Google is using the space above its search results to promote products owned by its parent company, Alphabet, Inc., it was reported on Thursday. The Internet search giant was found to utilize the precious space in order to push various products like its own Pixel phones as well as Nest smart thermostats and smokes detectors, and Android smart watches, The Wall Street Journal reported. Nest Labs is a Palo Alto, California-based company that manufactures thermostats, smoke detectors, and other home security products. It was acquired by Alphabet, Inc. in 2014 for $3.2billion. Google is using the space above its search results to promote products owned by its parent company, Alphabet.

Financial institutions (FI's) face many challenges, but one of the biggest of all is fighting fraud. With so much money and valuable data at stake, the financial system will always be a target for criminals and FI's must shoulder a lot of the responsibility for security. One of the most effective methods of keeping fraudsters at bay is staying up to date with the latest technologies and there are few areas of technological innovation more exciting than artificial intelligence (AI). It's true that we are still a long way off developing true AI, but the developments that have occurred in this field are already offering great potential for FI's. How do you ensure that your security safeguards and authentication processes are rigorous enough to combat fraud, while delivering the speed and reliability modern-day consumers expect?

In this week's news roundup for IT leaders, we bring you the latest in the ongoing debate about the impact of artificial intelligence on the future of jobs. This week, The World Economic Forum took place in Switzerland, where political and economic leaders along with executives from companies including IBM, Microsoft, Facebook and Google parent Alphabet gathered to discuss the impact of automation and artificial intelligence on the future of jobs and society. Speaking at the event, Google co-founder Sergey Brin said that the growth of AI has surprised him, but he's optimistic overall. As Adam Satariano reports for the Chicago Tribune, Brin said that automation would ultimately free up people to work on more intellectually demanding, creative or artistic pursuits while AI took care of the mundane tasks. In a Wall Street Journal recap of the event, Sam Schechner reports that while many executives and economists share an optimistic viewpoint, "some said this week that they also worry the spoils of the next revolution could be inequitably shared – and that the transition to new models of work could be brutal for many workers."

GRAUBUNDEN, SWITZERLAND – If your job involves inputting reams of data for a company, you might want to think about retraining in a more specialized field. After industrial robots and international trade put paid to many manufacturing jobs in the West, millions of white-collar workers could now be under threat from new technology such as artificial intelligence. The issue of how best to face up to this "Fourth Industrial Revolution" has been exercising politicians and business leaders this week at the World Economic Forum in the Swiss Alpine town of Davos. The progress of AI has been "staggering" in recent years, said Vishal Sikka, chief executive of Indian IT services giant Infosys. "But in many ways we are at the beginning of this evolution and we face the prospect of leaving a larger part of humanity behind than in any other (industrial) advance," he warned.

Machine learning has become a buzzword in the media these days. Recently Science magazine published a cover paper on Human-level concept learning through probabilistic program induction and shortly after Nature magazine devoted its cover story to AlphaGo, an AI program that defeated European Go Championship winner. Late on Tuesday night, Google's DeepMind AI group will play one of the world's best human Go players, Lee Se-dol of South Korea. The game will be live streamed on YouTube, and the stream is embedded at the end of this story. Many are now discussing the potential of artificial intelligence, asking questions such as "Can machines learn like a human?", "Will artificial intelligence surpass human intelligence?", To answer such questions, InfoQ interviewed Prof. Jürgen Schmidhuber, Scientific Director of The Swiss AI Lab IDSIA.

Key sectors of interest include Internet of Things, FinTech, Future of Work, Logis-cs/Transporta-on, eHealth, Security and others. BootstrapLabs is a Venture Capital firm based in Silicon Valley and focused on Applied Ar:ficial Intelligence About Us 3. Community of Founders, Intrapreneurs, AI/ML Experts, Execu-ves, Professors, Researchers, Investors focused on Innova-on, Technology and Entrepreneurship 30K PEOPLE Our Community 200K FOLLOWERS 1K ATTENDEES Our online community between BootstrapLabs core team and its closer advisors has over 200K followers. We see traffic on our website and deal flow referral coming from over 60 countries BootstrapLabs brought together over 1,000 people during 2016. Our community is a key pillar of our success and we organize many exclusive private and public AI centric events each year 4. Applied AI Digest #1 2016 Google's DeepMind Beats a Top Player at the Game of Go Zucks to create AI-Powered Jarvis JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC IBM Watson Head on the Future of AI Read Full Articles 5. Applied AI Digest #2 Artificial Intelligence Deals on the RiseCould AI Solve the World's Biggest Problems? Harvard is building an AI Engine as fast as the Brain Read Full Articles 6. Applied AI Digest #3 Is Big Data Still a Thing?

In the final months of the Obama White House, I was honored to be invited by the President's National Economic Council to discuss the recent report, Artificial Intelligence, Automation, and the Economy. I was joined by several other venture capitalists and entrepreneurs to comment on how the tech community sees AI -- its potential for positive impact as well as the implications for our workforce. As a VC at NextWorld Capital, a big area of my investment focus is on the companies that are digitizing and automating the physical world, including drones, the Internet of Things, and artificial intelligence. As an undergraduate and graduate student at MIT studying AI in the late 90's, I saw the commercial potential of technologies such as computer vision and robotics, but now I am convinced that AI is ready to drive systemic changes to businesses and services of all kinds. This new wave of technology will have an outsized impact on what I call the "field office," operated by deskless workers are building and servicing physical goods.

In this paper, a methodology is investigated for signal recovery in the presence of non-Gaussian noise. In contrast with regularized minimization approaches often adopted in the literature, in our algorithm the regularization parameter is reliably estimated from the observations. As the posterior density of the unknown parameters is analytically intractable, the estimation problem is derived in a variational Bayesian framework where the goal is to provide a good approximation to the posterior distribution in order to compute posterior mean estimates. Moreover, a majorization technique is employed to circumvent the difficulties raised by the intricate forms of the non-Gaussian likelihood and of the prior density. We demonstrate the potential of the proposed approach through comparisons with state-of-the-art techniques that are specifically tailored to signal recovery in the presence of mixed Poisson-Gaussian noise. Results show that the proposed approach is efficient and achieves performance comparable with other methods where the regularization parameter is manually tuned from the ground truth.

Statistical characteristics of network traffic have attracted a significant amount of research for automated network intrusion detection, some of which looked at applications of natural statistical laws such as Zipf's law, Benford's law and the Pareto distribution. In this paper, we present the application of Benford's law to a new network flow metric "flow size difference", which have not been studied before by other researchers, to build an unsupervised flow-based intrusion detection system (IDS). The method was inspired by our observation on a large number of TCP flow datasets where normal flows tend to follow Benford's law closely but malicious flows tend to deviate significantly from it. The proposed IDS is unsupervised, so it can be easily deployed without any training. It has two simple operational parameters with a clear semantic meaning, allowing the IDS operator to set and adapt their values intuitively to adjust the overall performance of the IDS. We tested the proposed IDS on two (one closed and one public) datasets, and proved its efficiency in terms of AUC (area under the ROC curve). Our work showed the "flow size difference" has a great potential to improve the performance of any flow-based network IDSs.

Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being. However, falling is a short term activity that occurs infrequently. This poses a challenge to traditional classification algorithms, because there may be very little training data for falls (or none at all). This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL. We propose three `X-Factor' Hidden Markov Model (XHMMs) approaches. The XHMMs model unseen falls using "inflated" output covariances (observation models). To estimate the inflated covariances, we propose a novel cross validation method to remove "outliers" from the normal ADL that serve as proxies for the unseen falls and allow learning the XHMMs using only normal activities. We tested the proposed XHMM approaches on two activity recognition datasets and show high detection rates for falls in the absence of fall-specific training data. We show that the traditional method of choosing a threshold based on maximum of negative of log-likelihood to identify unseen falls is ill-posed for this problem. We also show that supervised classification methods perform poorly when very limited fall data are available during the training phase.