As soon as Apple announced its plans to inject generative AI into the iPhone, it was as good as official: The technology is now all but unavoidable. AI has already colonized web search, appearing in Google and Bing. OpenAI, the 80 billion start-up that has partnered with Apple and Microsoft, feels ubiquitous; the auto-generated products of its ChatGPTs and DALL-Es are everywhere. Rarely has a technology risen--or been forced--into prominence amid such controversy and consumer anxiety. Certainly, some Americans are excited about AI, though a majority said in a recent survey, for instance, that they are concerned AI will increase unemployment; in another, three out of four said they believe it will be abused to interfere with the upcoming presidential election.

A study has revealed that they can remember another bonobo or chimpanzee for more than 25 years. Researchers used infra-red eye-tracking cameras to record where the primates gazed when they were shown side-by-side images of other bonobos or chimps. One picture was of a stranger, while the other was of a bonobo or chimp the participant had lived with for a year or more at some point in their life. They found the apes' eyes lingered significantly longer on images of those with whom they had previously lived, suggesting some degree of recognition. They also looked longer at apes with whom they'd had more positive relationships.

Pre-trained language models (LMs) have shown remarkable reasoning performance using explanations (or ``chain-of-thought'' (CoT)) for in-context learning. On the other hand, these reasoning tasks are usually presumed to be more approachable for symbolic programming. To make progress towards understanding in-context learning, we curate synthetic datasets containing equivalent (natural, symbolic) data pairs, where symbolic examples contain first-order logic rules and predicates from knowledge bases (KBs). Then we revisit neuro-symbolic approaches and use Language Models as Logic Programmer (LMLP) that learns from demonstrations containing logic rules and corresponding examples to iteratively reason over KBs, recovering Prolog's backward chaining algorithm. Comprehensive experiments are included to systematically compare LMLP with CoT in deductive reasoning settings, showing that LMLP enjoys more than 25% higher accuracy than CoT on length generalization benchmarks even with fewer parameters.

You are free to share this article under the Attribution 4.0 International license. Patients are 20% less likely to die of sepsis because a new AI system catches symptoms hours earlier than traditional methods, new research shows. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," says Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins University, and lead author of the studies, which evaluated more than a half million patients over two years.

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system, created by a Johns Hopkins researcher whose young nephew died from sepsis, scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and npj Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

As he entered Hong Kong Supermarket, Sam Lin scanned text messages from his wife instructing him how many red envelopes to buy. Three dozen, she wrote -- and make them large, to fit checks rather than folded wads of cash. Lin's nephews, nieces and in-laws will not have the thrill of pulling crisp bills out of their red Lunar New Year good luck envelopes when the Year of the Ox begins Friday. Normally, Lin goes to his credit union weeks ahead of the holiday to pre-order new bills -- a total of $900 to $1,000 for the kids and elders in his extended family. But with the possibility that the coronavirus could be lurking on $20 or $100 bills, Lin is one of many Asian Americans forgoing traditional cash to ring in the festivities.

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 and I both have full-time jobs and an 18-month-old son. I am pregnant with our second child, due in February. Since our son was born, my husband seems to have regressed.