The fields of artificial intelligence and machine learning are moving so quickly that any notion of ethics is lagging decades behind, or left to works of science fiction. This might explain a new study out of Shanghai Jiao Tong University, which says computers can tell whether you will be a criminal based on nothing more than your facial features. The bankrupt attempt to infer moral qualities from physiology was a popular pursuit for millennia, particularly among those who wanted to justify the supremacy of one racial group over another. But phrenology, which involved studying the cranium to determine someone's character and intelligence, was debunked around the time of the Industrial Revolution, and few outside of the pseudo-scientific fringe would still claim that the shape of your mouth or size of your eyelids might predict whether you'll become a rapist or thief. Not so in the modern age of Artificial Intelligence, apparently: In a paper titled "Automated Inference on Criminality using Face Images," two Shanghai Jiao Tong University researchers say they fed "facial images of 1,856 real persons" into computers and found "some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose-mouth angle."

But deciding to build an app in the first place was driven by a broader purpose. As one doctor put it, "If you want to do world-class sociology research, build Facebook."

From gender to education, the words used on social media carry impressions to others. Using publicly available tweets, social psychologists and computer scientists from the University of Pennsylvania Positive Psychology Center, Germany, and Australia are helping us to parse out the stereotypes formed by word choices on the social media channel Twitter. Utilizing natural language processing (NLP), a form of artificial intelligence, the researchers show where stereotyping goes from "plausible" to wrong. The research appears in Social Psychological and Personality Science. In a series of studies, participants were asked to categorize the authors of tweets based solely on the content of their social media posts.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

When the world shifted from desktop to smartphones, one thing didn't change: the existence of a screen on both devices. The screen shrank, but it remained the medium through which we interact with computers. For Google, that meant its core online advertising business -- visible search ads on a webpage -- remained intact and lucrative. Today, Google may be at the beginning of a new shift -- one toward artificially intelligent virtual assistants, in which we use our voice to interact with technology instead of our eyes. The problem with voice assistants is they don't have a screen on which to display ads.

With the nights now looming long, it's that time of season when we begin looking forward to the next year and thinking about what we need to change to stay competitive. There's not much more we can learn about 2016, but there's a lot we can learn from 2016. This article examines the ways content marketing and content marketing SEO will continue to evolve in the next 12 months. Taking more time to produce longer content is the big trend in content marketing. Marketers are spending a lot more time on each post and writing a lot longer than they did in the past. My company Orbit Media Studios just finished our third annual survey of 1,000 bloggers and the trend is clear. In 2016, the average blog post took 3 hours 16 minutes to write.

This work investigates the case of a network of agents that attempt to learn some unknown state of the world amongst the finitely many possibilities. At each time step, agents all receive random, independently distributed private signals whose distributions are dependent on the unknown state of the world. However, it may be the case that some or any of the agents cannot distinguish between two or more of the possible states based only on their private observations, as when several states result in the same distribution of the private signals. In our model, the agents form some initial belief (probability distribution) about the unknown state and then refine their beliefs in accordance with their private observations, as well as the beliefs of their neighbors. An agent learns the unknown state when her belief converges to a point mass that is concentrated at the true state. A rational agent would use the Bayes' rule to incorporate her neighbors' beliefs and own private signals over time. While such repeated applications of the Bayes' rule in networks can become computationally intractable, in this paper, we show that in the canonical cases of directed star, circle or path networks and their combinations, one can derive a class of memoryless update rules that replicate that of a single Bayesian agent but replace the self beliefs with the beliefs of the neighbors. This way, one can realize an exponentially fast rate of learning similar to the case of Bayesian (fully rational) agents. The proposed rules are a special case of the Learning without Recall.

The Schatten quasi-norm can be used to bridge the gap between the nuclear norm and rank function, and is the tighter approximation to matrix rank. However, most existing Schatten quasi-norm minimization (SQNM) algorithms, as well as for nuclear norm minimization, are too slow or even impractical for large-scale problems, due to the SVD or EVD of the whole matrix in each iteration. In this paper, we rigorously prove that for any p, p1, p2>0 satisfying 1/p=1/p1+1/p2, the Schatten-p quasi-norm of any matrix is equivalent to minimizing the product of the Schatten-p1 norm (or quasi-norm) and Schatten-p2 norm (or quasi-norm) of its two factor matrices. Then we present and prove the equivalence relationship between the product formula of the Schatten quasi-norm and its weighted sum formula for the two cases of p1 and p2: p1=p2 and p1\neq p2. In particular, when p>1/2, there is an equivalence between the Schatten-p quasi-norm of any matrix and the Schatten-2p norms of its two factor matrices, where the widely used equivalent formulation of the nuclear norm can be viewed as a special case. That is, various SQNM problems with p>1/2 can be transformed into the one only involving smooth, convex norms of two factor matrices, which can lead to simpler and more efficient algorithms than conventional methods. We further extend the theoretical results of two factor matrices to the cases of three and more factor matrices, from which we can see that for any 0

Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a layered (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov Chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods.

Google's Machine Learning framework TensorFlow was open-sourced in November 2015 [1] and has since built a growing community around it. TensorFlow is supposed to be flexible for research purposes while also allowing its models to be deployed productively. This work is aimed towards people with experience in Machine Learning considering whether they should use TensorFlow in their environment. Several aspects of the framework important for such a decision are examined, such as the heterogenity, extensibility and its computation graph. A pure Python implementation of linear classification is compared with an implementation utilizing TensorFlow. I also contrast TensorFlow to other popular frameworks with respect to modeling capability, deployment and performance and give a brief description of the current adaption of the framework.