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.

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.

The technology for autonomous vehicles has been around for a while, and major automakers and tech companies worldwide have invested billions of dollars in making it a reality. But according to industry analysts, it will be years before the automotive industry evolves to the point where most driving conditions can be handled by vehicles entirely independently without human intervention. Real-life situations, including making split-second decisions, dealing with quickly changing weather, and being able to see another motorist at a crosswalk, are best left to an attentive driver. Technology may be very useful; in some cases, when used appropriately, some of the modern automobile assist systems can even save lives. But driving is challenging; there are many types of roads, lanes, and weather conditions, so taking the same course of action is only sometimes the best.

A new study has found that Fourier analysis, a mathematical technique that has been around for 200 years, can be used to reveal important information about how deep neural networks learn to perform complex physics tasks, such as climate and turbulence modeling. This research highlights the potential of Fourier analysis as a tool for gaining insights into the inner workings of artificial intelligence and could have significant implications for the development of more effective machine learning algorithms. Fourier transformations reveal how deep neural network learns complex physics. One of the oldest tools in computational physics -- a 200-year-old mathematical technique known as Fourier analysis -- can reveal crucial information about how a form of artificial intelligence called a deep neural network learns to perform tasks involving complex physics like climate and turbulence modeling, according to a new study. The discovery by mechanical engineering researchers at Rice University is described in an open-access study published in the journal PNAS Nexus, a sister publication of the Proceedings of the National Academy of Sciences.

If you want to use ChatGPT as an app on your Apple Watch, then Petey could be the one to download. Developed by Hidde van der Ploeg, Petey allows you to type in, swipe or simply speak into your Watch for requests, and it will respond in a similar fashion to other ChatGPT services you may have used recently, such as Bing and MacGPT. If you're unaware, ChatGPT is an artificial intelligence assistant that can be implemented into apps and services. Similar to Siri and Google Assistant but much more powerful, it can give you recipes, methods to calm down anxious dogs, and much more. Petey is available for $4.99 / £4.99 on watchOS 6 and above, you don't even need to sign up for an OpenAI account compared to the Siri Shortcut that also enables ChatGPT on your wrist - it's one download, and you're good to go. AI is moving at a supersonic pace, with ChatGPT recently updated to version 4, bringing with it faster and more sophisticated answers.