Dyson has shown off a series of prototype robots it's developing, and announced plans to hire hundreds of engineers over the next five years in order to build robots capable of household chores. The images are designed to show off the fine motor skills of the machines, with arms capable of lifting plates out of a drying rack, vacuuming a sofa, or lifting up a children's toy. The company, best known for its range of vacuum cleaners, says that it aims to develop "an autonomous device capable of household chores and other tasks," with The Guardian noting that such a device could be released by 2030. It comes over half a decade after the company released its first robotic device, the Dyson 360 Eye robot vacuum cleaner, in 2014. Dyson has long emphasized its interest in AI and robotics to underpin its future products.

When most people think of blockchain, they think financial transactions. While crypto and tokens garner the lion's share of news, blockchain, and in particular smart contracts and more recent Web3 innovations, are becoming an indispensable part of the world's transition to clean energy. In a way, energy has evolved to become more adaptable to working with blockchain. Decentralization is at the core of Web3 and blockchain technologies – there are many players, in many locations, who interact but aren't centrally controlled. As keen followers of energy and clean technologies will know, the last five years has seen an exponential increase in the decentralization of power.

This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs

If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.

Artificial intelligence is the new electricity -- it's rapidly transforming every industry, enhancing our lives, and creating huge economic value. However, left unchecked, AI also has the potential to inflict harm upon humanity. We have seen cases where it has discriminated against minority groups and unintentionally promoted hateful and violence-inciting speech. How can we ensure that AI remains a force for positive change while reducing its capacity to cause harm? To address this complex problem, we joined forces with nine other startups (including global industry-leaders like Feedzai and Talkdesk) and six AI research centers to form the Center for Responsible AI.

For five years as a data analyst, I forecasted and analyzed Google's revenue. For six years as a data visualization specialist, I've helped clients and colleagues discover new features of the data they know best. Time and time again, I've found that by being more specific about what's important to us and embracing the complexity in our data, we can discover new features in that data. These features can lead us to ask better data-driven questions that change how we analyze our data, the parameters we choose for our models, our scientific processes, or our business strategies. My colleagues Ian Johnson, Mike Freeman, and I recently collaborated on a series of data-driven stories about electricity usage in Texas and California to illustrate best practices of Analyzing Time Series Data.

Lillian Pierson is a CEO & data leader that supports data professionals to evolve into world-class leaders & entrepreneurs. To date, she's helped educate over 1.3 million data professionals on AI and data science. Lillian has authored 6 data books with Wiley & Sons Publishers as well as 8 data courses with LinkedIn Learning. She's supported a wide variety of organizations across the globe, from the United Nations and National Geographic, to Ericsson and Saudi Aramco, and everything in between. She is a licensed Professional Engineer, in good standing.

SEOUL, May 23, 2022 – Hyundai Motor Group (the Group) announced on April 29 that it recently carried out a project with Microsoft Korea to prove digital twin technology is effective at predicting an electric vehicle's battery lifespan and optimizing its battery management and performance. Using Microsoft's cloud service Azure, the Group created digital twins of actual electric vehicles (EVs) with the aim to improve the accuracy of battery lifespan prediction and customize battery management systems for each EV model. Based on the project's success, the Group will implement digital twin technology as a way to improve battery performance going forward. Through this collaboration, the Group created digital twins of EVs in a virtual space based on various driving data collected from actual EVs in the real world, and used the virtual EVs to predict the battery lifespan of each vehicle. This high-level, data-integrated analysis model uses artificial intelligence (AI), machine learning and physical models to comprehensively analyze information, such as charging and discharging cycles as well as parking and driving environments.

The historical ways in which electricity was generated in large central power plants and delivered to passive customers through a one-way transmission and distribution network – as everyone knows – is radically changing to one where consumers can generate, store and consume a significant portion of their energy needs energy locally. This, however, is only the first step, soon to be followed by the ability to share or trade with others using the distribution network. More exciting opportunities are possible with the increased digitalization of BTM assets, which in turn can be aggregated into large portfolios of flexible load and generation and optimized using artificial intelligence and machine learning.

Using computing resources at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (Berkeley Lab), researchers at Argonne National Laboratory have succeeded in exploring important materials science questions and demonstrated progress using machine learning to solve difficult search problems. By adapting a machine-learning algorithm from board games such as AlphaGo, the researchers developed force fields for nanoclusters of 54 elements across the periodic table, a dramatic leap toward understanding their unique properties and proof of concept for their search method. The team published its results in Nature Communications in January. Depending on their scale--bulk systems of 100 nanometers versus nanoclusters of less than 100 nanometers--materials can display dramatically different properties, including optical and magnetic properties, discrete energy levels, and enhanced photoluminescence. These properties may lend themselves to new scientific and industry applications, and scientists can learn about them by developing force fields--computational models that estimate the potential energies between atoms in a molecule and between molecules--for each element or compound.