Summary: Automated Machine Learning has only been around for a little over two years and already there are over 20 providers in this space. However, a new European AML platform called Tazi, new in the US, is showing what the next generation of AML will look like. I've been a follower and a fan of Automated Machine Learning (AML) since it first appeared in the market about two years ago. I wrote an article on all five of the market entrants I could find at the time under the somewhat scary title'Data Scientists Automated and Unemployed by 2025!'. As time passed I tried to keep up with the new entrants.

Reinforcement Learning (RL) is an area of machine learning, where an agent learns by interacting with its environment to achieve a goal.In this course, you will be introduced to the world of reinforcement learning. You will learn how to frame reinforcement learning problems and start tackling classic examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole. You will explore the basic algorithms from multi-armed bandits, dynamic programming, TD (temporal difference) learning, and progress towards larger state space using function approximation, in particular using deep learning. You will also learn about algorithms that focus on searching the best policy with policy gradient and actor critic methods. Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game.

The artificial intelligence (AI) community has a clear message for researchers in South Korea: Don't make killer robots. Nearly 60 AI and robotics experts from almost 30 countries have signed an open letter calling for a boycott against KAIST, a public university in Daejeon, South Korea, that has been reported to be "develop[ing] artificial intelligence technologies to be applied to military weapons, joining the global competition to develop autonomous arms," the open letter said. In other words, KAIST might be researching how to make military-grade AI weapons. According to the open letter, AI experts the world over became concerned when they learned that KAIST -- in collaboration with Hanwha Systems, South Korea's leading arms company -- opened a new facility on Feb. 20 called the Research Center for the Convergence of National Defense and Artificial Intelligence. Given that the United Nations (U.N.) is already discussing how to safeguard the international community against killer AI robots, "it is regrettable that a prestigious institution like KAIST looks to accelerate the arms race to develop such weapons," the researchers wrote in the letter.

The risk for robotics and automation to displace human employees is very real, but these technologies might affect men and women differently. According to a new study from Website Builder Expert (WBE), robots will steal more jobs from women than men in certain countries and vice versa. For example, the report said, women in the US and the UK are more likely to lose their job to a robot than men in those countries are. However, female workers in Britain are "much more likely to lose their jobs to robots than American women," the report said. Men in the US are also safer from losing their jobs to automation than men in Britain.

A former US marine who lost her left arm at the shoulder in a motorcycle accident has been fitted with a bionic prosthetic arm that allows her to feel things through the fake fingers. Claudia Mitchell, 37, never thought she'd be able to do things like open water bottles or pick up items with her left thumb and forefinger again after her arm was amputated. However, through more than a decade of research trials a team at Cleveland Clinic has created a bionic prosthetic arm that allows her to do most of those things she didn't think would ever be possible. The cutting-edge technology is part of a booming field of research into prosthetics that could revolutionize movement for amputees. Claudia Mitchell, 37, lost her arm at the shoulder in a motorcycle accident in 2004.

London/Singapore:The scandal over the alleged abuse of Facebook Inc.'s user data is unfolding a long way from China, and yet privacy regulators in Beijing have been watching intently. Their interest shows how all three big players in the technology world--the Americans, Chinese and Europeans--are feeling their way to balance the demands of consumers for privacy and of governments for security. But they're also competing to shape the rules of the game for internet companies and, for the US and China in particular, to maximize access to data for a bigger goal: to dominate artificial intelligence, the next frontier in machine learning. "All of these governments and companies are in the middle of trying to figure out what data governance should look like," said Samm Sacks, senior fellow in the technology program at the Center for Strategic and International Studies, a Washington think tank. "And this is happening in a global context that will have implications for trade and for research and development in AI." New Chinese privacy standards take effect on 1 May.

WASHINGTON – Investigators on two congressional panels are looking into whether Russia acquired the data of millions of Facebook users and if companies with ties to then-2016 Republican election candidate Donald Trump's campaign played any role in providing it, three sources familiar with the inquiries said. On Tuesday, Facebook said it had deleted 70 Facebook accounts, 138 Facebook pages and 65 Instagram accounts that it said were "controlled" by the Internet Research Agency (IRA), a Russian propaganda arm based in St. Petersburg. Last September, Facebook disclosed that it had suspended about 470 "inauthentic accounts" that it believed were "likely operated out of Russia" and had purchased 3,000 ads which "appeared to focus on amplifying divisive social and political messages." And on Wednesday, the social media platform said the personal information of up to 87 million users, mostly in the United States, may have been improperly shared with political consultancy Cambridge Analytica, up from a previous news media estimate of more than 50 million. Chief Executive Officer Mark Zuckerberg will testify on April 10 and 11 to Congress.

Politicians worldwide are stealing one of the US government's best ideas by drawing up ambitious plans to make the most of advances in artificial intelligence. These AI manifestos, penned in Paris, Beijing, and elsewhere, follow the example of the Obama administration, which released a report on the technology toward the end of its tenure. This report did not include funding, but it made it clear that AI should be a key focus of government strategy. The Trump administration has abandoned this vision and has no intention of devising its own AI plan, say those working there. They say there is no need for an AI moonshot, and that minimizing government interference is the best way to make sure the technology flourishes.

Deep Neural Network (DNN) acoustic models often use discriminative sequence training that optimises an objective function that better approximates the word error rate (WER) than frame-based training. Sequence training is normally implemented using Stochastic Gradient Descent (SGD) or Hessian Free (HF) training. This paper proposes an alternative batch style optimisation framework that employs a Natural Gradient (NG) approach to traverse through the parameter space. By correcting the gradient according to the local curvature of the KL-divergence, the NG optimisation process converges more quickly than HF. Furthermore, the proposed NG approach can be applied to any sequence discriminative training criterion. The efficacy of the NG method is shown using experiments on a Multi-Genre Broadcast (MGB) transcription task that demonstrates both the computational efficiency and the accuracy of the resulting DNN models.

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concepts are represented by regions in this space. In this article, we extend our recent mathematical formalization of this framework by providing quantitative mathematical definitions for measuring relations between concepts: We develop formal ways for computing concept size, subsethood, implication, similarity, and betweenness. This considerably increases the representational capabilities of our formalization and makes it the most thorough and comprehensive formalization of conceptual spaces developed so far.