The new method, developed by a team led by Imperial College London researchers, could slash the energy cost of artificial intelligence (AI), which is currently doubling globally every 3.5 months. In a paper published today in Nature Nanotechnology, the international team have produced the first proof that networks of nanomagnets can be used to perform AI-like processing. The researchers showed nanomagnets can be used for'time-series prediction' tasks, such as predicting and regulating insulin levels in diabetic patients. Artificial intelligence that uses'neural networks' aims to replicate the way parts of the brain work, where neurons talk to each other to process and retain information. A lot of the maths used to power neural networks was originally invented by physicists to describe the way magnets interact, but at the time it was too difficult to use magnets directly as researchers didn't know how to put data in and get information out.

Self-Organizing Maps (SOMs, Kohonen networks) belong to neural network models of the unsupervised class. In this paper, we present the generalized setup for non-Euclidean SOMs. Most data analysts take it for granted to use some subregions of a flat space as their data model; however, by the assumption that the underlying geometry is non-Euclidean we obtain a new degree of freedom for the techniques that translate the similarities into spatial neighborhood relationships. We improve the traditional SOM algorithm by introducing topology-related extensions. Our proposition can be successfully applied to dimension reduction, clustering or finding similarities in big data (both hierarchical and non-hierarchical).

For more than 250 years, mathematicians have been trying to "blow up" some of the most important equations in physics: those that describe how fluids flow. If they succeed, then they will have discovered a scenario in which those equations break down -- a vortex that spins infinitely fast, perhaps, or a current that abruptly stops and starts, or a particle that whips past its neighbors infinitely quickly. Beyond that point of blowup -- the "singularity" -- the equations will no longer have solutions. They will fail to describe even an idealized version of the world we live in, and mathematicians will have reason to wonder just how universally dependable they are as models of fluid behavior. But singularities can be as slippery as the fluids they're meant to describe.

"I'm running out of time, but I'm also not gonna be like, 'I'm having a baby for the sake of having a baby,'" said the younger of the two. "One thing I would recommend," replied the older woman, "if it's an option: freeze your eggs." As a woman, you get to a certain age and babies – hypothetical, expected, realised – suddenly seem ubiquitous: in friendship circles, on social media, in targeted advertising for pregnancy tests and public health messages. But for women of my generation, the decision whether to have children feels more existentially fraught and morally complex than ever before. I have always wanted kids. I have always felt an uncomplicated joy at the chubbiness of babies' limbs and the infectiousness of a child's laughter.

If the groups under investigation are pre-specified, i.e., not a function of the observed data, then classical hypothesis tests will control the Type I error rate. However, it is increasingly common to want to test for a difference in means between groups that are defined through the observed data, e.g., via the output of a clustering algorithm. For instance, in single-cell RNA-sequencing analysis, researchers often first cluster the cells, and then test for a difference in the expected gene expression levels between the clusters to quantify up-or down-regulation of genes, annotate known cell types, and identify new cell types (Grün et al., 2015; Aizarani et al., 2019; Lähnemann et al., 2020; Zhang et al., 2019; Doughty & Kerkhoven, 2020). In fact, the inferential challenges resulting from testing data-guided hypotheses have been described as a "grand challenge" in the field of genomics (Lähnemann et al., 2020), and papers in the field continue to overlook this issue: as an example, seurat (Stuart et al., 2019), the state-of-the-art single-cell RNA sequencing analysis tool, tests for differential gene expression between groups obtained via clustering, with a note that "p-values [from these hypotheses] should be interpreted cautiously, as the genes used for clustering are the same genes tested for differential expression." Testing data-guided hypothesis also arises in the field of neuroscience (Kriegeskorte et al., 2009; Button, 2019), social psychology (Hung & Fithian, 2020), and physical sciences (Friederich et al., 2020; Pollice

Imagine if you could look at a snowflake at the South Pole and determine the size and the climate of all of Antarctica. Or study a randomly selected tree in the Amazon rain forest and, from that one tree--be it rare or common, narrow or wide, young or old--deduce characteristics of the forest as a whole. Or, what if, by looking at one galaxy among the hundred billion or so in the observable universe, one could say something substantial about the universe as a whole? A recent paper, whose lead authors include a cosmologist, a galaxy-formation expert, and an undergraduate named Jupiter (who did the initial work), suggests that this may be the case. The result at first seemed "crazy" to the paper's authors.

Thank you so much for signing up for my AI Newsletter. In the last few days, we were doing deep research in AI-related research updates and we were able to find these cool ones. But before you start reading, please Join Our Subreddit so you don't miss any updates. Researchers from Carnegie Mellon University recently published a paper that compares existing code models -- Codex, GPT-J, GPT-Neo, GPT-NeoX, and CodeParrot -- across programming languages. By comparing and contrasting various models, they want to offer more light on the landscape of code modeling design decisions, as well as fill in a major gap: no big open-source language model has been trained purely on code from several programming languages.

Fully robotic surgery may be a couple of decades away. But one thing could speed it up: billionaire engineer Elon Musk's very public mission to Mars. Early this year a fully autonomous robot completed a complex soft tissue surgery for the very first time at Johns Hopkins University. The Smart Tissue Autonomous Robot, or STAR, "excelled at suturing two ends of intestine--one of the most intricate and delicate tasks in abdominal surgery," Johns Hopkins reported. That said, there is a big caveat.

In late 1914, explorer Ernest Shackleton and 27 crewmen sailed into the icy waters around Antarctica. Their state-of-the-art ship Endurance stretched 144 feet, with three towering masts, its hull ultra-reinforced to resist crumpling in the floating ice. The crew's plan was to hike across the frozen continent, but the sea had other ideas. Endurance got stuck off the coast and was slowly crushed by the floating ice, forcing the men into one of the most famous feats of survival in history. They endured for over a year, scurrying across ice floes to hunt penguins and seals, before reaching an uninhabited island.

However, collecting meteorites in Antarctica is a physically stressful and dangerous business. But what if there was a "treasure map" showing the most likely places to find meteorites in Antarctica, for researchers to direct where to search? Researchers from Delft University of Technology in the Netherlands used artificial intelligence to create a type of treasure map to identify areas where meteorites can be found at high probability. Veronica Tollenaar, who led the study published in Science Advances, said: "Through our analysis, we learned that satellite observations of temperature, ice flow rate, surface cover and geometry are good indications of the location of meteorites-rich areas. We expect the treasure map to be 80% accurate."