This timely seminar focuses on the next steps for developing the AI sector in the UK - with sessions discussing the research, investment, skills and ethical challenges which underpin its development. The conference will provide an opportunity to assess the progress of the AI Sector Deal - which sets out the Government's approach to skills, infrastructure, research and finance in the sector. Delegates will consider what has been learnt so far, what more is needed to support the sector, and priorities for the Office of Artificial Intelligence and AI Council - set up to realise the full potential of AI to the economy as part of the Sector Deal. Delegates will discuss the key priorities for meeting the UK's AI skills demand including retaining AI graduates, developing a diverse workforce, and upskilling and reskilling the existing workforce. It follows the skills and talent package announced by the Government in February to support the growth of a world-leading AI sector in the UK - which includes plans for new industry-funded AI Masters, and 16 dedicated UK Research and Innovation AI Centres for Doctoral Training at universities.

Annually since 2016, Google has released a pair of flagship Pixel smartphones designed to showcase the very best of Android. This year was like any other with the debut of the Pixel 4 and Pixel 4 XL, which ship running Android 10. But what's unusual this time around is that the newest duo's hardware is perhaps just as compelling as their software. Gone is the two-tone rear cover that featured prominently on the original Pixel, Pixel 2, and Pixel 3 series, replaced with polished and grippy Corning Gorilla Glass 5. It's easier to grasp ahold of than that of the Pixel 3 and Pixel 3 XL, and it's more resistant to oily fingers and pocket lint. The Pixel 4 series is IP68 certified to withstand up to five feet of water for half an hour, which puts it on par with the outgoing Pixel 3 series. But both the Pixel 4 and the Pixel 4 XL are a good deal heavier than the Pixel 3 (5.71 The Pixel 4 series' frame is coated with a soft-touch material that's jet black on all three of the colorways -- Clearly White, Just Black, and the limited edition Oh So Orange. The haptics, which Google characterizes as "sharp and textured," feel great.

During a session at Google's I/O 2015 conference headlined by the Advanced Technologies and Projects Group (ATAP), engineers demoed what they called Project Soli, a novel gesture-recognition technology bound for handheld devices. The promise of the tech was that you could interact with things without actually touching them, which ostensibly would open up all manner of new ways of performing tasks. After a little over four years in development, it emerged in the Pixel 4 series as the gesture-detecting Motion Sense. So was it worth the wait? We used the Pixel 4 for a week to put Motion Sense through its paces.

IT automation and business automation aren't quite the same thing, but they produce a common side effect in their initial phases: They freak people out. If you tell someone that you're going to automate a lot of the work that they do today, they think that means they'll be out of a job tomorrow, or soon thereafter. "Every time there is a technology revolution, people fear they are going to lose their jobs, and that is not the case," says Denise Leaser, president of GreatBizTools. "History has shown that just the opposite happens. Some jobs go away, but far more jobs are created."

Artificial intelligence systems can – if properly used – help make government more effective and responsive, improving the lives of citizens. Improperly used, however, the dystopian visions of George Orwell's " 1984 " become more realistic. On their own and urged by a new presidential executive order, governments across the U.S., including state and federal agencies, are exploring ways to use AI technologies . As an AI researcher for more than 40 years, who has been a consultant or participant in many government projects, I believe it's worth noting that sometimes they've done it well – and other times not quite so well. The potential harms and benefits are significant.

An executive guide to the technology and market drivers behind the $135 billion robotics market. Schneider Electric's business is focused on providing energy and automation technology to help customers achieve efficiency and sustainability. Given the company's emphasis on delivering real-time automation, it's no surprise that Schneider has embarked on a broad strategy to transform its operations through the development of "smart" factories worldwide. Schneider's efforts around industrial automation, what it calls its Smart Factory Program, involves the use of the same Internet of Things (IoT) technologies in its manufacturing plants, facilities, and offices that the company offers to its customers. The centerpiece of the effort, which began in 2016, is EcoStruxure, open IoT-enabled architecture and platform that allows organizations to leverage connected devices, sensors and other things.

This blog post will take us through how a business which need to ensure customer due diligence (CDD) can automate the KYC (Know Your Customer) processes using deep learning and computer vision based solutions. But before we get started, let's familiarise ourselves with some basic terminology. Customer Due Diligence - CDD involves verifying that your customers are who they say they are and assessing the risks associated with each customer like the possibilities of fraud, money laundering, terrorism financing, etc. This includes verifying your customer's name, address, photograph by analysing bank documents, utility bills, etc. Anti Money Laundering - AML refers to a set of laws, regulations and procedures meant to prevent criminals from disguising illegally obtained assets and funds as legitimate income by safeguarding against trading illegal goods, tax evasion, market manipulation, corruption of public funds, etc. Know Your Business - KYB involves vetting a business trying to establish a relationship with a bank by determining their Ultimate Beneficial Owners (UBO) and enforcing compliance by assessing risks associated with the business. You can learn more about beneficial ownership structures and a risk based approach to counter laundering here.

While most of the buzz around artificial intelligence (AI) may seem new, the concept has been around for more than 60 years. American computer scientist John McCarthy, known as the "Father of AI," coined the term "artificial intelligence" in the 1950s, leading researchers across the United States to dig into the computer learning for processing equations and theorems. In the 1960s, computer scientists began creating machines similar to robots, and the first humanoid robot was built in Japan in 1972. Unfortunately, it was difficult for scientists to go any further due to the lack of advancement in data technology, and the period between the mid 1970s and early 1990s saw a drop-off in serious development. But in the 1990s, computers became more advanced.

Reinsurer Munich Re sees data analytics and artificial intelligence as key tools to open up new possibilities for managing and covering risk, and for supporting insurers across the entire value chain. "At Munich Re, our goal is to pioneer digital solutions for the insurance industry," said Doris Höpke, Member of the Board of Management "So we have been investing heavily in data analytics and artificial intelligence, in order to support our clients with innovative methods and new products," she explained in a statement ahead of the industry meeting in Baden-Baden. Munich Re says it is focused on combining primary insurers' portfolio data with external public sources, such as site geography, building construction, weather, or socio-economic status. By applying machine-learning principles, the company then looks to discover hidden loss drivers and respond with loss-prevention measures or by adjusting pricing. "This means faster claims estimates and handling, and better pricing as a result of improved accuracy in risk assessment," Hopke continued.

Machine Learning (ML) in healthcare has an affinity for patient-centred care and individual-level predictions. Both individual health and population health are not divergent, but at the same time, both are not the same and may require different approaches. ML in public health applications receives far less attention. The skills available to public health organizations to transition towards an integrated data analytics is limited. Hence the latest advances in ML and artificial intelligence (AI) have made very little impact on public health analytics and decision making.