Arun Uday is a former VC and currently the founder of Tring Chat, a mobile group chat app that allows users to search and join chat groups of interest to them. Chatbots seem to be climbing the proverbial peak of the tech hype curve with every passing day. Popular messaging apps have been attempting to outdo each other in terms of making their platforms open for bot development. Businesses are also rushing to embrace them, as evidenced by the interest they are eliciting from even staid institutions such as centuries-old banks. The frenetic action notwithstanding, the moot point, as always, is whether bots actually solve any real end-user need.

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a 'selection function' to reveal the relevant latent variables, and use this to obtain a compact approximation of the posterior distribution for EM; this can make inference possible where the number of possible latent states is e.g. exponential in the number of latent variables, whereas an exact approach would be computationally unfeasible. We learn the selection function entirely from the observed data and current EM state via Gaussian process regression. This is by contrast with earlier approaches, where selection functions were manually-designed for each problem setting. We show that our approach performs as well as these bespoke selection functions on a wide variety of inference problems: in particular, for the challenging case of a hierarchical model for object localization with occlusion, we achieve results that match a customized state-of-the-art selection method, at a far lower computational cost.

ARTIFICIAL INTELLIGENCE: The Humans Hiding Behind the Chatbots Ellen Huet Bloomberg "The company [X.ai] Multiple former AI trainers said that as recently as a few months ago, trainers looked over parts of almost all incoming e-mails--to evaluate what Amy guessed the user was saying--before Amy generated an auto response...Some people might find it unnerving to message a bot only to realize it's a human." ROBOTICS: Robots Could Hack Turing Test by Keeping Silent Tia Ghose Scientific American "Warwick was organizing Turing tests for the 60th anniversary of Turing's death when he and his colleague Huma Shah, also a computer scientist at Coventry University, noticed something curious: Occasionally, some of the AI chatbots broke and remained silent, confusing the interrogators. 'When they did so, the judge, on every occasion, was not able to say it was a machine,' Warwick told Live Science." HEALTH: A Medical Mystery of the Best Kind: Major Diseases Are in Decline Gina Kolata The New York Times "Something strange is going on in medicine. Major diseases, like colon cancer, dementia and heart disease, are waning in wealthy countries, and improved diagnosis and treatment cannot fully explain it...But Dr. Cummings, intrigued by the waning of disease, has a provocative idea for further investigation...Perhaps, he said, all these degenerative diseases share something in common, something inside aging cells themselves."

Maitreya One, a black futurist and hip-hop artist living in Harlem, steps off the Greyhound bus on a warm morning in Montgomery, Alabama. I walk up to him and give him a hug. Maitreya is a civil rights link from the past to the future--and one of the few African-American transhumanists I know. He is stepping off one bus in Montgomery--whose roots are tied to the spectacular Freedom Riders who challenged segregation laws in the early 1960s--and onto another: the Immortality Bus, whose mission is to spread radical science and promote transhumanist rights. Like others in the burgeoning transhumanism movement, Maitreya supports becoming a cyborg in the future, and he knows the coming controversy over such aims may end up as challenging as the civil rights era battles over racism. To transhumanists--some who want to become new biological species and others who want to become machines--a new civil rights age is looming.

When someone sends me something interesting, I can't help but share it so this insight from Susan Visser came at just the right moment. Susan and I have exchanged various ideas over the years, so here's her view of the key Fintech trends. Data is having a tremendous impact on customer experience, and through enhanced insight is boosting customer profitability in the financial sector. Take a brief glimpse at six trends in which fintech innovation is steering imaginative and profound approaches to rich customer experiences for financial consumers. The biggest trend in fintech today is centered around improving customer experiences.

Technology and data made possible advances in...technology and data. JL The Economist reports: New techniques have made training deep networks feasible. This takes a lot of number-crunching power, which became available when several AI research groups realised that graphical processing units (GPUs), the specialised chips used in PCs and video-games consoles to generate fancy graphics, were also well suited to running deep-learning algorithms. HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver.

Head chef robot "Andrew" flips Japanese pancakes during the "Kingdom of Robot" press preview at the Huis Ten Bosch amusement park on July 12 in Sasebo, Nagasaki, Japan. In the last 400 years or so, since the time of the scientific revolution, we have come to find it natural to suppose that the world is comprehensible. Nature and its laws, operating in things most small as well as in the cosmos as a whole, are understandable. And, yet, the biologist J.B.S. Haldane, quoted in Samuel Arbesman's intriguing new book coming out next week, Overcomplicated: Technology at the Limits of Comprehension, has written: "Now, my own suspicion is that the universe is not only queerer than we suppose, but it is queerer than we can suppose." It isn't just the world of physics that has come to seem so exceedingly strange -- at the level of the quantum and also that of the multiverse -- but consciousness itself.

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 part of'A Brief History of Game AI Up to AlphaGo'. Part 2 is here and part 3 is here. In this part, we shall cover the birth of AI and the very first game-playing AI programs to run on digital computers. On March 9th of 2016, a historic milestone for AI was reached when the Google-engineered program AlphaGo defeated the world-class Go champion Lee Sedol. Go is a two-player strategy board game like Chess, but the larger number of possible moves and difficulty of evaluation make Go the harder problem for AI.

This is the second part of'A Brief History of Game AI Up to AlphaGo'. Part 1 is here and part 3 is here. In this part, we shall cover just about four decades of progress, from the first victories of computers against people at Checkers and Chess all the way up to DeepBlue's victory against humanity's then-best living Chess player. By the late 1950s, the industrious engineers at IBM were far from the only ones working on AI -- excitement for the new field filled research groups in universities from the US to the Soviet Union. One such group was made up of Allen Newell and Herbert Simon (both attendants of the Dartmouth Conference) from Carnegie Mellon University, and Cliff Shaw from RAND Corporation. They collaborated on Chess AI from 1955 to 1958, culminating in "Chess Playing Programs and the Problem of Complexity"1 which both summarized existing Chess AI research and contributed new ideas that they tested with the NSS (Newell, Shaw, and Simon) Chess program. Just as Shannon noted that master players use intuition to think selectively about moves, Newell, Shaw and Simon considered heuristics to be an important aspect of human Chess-playing.