BAYESIAN DEEP LEARNING

#artificialintelligence

This article follows my previous one on Bayesian probability & probabilistic programming that I published few months ago on LinkedIn. And for the purpose of this article, I am going to assume that most this article readers have some idea what a Neural Network or Artificial Neural Network is. Neural Network is a non-linear function approximator. We can think of it as a parameterized function where the parameters are the weights & biases of Neural Network through which we will be typically passing our data (inputs), that will be converted to a probability between 0 and 1, to some kind of non-linearity such as a sigmoid function and help make our predictions or estimations. These non-linear functions can be composed together hence Deep Learning Neural Network with multiple layers of this function compositions.


Deep Bayesian Multi-Target Learning for Recommender Systems

arXiv.org Machine Learning

With the increasing variety of services that e-commerce platforms provide, criteria for evaluating their success become also increasingly multi-targeting. This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL). In this framework, target events are modeled as forming a Bayesian network, in which directed links are parameterized by hidden layers, and learned from training samples. The structure of Bayesian network is determined by model selection. We applied the framework to Taobao live-streaming recommendation, to simultaneously optimize (and strike a balance) on targets including click-through rate, user stay time in live room, purchasing behaviors and interactions. Significant improvement has been observed for the proposed method over other MTL frameworks and the non-MTL model. Our practice shows that with an integrated causality structure, we can effectively make the learning of a target benefit from other targets, creating significant synergy effects that improve all targets. The neural network construction guided by DBMTL fits in with the general probabilistic model connecting features and multiple targets, taking weaker assumption than the other methods discussed in this paper. This theoretical generality brings about practical generalization power over various targets distributions, including sparse targets and continuous-value ones.


Making the positive case for artificial intelligence - CBR

#artificialintelligence

In part, the critics of AI are driven by the knowledge that'white collar jobs' are the ones that are now under threat. Business leaders are frequently confronted by notions of job-killing automation and headlines on the variation of the theme that "Robots Will Steal Our Jobs." Elon Musk, CEO of Tesla, Silicon Valley figurehead, and champion of technology-driven innovation even goes a step further by suggesting AI is a fundamental threat to human civilisation. The robot on the assembly line is now a familiar image. AI in middle management is new.


Discovering Emerging Topics in Social Streams via Link Anomaly Detection

arXiv.org Machine Learning

Detection of emerging topics are now receiving renewed interest motivated by the rapid growth of social networks. Conventional term-frequency-based approaches may not be appropriate in this context, because the information exchanged are not only texts but also images, URLs, and videos. We focus on the social aspects of theses networks. That is, the links between users that are generated dynamically intentionally or unintentionally through replies, mentions, and retweets. We propose a probability model of the mentioning behaviour of a social network user, and propose to detect the emergence of a new topic from the anomaly measured through the model. We combine the proposed mention anomaly score with a recently proposed change-point detection technique based on the Sequentially Discounting Normalized Maximum Likelihood (SDNML), or with Kleinberg's burst model. Aggregating anomaly scores from hundreds of users, we show that we can detect emerging topics only based on the reply/mention relationships in social network posts. We demonstrate our technique in a number of real data sets we gathered from Twitter. The experiments show that the proposed mention-anomaly-based approaches can detect new topics at least as early as the conventional term-frequency-based approach, and sometimes much earlier when the keyword is ill-defined.


DeepMind Has Simple Tests That Might Prevent Elon Musk's AI Apocalypse

#artificialintelligence

You don't have to agree with Elon Musk's apocalyptic fears of artificial intelligence to be concerned that, in the rush to apply the technology in the real world, some algorithms could inadvertently cause harm. This type of self-learning software powers Uber's self-driving cars, helps Facebook identify people in social-media posts, and let's Amazon's Alexa understand your questions. Now DeepMind, the London-based AI company owned by Alphabet Inc., has developed a simple test to check if these new algorithms are safe.