VLMs have the potential to revolutionize driver and in-cabin monitoring by offering a more holistic understanding of the driving scene. Rather than focusing on individual variables, VLMs are trained to describe the entire scene, considering all crucial elements. This comprehensive approach allows them to construct a coherent narrative around the scene, leading to a more thorough assessment of the driver's situation. Despite the potential benefits, there is a notable lack of scientific research exploring the application of VLMs in this field. We aim to conduct an initial exploration of how these systems perform in tasks such as distraction detection, drowsiness detection, and gaze estimation. By evaluating their performance, we hope to determine whether they can match or even surpass state-of-the-art models, or identify areas where they fall short. To achieve this, we will utilize data from the Driver Monitoring Dataset (DMD), which contains extensive material of drivers in various states of drowsiness and distraction containing drivers doing several actions that imply distraction like texting, having a phone call, drinking water, besides driving safely, as well as detailed gaze annotations. By integrating VLMs into DMS, we expect the model to: Have better scene comprehension, enabling it to provide detailed descriptions and respond to queries through Visual Question Answering (VQA) tasks.

Large language models have achieved impressive performance on various natural language processing tasks. However, so far they have been evaluated primarily on benchmarks where all information in the input context is relevant for solving the task. In this work, we investigate the distractibility of large language models, i.e., how the model problem-solving accuracy can be influenced by irrelevant context. In particular, we introduce Grade-School Math with Irrelevant Context (GSM-IC), an arithmetic reasoning dataset with irrelevant information in the problem description. We use this benchmark to measure the distractibility of cutting-edge prompting techniques for large language models, and find that the model performance is dramatically decreased when irrelevant information is included. We also identify several approaches for mitigating this deficiency, such as decoding with self-consistency and adding to the prompt an instruction that tells the language model to ignore the irrelevant information.

With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policy makers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration index with three classes of engagement: "very engaged", "nominally engaged" and "not engaged at all". The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were "very engaged", "nominally engaged" and "not engaged at all". Additionally, the results also show that the students with best scores also have higher concentration indexes.

Large language models have achieved impressive performance on various natural language processing tasks. However, so far they have been evaluated primarily on benchmarks where all information in the input context is relevant for solving the task. In this work, we investigate the distractibility of large language models, i.e., how the model problem-solving accuracy can be influenced by irrelevant context. In particular, we introduce Grade-School Math with Irrelevant Context (GSM-IC), an arithmetic reasoning dataset with irrelevant information in the problem description. We use this benchmark to measure the distractibility of cutting-edge prompting techniques for large language models, and find that the model performance is dramatically decreased when irrelevant information is included. We also identify several approaches for mitigating this deficiency, such as decoding with self-consistency and adding to the prompt an instruction that tells the language model to ignore the irrelevant information.

Distracted driving and drowsy driving can be be dangerous and even fatal, but technologies like the ... [ ] KeepTruckin AI Dashcam can help monitor behavior and protect everyone. It can be very dangerous to drive on the highway. Vehicles are driving at a high rate of speed--many making poor decisions in the moment. Accidents--often fatal--are a regular occurrence and frequently result from drowsy or distracted drivers. KeepTruckin hopes to reduce the danger and improve safety for truck drivers with the help of artificial intelligence (AI).

Distracted driving and drowsy driving can be be dangerous and even fatal, but technologies like the ... [ ] KeepTruckin AI Dashcam can help monitor behavior and protect everyone. Vehicles are driving at a high rate of speed--many making poor decisions in the moment. Accidents--often fatal--are a regular occurrence and frequently result from drowsy or distracted drivers. KeepTruckin hopes to reduce the danger and improve safety for truck drivers with the help of artificial intelligence (AI). Distracted driving is dangerous for anyone, but it is particularly dangerous--for both the driver and the vehicles around him or her--for semi-truck drivers hauling cargo on our highways.

Computer scientists from the Netherlands and Spain have determined how a deep learning system learns better when distracted. The artificial intelligence (AI) is aimed at image recognition and can learn to recognize its surroundings. The team was able to simplify the learning process after forcing the system to focus on secondary characteristics.

I hate open world games, as a general rule. As someone with not a lot of free time, I like my games to be direct and challenging, taking me from level to level in an orderly fashion. Which is one reason why I find myself playing games like Dishonored or the Wolfenstein series time and again. I can go start to finish in a direct route, and the game rarely asks me to deviate course or worse - find a bunch of random materials and craft them. So I should hate The Legend of Zelda: Breath of the Wild. It's a sprawling, open world game with little direction except knowing you have to fight Ganon at the end.

Back in 1999, I read a great book by Bruce Sterling called "Distraction"; billed as a fictional view into the status of U.S. public service in the year 2044, the technology and cultural ideas packed into that book still resonate more than 15 years later. The book in part motivated me to strive to make a difference in public service, if only to avoid some of the more dystonia views in the book. Apparently I'm not the only one who found the book packed with ideas, Cory Doctorow also wrote a great review in 2008. The book's central premise: that all of us could suffer from "Distraction" from what really matters, especially in a world with 300 cable channels, 24/7 news, and always-on social media in the United States, is an idea that I'd like to explore more fully given our rapidly changing world of today. Technology is amoral, it is how we humans choose to use it that determines good vs. bad outcomes.