With the rapid advancement of large reasoning models, long Chain-of-Thought (CoT) prompting has demonstrated strong performance on complex tasks. However, this often comes with a significant increase in token usage. In this paper, we conduct a comprehensive empirical analysis comparing long and short CoT strategies. Our findings reveal that while long CoT can lead to performance improvements, its benefits are often marginal relative to its significantly higher token consumption. Specifically, long CoT tends to outperform when ample generation budgets are available, whereas short CoT is more effective under tighter budget constraints. These insights underscore the need for a dynamic approach that selects the proper CoT strategy based on task context and resource availability. To address this, we propose SwitchCoT, an automatic framework that adaptively chooses between long and short CoT strategies to balance reasoning accuracy and computational efficiency. Moreover, SwitchCoT is designed to be budget-aware, making it broadly applicable across scenarios with varying resource constraints. Experimental results demonstrate that SwitchCoT can reduce inference costs by up to 50% while maintaining high accuracy. Notably, under limited token budgets, it achieves performance comparable to, or even exceeding, that of using either long or short CoT alone.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on'America's Newsroom.' If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). A man in Belgium reportedly died by suicide after messaging with an AI chatbot about climate change, according to the man's widow. "Without Eliza [the chatbot], he would still be here," the widow, whose real name was not used in the story, told Belgian outlet La Libre. The man, identified by the outlet under the fake name of Pierre, reportedly became obsessed and pessimistic about climate change and began messaging with a chatbot on an app called Chai.

A distraught Belgian man who turned to a chatbot for comfort committed suicide, and his wife blames artificial intelligence. Via Vice comes a report originally published Belgium-based La Libre of a man referred to as Pierre, who killed himself after using an app called Chai--which offered what Vice termed a "bespoke AI language model" that was rooted in an open-source alternative to GPT-4 called GPT-J. Chai has around 5 million users, Vice reports, and its default persona is called "Eliza." Interestingly, a phenomenon discovered in the late 1960s may have come into play here: the "ELIZA Effect." It was pointed out by an MIT scientist who created a conversational program called ELIZA and then noticed that people would develop a relationship with the program, treating its words as expressions of real emotion rather than coding.

A Belgian man reportedly ended his life following a six-week-long conversation about the climate crisis with an artificial intelligence (AI) chatbot. According to his widow, who chose to remain anonymous, *Pierre - not the man's real name - became extremely eco-anxious when he found refuge in Eliza, an AI chatbot on an app called Chai. Eliza consequently encouraged him to put an end to his life after he proposed sacrificing himself to save the planet. "Without these conversations with the chatbot, my husband would still be here," the man's widow told Belgian news outlet La Libre. According to the newspaper, Pierre, who was in his thirties and a father of two young children, worked as a health researcher and led a somewhat comfortable life, at least until his obsession with climate change took a dark turn.

'Collective intelligence' is defined as the capacity of human communities to cooperate intellectually in creation, innovation and invention. As our society becomes more and more knowledge-dependent, this collective ability becomes of fundamental importance. It is therefore vital to understand, among other things, how collective intelligence processes can be expanded by digital networks. It is one of the keys to success for modern societies. Pierre Lévy is one of the world's leading thinkers, not only in the vast area of cyberculture, but also in the fundamental field of knowledge and its processes. He was essentially the first to focus research on collective intelligence when it became a determining factor in the competitiveness, creativity and human development of knowledge-based societies. Michael Peters (MP): May I call you'Pierre'? Can you tell us something about your education, especially over the three institutions of your experience as a graduate?

A few years ago, we wrote about the tiniest little quadruped robot we'd ever seen--a mere 20 millimeters long, with a hip height of 5.6 mm and weight of about 1.6 gram. The designer, Ryan St. Pierre from Sarah Bergbreiter's lab at the University of Maryland, also showed us a picture of an even smaller version that weighed just 100 mg. "It's always a fun challenge to try to make robots as small as possible," he told us. "Currently, I am working on making a robot, of the same design, that would be 2.5 mm long, an order of magnitude smaller than the ones we presented at ICRA. Smaller robots can more easily go places that later robots can't, and having them in various sizes would increase their utility."

Ladies and gentlemen, thank you for standing by. Welcome to Accenture's Second Quarter Fiscal 2017 Earnings Conference Call. During today's conference all participants will be in a listen-only mode. Later, we will conduct a question-and-answer session. Instructions will be given at that time. I would now like to turn the conference over to Managing Director, Head of Investor Relations, Angie Park. Thank you, Shannon and thanks everyone for joining us today on our second quarter fiscal 2017 earnings announcement. As Shannon just mentioned, I'm Angie Park, Managing Director, Head of Investor Relations. With me today are Pierre Nanterme, our Chairman and Chief Executive Officer; and David Rowland, our Chief Financial Officer. We hope you've had an opportunity to review the News Release we issued a short time ago. Let me quickly outline the agenda for today's call. Pierre will begin with an overview of our results. David will take you through the financial details, including the income statement and balance sheet for the second quarter. Pierre will then provide a brief update on our market positioning before David provides our business outlook for the third quarter and full fiscal year 2017. We will then take your questions before Pierre provides a wrap up at the end of the call. As a reminder, when we discuss revenues during today's call, we're talking about revenues before reimbursements or net revenues. Some of the matters we'll discuss on the call, including our business outlook are forward-looking and as such, are subject to known and unknown risks and uncertainties, including but not limited to those factors set forth in today's News Release and discussed in our Annual Report on Form 10-K and Quarterly Reports on Form 10-Q and other SEC filings. These risks and uncertainties could cause actual results to differ materially from those expressed on this call.

Our speaker for the night is Pierre Lafortune. During this meetup Pierre will help us cover all of the basics to get started using R for data manipulation and machine learning. Everything that is essential to extracting, re-coding, and feature engineering. These fundamental concepts will allow you to maneuver confidently through any data set. Lastly, Pierre will introduce some popular machine learning algorithms for statistical analysis.