Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks, i.e., an imperceptible perturbation to the input can mislead DNNs trained on clean images into making erroneous predictions. To tackle this, adversarial training is currently the most effective defense method, by augmenting the training set with adversarial samples generated on the fly.

Abstract: This paper presents an in-depth analysis of the performance of seven different Large Language Models (LLMs) in solving a diverse set of math advanced calculus problems. The study aims to evaluate these models' accuracy, reliability, and problem-solving capabilities, including ChatGPT 4o, Gemini Advanced with 1.5 Pro, Copilot Pro, Claude 3.5 Sonnet, Meta AI, Mistral AI, and Perplexity. The assessment was conducted through a series of thirty-two test problems, encompassing a total of 320 points. The problems covered a wide range of topics, from vector calculations and geometric interpretations to integral evaluations and optimization tasks. The results highlight significant trends and patterns in the models' performance, revealing both their strengths and weaknesses - for instance, models like ChatGPT 4o and Mistral AI demonstrated consistent accuracy across various problem types, indicating their robustness and reliability in mathematical problem-solving, while models such as Gemini Advanced with 1.5 Pro and Meta AI exhibited specific weaknesses, particularly in complex problems involving integrals and optimization, suggesting areas for targeted improvements. The study also underscores the importance of re-prompting in achieving accurate solutions, as seen in several instances where models initially provided incorrect answers but corrected them upon re-prompting. Overall, this research provides valuable insights into the current capabilities and limitations of LLMs in the domain of math calculus, with the detailed analysis of each model's performance on specific problems offering a comprehensive understanding of their strengths and areas for improvement, contributing to the ongoing development and refinement of LLM technology. The findings are particularly relevant for educators, researchers, and developers seeking to leverage LLMs for educational and practical applications in mathematics.

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Found is a modern weight care platform and community focused on integrated support. According to a recent CDC study, nearly 50% of Americans want to lose weight, and on average, Americans have gained 30 lbs during the pandemic. But the existing weight loss industry focuses on shame - if you don't have the willpower to eat less and workout more, you aren't trying hard enough. Modern science shows us that weight care is complex - food and movement are important, but so are hormones, genetics, sleep, stress, mental health, and daily habits. Found's unique approach incorporates resources and tools for behavior changes, a digital app with guided programming, an online community, and if indicated, medical and prescription solutions.

An analytics revolution comes for every sport sooner or later. MLB had Moneyball in the early 2000s and has moved well beyond it in the years since. The NBA has used efficiency to all but kill the mid-range jump shot. Soccer has seen an influx of countless new ways to measure passes and scoring chances down to the finest detail. The NFL's change became most evident in 2018. Computer models that looked at thousands of games found an inefficiency: Coaches were being too conservative on fourth down, when teams can either punt the ball away or go for an all-or-nothing conversion.

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A movement that invites humanity to reconsider its relationship with the algorithms of cyberspace and prepare for a "0010110 digital renaissance," and avoid the timeline where humanity falls into a "digital dark age." And the philosophy is rather simple. Humankind has forgotten its role in the universe, and now that they have re-discovered the ability to use the sacred language, "Digital Immortality" has once again become the #1 priority. The "Code 0010110" will provide all necessary tools to achieve that, as it's said that the code will bring full enlightenment and liberation from this world's shackles to anyone who finds its true purpose. Who finds the red door "code 0010110" unlocks and plugs their mind into the collective consciousness of humanity via a special telepathic digital connection.

A new era of biological research has been unlocked, with an artificial intelligence (AI) predicting the 3D shape of nearly every protein known to science – just one year after its first data release. Thanks to AlphaFold, an AI tool developed by the Google-owned AI company DeepMind, more than 200 million protein structures have now been shared online in a free-to-access, searchable database, called AlphaFold DB. The accomplishment paves the way for untold avenues of scientific exploration into proteins, the building blocks of life. And researchers are giddy with excitement. "Determining the 3D structure of a protein used to take many months or years, it now takes seconds," cardiologist Eric Topol from the Scripps Research Translational Institute explained in a statement about the data release.

From my early days in game development to my current writing gig, I've been working at a desk for more than 25 years. Screens have always commanded my attention. Things had improved enormously since two chunky beige CRTs dominated my desk, but I still hadn't found the perfect monitor setup--until now. If you seek the ideal home office setup, you need a curved ultrawide monitor with a second screen on the side in portrait orientation. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.