Billions of dollars have gone into the development of Autonomous Vehicle (AV) technology, with some manufacturers slowly introducing more sophisticated cruise control, braking and stability systems, driver alerts and other features, and others who are "going the extra mile" with full-on automation of vehicles including delivery trucks and shuttles within confined geographies (college campuses for example). The forecasts for truly autonomous vehicles interacting on public roads and highways range greatly, and public opinion varies; while drivers understand the benefits, they are also wary of letting go of the wheel, or others letting go of the wheel. Stories in the media, including those about fatal collisions, are not helping. There are no fully autonomous cars and trucks on public roads without at least a human overseer on board, which is prudent given the early nature of the evolution of AVs. Despite what some may perceive as constraints to innovation, an enormous amount of energy, attention and investment is underway in the United States of America, and where public agencies work with the AV "ecosystem," we can expect to see real progress as we head into 2020 making the next decade spurred along by the roll out of 5G networks, more lit fiber and other infrastructure improvements.

Oct 18: AI and the Evolution of Cloud Computing: Evaluating How Financial Data is Stored, Protected, and Maintained by Cloud Providers Congressional Hearing Friday, Oct 18, 9:30am EDT. Financial Services Committee Hearing -- Task Force on Artificial Intelligence: AI and the Evolution of Cloud Computing: Evaluating How Financial Data is Stored, Protected, and Maintained by Cloud Providers. Oct 17: US Stocks were UP Thursday, driven by solid Q3 earnings reports from Netflix, Morgan Stanley and others, as well as reports of a UK-EU Brexit deal. More than one-tenth of all the companies in the S&P 500 have reported Q3 earnings results so far, and most are coming in above analysts' expectations. Oct 16: US stocks end slightly lower after weak U.S. retail sales and tension with China offsets good corporate earnings, e.g., IBM (pdf): IBM reports solid earnings, but Q3 sales mixed, other info here; Netflix Q3 results (pdf) and other info here. Netflix $NFLX rises after earnings show subscriber ...

What will work look like when everything is digitally transformed, artificial intelligence is optimally deployed and robots are embedded in every aspect of our economy? That's hard to know, but one company that's focused on making work work better, and helping businesses digitally transform, is Box. Box, which went public in 2015, still acts very much like a Silicon Valley startup. Except, of course, grown up, with partnerships with Google and IBM and just about any other important company you can think of. Elements of this image furnished by NASA.

Machine learning technology is poised to be huge thing in financial services. In fact, two-thirds of UK-based firms are already using it. That is according to two of the UK's top financial regulators. The Financial Conduct Authority (FCA) and the Bank of England have taken a deep dive into how the financial services industry in the country is using machine learning. The research is based on a survey sent out to 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms.

In our last blog topic on data lineage "Top 6 Open Source Data Lineage Tools", we discussed on what is data lineage and importance of data lineage along with top open-source & paid data lineage tools. In this blog, we will cover the top 10 real-life data lineage examples. This blog will focus on the significance and benefits of data lineage for below mentioned companies. Standard Chartered, a British multinational bank, needs no formal introduction. The bank is one of the global leaders not only in terms of the users but also in terms of its data analytics sophistication.

With over 600,000 bridges across the United States, nearly 8% of which are deemed structurally deficient, monitoring and maintaining our country's infrastructure is critical to ensuring the safety of American motorists. Dynamic Infrastructure, a New York- and Tel Aviv-based startup, is currently implementing an innovative artificial intelligence system that allows infrastructure operators to observe condition changes in real time. The system provides live, cloud-based, 3-dimensional images of the bridge or tunnel, while detecting and alerting the operators to any observed change before it results in a collapse. "The world faces an infrastructure crisis," said Saar Dickman, co-founder and CEO of Dynamic Infrastructure. "Specifically, deficient bridges and tunnels represent a severe infrastructure challenge in the U.S. and worldwide and their poor condition leads to life losses and millions in unplanned expenditures."

Google will receive five years' worth of NHS patients' sensitive records under the terms of a deal signed last month, despite controversy over similar contracts in the past. The extent to which patient data has been shared between an NHS trust in England and AI company DeepMind was first revealed by New Scientist in 2016 and later ruled that it failed to comply with the law by the data watchdog for failures over informing patients.

Banks have been in the business of deciding who is eligible for credit for centuries. But in the age of artificial intelligence (AI), machine learning (ML), and big data, digital technologies have the potential to transform credit allocation in positive as well as negative directions. Given the mix of possible societal ramifications, policymakers must consider what practices are and are not permissible and what legal and regulatory structures are necessary to protect consumers against unfair or discriminatory lending practices. In this paper, I review the history of credit and the risks of discriminatory practices. I discuss how AI alters the dynamics of credit denials and what policymakers and banking officials can do to safeguard consumer lending.

Who's involved: German re/insurer Talanx and US-headquartered automation software provider, WorkFusion. What's happening: Talanx and WorkFusion have agreed a strategic partnership. In an initial step, the software from WorkFusion is being used for automated checking and processing in the claims division at Talanx's subsidiary HDI. The software will initially be used for invoices dealing with glass breakage and motor insurance. Significance of development: Talanx has described the new software as "automation 4.0" because the artificial intelligence platform can take end-to-end decisions, known as Intelligent Process Automation.

Today's commercial aircraft are typically manufactured in sections, often in different locations--wings at one factory, fuselage sections at another, tail components somewhere else--and then flown to a central plant in huge cargo planes for final assembly. But what if the final assembly was the only assembly, with the whole plane built out of a large array of tiny identical pieces, all put together by an army of tiny robots? That's the vision that graduate student Benjamin Jenett, working with Professor Neil Gershenfeld in MIT's Center for Bits and Atoms (CBA), has been pursuing as his doctoral thesis work. It's now reached the point that prototype versions of such robots can assemble small structures and even work together as a team to build up a larger assemblies. The new work appears in the October issue of the IEEE Robotics and Automation Letters, in a paper by Jenett, Gershenfeld, fellow graduate student Amira Abdel-Rahman, and CBA alumnus Kenneth Cheung SM '07, Ph.D. '12, who is now at NASA's Ames Research Center, where he leads the ARMADAS project to design a lunar base that could be built with robotic assembly. "This paper is a treat," says Aaron Becker, an associate professor of electrical and computer engineering at the University of Houston, who was not associated with this work.