Can an arbitrarily intelligent reinforcement learning agent be kept under control by a human user? Or do agents with sufficient intelligence inevitably find ways to shortcut their reward signal? This question impacts how far reinforcement learning can be scaled, and whether alternative paradigms must be developed in order to build safe artificial general intelligence. In this paper, we use an intuitive yet precise graphical model called causal influence diagrams to formalize reward tampering problems. We also describe a number of modifications to the reinforcement learning objective that prevent incentives for reward tampering. We verify the solutions using recently developed graphical criteria for inferring agent incentives from causal influence diagrams. Along the way, we also compare corrigibility and self-preservation properties of the various solutions, and discuss how they can be combined into a single agent without reward tampering incentives.

This will be held in parallel to the Photonica 2019 Conference (www.photonica.ac.rs). If you are having trouble viewing this on your mobile, switch to Desktop Site version for the best viewing experience.

The SMART Forecasting team at Walmart Labs is tasked with providing demand forecasts for over 70 million store-item combinations every week! For example, just how much of every type of ginger needs to go to every Walmart store in the U.S., every week for the next 52 weeks, with the goal of improving in stocks and reducing food waste. Our algorithm strategy was to build a suite of machine learning models and deploy them at scale to generate bespoke solutions for (oh so many!) store-item-week combinations. Random Forests would be part of this suite. We went through the traditional model development workflow of data discovery, identifying demand drivers, feature engineering, training, cross validation and testing.

SANTA CLARA, CA – Micro Focus (LSE: MCRO; NYSE: MFGP) today announced the general availability of Service Management Automation X (SMAX) 2019.05. SMAX is the first application suite for Enterprise Service Management and IT Service Management built on machine learning and analytics, powered by an embedded CMDB and Discovery to help drive down costs and speed up time to resolution. Built-in best practices are quickly and easily configured and extended in an entirely codeless way with the SMAX Studio enabling customers to achieve faster time to value. The scalable, multi-tenant cloud-native solution delivers significantly lower cost of ownership and enables customers to deploy on their choice of public or private cloud. SMAX is also available as-a-service by Micro Focus partners worldwide.

If we're going to map the world, we're not going to do it with ever-greater volumes of elbow grease. There's just too much work to do. AI and computer vision are helpful assistants in this task, however, as a Facebook effort has shown, laying down hundreds of thousands of miles of previously unmapped roads in Thailand and other less well-covered countries. The problem is simply that there's a whole lot of Earth and only a handful of people actually making maps of it. Sure, Google and Apple have dueling products -- but their focus is on businesses in cities and accurate navigation, not including every dirt path and gravel road.

While the United States currently has an edge in the race to develop artificial intelligence, China is rapidly gaining ground as Europe falls behind, according to a report released today by the Center for Data Innovation. The study arrives amid a wide-ranging debate about which region has gained AI leadership, and the implications that holds for dominating cutting-edge technologies such as autonomous vehicles and other forms of automation. The winners of an AI arms race could hold a significant economic advantage in the decades to come. There has been growing concern among U.S. tech companies and policymakers that China's initiative to make it dominant in AI by 2030 is allowing it to dictate this critical field. The ability of its central government to allow sweeping data gathering and determine official champions to lead this charge seems to have given its efforts significant momentum.

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. And it doesn't help reduce the confusion when every tech vendor rebrands their products as AI. So, what do these terms really mean? What are overlaps and differences? And most importantly, what can this do for your business?

Artificial intelligence has promised to revolutionize our lives, taking over the mundane tasks of daily existence, from prewriting "smart" email replies to driving our car through rush hour traffic. In the PR realm, AI has been touted as equal parts something to celebrate (no more manual coverage reports!) and fear (er, so long, means of employment). But the truth, as usual, lies somewhere in between. Some form of intelligent technology is already embedded in the PR industry, from the tools we use to find new audiences and monitor evolving conversations to modern media placement. Bloomberg News uses AI to generate coverage on some 3,500 earnings reports every quarter.

Our voices may convey subtle clues about our mood and psychological state. Now, scientists are using artificial intelligence to pick up these clues, with the aim of building voice-analyzing technologies that can identify individuals in need of mental-health care. But others caution they could do more harm than good. At the University of Alberta, computing science PhD student Mashrura Tasnim has developed a machine-learning model that can recognize the speech qualities of people with depression. Her goal is to create a smartphone application that would monitor users' conversations and alert their emergency contacts or mental-health professionals when it detects depression.

Scale AI Inc., a three-year-old startup run by a 22-year-old, is teaching machines how to see. For that, it just joined Silicon Valley's list of unicorns with a fresh $100 million investment that puts its valuation above the coveted $1 billion mark, and its artificial intelligence (AI) technology has already attracted big-name customers in the field for autonomous vehicles, according to Bloomberg. Alphabet Inc.'s (GOOGL) Waymo, General Motor Co.'s (GM) Cruise, and Uber Technologies Inc. (UBER) are all buying what Scale has to offer, because well, self-driving cars are machines that need to be able to see. Scale stands out because it has built a set of software tools that are significantly reducing the time it takes to train a machine how to process and interpret visual imagery. And less time means lower costs.