Designing for the social, cultural, and ethical implications of ML are just as important as its technical advances.

Teach a robot to open a door, and it ought to unlock a lifetime of opportunities. Just over a year after graduating from Alphabet's X moonshot lab, the team that trained over a hundred wheeled, one-armed robots to squeegee cafeteria tables, separate trash and recycling, and yes, open doors, is shutting down as part of budget cuts spreading across the Google parent, a spokeswoman confirmed. "Everyday Robots will no longer be a separate project within Alphabet," says Denise Gamboa, director of marketing and communications for Everyday Robots. "Some of the technology and part of the team will be consolidated into existing robotics efforts within Google Research." The robotics venture is the latest failed bet for X, which in the past decade also spun out internet-beaming balloons (Loon) and power-generating kites (Makani) before deeming them too commercially inviable to keep afloat.

Presently, enterprises have implemented advanced artificial intelligence (AI) technologies to support business process automation (BPA), provide valuable data insights, and facilitate employee and customer engagement.7 However, developing and deploying new AI-enabled applications poses some management and technology challenges.3,5,12,15 Management challenges include identifying appropriate business use cases for AI-enabled applications, lack of expertise in applying advanced AI technologies, and insufficient funding. Concerning technology challenges, organizations continuously encounter obsolete, incumbent information technology (IT)/information systems (IS) facilities; difficulty and complexity integrating new AI projects into existing IT/IS processes; immature and underdeveloped AI infrastructure; inadequate data quantity and poor-quality learning requirements; growing security problems/threats; and inefficient data preprocessing assistance. Furthermore, major cloud service vendors (for example, Amazon, Google, and Microsoft) and third-party vendors (for instance, Salesforce and Sense-Time) have stepped up efforts as major players in the AI-as-a-service (AIaaS) race by integrating cloud services with AI core components (for example, enormous amounts of data, advanced learning algorithms, and powerful computing hardware).4 Although AIaaS offerings allow companies to leverage AI power without investing massive resources from scratch,8 numerous issues have emerged to hinder the development of desired AI systems. For example, current AI offerings are recognized as a fully bundled package, offering less interoperability between different vendors and causing vendor lock-in and proprietary concerns.

The combined power of AI and robotics is revolutionizing mobility and manufacturing. Automated vehicles, airplanes, people movers, and warehouse robots are improving in their range, flexibility, situational awareness, and intelligence, while better technology, a hunger for increased productivity and efficiency, and the pressures of covid-19 lockdowns have fueled investment in autonomous systems. In 2020 and…

San Francisco's board of supervisors recently voted to let their police deploy robots equipped with lethal explosives – before backtracking several weeks later. In America, the vote sparked a fierce debate on the militarisation of the police, but it raises fundamental questions for us all about the role of robots and AI in fighting crime, how policing decisions are made and, indeed, the very purpose of our criminal justice systems. In the UK, officers operate under the principle of "policing by consent" rather than by force. But according to the 2020 Crime Survey for England and Wales, public confidence in the police has fallen from 62% in 2017 to 55%. One recent poll asked Londoners if the Met was institutionally sexist and racist.

But one effort from the US Department of Defense does seem to be effective: xView2. Though it's still in its early phases of deployment, this visual computing project has already helped with disaster logistics and on the ground rescue missions in Turkey. An open-source project that was sponsored and developed by the Pentagon's Defense Innovation Unit and Carnegie Mellon University's Software Engineering Institute in 2019, xView2 has collaborated with many research partners, including Microsoft and the University of California, Berkeley. It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity much faster than is possible with current methods. Ritwik Gupta, the principal AI scientist at the Defense Innovation Unit and a researcher at Berkeley, tells me this means the program can directly help first responders and recovery experts on the ground quickly get an assessment that can aid in finding survivors and help coordinate reconstruction efforts over time.

The progress of artificial intelligence models over the past few years has been faster than almost anyone expected. Some advances have left society scrabbling to adapt. Teachers are struggling to stop students using chatbots to write their essays, artists claim they are losing paid work to image-creating AIs and efforts are under way in some places to replace journalists with large language models. But bigger changes are afoot.

Chatbots like Bing have kicked off a major new AI arms race between the biggest tech companies. Though Google, Microsoft, Amazon and Facebook have invested in AI tech for years, it's mostly worked to improve existing products, like search or content-recommendation algorithms. But when the start-up company OpenAI began making public its "generative" AI tools -- including the popular ChatGPT chatbot -- it led competitors to brush away their previous, relatively cautious approaches to the tech.

After years selling its controversial Full-Self Driving software upgrade for thousands of dollars, Tesla today issued a recall for every one of the nearly 363,000 vehicles using the feature. The move was prompted by a US government agency saying the software had in "rare circumstances" put drivers in danger and could increase the risk of a crash in everyday situations. Recalls are common in the auto industry and mostly target particular parts or road situations. Tesla's latest recall is sweeping, with the National Highway Traffic Safety Administration saying the Full Self-Driving software can break local traffic laws and act in a way the driver doesn't expect in a grab bag of road situations. According to the agency's filing, those include driving through a yellow light on the verge of turning red; not properly stopping at a stop sign; speeding, due to failing to detect a road sign or because the driver has set their car to default to a faster speed; and making unexpected lane changes to move out of turn-only lanes when going straight through an intersection.

The Bing chatbot is powered by a kind of artificial intelligence called a neural network. That may sound like a computerized brain, but the term is misleading. A neural network is just a mathematical system that learns skills by analyzing vast amounts of digital data. As a neural network examines thousands of cat photos, for instance, it can learn to recognize a cat. Most people use neural networks every day. It's the technology that identifies people, pets and other objects in images posted to internet services like Google Photos.