What happens when AI runs out of pictures? A hospital may only ever collect a few dozen scans of a rare condition - for example, an unusual tumour. The radiology department wants software to flag this on a scan - not to replace the specialist, but so a hospital without one still gets their scan checked the same way. The clinicians know what they're looking for. Over a decade, the hospital might gather 40 confirmed cases.

People become more suspicious of a humanoid robot that makes errors, especially when the robot is an expressive conversation partner. In our new study published in the journal Science Robotics, we had 50 people hold conversations and make joint decisions with the commercial humanoid robot Pepper, which is designed to be expressive and recognize emotions. Sometimes we had the robot give sound advice. Sometimes we had it make conversational mistakes, interrupting people or pushing illogical suggestions. For some participants, the robot was animated, using gestures, eye contact and nods.

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On Rashomon sets, the mathematics of simplicity, and why we don't need black boxes: an interview with Cynthia Rudin Welcome back to AI Pioneers - in-depth conversations with those shaping the field . This time, we speak with Cynthia Rudin, a trailblazer in the field of interpretable machine learning. Winner of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, Cynthia's algorithms are already predicting seizures, aiding crime detection, and powering biological research . We discuss black boxes, Rashomon sets, and what's next for her lab - from cancer detection to interpretable AI-generated music. Can you tell me a bit about your background - what drew you into the field of interpretable machine learning?

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Dogs can tell if you're angry, scared, or sad Our canine companions may really understand what we're going through, new study suggests. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Four canine study participants--Morante, Kun-kun, Odin, and Molly--are helping scientists understand more about how dogs recognize human emotions. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Any dog lover will tell you their canine friend picks up on their mood and emotions. Now scientists have worked out exactly what is going on inside a dog's brain when it sees a human - even a complete stranger - smiling or scowling. Their research involved 14 dogs in total who all sat very still in a brain scanner while they were shown pictures of people's faces. Photographs of happy, smiling human faces made an area of the brain that is associated with reward light up with activity. This reward circuitry is the part of the brain that is activated when we enjoy and remember good things, like tasty food or praise.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Do not try this at home. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . X-ray imaging is best left to the professionals for a good reason.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Radiographs of the hand were acquired (A) preflight by a crewmember, (B) in-flight on day 1 after launch (L+1) by a crewmember, and (C) postflight by a non-crew operator using the same imaging protocol. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . For more than 40 years, ultrasound has remained the sole medical imaging method in space, but not by choice.

Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols. Retraining DNN models to address these distribution shifts is often impractical due to the high cost of acquiring and annotating new medical datasets. To address this, we introduce VarDeepPCA, a novel lightweight variational DNN framework designed to restore/refine degraded segmentation maps by leveraging intrinsic geometric priors. Unlike existing approaches that require target-domain data or extensive pre-training, our VarDeepPCA explicitly learns a distribution of valid anatomical geometries using only small in-distribution (ID) datasets. Theoretically, our novel variational learning framework leverages a reinterpretation of the softmax mapping to implicitly perform exact distribution modeling, thereby enabling computationally efficient, sampling-free learning and inference. This also enables VarDeepPCA to provide uncertainty estimates associated with its restored segmentation maps. We empirically validate our framework across 4 distinct clinical applications, using 14 publicly available datasets, involving segmentation of the myocardium, neuroretinal rim, prostate, and fetal head. Comparisons against 15 existing methods demonstrate that VarDeepPCA consistently restores segmentation maps produced by the existing methods on OOD data to (i) significantly improve anatomical plausibility of geometries and clinical utility of the segmentations, and (ii) significantly reduce errors, without needing any more training data than that used by existing methods.