Isometric exercises appeal to individuals seeking convenience, privacy, and minimal dependence on equipments. However, such fitness training is often overdependent on unreliable digital media content instead of expert supervision, introducing serious risks, including incorrect posture, injury, and disengagement due to lack of corrective feedback. To address these challenges, we present a real-time feedback system for assessing isometric poses. Our contributions include the release of the largest multiclass isometric exercise video dataset to date, comprising over 3,600 clips across six poses with correct and incorrect variations. To support robust evaluation, we benchmark state-of-the-art models-including graph-based networks-on this dataset and introduce a novel three-part metric that captures classification accuracy, mistake localization, and model confidence. Our results enhance the feasibility of intelligent and personalized exercise training systems for home workouts. This expert-level diagnosis, delivered directly to the users, also expands the potential applications of these systems to rehabilitation, physiotherapy, and various other fitness disciplines that involve physical motion.

In laparoscopy surgical training and evaluation, real-time detection of surgical actions with interpretable outputs is crucial for automated and real-time instructional feedback and skill development. Such capability would enable development of machine guided training systems. This paper presents a rapid deployment approach utilizing automated machine learning methods, based on surgical action data collected from both experienced and trainee surgeons. The proposed approach effectively tackles the challenge of highly imbalanced class distributions, ensuring robust predictions across varying skill levels of surgeons. Additionally, our method partially incorporates model transparency, addressing the reliability requirements in medical applications. Compared to deep learning approaches, traditional machine learning models not only facilitate efficient rapid deployment but also offer significant advantages in interpretability. Through experiments, this study demonstrates the potential of this approach to provide quick, reliable and effective real-time detection in surgical training environments

Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence levels of both AI and users. However, this process would likely be made more difficult by the fact that AI confidence may influence users' self-confidence and its calibration. To explore these dynamics, we conducted a randomized behavioral experiment. Our results indicate that in human-AI decision-making, users' self-confidence aligns with AI confidence and such alignment can persist even after AI ceases to be involved. This alignment then affects users' self-confidence calibration. We also found the presence of real-time correctness feedback of decisions reduced the degree of alignment. These findings suggest that users' self-confidence is not independent of AI confidence, which practitioners aiming to achieve better human-AI collaboration need to be aware of. We call for research focusing on the alignment of human cognition and behavior with AI.

When you were a kid, you probably had piano lessons at some point. Back then, it may not have seemed cool enough to stick with, but in adulthood, you might be wishing you had some musical skill. Skoove is an innovative new way to learn the piano, trusted by more than one million people worldwide. This interactive program offers lessons for beginners, intermediate, and advanced players, utilizing artificial intelligence to recognize the notes you play and give you real-time feedback. The system learns your weaknesses and plans your next exercises, giving you a fully personalized plan to learn and practice notes, chords, and techniques.

AI is transforming software development from code to deployment. It's helping us find a new way of inventing technology. Algorithm-based, machine learning is used to speed up the software development cycle. AI supports developers in optimizing their software workflow at each stage of the development process. As AI continues to bring in new developments for software developers, we can expect great things in the future.

Analytics and business intelligence (BI) have long been understood to be fundamental to business success. Today, powerful technologies, including artificial intelligence (AI) and machine learning (ML), make it possible to gain deeper insights into all areas of business activity in order to drive efficiency, reduce waste and gain a better understanding of customers. Truly benefiting from analytics – particularly the most advanced and powerful analytics techniques involving AI – requires developing a top-to-bottom culture of data literacy throughout an organization and this, in my experience, is where many businesses are still failing. This is highlighted by one particular statistic that came up during my recent webinar conversation with Amir Orad, CEO of Sisense. Orad told me that according to his observations, 80 percent of employees in the average organization simply aren't leveraging the analytics that, in theory, are available to them.

I co-authored a book that claims consciousness has been "solved". One of the greatest neuroscientists of our generation who is largely ignored within the field and unknown outside has conclusively put this thousand-year mystery to rest after sixty-five years of work. Many are skeptical of this claim, as you might guess. This article is not another attempt to convince the skeptics. Instead, it is to help understand why it is hard for us to believe we have an answer to the mystery of consciousness. It is to help understand why understanding consciousness is more important now -- at the dawn of the AI age -- than ever before in the history of humanity.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! When we think about AI and voice recognition, we typically think of one of two suboptimal scenarios. The first is your Amazon Alexa sitting at home, possibly eavesdropping on your everyday conversations and feeding advertising algorithms so you buy the right kind of lawn mower. The second scenario is clunky transcription software, auto-subtitling our videos and TV shows, often to inaccurate (and humorous) effect. In reality, though, there are some deeply exciting developments happening in the AI voice recognition space right now.

Hear from top leaders discuss topics surrounding AL/ML technology, conversational AI, IVA, NLP, Edge, and more. When we think about AI and voice recognition, we typically think of one of two suboptimal scenarios. The first is your Amazon Alexa sitting at home, possibly eavesdropping on your everyday conversations and feeding advertising algorithms so you buy the right kind of lawn mower. The second scenario is clunky transcription software, auto-subtitling our videos and TV shows, often to inaccurate (and humorous) effect. In reality, though, there are some deeply exciting developments happening in the AI voice recognition space right now. Advances in AI mean that it is now possible to create complex programs and models that can analyze and score speech.

For more than a decade, people have been trying to teach cars how to drive. In the not-too-distant future, this effort may come full circle, with cars teaching people how to drive; last week, General Motors applied for a patent on an autonomous vehicle equipped to "train drivers." Self-driving cars have taken a lot longer to come about than was predicted, with complications relating to technology, safety, and regulations all throwing wrenches in the spokes of progress. Google was one of the first companies to invest heavily in driverless vehicle development, launching its self-driving car project in early 2009 out of its X lab (also known as the Moonshot Factory). As recently as 2015, auto industry insiders predicted fully self-driving cars would be on the road by 2020. That wasn't the case, and two years later we're still waiting for the day we can kick back, put our feet up, and watch the scenery go by as autonomous cars deliver us to our destinations.