Paul Kemp: Welcome to another episode of The App Guy Podcast. I am your host, it's Paul Kemp. This is a very, very special episode. We actually made it to #500, can you believe it? Now, before I just start celebrating, I have a terrific episode lined up for you. Skype is the reasons why I can get so many guests on the show.

In multiagent systems, social choice functions can help aggregate the distinct preferences that agents have over alternatives, enabling them to settle on a single choice. Despite the basic manipulability of all reasonable voting systems, it would still be desirable to find ways to reach plausible outcomes, which are stable states, i.e., a situation where no agent would wish to change its vote. One possibility is an iterative process in which, after everyone initially votes, participants may change their votes, one voter at a time. This technique, explored in previous work, converges to a Nash equilibrium when Plurality voting is used, along with a tie-breaking rule that chooses a winner according to a linear order of preferences over candidates. In this paper, we both consider limitations of the iterative voting method, as well as expanding upon it. We demonstrate the significance of tie-breaking rules, showing that no iterative scoring rule converges for all tie-breaking. However, using a restricted tie-breaking rule (such as the linear order rule used in previous work) does not by itself ensure convergence. We prove that in addition to plurality, the veto voting rule converges as well using a linear order tie-breaking rule. However, we show that these two voting rules are the only scoring rules that converge, regardless of tie-breaking mechanism.

Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better performance than traditional batch methods. However, most of these DSO methods are not scalable enough. In this paper, we propose a novel DSO method, called \underline{s}calable \underline{c}omposite \underline{op}timization for l\underline{e}arning~({SCOPE}), and implement it on the fault-tolerant distributed platform \mbox{Spark}. SCOPE is both computation-efficient and communication-efficient. Theoretical analysis shows that SCOPE is convergent with linear convergence rate when the objective function is convex. Furthermore, empirical results on real datasets show that SCOPE can outperform other state-of-the-art distributed learning methods on Spark, including both batch learning methods and DSO methods.

Accordingly, attendees at the upcoming 20th Cloud Expo at the Javits Center in New York, June 6-8, 2017, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track. Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses. FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds. More than US$20 billion in venture capital is being invested in FinTech this year.

From Bhutan's gross national happiness index to new year's resolutions, we are all fascinated with ways to get happier, but is technology getting us any closer? In collaboration with the Royal Society – please join us on January 16th-17th for 28 hours of hacking happiness and explore how machine learning can be used to measure, understand, predict and increase happiness. In this multidisciplinary hackathon, we hope to attract professionals from different disciplines, including machine learning, data science, design and software engineering to come together and develop innovative solutions addressing happiness. We are especially interested in ideas that emphasise human-AI collaboration. We would be interested to see ideas that address ways in which new machine learning systems can work together with people in a broad variety of topics such as conversational assistants, interactive AI, active learning, human-agent collaboration, crowd-sourcing or citizen science.

I have been pondering the security technology encroachments into public life, particularly regarding CCTV monitoring. There was a time, it seems now very long ago, that the UK was awash in CCTV. Hundreds of millions of dollars, and over four million (and counting) CCTV cameras later, the UK is the most surveilled society on earth. We were assured that would never happen in the US, or other developed countries. Still, if you have nothing to hide… Today, London and Beijing have over 400,000 CCTV each (proving politics is no guarantee either way). In the US there are over 30 million CCTV cameras, mostly in private hands.

The government should play a bigger role in developing new tools based on artificial intelligence, or we could miss out on revolutionary applications because they don't have obvious commercial upside. That was the message from prominent AI technologists and researchers at a Senate committee hearing last week. They agreed that AI is in a crucial developmental moment, and that government has a unique opportunity to shape its future. They also said that the government is in a better position than technology companies to invest in AI applications aimed at broad societal problems. Today just a few companies, led by Google and Facebook, account for the lion's share of AI R&D in the U.S.

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Japan's space program, JAXA, soars this weekend after its robotic cargo spacecraft began its four-day journey to the International Space Station (ISS) on Friday. Onboard the spacecraft, called Kounotori (after the Japanese word for "white stork"), are more than four tons of cargo, including JAXA's massive debris clearing space whip and a new array of lithium ion batteries for the space station's solar arrays. Friday's cargo launch is particularly important after the failure of a Russian Progress cargo launch earlier this month. Several other cargo launches have met similar fates over the past two years. "Spaceflight's not an easy thing," said NASA astronaut Peggy Whitson in an interview aboard the ISS.

Starred articles are candidates for the picture of the week. A comprehensive list of all past resources is found here. We are in the process of automatically categorizing them using indexation and automated tagging algorithms. Combining the Strengths of MLlib, scikit-learn, and R What is Machine Learning and Predictive Analytics? A Real World Exa... How William Cleveland Turned Data Visualization Into a Science * OpenText Data Visualization – Red Carpet Edition ** Sharing & Preserving Beautiful Graphs With Your Data A Complete Tutorial to learn Data Science in R from Scratch How to create confounders with regression: a lesson from causal inf... What is Machine Learning and Predictive Analytics?