A phone-banking tool powered entirely by artificial intelligence is getting its first real-world test in a Pennsylvania Democrat's congressional campaign. The chatbot, named Ashley, calls voters and engages in two-way, interactive conversations about candidate Shamaine Daniels, one of seven Democrats running so far in next year's primary. The voice tool from the startup Civox represents one of many ways AI technology is breaking into politics ahead of the 2024 campaigns, but experts say its direct contact with voters could threaten data security and has the potential to undermine voter trust. Daniels announced the partnership with Civox on Tuesday, saying the the first-of-its-kind political campaign tool had already completed more than 1,000 calls with likely Democratic primary voters in Pennsylvania's 10th House district, which includes the state capital, Harrisburg. Unlike other robocallers, Ashley doesn't use canned responses or give call recipients a menu of options.

Democrat Shamaine Daniels is running for Congress, eyeing a seat held by Trump-aligned Republican Rep. Scott Perry, who played a key role challenging the 2020 election results. Daniels, who lost to Perry by less than 10 points last year, hopes a new weapon will help her underdog candidacy: Ashley, an artificial intelligence campaign volunteer. Ashley is not your typical robocaller; none of her responses are canned or pre-recorded. Her creators, who intend to mainly work with Democratic campaigns and candidates, say she is the first political phone banker powered by generative AI technology similar to OpenAI's ChatGPT. She is capable of having an infinite number of customized one-on-one conversations at the same time.

Doctors believe artificial intelligence is now saving lives after a major advancement in breast cancer screenings. AI is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. People suffering from dementia could live more independently thanks to a pair of AI-powered socks that can track everything from a patient's heart rate to movement. Called "SmartSocks," the AI-powered apparel was created in partnership between the University of Exeter and researchers at the start-up company Milbotix, according to SWNS. The socks can monitor a patient's heart rate, sweat levels and motion to prevent falls while also promoting independence for those with dementia. "I came up with the idea for SmartSocks while volunteering in a dementia care home," SmartSocks creator Zeke Steer, CEO of Milbotix, told SWNS.

Emotions play an essential role in human communication. Developing computer vision models for automatic recognition of emotion expression can aid in a variety of domains, including robotics, digital behavioral healthcare, and media analytics. There are three types of emotional representations which are traditionally modeled in affective computing research: Action Units, Valence Arousal (VA), and Categorical Emotions. As part of an effort to move beyond these representations towards more fine-grained labels, we describe our submission to the newly introduced Emotional Reaction Intensity (ERI) Estimation challenge in the 5th competition for Affective Behavior Analysis in-the-Wild (ABAW). We developed four deep neural networks trained in the visual domain and a multimodal model trained with both visual and audio features to predict emotion reaction intensity. Our best performing model on the Hume-Reaction dataset achieved an average Pearson correlation coefficient of 0.4080 on the test set using a pre-trained ResNet50 model. This work provides a first step towards the development of production-grade models which predict emotion reaction intensities rather than discrete emotion categories.

AI strategist Lisa Palmer and privacy consultant Jodi Daniels discuss privacy concerns around the acquisition of biometric data. A significant expansion in Artificial intelligence (AI) facial recognition technology is increasingly being deployed to catch criminals, but experts express concern about the impact on personal privacy and data. According to the Allied Market Research data firm, the facial recognition industry, which was valued at $3.8 billion in 2020, will have grown to $16.7 billion by 2030. Lisa Palmer, an AI strategist, said it is important to understand that an individual's data largely feeds what happens from an AI perspective, especially within a generative framework. While there has been data recorded on citizens for decades, today's surveillance is different because of the quantity and quality of the data recorded as well as how it's being used, according to Palmer.

Cancer is a disease characterized by the abnormal growth and division of cells in the body. Tumors can affect any part of the body and can be benign (non-cancerous) or malignant (cancerous), spreading to other parts of the body through the bloodstream or lymph system. Scientists at the University of California, San Francisco (UCSF) and IBM Research have created a virtual library of thousands of "command sentences" for cells using machine learning. These "sentences" are based on combinations of "words" that direct engineered immune cells to find and continuously eliminate cancer cells. This research, which was recently published in the journal Science, is the first time that advanced computational techniques have been applied to a field that has traditionally progressed through trial-and-error experimentation and the use of pre-existing molecules rather than synthetic ones to engineer cells.

Using new machine learning techniques, researchers at UC San Francisco (UCSF), in collaboration with a team at IBM Research, have developed a virtual molecular library of thousands of "command sentences" for cells, based on combinations of "words" that guided engineered immune cells to seek out and tirelessly kill cancer cells. The work, published online Dec. 8, 2022, in Science, represents the first time such sophisticated computational approaches have been applied to a field that, until now, has progressed largely through ad hoc tinkering and engineering cells with existing, rather than synthesized, molecules. The advance allows scientists to predict which elements -- natural or synthesized -- they should include in a cell to give it the precise behaviors required to respond effectively to complex diseases. "This is a vital shift for the field," said Wendell Lim, PhD, the Byers Distinguished Professor of Cellular and Molecular Pharmacology, who directs the UCSF Cell Design Institute and led the study. "Only by having that power of prediction can we get to a place where we can rapidly design new cellular therapies that carry out the desired activities."

Using new machine learning techniques, researchers at UC San Francisco (UCSF), in collaboration with a team at IBM Research, have developed a virtual molecular library of thousands of "command sentences" for cells, based on combinations of "words" that guided engineered immune cells to seek out and tirelessly kill cancer cells. The work, published online Dec. 8, 2022, in Science, represents the first time such sophisticated computational approaches have been applied to a field that until now has progressed largely through ad hoc tinkering and engineering cells with existing--rather than synthesized--molecules. The advance allows scientists to predict which elements--natural or synthesized--they should include in a cell to give it the precise behaviors required to respond effectively to complex diseases. "This is a vital shift for the field," said Wendell Lim, Ph.D., the Byers Distinguished Professor of Cellular and Molecular Pharmacology, who directs the UCSF Cell Design Institute and led the study. "Only by having that power of prediction can we get to a place where we can rapidly design new cellular therapies that carry out the desired activities."

A version of this article was published in TIME's newsletter Into the Metaverse. You can find past issues of the newsletter here. Technology and fiction have long shared a symbiotic relationship. Just as writers dreamed up fantastical worlds based on imagined technologies, those same worlds have inspired engineers, technologists, and scientists--spurring breakthroughs as well as thorny philosophical questions about their work. The term "metaverse" itself comes from Neal Stephenson's Snow Crash; the comic strip Dick Tracy inspired the cell phone.

More companies are successfully exploiting predictive maintenance systems that combine AI and IoT sensors to collect data that anticipates breakdowns and recommends preventive action before break or machines fail, in a demonstration of an AI use case with proven value. This growth is reflected in optimistic market forecasts. The predictive maintenance market is sized at $6.9 billion today and is projected to grow to $28.2 billion by 2026, according to a report from IoT Analytics of Hamburg, Germany. The firm counts over 280 vendors offering solutions in the market today, projected to grow to over 500 by 2026. "This research is a wake-up call to those that claim IoT is failing," stated analyst Fernando Bruegge, author of the report, adding, "For companies that own industrial assets or sell equipment, now is the time to invest in predictive maintenance-type solutions."