This study presents a novel framework for 3D gaze tracking tailored for mixed-reality settings, aimed at enhancing joint attention and collaborative efforts in team-based scenarios. Conventional gaze tracking, often limited by monocular cameras and traditional eye-tracking apparatus, struggles with simultaneous data synchronization and analysis from multiple participants in group contexts. Our proposed framework leverages state-of-the-art computer vision and machine learning techniques to overcome these obstacles, enabling precise 3D gaze estimation without dependence on specialized hardware or complex data fusion. Utilizing facial recognition and deep learning, the framework achieves real-time, tracking of gaze patterns across several individuals, addressing common depth estimation errors, and ensuring spatial and identity consistency within the dataset. Empirical results demonstrate the accuracy and reliability of our method in group environments. This provides mechanisms for significant advances in behavior and interaction analysis in educational and professional training applications in dynamic and unstructured environments.

Usual weather prediction systems have the capacity to generate around 50 predictions for the week ahead. FourCastNet can instead predict thousands of possibilities, accurately capturing the risk of rare but deadly disasters and thereby giving vulnerable populations valuable time to prepare and evacuate. The hoped-for revolution in climate modeling is just the beginning. With the advent of AI, science is about to become much more exciting--and in some ways unrecognizable. The reverberations of this shift will be felt far outside the lab; they will affect us all.

There's a common perception that artificial intelligence (AI) will help streamline our work. There are even fears that it could wipe out the need for some jobs altogether. But in a study of science laboratories I carried out with three colleagues at the University of Manchester, the introduction of automated processes that aim to simplify work--and free people's time--can also make that work more complex, generating new tasks that many workers might perceive as mundane. In the study, published in Research Policy, we looked at the work of scientists in a field called synthetic biology, or synbio for short. Synbio is concerned with redesigning organisms to have new abilities.

The language in Nature was pretty mild as far as freakouts go. ChatGPT and other similar A.I. tools, the editors wrote, threaten "the transparency and trust-worthiness that the process of generating knowledge relies on … ultimately, research must have transparency in methods, and integrity and truth from authors." The editor of Nature's chief rival, Science, similarly blew his stack in a most genteel manner: "An AI program cannot be an author. A violation of these policies will constitute scientific misconduct no different from altered images or plagiarism of existing works," he wrote. These might seem like gentle warnings, but to academics who submit research papers to peer-reviewed journals like Science and Nature, the specter of being charged with research misconduct--potentially a career-wrecking accusation--for using A.I. is about as subtle as an air-raid siren.

By automating scientific processes and introducing artificial intelligence for decision-making, TRI's new closed-loop research platforms free up scientists' time for more creative tasks. When I first started graduate school almost 10 years ago, I was mixing ingredients by hand, writing down reaction conditions on a piece of paper, and grabbing a quick lunch in between lab sessions. At that time, the idea of a robot doing my experiments -- or using machine learning to predict the outcomes of my reactions -- would have never occurred to me. I accepted a future as a scientist where I would only be able to explore a tiny fraction of the billions of possible materials in the universe by hand. If lucky, a scientific discovery might arrive serendipitously as I became better at making "educated guesses."

Artificial intelligence (AI) has become a buzzword in technology in both civilian and military contexts. With interest comes a radical increase in extravagant promises, wild speculation, and over-the-top fantasies, coupled with funding to attempt to make them all possible. In spite of this fervor, AI technology must overcome several hurdles: it is costly, susceptible to data poisoning and bad design, difficult for humans to understand, and tailored for specific problems. No amount of money has eradicated these challenges, yet companies and governments have plunged headlong into developing and adopting AI wherever possible. This has bred a desire to determine who is "ahead" in the AI "race," often by examining who is deploying or planning to deploy an AI system.

Artificial intelligence (AI) has become a buzzword in technology in both civilian and military contexts. With interest comes a radical increase in extravagant promises, wild speculation, and over-the-top fantasies, coupled with funding to attempt to make them all possible. In spite of this fervor, AI technology must overcome several hurdles: it is costly, susceptible to data poisoning and bad design, difficult for humans to understand, and tailored for specific problems. No amount of money has eradicated these challenges, yet companies and governments have plunged headlong into developing and adopting AI wherever possible. This has bred a desire to determine who is "ahead" in the AI "race," often by examining who is deploying or planning to deploy an AI system.

Is science just another opinion? As the weeks unfold into months in the COVID-19 pandemic, scientists have struggled to understand the disease, how best to treat it, and how to find a vaccine. The frustration over new outbreaks and the difficulties of containing the disease have embroiled mainstream politics. Some politicians, claiming their policies are science-based, handpick scientists whose expert opinions align with their political views. Scientists appear on talk-show panels where their expert opinions are treated like the political opinions--with admiration if they agree with yours, disdain if they do not.

A few months into my postdoc, an Excel spreadsheet dealt me quite a blow. As I was preparing to perform some statistical analyses, I made a horrifying discovery: some of my sample metadata had been incorrectly merged into a single Excel spreadsheet. The metadata had to be fixed, and all of the preliminary analyses I had done had to be repeated. Sadly, even after fixing my metadata, the dataset was unsalvageable. Not enough samples had been collected and categorical metadata were missing for some samples -- there were no statistical tests I could do to identify any meaningful patterns.

Artificial intelligence and augmented intelligence promise to transform every industry, from transportation and manufacturing to finance and healthcare. Sales operations is no exception. In fact, according to SiriusDecisions, AI will play a pivotal role in reshaping sales management processes in 2019. The firm foresees sales strategy and planning, including sales compensation plan design, assessment and execution feeding off a single data repository that integrates with AI solutions.* Forward-thinking organizations are already leveraging augmented intelligence to build their commission plans, putting time-consuming manual processes in the past.