Mojtaba Arvin is a computer programmer who's fascinated by artificial intelligence . Right now, he spends his time as an information technology student .

This is the first in Blue Hill Research's occasional blog series "Questioning Authority with Toph Whitmore." As co-founder (with friend Michael Stonebraker) of Vertica, Andy Palmer ambitiously sought nothing less than to reinvent the database. In 2013, he and Stonebraker moved up the data value chain and founded Tamr, the Cambridge, MA-based software company aiming to provide a unified view of data in the modern enterprise. Palmer joined me for a discussion in which he talked Tamr, predicted the future of enterprise data management, and introduced a rather colorful (yet apt) analogy of which, he admits, his marketing team is less than fond. TOPH WHITMORE: Tell me about the genesis of Tamr.

Ford Motor Co. intends to have a fully driverless vehicle -- no steering wheel, no pedals -- on the road within five years. The car will initially be used for commercial ride-hailing or ride-sharing services; sales to consumers will come later. Ford CEO Mark Fields announced the new goal Tuesday at the company's Silicon Valley campus in Palo Alto, California. "This is a transformational moment in our industry and it is a transformational moment for our company," Fields said. Ford's approach to the autonomous car breaks from many other companies, like Mercedes-Benz and Tesla Motors, which plan to gradually add self-driving capability to traditional cars.

Intel Scalable Systems Framework (Intel SSF) reduces confusion given the wealth of new technologies now available to HPC customers, and offers guidance for the right mix of balanced and validated hardware and software technologies. Intel SSF incorporates a host of software and hardware technologies including Intel Omni-Path Architecture (Intel OPA), Intel Optane SSDs built on 3D XPoint technology, and new Intel Silicon Photonics – plus it incorporates Intel's compute and storage products, including Intel Xeon processors, Intel Xeon Phi processors, and Intel Enterprise Edition for Lustre* software. Benchmarks show that a combination of Intel SSF technologies (Intel Xeon Phi and Intel OPA) provide significantly better scaling and performance when training deep learning neural networks than GPU-based products on well-known benchmarks such as AlexNet and GoogleNet [1]. These and other deep-learning benchmarks can be viewed on the Intel machine learning portal. Intel Xeon Phi processors deliver superior neural networking training performance using up to seventy two (72) processing cores per processor where each core contains two Intel AVX-512 vector processing units.

Forecasting the future isn't easy but some things are predictable. It is predictable, for example, that every now and then everyone will get excited about the imminent arrival of machines that think like people and that will therefore destroy people's jobs. This triggers a mix of enthusiasm and paranoia. But after the humanoid machines fail to materialize as predicted, everyone calms down and gets back to work. In 2016, we are approaching the end of one of those periodic bouts of excitement.

The U.K.'s Brexit vote was something few prognosticators saw coming prior to the June 23 referendum. But once the results were in, it was clear the vote to leave the European Union would have a major impact on financial markets. The pound sterling fell in value by 11% two days after the vote, and both the Dow Jones Industrial Average and the London Stock Exchange's FTSE 100 index lost more than 2% of their total value. This left millions of traders all over the world scrambling to find safer investment positions. But at least one group of investors was relatively calm, according to Omer Cedar, CEO of Omega Point Research Inc., a New York-based software company that sells analytics tools to help investment managers review their portfolios for risks.

Before the end of this year, Darktrace plans to release its Antigena technology. Antigena is designed to replicate the function of human antibodies, which identify and neutralise bacteria and viruses, by neutralising cyber threats automatically without human intervention. Darktrace is researching how information security teams respond to situations with a view to enabling the system not only to learn what they do, but also to predict what they will do and then use that information to offer better support information. "This is the kind of thing that really interests us, and is the kind of envelope-pushing, self-learning, machine-learning, AI-type stuff that we really want to get into," said Palmer. "An entirely AI security operations centre is not an unreasonable objective for us to have as researchers, and is certainly one of our goals, especially considering how quickly technology is moving in areas such as self-driving cars, which not long ago were considered to be pure fiction."

NEW YORK (Reuters) – IBM is in an unusual fix in telling big U.S. banks they can use its Watson software of Jeopardy-winning fame as a cost-saving solution: bankers say they like it, but cannot afford it. IBM is in good company. Banks are in the fifth year of their belt-tightening campaigns that began in 2011, chasing billions of dollars' worth of savings, and vendors that offer everything from technology to janitorial services are getting squeezed. With persistently low interest rates hurting revenue and businesses like bond trading hemmed in by new regulations, few on Wall Street expect the austerity to end any time soon. For IBM the irony lies in the fact that senior bank executives say they believe its artificial intelligence software could help them achieve cost-cutting goals in coming years, but are not ready to pay for Watson today.

By beating humans at games of Go and Jeopardy, artificial intelligence engines like Google's DeepMind and IBM's Watson have captured attention for their promise of solving bigger human problems. Watson, for example, is being enlisted to help doctors predict cancer in patients. The American internet pioneer Douglas Engelbart suggests that AI's grandest promise is the amplification of human ability. Whether it's automating rote cognitive tasks like tagging people in photos or assisting in complex work flows like cancer treatment, the human-augmentation promise feels almost inevitable in every product and domain. Self-driving cars rely on massive amounts of data collected over several years from efforts like Google's people-powered street canvassing, which provides the ability to "see" roads.

Ford Motor Co. became the world's first automaker to announce hard plans to deliver driverless cars. "We'll have mass-produced, fully autonomous cars on the road in five years," said Raj Nair, head of global product development, at an event Tuesday in Palo Alto, Calif. Ford said the cars would be first used for ride hailing and ride sharing, but no companies were named. A few years after that, the company plans to roll out driverless cars to consumers. All the carmakers, plus Google and maybe Apple, are working on driverless cars, but none has announced firm dates.