several modifications to the basic method, derived from empirically successful heuristics developed in the context of SGD. Numerical results demonstrate wide applicability and robustness of the proposed algorithm.

This database provided a comprehensive record of opioids dispensed at California pharmacies to civilian, non–U.S. Department of Veterans Affairs, and non-institutionalized patients treated by clinicians in our sample. Descriptive and inferential statistics were carried out with the Stata software (6). The cmp command in Stata was used to compute a difference-in-differences estimator within a mixed-model two-part linear regression analysis (7). The difference-in-differences estimator compared the average change over time in milligram morphine equivalents (MMEs) dispensed for prescribers in the intervention group with the average change over time for prescribers in the control group.

While the field of artificial intelligence (AI) has been around for some 60 years, it's now finally a part of our daily lives -- including how we work, bank, shop, interact, invest, drive and get insured. The term AI means different things to different people, but at PwC we think about it on a continuum, moving from assisted to augmented and, finally, autonomous intelligence. Here, I am primarily focusing on assisted intelligence -- applications that help us better perform tasks we're already doing today. This includes things like email filtering, automated processing of insurance claims and customer service chatbots, just to name a few applications. Of course when you're talking about AI, the question of automation and its potential to replace human jobs isn't far behind.

In building a case for an American manufacturing renaissance, economists cite increasing productivity, cheap natural gas, and rising value-added figures to show that manufacturing is in good shape and will get better. Some of these positivists also claim that rising labor costs in Asia and the creation of U.S. manufacturing jobs since 2010 are evidence of a big turnaround in manufacturing. There are also some mysterious predictions, shared without data to back them up, that manufacturing exports will grow and imports will shrink. Manufacturing has been battered so badly by China and other Asian countries and by American multinational corporation offshoring that people are desperate for positive news. But the question is, are these stories based on truth or are they just "happy talk"? For example, the McKinsey Global Institute says growth in manufacturing is right around the corner.

The Chinese government has wholeheartedly embraced surveillance technology to exercise control over its citizenry in ways both big and small. It's facial-scanning passers-by to arrest criminals at train stations, gas pumps, and sports stadiums and broadcasting the names of individual jaywalkers. Government-maintained social credit scores affect Chinese citizens' rights and privileges if they associate with dissidents. In Tibet and Xinjiang, the government is using facial recognition and big data to surveil the physical movements of ethnic minorities, individually and collectively, to predict and police demonstrations before they even start. China is even using facial recognition to prevent the overuse of toilet paper in some public bathrooms.

Earlier this year a report by the King's Fund highlighted the tremendous difficulties startups have in scaling up their technologies in the healthcare sector. It cited things such as a lack of appetite for change and insufficient resources to scale up successful pilots as key factors holding back innovation in the sector. Such conclusions are not new however, with many shared with previous reports on the topic. For instance, the King's Fund report follows on from the Accelerated Access Review, which was designed to speed up the introduction of technologies and innovations into the NHS. Many of the recommendations from that are shared with the King's Fund report, as they are with another report from the Health Foundation.

I Know First was selected from over 200 companies to be part of the fintech showroom in Tokyo, Japan on May 23rd, 2018. The showroom event, as well as a housewarming party, were hosted by JIAM (Japan International Asset Management Consortium, where I Know First presented their Algorithmic AI solutions using machine-learning algorithms. As part of the showroom event, I Know First gave a short pitch under 5 minutes followed by a deeper dive and demo if visiting client were interested. JIAM are a not-for-profit organization, which assists international asset management firms, asset management technology firms and talented financial professionals in tapping into the Japanese market. Through collaboration with Japanese government agencies (primarily the Tokyo Metropolitan Government) and industry associations, JIAM's goal is to bolster the Japanese asset management industry and support firms and individuals associated with it.

An unprecedented wave of rank-and-file rebellion is sweeping Big Tech. At one company after another, employees are refusing to help the US government commit human rights abuses at home and abroad. At Google, workers organized to shut down Project Maven, a Pentagon project that uses machine learning to improve targeting for drone strikes – and won. At Amazon, workers are pushing Jeff Bezos to stop selling facial recognition to police departments and government agencies, and to cut ties with Immigration and Customs Enforcement (Ice). At Microsoft, workers are demanding the termination of a $19.4m cloud deal with Ice.