Enterprises know they want to do machine learning, but they also know they can't afford to think too long or too hard about it. They need to act, and they have specific business problems that they want to solve. And they know instinctively and anecdotally from the experience of the hyperscalers and the HPC centers of the world that machine learning techniques can be utterly transformative in augmenting existing applications, replacing hand-coded applications, or creating whole new classes of applications that were not possible before. They also have to decide if they want to run their AI workloads on-premise or on any one of a number of clouds where a lot of the software for creating models and training them are available as a service. And let's acknowledge that a lot of those models were created by the public cloud giants for internal workloads long before they were peddled as a service.

Drones and robots could form part of a new radiation-monitoring system at the Chernobyl power station in Ukraine, as scientists at the plant fear that existing sensor networks built after the nuclear accident in 1986 have been at least partially destroyed by Russian troops. When Russia seized the Chernobyl plant in February, the sensors monitoring gamma radiation levels quickly went offline and most remained that way.

Next time you pass a farm where a modern tractor is cruising around a field, take a closer look. While there is a farmer sitting in the cab, the vehicle might be driving itself. That tractor is often operating on auto pilot using semi-autonomous, self-driving technology. While the tractor plows along thanks to features like autosteer and computer-assisted technologies for applying fertilizers or pesticides, the farmer can send work texts or emails, pay bills or even flip through Instagram stories or TikTok videos. For farmers, this kind of efficiency is not a luxury.

Venture capital firms are eager to allocate money into robotics firms in a wide range of industries from shipping to healthcare as more automation became a focus during the global pandemic. In 2021, funding globally for robotics and drone companies rose to $14.9 billion, according to PitchBook. The amount of funding raised for robotics in 2022 has been steady. By January, VCs allocated $560 million of funding into robotics startups, excluding the round for Wandelbots, a German industrial robotics company that raised $84 million for its Series C round in January. The funding for the company's no-code platform came from U.S. investor Insight Partners, which led the round and was also supported by its existing backers - Microsoft (MSFT) - Get Microsoft Corporation Report, 83North, Next47, Paua, Atlantic Labs and EQT.

"It feels like you have a new set of eyes," said Barbara Han, a disease ecologist at the Cary Institute of Ecosystem Studies in Millbrook, N.Y., who collaborates with Dr. Carlson. "You just can't see in as many dimensions as the model can." Dr. Han first came across machine learning in 2010. Computer scientists had been developing the technique for decades, and were starting to build powerful tools with it. These days, machine learning enables computers to spot fraudulent credit charges and recognize people's faces.

Shobita Parthasarathy says that LLMs could help to advance research, but their use should be regulated. Machine-learning algorithms that generate fluent language from vast amounts of text could change how science is done -- but not necessarily for the better, says Shobita Parthasarathy, a specialist in the governance of emerging technologies at the University of Michigan in Ann Arbor. In a report published on 27 April, Parthasarathy and other researchers try to anticipate societal impacts of emerging artificial-intelligence (AI) technologies called large language models (LLMs). These can churn out astonishingly convincing prose, translate between languages, answer questions and even produce code. The corporations building them -- including Google, Facebook and Microsoft -- aim to use them in chatbots and search engines, and to summarize documents. They sometimes parrot errors or problematic stereotypes in the millions or billions of documents they're trained on.

University of Pittsburgh School of Medicine data scientists and UPMC neurotrauma surgeons have created a prognostic model that uses automated brain scans and machine learning to inform outcomes in patients with severe traumatic brain injuries (TBI). Their findings are published in the journal Radiology, in a paper titled, "Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans." The researchers demonstrated that their advanced machine-learning algorithm can analyze brain scans and relevant clinical data from TBI patients to quickly and accurately predict survival and recovery six months after the injury. "Every day, in hospitals across the United States, care is withdrawn from patients who would have otherwise returned to independent living," said co-senior author David Okonkwo, MD, PhD, professor of neurological surgery at Pitt and UPMC. "The majority of people who survive a critical period in an acute care setting make a meaningful recovery--which further underscores the need to identify patients who are more likely to recover."

HPE has released a pair of systems designed to broaden the uptake and speed deployment of machine learning among enterprises. Swarm Learning is aimed at bringing the wisdom of crowds to machine learning modeling without sacrificing security, while the Machine Learning Development System is meant to offer a one-box training solution for companies that would otherwise have had to design and build their own machine learning infrastructure. The Machine Learning Development System is available in physical footprints of several different sizes, but the company says a "small configuration" uses an Apollo 6500 Gen10 compute server to provide the horsepower for machine learning training, HPE ProLiant DL325 servers and Aruba CX 6300 switches for management of system components, and NVIDIA's Quantum InfiniBand networking platform, along with HPE's specialist Machine Learning Development Environment and Performance Cluster management software suites. According to IDC research vice president Peter Rutten, it's essentially bringing HPC (high performance computing) capabilities to enterprise machine learning, something that would usually require enterprises to architect their own systems. "It is the kind of system that businesses are really looking for, now that AI is more mature," he said.

When COVID-19 hit, an enterprising group of pupils in Abuja, Nigeria, used their robotics class to design and build a simple robot to cut down on interpersonal contact in hospitals. Using only scraps they found around the classroom, they each contributed to the ideas, concept, mechanics and AI elements of their robot "Mairabot" – which earned praise from health officials and their teachers alike. Mairabot, by filmmaker Philip Okpokoro, introduces us to Nabila Abbas and her fellow students in this short, inspiring film. Philip Okpokoro is a Nigerian director and cinematographer with an impressive record in both documentary and live TV directing. He has directed a wide array of film projects from high-end live TV to intimate documentaries for global broadcasters, and has been awarded for best director of photography.

Roboticists have designed all sorts of jumping robots over the years, and many of them have been inspired by biology. But, as diverse as the natural world is, evolution hasn't cracked every option.