That there's little love lost between Donald Trump and Silicon Valley is hardly a new revelation. But while a Trump Administration's relationship with the tech elite is bound to be more distant and chillier than a Clinton Administration's would have been, its policies may simply be sub-optimal for the tech sector rather than disastrous -- and with some notable silver linings. On one hand, a lack of the close ties prominent tech execs formed with the Obama Administration, and appeared set to maintain with a Clinton Administration, could cause some problems. Obama, who recently guest-edited an issue of Wired magazine and gave the publication a thoughtful interview on various tech topics, has been willing to lend an ear to Silicon Valley's views on issues such as autonomous driving regulations, encryption keys, artificial intelligence research and STEM education funding. Trump, who appears to know much less about tech and briefly called for a boycott of Apple (AAPL) in response to the company's unwillingness to help break the encryption on an iPhone used by one of the San Bernardino shooters, probably won't be holding talks with the likes of Tim Cook and Facebook's (FB) Mark Zuckerberg and Sheryl Sandberg as frequently.

But that is not to say it is perfect by any stretch of the imagination. "Deep learning has led to some big advances in computer vision, natural language processing, and other areas," Tommi Jaakkola, a Massachusetts Institute of Technology professor of electrical engineering and computer science, told Digital Trends. "It's tremendously flexible in terms of learning input/output mappings, but the flexibility and power comes at a cost. That is it that it's very difficult to work out why it is performing a certain prediction in a particular context." This black-boxed lack of transparency would be one thing if deep learning systems were still confined to being lab experiments, but they are not.

Almost every action we do on the Internet or on mobile applications is recorded in files known as web logs. These logs can be very voluminous, providing a classic example of Big Data. Data science and Machine Learning algorithms can provide a way of extricating value from web logs. At the OVH Summit on the 11th of October, I presented a workshop on getting value out of web logs through Machine Learning with Dataiku DSS. In this article, I will run through the aspects of that presentation.

Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. This is a summary (with links) to an article series that's intended to be a comprehensive, in-depth guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques in the field. The first chapter of the series starts with both a formal and informal definition of machine learning. This is followed by a discussion of the machine learning process end-to-end, the different types of machine learning, potential goals and outputs, and a categorized overview of the most widely used machine learning algorithms.

Science fiction has, for many years, looked to a future in which robots are intelligent and cyborgs are commonplace. The Terminator, The Matrix, Blade Runner and I, Robot are all good examples of this vision. But until the last decade, consideration of what this might actually mean in the future was unnecessary because it was all science fiction, not scientific reality. Now, however, science has not only done some catching up; it's also introduced practicalities that the original story lines didn't appear to include (and, in some cases, still don't include). What we consider here are several different experiments linking biology and technology together in a cybernetic way--essentially ultimately combining humans and machines in a relatively permanent merger.

Google has built machine learning systems that can create their own cryptographic algorithms -- the latest success for AI's use in cybersecurity. But what are the implications of our digital security increasingly being handed over to intelligent machines? Google Brain, the company's California-based AI unit, managed the recent feat by pitting neural networks against each other. Two systems, called Bob and Alice, were tasked with keeping their messages secret from a third, called Eve. None were told how to encrypt messages, but Bob and Alice were given a shared security key that Eve didn't have access too.

Artificial intelligence and robotics are coming into our lives more than ever before and have the potential to transform healthcare, transport, manufacturing, even our domestic chores. Mary "Missy" Cummings, Director of the Humans and Autonomy Lab (HAL) at Duke University, and co-chair of the Global Future Council on Artificial Intelligence and Robotics, says the technology will work best in collaboration with humans. While cab drivers may fear for their jobs, she envisages a worldwide shortage of roboticists in 2030.

CAMBRIDGE, MA - 10 Nov 2016: IBM Watson Health (NYSE: IBM) and the Broad Institute of MIT and Harvard today announced a research initiative aimed at discovering the basis of cancer drug resistance. The five year, $50 million project will study thousands of drug resistant tumors and draw on Watson's computational and machine learning methods to help researchers understand how cancers become resistant to therapies. The anonymized data will be made available to the scientific community to catalyze research worldwide. To help understand how cancers become resistant to specific therapies, Broad Institute will generate tumor genome sequence data from patients who initially respond to treatment but who then become drug-resistant. Broad will use new genome-editing methods to conduct large-scale cancer drug resistance studies in the laboratory, to help identify tumors' specific vulnerabilities.

More chatbots will begin to solve real-world problems. In many instances, early chatbots seemed more like technologies in search of problems than customer-centric solutions. As the chatbot hype subsides, technologies mature, and companies get feedback from customers, the problems that chatbots tackle will become more obvious, and, in turn, more valuable. An example is our own ReplyYes' The Edit, which endeavors to solve the problem of product discovery for music lovers. Through a use of progressive disclosure, short keyword interactions, and machine-based curation of vinyl albums, we give customers a personalized and serendipitous experience to help them find music they love.

Artificial intelligence is already everywhere. Robots are performing surgeries, courts use AI to help determine sentencing and bots trade on the stock market all day. Last week, 150 academics and industry experts published the U.S. Roadmap for Robotics -- just ahead of the presidential election -- to help guide Congress as it moves to figure out how to allocate federal funds to encourage innovation, keep humans safe and, importantly, make sure America remains a global leader. The first Roadmap for Robotics report, published in 2009, inspired the Obama administration to launch the National Robotics Initiative in 2011, a program that allocated $70 million to advancing robotics research in the United States. The 2016 report is a 100-page tome packed with specific, technical recommendations that the contributors believe will be important for Congress to fund and support as robotics starts to take center stage across U.S. industries.