A piece of glass with tiny little bumps on it can be used to identify objects. The "smart glass" could eventually be a more compact than using cameras and computers to achieve the same aim. Machine learning algorithms are becoming good at identifying objects but using them usually requires a camera and a computer.

A study conducted at the University of Jyväskylä Faculty of Information Technology's Digital Health Intelligence Laboratory used machine learning to predict anterior cruciate ligament injuries. The largest data set collected for this purpose was used, but the results show that even machine learning cannot develop a sufficiently effective model to predict injuries in individual athletes. Anterior cruciate ligament (ACL) injuries are common in team sports and cutting sports. Preventing them is important for both elite and amateur athletes. Multiple injury risk factors have been recognized in previous research, but the actual prediction of ACL injuries is still a matter of controversy.

What does AI bring to interventional surgical robots? Interventional surgical robots remove the physician from X-ray hazards, enable surgeries and stenting without compromising safety, and allow increased precision. Image navigation is the eye and brain of interventional robots, playing a crucial role in both diagnoses and as the primary guidance tool during interventions. Fortunately, powerful artificial intelligence (AI) technology is penetrating the medical imaging arena, holding significant promise for creating an'eye-hand-brain' collaborative system for interventional robots and optimizing fluoroscopic interventional procedures. From preoperative treatment plans to intraoperative imaging navigation and postoperative imaging follow-ups, AI can help realize image-guided precision medical visualization and provide physicians with additional information not available through conventional approaches.

In their paper FAIR-FATE: Fair Federated Learning with Momentum, Teresa Salazar, Miguel Fernandes, Helder Araujo, and Pedro Henriques Abreu develop a fairness-aware federated learning algorithm which aims to achieve group fairness while maintaining classification performance. Here, Teresa tells us more about their work. With the widespread use of machine learning algorithms to make decisions which impact people's lives, the area of fairness-aware machine learning has been receiving increasing attention. Fairness-aware machine learning algorithms ensure that predictions do not prejudice unprivileged groups of the population with respect to sensitive attributes such as race or gender. However, the focus has been on centralized machine learning, with decentralized methods receiving little attention.

There's an age-old adage in biology: structure determines function. In order to understand the function of the myriad proteins that perform vital jobs in a healthy body--or malfunction in a diseased one--scientists have to first determine these proteins' molecular structure. But this is no easy feat: protein molecules consist of long, twisty chains of up to thousands of amino acids, chemical compounds that can interact with one another in many ways to take on an enormous number of possible three-dimensional shapes. Figuring out a single protein's structure, or solving the "protein-folding problem, can take years of finicky experiments. But earlier this year an artificial intelligence program called AlphaFold, developed by the Google-owned company DeepMind, predicted the 3-D structures of almost every known protein--about 200 million in all. DeepMind CEO Demis Hassabis and senior staff research scientist John Jumper were jointly awarded this year's $3-million Breakthrough Prize in Life ...

You know that text autocomplete function that makes your smartphone so convenient -- and occasionally frustrating -- to use? Well, now tools based on the same idea have progressed to the point that they are helping researchers to analyse and write scientific papers, generate code and brainstorm ideas. The tools come from natural language processing (NLP), an area of artificial intelligence aimed at helping computers to'understand' and even produce human-readable text. Called large language models (LLMs), these tools have evolved to become not only objects of study but also assistants in research. LLMs are neural networks that have been trained on massive bodies of text to process and, in particular, generate language.

Australian surf lifesavers are increasingly using drones to spot sharks at the beach before they get too close to swimmers. But just how reliable are they? Discerning whether that dark splodge in the water is a shark or just, say, seaweed isn't always straightforward and, in reasonable conditions, drone pilots generally make the right call only 60% of the time. While this has implications for public safety, it can also lead to unnecessary beach closures and public alarm. Engineers are trying to boost the accuracy of these shark-spotting drones with artificial intelligence (AI).

How similar are bears and bulls? If you ask a biologist, she might say that they are pretty similar, since they are both four-legged mammals found in North America. However, if you ask an economist, he might say they are polar opposites, since they are used to describe distinct stock market conditions. The unique way in which individuals organize their semantic knowledge, or general information gained through life experiences, could cause two people to judge the similarity between two animals in very different ways. Scientists have been trying to understand the structure of semantic knowledge for a long time, in large part because it may lead to deeper insights about human behavior.

Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which make them extremely versatile for a number of applications: separating petrochemicals and gases, mimicking DNA, producing hydrogen, and removing heavy metals, fluoride anions, and even gold from water are just a few examples. MOFs are the focus of Professor Berend Smit's research at EPFL School of Basic Sciences, where his group employs machine learning in the discovery, design, and even categorization of the ever-increasing MOFs that currently flood chemical databases. In a new study, Smit and his colleagues have developed a machine-learning model that predicts the heat capacity of MOFs. "This is about very classical thermodynamics," says Smit. "How much energy is needed to heat up a material by one degree? Until now, all engineering calculations have assumed that all MOFs have the same heat capacity, for the simple reason that there is hardly any data available."

Self-driving car developer Argo AI suddenly announced that it was closing its doors this week. Some of its 1,800-odd employees, winnowed already by summer layoffs, are to be offered jobs to "work on automated technology with either Ford or Volkswagen," Catherine Johnsmeyer, an Argo spokesperson, said in a statement. The two auto giants had sunk some $3.6 billion into Argo and owned most of it. Now, they had decided to pull the plug. The end of Argo is just the latest sign that the global effort to get cars to drive themselves is in trouble--or at least more complex than once thought.