The Dead Sea Scrolls are surely among the most historically and biblically important objects known to humankind. Found in caves near the Dead Sea nearly 100 years ago, these ancient manuscripts have transformed understanding of Jewish and Christian origins. Penned upon the 1,000 scrolls were profound religious texts, hymns, prayers, legal codes, commentaries and more. Until now, the scrolls have been assumed to date somewhere between the third century BC and the first century AD. But according to a new AI analysis, some of the scrolls date back as far as the fourth century BC – nearly 2,500 years ago.

University of Waterloo researchers have developed new artificial intelligence (AI) technology that can accurately analyze pitcher performance and mechanics using low-resolution video of baseball games. The system, developed for the Baltimore Orioles by the Waterloo team, plugs holes in much more elaborate and expensive technology already installed in most stadiums that host Major League Baseball (MLB), whose teams have increasingly tapped into data analytics in recent years. Waterloo researchers convert video of a pitcher's performance into a two-dimensional model that PitcherNet's AI algorithm can later analyze. Those systems, produced by a company called Hawk-Eye Innovations, use multiple special cameras in each park to catch players in action, but the data they yield is typically available to the home team that owns the stadium those games are played in. To add away games to their analytics operation, as well as use smartphone video taken by scouts in minor league and college games, the Orioles asked video and AI experts at Waterloo for help about three years ago.

In the rapidly evolving landscape of artificial intelligence (AI), the ability to comprehensively analyze and understand complex data is paramount. However, the ever-increasing complexity of real data requires sophisticated AI pipelines that seamlessly integrate different stages such as data collection, preparation, model generation, and evaluation. Such a pipeline can be illustrated as a chain of distinct yet interdependent stages, each contributing to the overarching goal of turning data into actionable intelligence. Like a well-coordinated symphony, these stages require harmonious collaboration to achieve optimal results. The concept of AI pipelines therefore represents more than just a linear progression; it signifies the orchestration of diverse processes to accomplish a larger purpose. At the core of this revolution lie AI pipelines, intricate networks of interconnected data processing and analysis steps designed to transform raw data into meaningful insights or outcomes using AI techniques. The evolution of simple AI models to adaptive, systematic AI pipelines has ushered in a new era of data-driven decision-making by solving complex tasks in an ever-changing environment. Making AI understandable, accessible and usable by everyone in every domain requires a domain-independent, easy-to-use pipeline architecture that can be integrated into a complex ecosystem of experts and non-experts. However, the design and implementation of such pipelines often prove challenging due to the intricate interplay of technical components and the diverse requirements of different application domains.

Machine learning models trained with differentially-private (DP) algorithms such as DP-SGD enjoy resilience against a wide range of privacy attacks. Although it is possible to derive bounds for some attacks based solely on an $(\varepsilon,\delta)$-DP guarantee, meaningful bounds require a small enough privacy budget (i.e., injecting a large amount of noise), which results in a large loss in utility. This paper presents a new approach to evaluate the privacy of machine learning models against specific record-level threats, such as membership and attribute inference, without the indirection through DP. We focus on the popular DP-SGD algorithm, and derive simple closed-form bounds. Our proofs model DP-SGD as an information theoretic channel whose inputs are the secrets that an attacker wants to infer (e.g., membership of a data record) and whose outputs are the intermediate model parameters produced by iterative optimization. We obtain bounds for membership inference that match state-of-the-art techniques, whilst being orders of magnitude faster to compute. Additionally, we present a novel data-dependent bound against attribute inference. Our results provide a direct, interpretable, and practical way to evaluate the privacy of trained models against specific inference threats without sacrificing utility.

The Research Brief is a short take about interesting academic work. My colleagues and I have developed a flexible, stretchable electronic device that runs machine-learning algorithms to continuously collect and analyze health data directly on the body. The skinlike sticker, developed in my lab at the University of Chicago's Pritzker School of Molecular Engineering, includes a soft, stretchable computing chip that mimics the human brain. To create this type of device, we turned to electrically conductive polymers that have been used to build semiconductors and transistors. These polymers are made to be stretchable, like a rubber band.

Artificial intelligence has found disparities in the amount of airtime women and men were given on CNN, FOX News and MSNBC - females had a 10 percent less chance of speaking during political discussions because male speakers constantly interrupted them. The discovery was made by researchers at Rochester Institute of Technology who analyzed 625,409 dialogues hosted on the three news cable networks from January 2000 through July 2021. The technology revealed women received an average of 72.8 words per chance to speak compared to 81.4 for male speakers and women were interrupted 39.4 percent of the time during discussions - this is compared to the 35.9 percent of the time for men. The team believes their AI could be used during talk shows, interviews and political debates to identify a serial interrupter in real-time, but the study also reinforces previous research that found men interrupt women more to show their dominance. AI analyzed thousands of dialogues from news segments on the three networks and found woman are given a 10 percent less chance at speaking because men interrupt them.

Despite society generally being more open about personal health in the 21st century, penis health in particular is still something that's not openly discussed as much. Perhaps it's taboo in certain cultures, or maybe men just suffer from too much toxic masculinity and feel ashamed or embarrassed about the topic. This becomes a huge disadvantage for sexually-active men who are afraid to ask questions, or ask for help regarding penis-related issues. But Singapore-based startup HeHealth may have just the solution for weary men needing very important answers – in the form of a mobile app that uses AI to analyze a photo of your penis for any signs of sexually-transmitted diseases (STDs). All done in absolute discretion, of course.

Though techno-thriller The Circle (2017) is more a comment on the ethical implications of social networks than the practicalities of external video analytics, the improbably tiny'SeeChange' camera at the center of the plot is what truly pushes the movie into the'science-fiction' category. A wireless and free-roaming device about the size of a large marble, it's not the lack of solar panels or the inefficiency of drawing power from other ambient sources (such as radio waves) that makes SeeChange an unlikely prospect, but the fact that it's going to have to compress video 24/7, on whatever scant charge it's able to maintain. Powering cheap sensors of this type is a core area of research in computer vision (CV) and video analytics, particularly in non-urban environments where the sensor will have to eke out the maximum performance from very limited power resources (batteries, solar, etc.). In cases where such an edge IoT/CV device of this type must send image content to a central server (often through conventional cell coverage networks), the choices are hard: either the device needs to run some kind of lightweight neural network locally in order to send only optimized segments of relevant data for server side processing; or it has to send'dumb' video for the plugged-in cloud resources to evaluate. Though motion-activation through event-based Smart Vision Sensors (SVS) can cut down this overhead, that activation monitoring also costs energy.

Psychedelics, also known as hallucinogenic drugs have been widely stigmatized as dangerous illegal drugs. These drugs are psychoactive drugs that are used to alter sensory perceptions, energy levels, and thought processes. But very little is really known about what these substances actually do to our brains. AI is crucial to unlocking the potential of psychedelic drugs. To better understand how these subjective effects manifest in the brain, some scientists are using AI methods to figure it out and drug companies are now employing artificial intelligence in their research.

A study of bird songs conducted in the Sierra Nevada mountain range in California generated a million hours of audio, which AI researchers are working to decode to gain insights into how birds responded to wildfires in the region, and to learn which measures helped the birds to rebound more quickly. Scientists can also use the soundscape to help track shifts in migration timing and population ranges, according to a recent account in Scientific American. More audio data is coming in from other research as well, with sound-based projects to count insects and study the effects of light and noise pollution on bird communities underway. "Audio data is a real treasure trove because it contains vast amounts of information," stated ecologist Connor Wood, a Cornell University postdoctoral researcher, who is leading the Sierra Nevada project. "We just need to think creatively about how to share and access that information."