Whether you love or hate it, artificial intelligence is here to stay. The question is -- particularly in Japan, which is facing a severely aging population and shrinking of its workforce -- will AI perform tasks that are beneficial to society as a whole? AI has seen phenomenal success with a variety of tasks, such as performing facial recognition and piloting autonomous vehicles, but there are obvious concerns about the negative impacts of the technology on society, such as people losing their jobs to automation, as well as the dangers of new-generation weapons. "It is crucial to have design methodologies for AI that are truly beneficial to humans," Hideki Asoh, deputy director of the Artificial Intelligence Research Center at the National Institute of Advanced Industrial Science and Technology, said in a recent interview. The beneficial AI movement, promoted by distinguished researchers and leaders in the AI community, including professors Max Tegmark at the Massachusetts Institute of Technology and Stuart Russell at the University of California, Berkeley, is gaining worldwide attention in the discussion of AI safety.

It has been said that to give a man a fish is to feed him for a day, whereas to teach a man to fish is to feed him for life. Forward-looking financial service companies are similarly finding that giving computers instructions is not nearly as fruitful as teaching them to write their own. From assessing credit risks to beefing-up the security of their own networks, fintech startups, in particular, are turning to machine learning finance-based solutions in order to work smarter rather than harder. Considering that over 200 leading financial institutions will attend the upcoming October 2016 Machine Learning Fintech Conference, investment in this subset of artificial intelligence (AI) seems to be a wise move, indeed, for companies that don't want to be left behind. With leading banks starting to invest in AI, and machine learning in particular, fintech companies will be significantly disadvantaged if they fail to do likewise.

The FDA has been approving its fair share of AI-powered medical technology, but its latest might be particularly helpful if you ever have a nasty fall. The agency has greenlit Imagen's OsteoDetect, an AI-based diagnostic tool that can quickly detect distal radius wrist fractures. Its machine learning algorithm studies 2D X-rays for the telltale signs of fractures and marks them for closer study. It's not a replacement for doctors or clinicians, the FDA stressed -- rather, it's to improve their detection and get the right treatment that much sooner. The approval came relatively quickly by using the De Novo premarket review pathway, which streamlines the process for products with "low to moderate risk."

Best listening experience is on Chrome, Firefox or Safari. Subscribe to Ask the CIO's audio interviews on Apple Podcasts or PodcastOne. The way NASA's shared services office used to process grants was manual, full of paper and required the scanning of documents. Many would consider that approach to be "low-value" work -- the kind the Trump administration wants agencies to stop doing. Insight by the Trezza Media Group: Technology experts discuss secure cloud computing strategies in this free webinar.

The FDA has been approving its fair share of AI-powered medical technology, but its latest might be particularly helpful if you ever have a nasty fall. The agency has greenlit Imagen's OsteoDetect, an AI-based diagnostic tool that can quickly detect distal radius wrist fractures. Its machine learning algorithm studies 2D X-rays for the telltale signs of fractures and marks them for closer study. It's not a replacement for doctors or clinicians, the FDA stressed -- rather, it's to improve their detection and get the right treatment that much sooner. The approval came relatively quickly by using the De Novo premarket review pathway, which streamlines the process for products with "low to moderate risk."

Artificial intelligence software capable of interpreting images, matching faces and analysing patterns of communication is being piloted by UK police forces to speed up examination of mobile phones seized in crime investigations. Cellebrite, the Israeli-founded and now Japanese-owned company behind some of the software, claims a wider rollout would solve problems over failures to disclose crucial digital evidence that have led to the collapse of a series of rape trials and other prosecutions in the past year. However, the move by police has prompted concerns over privacy and the potential for software to introduce bias into processing of criminal evidence. As police and lawyers struggle to cope with the exponential rise in data volumes generated by phones and laptops in even routine crime cases, the hunt is on for a technological solution to handle increasingly unmanageable workloads. Some forces are understood to have backlogs of up to six months for examining downloaded mobile phone contents.

Deep neural networks made a striking entree in machine learning and quickly became state-of-the-art algorithms in many tasks such as computer vision [1, 2, 3, 4], speech recognition and generation [5, 6] or natural language processing [7, 8]. However, deep neural networks are known for being very sensitive to their input, and adversarial examples provide a good illustration of their lack of robustness [9, 10]. Indeed, a well-chosen small perturbation of the input image can mislead a neural network and significantly decrease its classification accuracy. One metric to assess the robustness of neural networks to small perturbations is the Lipschitz constant (see Definition 1), which upper bounds the relationship between input perturbation and output variation for a given distance. For generative models, the recent Wasserstein GAN [11] improved the training stability of GANs by reformulating the optimization problem as a minimization of the Wasserstein distance between the real and generated distributions [12]. However, this method relies on an efficient way of constraining the Lipschitz constant of the critic, which was only partially addressed in the original paper, and the object of several followup works [13, 14]. Recently, the Lipschitz continuity was used in order to improve the state-of-the-art in several deep 1 learning topics: (1) for robust learning, avoiding adversarial attacks was achieved in [15] by constraining local Lipschitz constants in neural networks.

Silicon Valley is not a place known for its religiosity, yet a remarkable number of tech leaders and workers have an irrational belief in the Singularity. For those of you not mainlining Reddit, here's the gist of the argument: The "Singularity" is a term for a theoretical event predicted by several mildly famous technologists. In their telling, advancement of computer technology will ultimately lead to a self-improving artificial intelligence. The first self-aware AI will bootstrap itself at such an incredible rate that eventually it will outstrip our capacities to help it--much less understand it. At that point, all bets are off.

Immigration officials have abandoned their pursuit of a controversial machine-learning technology that was a pillar of the Trump administration's ''extreme vetting'' of foreign visitors, dealing a reality check to the goal of using artificial intelligence to predict human behavior. Immigration and Customs Enforcement officials told tech-industry contractors last summer that they wanted a system for their ''extreme vetting initiative'' that could automatically mine Facebook, Twitter, and the broader Internet to determine whether a visitor might commit criminal or terrorist acts or was a ''positively contributing member of society.'' But ICE quietly dropped the machine-learning requirement from its request in recent months, opting instead to hire a contractor that can provide training, management, and human personnel who can do the job. Federal documents say the contract is expected to cost more than $100 million and be awarded by the end of the year. After gathering ''information from industry professionals and other government agencies on current technological capabilities,'' ICE spokeswoman Carissa Cutrell said, the focus of what the agency now calls its Visa Lifecycle Vetting program ''shifted from a technology-based contract to a labor contract.''

When it comes to the world of "big data", whoever has the most data has an advantage. And whoever has the most and best data scientists with the best tools to crunch that data usefully, build on that advantage. That's a key reason CIOs now look at all the data their companies collect and enlist data scientists and new tools to try to wring competitive advantage from what they learn. Entire economic blocs and countries are also upping the investment ante as they seek to slake their thirst for data and the riches machine learning and other AI technologies can bring. For example, the European Union's Digital Market Commission in April said it would increase its artificial intelligence R&D spending to $1.8 billion.