Teleoperating a physical robot could become an important job in future, according to Sanctuary AI, based in Vancouver, Canada. The company also believes that this might provide a way to train robots how to perform tasks that are currently well out of their (mechanical) reach, and imbue machines with a physical sense of the world some argue is needed to unlock human-level artificial intelligence. Industrial robots are powerful, precise, and mostly stubbornly stupid. They cannot apply the kind of precision and responsiveness needed to perform delicate manipulation tasks. That's partly why the use of robots in factories is still relatively limited, and still requires an army of human workers to assemble all the fiddly bits into the guts of iPhones.

On the top floor of a squat Singapore industrial estate, wedged between a railway depot and water reclamation plant, is a young security firm that's shooting for the stars. Well, shooting for anything beneath the stars that shouldn't be there, technically speaking. TRD is one of the world's leading purveyors of anti-drone technology--a burgeoning industry worth some $1.1 billion last year and projected to grow to $7.4 billion by 2032. "Anti-drone is the hot topic right now," says TRD CEO Sam Ong, a former officer in the Singapore Armour Corps, where he specialized in tank technology. "Unmanned warfare is taking center stage, especially in the Ukraine war."

Google's long-awaited, AI-powered chatbot, Bard, is here. The company rolled it out to the public on Tuesday, and anyone with a Google account can join the waitlist to get access. Though it's a standalone tool for now, Google is expected to put some of this technology into Google Search in the future. But in contrast to other recent AI chatbot releases, you shouldn't expect Bard to fall in love with you or threaten world domination. Bard is, so far, pretty boring.

UNTIL Microsoft curtailed the capabilities of its Bing chatbot – codenamed Sydney and powered by an advanced version of OpenAI's ChatGPT model – there were a chaotic few days last month when it was threatening, cajoling, falling in love with and terrifying its beta testers. Even journalists who regularly write about artificial intelligence expressed surprise: they know these programs are just statistical models of the language on the internet, but they still found Sydney's "personality" unsettling and eerily human. Bing's chatbot has yet to be rolled out to the world at large and its curtailment has prevented it from going off the rails again, but it remains unnerving.

In June 2021, Dave Zvenyach, director of a group tasked with improving digital access to US government services, sent a Slack message to his team. He'd decided that Login.gov, which provides a secure way to access dozens of government apps and websites, wouldn't use selfies and face recognition to verify the identity of people creating new accounts. "The benefits of liveness/selfie do not outweigh any discriminatory impact," he wrote, referring to the process of asking users to upload a selfie and photo of their ID so that algorithms can compare the two. Face recognition technology has become more accurate, but many systems have been found to work less reliably for women with dark skin, people who identify as Asian, or people with a nonbinary gender identity. Yet Zvenyach's pronouncement also put Login.gov and US agencies using the service at odds with federal security guidelines.

While artificial intelligence continues to transform health care, the tech has an Achilles heel: training AI systems to perform specific tasks requires a great deal of annotated data that engineers sometimes just don't have or cannot get. In a perfect world, researchers would be able to digitally generate the exact data they need when they need it, unlocking new capabilities of AI. In reality, however, even digitally generating this data is tricky because real-world data, especially in medicine, is complex and multi-faceted. But solutions are in the pipeline. Researchers in the Whiting School of Engineering's Laboratory for Computational Sensing and Robotics have created software to realistically simulate the data necessary for developing AI algorithms that perform important tasks in surgery, such as X-ray image analysis.

When rumors began swirling last month about the Chinese search giant Baidu working on a chatbot to rival OpenAI's ChatGPT, it seemed like the perfect move. Baidu has invested heavily in artificial intelligence over the past decade and could harness the technology for its leading search engine, as Microsoft has done for Bing and Google says it will do too. Yet when Baidu unveiled Ernie Bot, or 文心一言 "Wenxin Yiyan" in Chinese, in Beijing earlier this month, the news fell flat. Robin Li, Baidu's CEO, admitted halfway through the launch stream that demos of Ernie Bot answering general knowledge questions, summarizing information from the web, and generating images were prerecorded, leading to snarky commentary on Chinese social media. It didn't help that OpenAI had introduced a major upgrade, called GPT-4, to the AI technology that powers ChatGPT only the day before.

Moatasem Jibril, a young man from Sudan, is realising his dream of conducting technological experiments to manufacture robots by using recycled electronic waste. Despite modest capabilities and living in a mud house in the city of Omdurman, west of the capital, Khartoum, Jibril did not give up on his dream of making a robot, even after having to quit university due to the deteriorating economic conditions of his family. For about ten years, Jibril has been trying to create robots in a narrow space inside his family house, and he challenges poverty by working daily in the market to earn money to purchase the materials he needs for his project. He hopes that his dream will be funded by any businessman or institution. Sudan is suffering from many crises, starting with a shortage of basic and imported commodities, as well as the depreciation of the local currency, in addition to the government's measures to lift fuel subsidies at the request of the International Monetary Fund in 2021.

Slip Robotics demonstrated its automated trailer loading/unloading system (ATLS) at ProMat 2023. The omnidirectional robot is designed to carry up to 8 full pallets and a total of 6 tons. It is capable of autonomously driving into a tractor-trailer for transport to the next destination. Three ATLS robots can fit inside a typical tractor-trailer. Fork truck operators do not need to enter the trailer and instead load and unload pallets onto the Slip ATLS in the open loading dock.

With the public release of ChatGPT and Microsoft's $10-billion investment into OpenAI, artificial intelligence (AI) is quickly gaining mainstream acceptance. For enterprise networking professionals, this means there is a very real possibility that AI traffic will affect their networks in major ways, both positive and negative. As AI becomes a core feature in mission-critical software, how should network teams and networking professionals adjust to stay ahead of the trend? Andrew Coward, GM of Software Defined Networking at IBM, argues that the enterprise has already lost control of its networks. The shift to the cloud has left the traditional enterprise network stranded, and AI and automation are required if enterprises hope to regain control.