Check out the "Decentralized data markets for training AI models" session at the Artificial Intelligence Conference in San Francisco, September 4-7, 2018. Hurry--early price ends July 20. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the data community about the many interesting opportunities and challenges in data itself. Much of the focus of recent press coverage has been on algorithms and models, specifically the expanding utility of deep learning.

Thousands of the top names in tech have come together to take a stand against the development of killer robots. Tesla and SpaceX CEO Elon Musk, who has long been outspoken about the dangers of AI, joined the founders of Google DeepMind, the XPrize Foundation, and over 2,500 individuals and companies in signing a pledge this week condemning lethal autonomous weapons. The industry experts vowed to'neither participate in nor support' the use of such weapons, arguing that'the decision to take a human life should never be delegated to a machine.' The move comes amid growing fears of AI systems that could target and kill a person without a human at the reins to make the final judgement call. 'There is an urgent opportunity and necessity for citizens, policymakers, and leaders to distinguish between acceptable and unacceptable uses of AI,' industry experts argue in the pledge, which was released on Wednesday at the 2018 International Joint Conference on Artificial Intelligence in Stockholm.

AUTOMATICALLY investing through an artificial intelligence (AI) feature is better value than using traditional financial advisers, according to Crowd2Fund. The peer-to-peer lender said that financial advisers can be expensive as they often charge hefty wealth management fees. By contrast, Crowd2Fund charges one per cent from each payment and offers a Smart-Invest feature. This uses AI to automatically invest funds into live opportunities on its platform. "Smart-Invest is aimed at retail investors looking to maximise their return on investment and their financial goals," said Chris Hancock, chief executive and founder of Crowd2Fund.

Thousands of scientists who specialise in artificial intelligence (AI) have declared that they will not participate in the development or manufacture of robots that can identify and attack people without human oversight. Demis Hassabis at Google DeepMind and Elon Musk at the US rocket company SpaceX are among more than 2,400 signatories to the pledge which intends to deter military firms and nations from building lethal autonomous weapon systems, also known as Laws. The move is the latest from concerned scientists and organisations to highlight the dangers of handing over life and death decisions to AI-enhanced machines. It follows calls for a preemptive ban on technology that campaigners believe could usher in a new generation of weapons of mass destruction. Orchestrated by the Boston-based organisation, The Future of Life Institute, the pledge calls on governments to agree norms, laws and regulations that stigmatise and effectively outlaw the development of killer robots.

The majority of robots are white. Do a Google image search for "robot" and see for yourself: The whiteness is overwhelming. There are some understandable reasons for this; for example, when we asked several different companies why their social home robots were white, the answer was simply because white most conveniently fits in with other home decor. But a new study suggests that the color white can also be a social cue that results in a perception of race, especially if it's presented in an anthropomorphic context, such as being the color of the outer shell of a humanoid robot. In addition, the same issue applies to robots that are black in color, according to the study.

Toby discusses his early dreams of building thinking machines inspired by science fiction - and covers AI Ethics and current to near term applicability in intelligent systems. Toby Walsh is a leading researcher in Artificial Intelligence. He was recently named in the inaugural Knowledge Nation 100, the one hundred "rock stars" of Australia's digital revolution. He is Guest Professor at TU Berlin, Scientia Professor of Artificial Intelligence at UNSW and leads the Algorithmic Decision Theory group at Data61, Australia's Centre of Excellence for ICT Research. He has been elected a fellow of the Australian Academy of Science, and has won the prestigious Humboldt research award as well as the 2016 NSW Premier's Prize for Excellence in Engineering and ICT.

Are you ready to develop and show off your machine learning skills in a way that has a positive impact on the world? If so, get your hands on an AWS DeepLens video camera and join the AWS DeepLens Challenge! About the Challenge Working together with our friends at Intel, we are launching the first in a series of eight themed challenges today, all centered around improving the world in some way. Each challenge will run for two weeks and is designed to help you to get some hands-on experience with machine learning. We will announce a fresh challenge every two weeks on the AWS Machine Learning Blog. Each challenge will have a real-world theme, a technical focus, a sample project, and a subject matter expert.

While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal. Likewise, we demonstrate that the common practice of using very few epochs during the main NAS and much larger numbers of epochs during a post-processing step is inefficient due to little correlation in the relative rankings for these two training regimes. To combat both of these problems, we propose to use a recent combination of Bayesian optimization and Hyperband for efficient joint neural architecture and hyperparameter search.

An important task for a recommender system to provide interpretable explanations for the user. This is important for the credibility of the system. Current interpretable recommender systems tend to focus on certain features known to be important to the user and offer their explanations in a structured form. It is well known that user generated reviews and feedback from reviewers have strong leverage over the users' decisions. On the other hand, recent text generation works have been shown to generate text of similar quality to human written text, and we aim to show that generated text can be successfully used to explain recommendations. In this paper, we propose a framework consisting of popular review-oriented generation models aiming to create personalised explanations for recommendations. The interpretations are generated at both character and word levels. We build a dataset containing reviewers' feedback from the Amazon books review dataset. Our cross-domain experiments are designed to bridge from natural language processing to the recommender system domain. Besides language model evaluation methods, we employ DeepCoNN, a novel review-oriented recommender system using a deep neural network, to evaluate the recommendation performance of generated reviews by root mean square error (RMSE). We demonstrate that the synthetic personalised reviews have better recommendation performance than human written reviews. To our knowledge, this presents the first machine-generated natural language explanations for rating prediction.

Google Cloud Platform is down (cloud.google.com) Credit card thieves using free-to-play apps to launder their ill-gotten gains (kromtech.com) How SSH port became 22 (www.ssh.com) Federal Reserve chair says decline in workers' share of profits'very troubling' (www.latimes.com) Trump's sycophants sink to new lows after (back.ly) U.S. To Make More Drugs Easily Available, Cutting Role Docs Play (www.bloombergquint.com) Iron Ox is hiring a Project Manager to help build the robotic farm (jobs.lever.co) Facebook's algorithm change leads to plummeting traffic and layoffs (thelogic.co)