Curvature information -- particularly, the largest eigenvalue of the lossHessian, known as the sharpness -- often forms the basis for learning ratetuners. However, recent work has shown that the curvature information undergoescomplex dynamics during training, going from a phase of increasing sharpness toeventual stabilization. We analyze the closed-loop feedback effect betweenlearning rate tuning and curvature. We find that classical learning rate tunersmay yield greater one-step loss reduction, yet they ultimately underperform inthe long term when compared to constant learning rates in the full batch regime.These models break the stabilization of the sharpness, which we explain using asimplified model of the joint dynamics of the learning rate and the curvature.To further investigate these effects, we introduce a new learning rate tuningmethod, Curvature Dynamics Aware Tuning (CDAT), which prioritizes long termcurvature stabilization over instantaneous progress on the objective. In thefull batch regime, CDAT shows behavior akin to prefixed warm-up schedules on deeplearning objectives, outperforming tuned constant learning rates.

Construction workers face significant risks of work-related musculoskeletal disorders (WMSDs), driven by repetitive tasks, heavy load handling, and non-neutral postures in dynamic, unpredictable environments [1, 10]. In the U.S., construction workers experience an 11% higher WMSD rate than the average across industries, with the back and shoulders most affected [10]. While exoskeletons show promise in reducing physical strain--passive designs lowering back muscle activity by 10-40% and active ones achieving up to 80% reductions across multiple regions [5]--their practical deployment remains limited by discomfort and poor alignment with human movements, particularly in construction settings [6]. Central to these limitations is the challenge of accurately recognizing user intent across varied tasks, a gap that restricts effective collaboration [3, 34]. This misalignment heightens safety risks, as powered exoskeletons may generate destructive forces if their controlled output deviates from the user's intent [34]. Addressing this locomotion intent recognition challenge is pivotal to unlocking effective exoskeleton assistance in construction, particularly for diverse, safety-critical tasks like ladder climbing and obstacle navigation. Traditional evaluation of assistive technologies like lower-limb exoskeletons has focused narrowly on routine tasks such as straight walking [27], neglecting these critical locomotion modes and requiring a shift beyond conventional control paradigms that lack flexibility for dynamic contexts. Construction tasks are highly variable, requiring workers to adapt to shifting demands, irregular workflows, and unstructured environments where movement patterns are unpredictable [10]. This variability complicates the implementation of assistive technologies, as rigid control approaches struggle to accommodate rapid task transitions and environmental uncertainty.

As the US federal government struggles to meaningfully regulate AI--or even function--New York City is stepping into the governance gap. The city introduced an AI Action Plan this week that mayor Eric Adams calls a first of its kind in the nation. The set of roughly 40 policy initiatives is designed to protect residents against harm like bias or discrimination from AI. It includes development of standards for AI purchased by city agencies and new mechanisms to gauge the risk of AI used by city departments. New York's AI regulation could soon expand still further.

Artificial Intelligence (AI) is appreciated for its potential to solve some of the biggest challenges that mankind is faced with -- from drug discovery to climate change to poverty reduction and beyond. While it has made its way into many daily consumer uses, the lack of widespread enterprise applications is often cited by critics as a reality check over the hype cycle. What is certain in the coming year and beyond is that AI will continue to push the boundaries of what is possible in both consumer and business. Companies are already automating mind-numbing repetitive workplace tasks, empowering employees to focus on higher-value, creative problem-solving. A McKinsey survey showed that enterprise adoption was up 6 per cent from the previous year to 56 per cent in 2021.

Mobile app technology has reduced the gap between the global audience and the businesses. In less than a decade, applications became mainstream in the business world, because of its wide-ranging functionalities. As a flourishing concept in the market, it has been bombarded with a lot of different technologies. And today we will talk about one such technology. Yes, we are talking about mobile app development.

This two-day instructor-led course focuses on Data Science and you will learn about practice of data science, Python programming language, Azure Machine Learning Studio and how they can be used to explore and transform data, and create, validate, deploy and consume machine learning models. The primary audience for this course is individuals who need to master the basics of data science and who plan an implementation of data science solution. The following materials are included as part of the course. Dinesh is an experienced professional and database enthusiast with skills in database management systems and business intelligence, especially on the Microsoft SQL Server product suite. Possessing over 16 years of experience on data related technologies, he does training, consulting, and is a top contributor to the local SQL Server community.

Readers of this column know that Artificial intelligence (AI) is hot. Managing Director at Redpoint Capital's Tomasz Tunguz recently pointed out that the AI startup ecosystem blossomed in just a few years: there are 400 AI startups today and there were none 8 years ago. You might also think that the hype is getting out of hand when you read this week's Gartner report on Artificial Intelligence: the research group claims that AI Augmentation Will Create $2.9 Trillion of Business Value in 2021. If you're feeling "behind the AI ball", you might find solace the latest AI report from the UK's Defence Science and Technology Laboratory; they found that, of the start-ups claiming to use AI, only 40% actually did … And, despite its bullish views on the future of AI, Gartner did find major impediments to "AI Success". The research firm's latest survey shows that the #1 issue is skilled help and difficulty in understanding AI use cases.

Infrared light flooded down invisibly as I eyed the pastries in Amazon's new convenience store in downtown San Francisco. It helped cameras mounted on the store's ceiling detect that I picked up a croissant, then put it back. My flirtation with a $3.19 morsel of flaky pastry was recorded during a preview of the Amazon Go store that opened in San Francisco's financial district this morning. As in the five other such stores in Seattle and Chicago, shoppers gain entry by scanning a QR code in the Amazon Go mobile app to open a subway-style entry gate. Hundreds of cameras on the ceiling, plus sensors in the shelves, then record what each person picks up, so they can walk out without having to visit a checkout.

That patent, awarded April 25, 1961, recognizes Robert Noyce as the inventor of the silicon integrated circuit (IC). Integrated circuits forever changed how computers were made while adding power to a process of another kind: the growth of a then-nascent field called artificial intelligence (AI). And the potential of Noyce's invention truly took flight when he and Gordon Moore founded Intel on July 18, 1968. Fifty years later, the "eternal spring" of artificial intelligence is in full swing. To understand how we arrived, here's the truth in a nutshell: The rise of artificial intelligence is intertwined with the history of faster, more robust microprocessors.

Whether consumers know it or not, three next-generation technologies are playing a major role in shaping their experience with brands -- and the future of consumer goods marketing: artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). To keep pace and effectively compete in an increasingly connected marketplace, brands are investing in these three technologies to continually fine-tune their customer strategies, using hyper-personalized information across touchpoints. Have you ever wondered how Netflix makes movie and TV show recommendations, how Facebook prompts friends to be tagged in photos, and how Alexa, Siri and Google Now assist in our day-to-day activities? These are real-life examples of machine learning -- a subset of AI. ML uses a customer's historic data and behavioral patterns to create high-quality predictions of their future behavior.