Our finding continues the modern trend of achieving strong results with decades-old ideas. For example, in 2012, the "AlexNet" paper showed how to design, scale and train convolutional neural networks (CNNs) to achieve extremely strong results on image recognition tasks, at a time when most researchers thought that CNNs were not a promising approach to computer vision. Similarly, in 2013, the Deep Q-Learning paper showed how to combine Q-Learning with CNNs to successfully solve Atari games, reinvigorating RL as a research field with exciting experimental (rather than theoretical) results. Likewise, our work demonstrates that ES achieves strong performance on RL benchmarks, dispelling the common belief that ES methods are impossible to apply to high dimensional problems. ES is easy to implement and scale.

Washington (AFP) - Are robots coming for your job? Although technology has long affected the labor force, recent advances in artificial intelligence and robotics are heightening concerns about automation replacing a growing number of occupations, including highly skilled or "knowledge-based" jobs. Just a few examples: self-driving technology may eliminate the need for taxi, Uber and truck drivers, algorithms are playing a growing role in journalism, robots are informing consumers as mall greeters, and medicine is adapting robotic surgery and artificial intelligence to detect cancer and heart conditions. Of 700 occupations in the United States, 47 percent are at "high risk" from automation, an Oxford University study concluded in 2013. A McKinsey study released this year offered a similar view, saying "about half" of activities in the world's workforce "could potentially be automated by adapting currently demonstrated technologies."

It's not yet clear whether, with the rise of artificial intelligence, workforce automation will lead to an overall rise or drop in human job creation. That's the argument put forward by University of Geneva professor and tax lawyer Xavier Oberson. Oberson argues that as robots take over more and more jobs – particularly in the industry and service sectors – there will be a rise in unemployment and a corresponding drop in tax and social security receipts by governments all over the world. He believes that imposing a tax on work done by robots could help offset these losses. Logistically, he says this could be managed by creating a "legal entity" representing robots, just as is done today for corporations.

In a previous story, I wrote about how a Game Theoretic approach was influencing developments in the Deep Learning field. In this story, I now write about DeepMind's latest foray into this exciting area. Yesterday, February 19th 2017), DeepMind presents their latest research on this subject titled "Understanding Agent Cooperation". The gist of the research is that, they employed Deep Reinforcement Learning networks in two game environments to study their behavior. The motivation is to study multi-agent systems to better understand and control these kinds of systems. In a previous story (see: "Five Capability Levels of Deep Learning", I laid out a road map as to how Deep Learning will evolve in even greater capabilities.

In distributed function computation, each node has an initial value and the goal is to compute a function of these values in a distributed manner. In this paper, we propose a novel token-based approach to compute a wide class of target functions to which we refer as "Token-based function Computation with Memory" (TCM) algorithm. In this approach, node values are attached to tokens and travel across the network. Each pair of travelling tokens would coalesce when they meet, forming a token with a new value as a function of the original token values. In contrast to the Coalescing Random Walk (CRW) algorithm, where token movement is governed by random walk, meeting of tokens in our scheme is accelerated by adopting a novel chasing mechanism. We proved that, compared to the CRW algorithm, the TCM algorithm results in a reduction of time complexity by a factor of at least $\sqrt{n/\log(n)}$ in Erd\"os-Renyi and complete graphs, and by a factor of $\log(n)/\log(\log(n))$ in torus networks. Simulation results show that there is at least a constant factor improvement in the message complexity of TCM algorithm in all considered topologies. Robustness of the CRW and TCM algorithms in the presence of node failure is analyzed. We show that their robustness can be improved by running multiple instances of the algorithms in parallel.

We propose a scheme for training a computerized agent to perform complex human tasks such as highway steering. The scheme is designed to follow a natural learning process whereby a human instructor teaches a computerized trainee. The learning process consists of five elements: (i) unsupervised feature learning; (ii) supervised imitation learning; (iii) supervised reward induction; (iv) supervised safety module construction; and (v) reinforcement learning. We implemented the last four elements of the scheme using deep convolutional networks and applied it to successfully create a computerized agent capable of autonomous highway steering over the well-known racing game Assetto Corsa. We demonstrate that the use of the last four elements is essential to effectively carry out the steering task using vision alone, without access to a driving simulator internals, and operating in wall-clock time. This is made possible also through the introduction of a safety network, a novel way for preventing the agent from performing catastrophic mistakes during the reinforcement learning stage.

Datasets with hundreds of variables and many missing values are commonplace. In this setting, it is both statistically and computationally challenging to detect true predictive relationships between variables and also to suppress false positives. This paper proposes an approach that combines probabilistic programming, information theory, and non-parametric Bayes. It shows how to use Bayesian non-parametric modeling to (i) build an ensemble of joint probability models for all the variables; (ii) efficiently detect marginal independencies; and (iii) estimate the conditional mutual information between arbitrary subsets of variables, subject to a broad class of constraints. Users can access these capabilities using BayesDB, a probabilistic programming platform for probabilistic data analysis, by writing queries in a simple, SQL-like language. This paper demonstrates empirically that the method can (i) detect context-specific (in)dependencies on challenging synthetic problems and (ii) yield improved sensitivity and specificity over baselines from statistics and machine learning, on a real-world database of over 300 sparsely observed indicators of macroeconomic development and public health.

Bilinear models provide rich representations compared with linear models. They have been applied in various visual tasks, such as object recognition, segmentation, and visual question-answering, to get state-of-the-art performances taking advantage of the expanded representations. However, bilinear representations tend to be high-dimensional, limiting the applicability to computationally complex tasks. We propose low-rank bilinear pooling using Hadamard product for an efficient attention mechanism of multimodal learning. We show that our model outperforms compact bilinear pooling in visual question-answering tasks with the state-of-the-art results on the VQA dataset, having a better parsimonious property. Bilinear models (Tenenbaum & Freeman, 2000) provide richer representations than linear models. To exploit this advantage, fully-connected layers in neural networks can be replaced with bilinear pooling. The outer product of two vectors (or Kroneker product for matrices) is involved in bilinear pooling, as a result of this, all pairwise interactions among given features are considered. Recently, a successful application of this technique is used for fine-grained visual recognition (Lin et al., 2015).

Over the course of March of the Machines, there has been a lot of talk about machine learning and deep learning, and the jobs arising from them, but what is it like to work in that field? When we talk about emerging technologies and the future of tech, deep learning is an area that crops up again and again. It will be the driving force behind the development of AI and robotics, and already plays an essential part in the creation of tech we use on a daily basis. But what is it like to work in this evolving sector? We asked Kevin McGuinness, research fellow at the Insight Centre for Data Analytics, Dublin City University (DCU), about what he's doing with deep learning and how the area is changing.

Artificial intelligence is all around. Tech giants and startups are cultivating their AI technology to create better ways of living such as riding on driverless cars and making payment by scanning your face. While this lies on the grounds that AI's practical skills can ultimately replace human labor, one startup believes that AI can be an emotional companion to human. Shanghai-based startup Emotibot made an AI-powered bot that can complete practical tasks as well as have a conversation with you. Corporates who want more customer interaction are in talk with the company to source their technology to humanize their online customer service.