Harvey Castro talks about how AI cold be used in cold cases and the symbiotic relationship between AI and a detective. There's no question that artificial intelligence is changing our lives. A bot that sounds almost human can author your emails, teach you a new language, book your trips or even be your friend. Check out direct links to try those out here. One woman I spoke with on my national radio show even married her AI companion.

Nvidia, the company that dominates the market for graphics processing units, was once known mostly in the video game world. But these days, Nvidia GPUs are also the go-to source for the massive computing power needed to run generative A.I. systems--and the recent explosion in A.I. hype has propelled the company's stock into the stratosphere. Nvidia briefly hit a trillion-dollar valuation, putting itself in league with tech giants like Alphabet and Apple and launching a bit of a frenzy in the markets. Nvidia is looking like the first big stock win of the A.I. era, and investors are salivating. On Sunday's episode of What Next: TBD, I spoke with Don Clark, a freelance reporter who specializes in the chips industry, about how Nvidia rode the A.I. revolution, became the hottest chipmaker in the world, and made the entire A.I. craze suddenly seem very real.

Advances in voice technology and voice user interfaces (VUIs) -- such as Alexa, Siri, and Google Home -- have opened up the potential for many new types of interaction. However, despite the potential of these devices reflected by the growing market and body of VUI research, there is a lingering sense that the technology is still underused. In this paper, we conducted a systematic literature review of 35 papers to identify and synthesize 127 VUI design guidelines into five themes. Additionally, we conducted semi-structured interviews with 15 smart speaker users to understand their use and non-use of the technology. From the interviews, we distill four design challenges that contribute the most to non-use. Based on their (non-)use, we identify four opportunity spaces for designers to explore such as focusing on information support while multitasking (cooking, driving, childcare, etc), incorporating users' mental models for smart speakers, and integrating calm design principles.

OpenAI has released the Chat Generative Pre-trained Transformer (ChatGPT) and revolutionized the approach in artificial intelligence to human-model interaction. Several publications on ChatGPT evaluation test its effectiveness on well-known natural language processing (NLP) tasks. However, the existing studies are mostly non-automated and tested on a very limited scale. In this work, we examined ChatGPT's capabilities on 25 diverse analytical NLP tasks, most of them subjective even to humans, such as sentiment analysis, emotion recognition, offensiveness, and stance detection. In contrast, the other tasks require more objective reasoning like word sense disambiguation, linguistic acceptability, and question answering. We also evaluated GPT-4 model on five selected subsets of NLP tasks. We automated ChatGPT and GPT-4 prompting process and analyzed more than 49k responses. Our comparison of its results with available State-of-the-Art (SOTA) solutions showed that the average loss in quality of the ChatGPT model was about 25% for zero-shot and few-shot evaluation. For GPT-4 model, a loss for semantic tasks is significantly lower than for ChatGPT. We showed that the more difficult the task (lower SOTA performance), the higher the ChatGPT loss. It especially refers to pragmatic NLP problems like emotion recognition. We also tested the ability to personalize ChatGPT responses for selected subjective tasks via Random Contextual Few-Shot Personalization, and we obtained significantly better user-based predictions. Additional qualitative analysis revealed a ChatGPT bias, most likely due to the rules imposed on human trainers by OpenAI. Our results provide the basis for a fundamental discussion of whether the high quality of recent predictive NLP models can indicate a tool's usefulness to society and how the learning and validation procedures for such systems should be established.

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.