AI and data analytics will allow the utility firms to optimize the management of customer data and connect with the customers at a deeper level. FREMONT, CA: Public utility companies have failed in maintaining customer experience levels. The inability to live up to the customers' expectations can be partly attributed to the lack of competition in the sector. However, there is another aspect to it too. Public utility companies had conventionally utilized the legacy systems that are slow and lack the efficiency required to meet the current service demands.

Oct. 28, 2019, 2:00 PM - 3:30 PM Saal 6 - Europe This session includes representatives of universities, research institutes, multinational corporations, federal agencies, non-profit organizations, and intergovernmental bodies (ITU/WHO FG-AI4H), which are based on several continents. Despite these differences, all participants in the session share a common goal: ensuring that AI solutions for health are safe for patient use.

Back in October 2017, publication of the Hall & Pesenti review, Growing the Artificial Intelligence Industry in the UK, caused something of a stir – if subsequent actions are anything to go by. Seven months after the independent, government commissioned study hit ministers' desks, the AI Sector Deal was announced. Trumpeted as a £1 billion Whitehall vote of confidence in the industry – which has deep, historic roots in the UK – closer examination revealed it to be a more conservative £300 million of public funds backed by a promise of £700 million from industrial and academic partnerships. It still provided a welcome boost to the country's ambitions, even if the announcement itself was fumbled by the mandarins of SW1. At a Westminster eForum event in February 2018, the Deal was trailed for publication in early March. However, policymakers sat on it until May, when it finally emerged days after the EU had published its own AI strategy to the tune of €20 billion – a pointless and frustrating own goal for the UK.

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Inphi Corp., is buying most of eSilicon; while Synopsys will acquire the fabless vendor's embedded memory and interface intellectual property (IP) business. Inphi is to pay $216 million for eSilicon in both cash and assumption of debt, while the price that Synopsys paid for the memory assets was not disclosed. Targeting high-bandwidth networking, high-performance computing, artificial intelligence (AI) and 5G infrastructure markets, its IP includes configurable 7nm 56G/112G SerDes plus networking-optimized 16/14/7nm FinFET IP platforms featuring HBM2 PHY, ternary content-addressable memory (TCAM), specialized memory compilers and I/O libraries. Its neuASIC platform provides AI-specific IP and a modular design methodology to create ASICs. Speaking of the acquisitions, Jack Harding, president and CEO of eSilicon, said, "Our engineering talent, IP and customer relationships in networking, data-center and cloud, telecom 5G infrastructure and AI will help enhance their respective offerings." The Inphi acquisition of eSilicon is expected to close this quarter subject to US and Vietnamese regulatory approval (eSilicon has been in Vietnam since 2010).

We had the opportunity to meet with Eero Laaksonen, CEO of as part of the IT Press Tour in San Francisco. Valoh is the Finnish word for lantern shark, a deep-dwelling, self-illuminating fish. Eero believes automation is key to improving quality of life and that deep learning makes things that are not scalable today, scalable like looking at medical images and autonomous cars. Valohai strives to push all industries forward with the ability to do meaningful things faster.

Devices communicate with a central server periodically to learn a global model. Federated learning helps preserve user privacy and reduce strain on the network by keeping data localized. How does it differ from traditional large-scale machine learning, distributed optimization, and privacy-preserving data analysis? What do we understand currently about federated learning, and what problems are left to explore? In this post, we briefly answer these questions, and describe ongoing work in federated learning at CMU.

What was the first thing that you did this morning when you woke up? And what was the last thing that you did before you went to bed last night? Chances are that many of us -- probably most of us -- were on our smartphones. Our day-to-day consumption of all things digital is increasingly being analyzed and dictated by algorithms: what we see (or don't see) in our news and social media feeds, the products we buy, the music we listen to. When we type a query into a search engine, the results are determined and ranked by based on what is deemed to be "useful" and "relevant." Serendipity has often been replaced by curated content, with all of us enveloped inside our own personalized bubbles.

New York-based startup Barsys recently announced the Coaster, a smart saucer that guides the user on how to create a particular cocktail. By connecting the coaster to a smartphone with the complimentary app downloaded, users can choose from Barsys' existing library of cocktails or input their own recipe. Once a cocktail is selected, the user can simply start pouring ingredients one by one, and with each new alcohol or mixer, the coaster will light up when the correct amount has been added to the glass. The Barsys Coaster is regularly priced at US$149 (S$203), but interested customers can pre-order it here for US$95 (S$130). The device will start shipping in December.

In its Crossing the Quality Chasm: A New Health System for the 21st Century report, the Institute for Healthcare Improvement (IHI) identifies six distinct "Aims for Improvement" addressing the divide between good health care and the health care that people actually receive. Patient-centered service is a leading focus. Donald M. Berwick, MD, MPP, former president and chief executive officer of IHI and former administrator of CMS said, "We are guests in our patients' lives instead of hosts in our healthcare organizations." Being guests in the lives of patients means being welcomed in, not waited for. This requires that we prioritize patient convenience–something artificial intelligence (AI) in healthcare can help deliver.