The market for anti-money laundering (AML) compliance jobs may be on the rise in Nordic countries, but don't count on that trend continuing. Advances in technology could render many compliance roles at Nordea Bank and Danske Bank obsolete, Bloomberg reported Monday. While Helsinki-based Nordea Bank relies on hundreds of employees to help scrutinize billions of transactions for signs of criminal activity, the system is costly and inefficient, and one the lender hopes to move away from, Mikael Bjertrup, head of the bank's financial crime prevention unit, told the news outlet. The bank, which currently uses machine-learning algorithms to close approximately 20 percent of its suspicious transaction alerts, is seeking to increase that total to 80 percent--a shift that would scale back the number of compliance officers needed by the institution, according to the report. "We'll be fewer people in the future, but our defense will be better," Bjertrup told Bloomberg.

Following the attacks in Christchurch, New Zealand, in March, social media companies have once again come under growing pressure to "do …

Nvidia Corp on Monday said it will make its chips work with processors from ARM Holdings Inc to build supercomputers, deepening Nvidia's push into systems that are used for modeling both climate change predictions and nuclear weapons. Nvidia was long known as a supplier of graphics chips for personal computers to make video games look more realistic, but researchers now also use its chips inside data centers to speed up artificial intelligence computing work such as training computers to recognize images. To do so, Nvidia's so-called accelerator chips work alongside central processors from companies such as Intel Corp and International Business Machines Corp. At a supercomputing conference held in Germany on Monday, Nvidia said its accelerator chips will work with ARM processors by the end of the year. ARM, owned by Japan's SoftBank Group Corp, provides the underlying processor technology for the chips in most mobile phones. But companies such as Ampere Computing, headed by Intel's former president, have been working to take those chips into data centers, where Intel's chips are dominant.

AI is in full gold rush mode. Every day we hear headlines of AI companies raising vast sums of capital to give them the resources to prospect the veins of AI gold. The money is flowing to these new frontiers. In the US venture capital funding for AI grew 72% year over year to a whopping $9.3B in 2018. Dataminr, a New York based AI and machine learning company that makes sense of news and information in real time, raised US$392 million in 2018, for example.

USC Viterbi Professors Burcin Becerik-Gerber and Gale Lucas launch CENTIENTS, a center aimed at fostering research and collaboration toward human-centered design and integration of intelligent technologies into built environments. In the 1960s cartoon The Jetsons, the future was a world full of self-driving cars and sassy, meticulous robots. Individuals, like the patriarch George, could move through space--and the shower--without having to lift a finger. The mechanisms around him played a pivotal role in making decisions on his behalf, based on a learned understanding of his most basic preferences. For a while, this future seemed distant, but upon us now is an unprecedented opportunity to merge human behavior and preferences with automation to create a personalized, dynamic and improved daily reality for individuals at work and at home.

The concept of beauty and cosmetics has advanced since the ancient Egyptian standards of smooth hair, clear skin, and beautiful eyes to include hair color and extensions, face contouring, and eyebrow enhancement. For each curve on our face, there now exists a product available to enhance and project it beautifully. The definition of beauty has changed over the years. Growing global economies, increasing disposable incomes, changes in lifestyle (indulgence in cosmetics for skin care, salon and spa treatments), and climate changes have resulted in increased skin care demands. The popularity of natural and organic beauty products, particularly in the United States and European countries, has also fostered the growth of the cosmetics market.

Amazon's Alexa voice assistant faces a massive challenge: Operating not only as a multi-lingual product, but also ensuring that all regional variants of languages it supports are well understood by Alexa, too. To help accomplish that, Alexa has been retrained entirely for every variant needed -- a time and resource-heavy activity. But a new machine learning-based method for training speech recognition created by Alexa's AI team could mean a lot less rework in building out models for new variants of existing languages. In a paper presented to the North American Chapter of the Association for Computational Linguistics, Amazon Alexa AI Senior Applied Science Manager Young-Bum Kim and his colleagues laid out a new system that was able to demonstrate improvements in accuracy of 18%, 43%, 115% and 57%, respectively, on four variants of English (from the U.S., the U.K., India and Canada) used in the trial. The team managed this by implementing a means through which it can tweak its learning algorithm to focus its attention more heavily on just a locale-specific model when it knows in advance that answers to requests from users made in that domain are highly region-specific (i.e. when asking to find a good nearby restaurant) versus when the results are going to be relatively similar regardless of where the request is being made. Alexa's team then combined their locale-specific models into one and also added their location-independent model for the language, and found the improvements measured above.

Self-driving vehicles, dating apps which give out relationship advice, humanoid robots that crack jokes and get upset... With a global market that is expected to reach US$35,870 million by 2025 from its direct revenue sources, artificial intelligence (AI) is no longer just the subject of science fiction books. According to a study carried out by IDC, in the ASEAN region, AI adoption rates are currently on the rise and growth has almost doubled in comparison to last year. When it comes to adopting this emerging technology, Indonesia is leading the way, with 24.6% of companies already embracing AI in some capacity. Thailand comes in second and the bronze medal goes to Singapore. This is somewhat surprising, considering the city state is normally something of a trailblazer in the region when it comes to embracing new technologies.

The UK, which spends more than £2bn on video surveillance each year, is to mark National Surveillance Camera Day on 20 June as part of the National Surveillance Camera Strategy. The aim of the national event is to raise awareness about surveillance cameras and to encourage debate about the use of surveillance cameras in modern society by highlighting how they are used in practice, why they are used and who is using them. The initiative by the Surveillance Camera Commissioner (SCC) and the Centre for Research into Information, Surveillance and Privacy (Crisp) is also aimed at starting a nationwide conversation about how camera technology is evolving, especially around automatic face recognition and artificial intelligence (AI). The organisers hope that the resultant public debate will help inform policy-makers and service providers regarding societally acceptable surveillance practices and legitimacy for surveillance camera systems that are delivered in line with society's needs. As part of the initiative, the SCC is encouraging surveillance camera control centres to throw their "doors open" so that the public can see how they operate.

The adoption of artificial intelligence (AI) is rapidly taking hold across global business, according to a new McKinsey Global Survey on the topic.1 1.The online survey was in the field from February 6 to February 16, 2018, and garnered responses from 2,135 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. To adjust for differences in response rates, the data are weighted by the contribution of each respondent's nation to global GDP. AI, typically defined as the ability of a machine to perform cognitive functions associated with human minds (such as perceiving, reasoning, learning, and problem solving), includes a range of capabilities that enable AI to solve business problems. The survey asked about nine in particular,2 2.The nine capabilities are natural-language text understanding, natural-language speech understanding, natural-language generation, virtual agents or conversational interfaces, computer vision, machine learning, physical robotics, autonomous vehicles, and robotic process automation (RPA). Some would argue that RPA should not be classified as AI in and of itself, but in our experience, RPA systems are increasingly incorporating AI capabilities.