The winners of three IJCAI awards have been announced. These three distinctions are: the Award for Research Excellence, the John McCarthy Award and the Computers and Thought Award. The Research Excellence award is given to a scientist who has carried out a program of research of consistently high quality throughout an entire career yielding several substantial results. The winner of the 2023 Award for Research Excellence is Sarit Kraus, Professor of Computer Science, Bar-Ilan University, Israel. Professor Kraus is recognized for her pioneering work on the study of interactions among self-interested agents, creating the field of automated negotiation, and developing methods for coalition formation and teamwork, both as formal models and real-world implementations.

Wildfires in Canada have led to a surge in air pollution levels in the US, with New York currently having the worst air quality of any major city. There are plenty of images of N95-mask-wearing people walking down smog-blighted streets that wouldn't look out of place in many a dystopia. Many states and cities have urged folks to stay inside unless they absolutely need to leave, and they're pumping out as much Air Quality Index data as they can. But do you actually know what the Air Quality Index is, or what it's for? We've done an AQI deep dive, exploring how it works and how you can keep yourself informed and safe. And, on the subject of being safe, we've also knocked up a guide for how to make a quick-and-dirty box fan air filter.

It is a debate sure to ruffle feathers, but anything beyond the Watford Gap really should be classed as the north of England, a study suggests. This is the critical line at which high street bakery Greggs, the beacon of northernness, becomes more popular than the southerners' sandwich shop of choice, Pret A Manger, an academic study has worked out using artificial intelligence. If the national consumption of steak bakes versus houmous-filled wraps and smashed avocado on toast were not convincing enough, the researchers also looked at the distribution of Morrisons and Waitrose supermarkets across England. This too put the north-south divide within two miles of the Watford Gap. Both calculations agree that Birmingham, Coventry and Leicester are technically in the north of England. But bizarrely, the Pret and Greggs dividing line shows that Cornwall is northern.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? Submit it here or post it in the Slate Parenting Facebook group. My husband is very involved with the kids. He's a good father--he does the hard parts of parenting, happily.

Recent advances in deep learning have witnessed many successful unsupervised image-to-image translation models that learn correspondences between two visual domains without paired data. However, it is still a great challenge to build robust mappings between various domains especially for those with drastic visual discrepancies. In this paper, we introduce a novel versatile framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), that improves the quality, applicability and controllability of the existing translation models. The key idea of GP-UNIT is to distill the generative prior from pre-trained class-conditional GANs to build coarse-level cross-domain correspondences, and to apply the learned prior to adversarial translations to excavate fine-level correspondences. With the learned multi-level content correspondences, GP-UNIT is able to perform valid translations between both close domains and distant domains. For close domains, GP-UNIT can be conditioned on a parameter to determine the intensity of the content correspondences during translation, allowing users to balance between content and style consistency. For distant domains, semi-supervised learning is explored to guide GP-UNIT to discover accurate semantic correspondences that are hard to learn solely from the appearance. We validate the superiority of GP-UNIT over state-of-the-art translation models in robust, high-quality and diversified translations between various domains through extensive experiments.

People in Texas sounded off on AI job displacement, with half of people who spoke to Fox News convinced that the tech will rob them of work. Don McLean, the one-man creative force behind the hit songs "American Pie," "Vincent (Starry, Starry Night)," "And I Love You So," "Castles in the Air," and other songs, albums, tours and projects, shared thoughts about artificial intelligence, music, creativity and authenticity with Fox News Digital in a recent phone interview amid his current "American Pie" 50th anniversary tour. "When you talk about artificial intelligence right now -- I'm not sure what that means at the moment, but clearly it's evolving," he said from California, where he was making several tour stops after returning from concert performances in Australia. "With any technology, you have an inflection point where it takes off," said McLean. "Today, AI has merely presented itself -- but the inflection point hasn't been reached yet. He added, "I also want to say that before a form of artificial intelligence was in use -- and it's been in use for many years -- the tape recorder and the photographic lens were both honest. If you took a picture, that was the way something looked." However, in current times, he said, "you have all this photoshopping and massaging and whatnot, so now the camera lies.

Dear Prudence is Slate's advice column. For this edition, Alicia Montgomery, Slate's vice president of audio, will be filling in as Prudie. My friend Kari and I have been close since we were college roommates (we are now just about 40). Kari has been with her long-distance girlfriend Lora for the last four years, and recently Lora has been talking about moving to Kari and my town in order to better facilitate having a baby. The road for them is going to be long, given the mechanics and their ages, but they have all systems go from their doctors. The problem is that I know Kari is not 100 percent committed to Lora; she says she's not sure she's the one and has built (but not, to my knowledge, deployed) dating profiles on multiple sites and expresses jealousy to me quite often about my adventurous dating life.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. Meet Ezra, the full-body cancer screener that just might save your life. Combining MRI imaging technology with artificial intelligence, Ezra scans for possible cancer in the human body in up to 13 organs. It also monitors for hundreds of other conditions, such as brain aneurysms or fatty liver disease.

With machine learning conferences growing ever larger, and reviewing processes becoming increasingly elaborate, more data-driven insights into their workings are required. In this report, we present the results of a survey accompanying the first "Learning on Graphs" (LoG) Conference. The survey was directed to evaluate the submission and review process from different perspectives, including authors, reviewers, and area chairs alike. The first "Learning on Graphs" (LoG) Conference (9-12 December, 2022) was remarkable in more ways than one: starting from scratch, the conference aims to be the place for graph learning research, making use of an advisory committee that consists of international experts in the field. Moreover, at is core, LoG wants to be known for its exceptional review quality.

Nanomechanical resonant sensors are used in mass spectrometry via detection of resonance frequency jumps. There is a fundamental trade-off between detection speed and accuracy. Temporal and size resolution are limited by the resonator characteristics and noise. A Kalman filtering technique, augmented with maximum-likelihood estimation, was recently proposed as a Pareto optimal solution. We present enhancements and robust realizations for this technique, including a confidence boosted thresholding approach as well as machine learning for event detection. We describe learning techniques that are based on neural networks and boosted decision trees for temporal location and event size estimation. In the pure learning based approach that discards the Kalman filter, the raw data from the sensor are used in training a model for both location and size prediction. In the alternative approach that augments a Kalman filter, the event likelihood history is used in a binary classifier for event occurrence. Locations and sizes are predicted using maximum-likelihood, followed by a Kalman filter that continually improves the size estimate. We present detailed comparisons of the learning based schemes and the confidence boosted thresholding approach, and demonstrate robust performance for a practical realization.