Hollywood legend Robert Downey Jr. is putting down his Iron Man suit and taking up the bigger challenge of cleaning up the Earth. Iron Man's on-screen nemesis Thanos had a radical way of reducing humanity's impact on the Earth, but Downey Jr. hopes to use a more humane solution. Appearing on-stage at Amazon's new re:MARS (Machine Learning, Automation, Robotics and Space) conference in Las Vegas, Downey Jr. launched an initiative called Footprint Coalition. "I don't pretend to understand the complexities we face as a species, just because I portrayed a genius in my professional life. My scholastic achievement peaked at a correctional finishing," Downey Jr. joked, in reference to being incarcerated on drug charges and never making it past high school.

Amazon on Wednesday announced Alexa Conversations, a deep learning-based approach for developers to create natural voice experiences on Alexa. The toolset, currently in preview, lets developers build natural skill dialogs with fewer lines of code and less training data, Amazon said. To use Conversations, developers provide API access to their skills' functionality, a sample of dialogs annotated with the prompts that Alexa will say to say to the customer, and the actions they expect the customer to take. Alexa Conversations' AI finishes the job, using the input data to generate dialog flows and variations. "It's way easier to build a complex voice experience with Alexa Conversations due to its underlying deep-learning-based dialog modeling,"said Rohit Prasad, Alexa vice president and head scientist, at Amazon's re:MARS conference in Las Vegas Wednesday.

Amazon on Wednesday announced Alexa Conversations, a deep learning-based approach for developers to create natural voice experiences on Alexa. The toolset, currently in preview, lets developers build natural skill dialogs with fewer lines of code and less training data, Amazon said. To use Conversations, developers provide API access to their skills' functionality, a sample of dialogs annotated with the prompts that Alexa will say to say to the customer, and the actions they expect the customer to take. Alexa Conversations' AI finishes the job, using the input data to generate dialog flows and variations. "It's way easier to build a complex voice experience with Alexa Conversations due to its underlying deep-learning-based dialog modeling,"said Rohit Prasad, Alexa vice president and head scientist, at Amazon's re:MARS conference in Las Vegas Wednesday.

Ethical and responsible AI development is a top concern for IT Leaders. In our new study, The AI Ethics Deficit, we found that 94% of IT Leaders in the US and UK believe more attention needs to be paid to corporate responsibility and ethics in AI development. A further 87% of IT Leaders believe AI development should be regulated to ensure it serves the best interests of business, governments, and citizens alike. Together with independent research firm Vanson Bourne, we surveyed 300 IT Leaders representing organizations with more than 1,000 employees in the US and UK, to understand their views and perspectives on the future development of AI.

Agerris, an Australia-based robotics and AI platform for agriculture, announced over the weekend that it has raised $6.5 million (AUSD) in seed funding from Uniseed, Carthona Capital and BridgeLane Group. The startup was founded by Professor Salah Sukarrieh and began as research at the Australian Centre for Field Robotics at the University of Sydney (which is also a partner in Uniseed). From the looks of it, Agerris is building a modular robotics and AI platform that has broad applications for both plant and livestock farmers. According to a University of Sydney news post, Agerris has two main products. The "Swagbot" can autonomously monitor and identify weed issues, detect food and crops through computer vision, as well as herd livestock.

The age of automation, and on the near horizon, artificial intelligence (AI) technologies offer new job opportunities and avenues for economic advancement, but women face new challenges overlaid on long-established ones. Between 40 million and 160 million women globally may need to transition between occupations by 2030, often into higher-skilled roles. To weather this disruption, women (and men) need to be skilled, mobile, and tech-savvy, but women face pervasive barriers on each, and will need targeted support to move forward in the world of work. A new McKinsey Global Institute (MGI) report, The future of women at work: Transitions in the age of automation (PDF–2MB), finds that if women make these transitions, they could be on the path to more productive, better-paid work. If they cannot, they could face a growing wage gap or be left further behind when progress toward gender parity in work is already slow. This new research explores potential patterns in "jobs lost" (jobs displaced by automation), "jobs gained" (job creation driven by economic growth, investment, demographic changes, and technological innovation), and "jobs changed" (jobs whose activities and skill requirements change from partial automation) for women by exploring several scenarios of how automation adoption and job creation trends could play out by 2030 for men and women given current gender patterns in the global workforce. These scenarios are not meant to predict the future; rather, they serve as a tool to understand a range of possible outcomes and identify interventions needed.

A new tool launched by privacy activists offers to help travelers avoid increasingly invasive facial recognition technologies in airports. Activist groups Fight for the Future, Demand Progress and CREDO on Wednesday unveiled a new website called AirlinePrivacy.com, The site also helps customers to directly book flights with airlines that don't use facial recognition technologies. Airlines' use of facial recognition technology is raising fresh questions about privacy and data security, advocates have argued. Instead of verifying passengers' details by scanning a boarding pass, the technology – which is provided by government agencies – scans passengers' face and sends that information to border control to verify identity and flight details.

Exploration strategy design is one of the challenging problems in reinforcement learning~(RL), especially when the environment contains a large state space or sparse rewards. During exploration, the agent tries to discover novel areas or high reward~(quality) areas. In most existing methods, the novelty and quality in the neighboring area of the current state are not well utilized to guide the exploration of the agent. To tackle this problem, we propose a novel RL framework, called \underline{c}lustered \underline{r}einforcement \underline{l}earning~(CRL), for efficient exploration in RL. CRL adopts clustering to divide the collected states into several clusters, based on which a bonus reward reflecting both novelty and quality in the neighboring area~(cluster) of the current state is given to the agent. Experiments on a continuous control task and several \emph{Atari 2600} games show that CRL can outperform other state-of-the-art methods to achieve the best performance in most cases.

One of the main challenges in the construction of oil and gas wells is the need to detect and avoid abnormal situations, which can lead to accidents. Accidents have some indicators that help to find them during the drilling process. In this article, we present a data-driven model trained on historical data from drilling accidents that can detect different types of accidents using real-time signals. The results show that using the time-series comparison, based on aggregated statistics and gradient boosting classification, it is possible to detect an anomaly and identify its type by comparing current measurements while drilling with the stored ones from the database of accidents.

We consider the setup of stochastic multi-armed bandits in the case when reward distributions are piecewise i.i.d. and bounded with unknown changepoints. We focus on the case when changes happen simultaneously on all arms, and in stark contrast with the existing literature, we target gap-dependent (as opposed to only gap-independent) regret bounds involving the magnitude of changes $(\Delta^{chg}_{i,g})$ and optimality-gaps ($\Delta^{opt}_{i,g}$). Diverging from previous works, we assume the more realistic scenario that there can be undetectable changepoint gaps and under a different set of assumptions, we show that as long as the compounded delayed detection for each changepoint is bounded there is no need for forced exploration to actively detect changepoints. We introduce two adaptations of UCB-strategies that employ scan-statistics in order to actively detect the changepoints, without knowing in advance the changepoints and also the mean before and after any change. Our first method \UCBLCPD does not know the number of changepoints $G$ or time horizon $T$ and achieves the first time-uniform concentration bound for this setting using the Laplace method of integration. The second strategy \ImpCPD makes use of the knowledge of $T$ to achieve the order optimal regret bound of $\min\big\lbrace O(\sum\limits_{i=1}^{K} \sum\limits_{g=1}^{G}\frac{\log(T/H_{1,g})}{\Delta^{opt}_{i,g}}), O(\sqrt{GT})\big\rbrace$, (where $H_{1,g}$ is the problem complexity) thereby closing an important gap with respect to the lower bound in a specific challenging setting. Our theoretical findings are supported by numerical experiments on synthetic and real-life datasets.