The study explores mitigating overconfidence bias in LLMs to improve their reliability. We introduce a knowledge transfer (KT) method utilizing chain of thoughts, where "big" LLMs impart knowledge to "small" LLMs via detailed, sequential reasoning paths. This method uses advanced reasoning of larger models to fine-tune smaller models, enabling them to produce more accurate predictions with calibrated confidence. Experimental evaluation using multiple-choice questions and sentiment analysis across diverse datasets demonstrated the KT method's superiority over the vanilla and question-answer pair (QA) fine-tuning methods. The most significant improvement in three key metrics, where the KT method outperformed the vanilla and QA methods by an average of 55.3% and 43.1%, respectively. These findings underscore the KT method's potential in enhancing model trustworthiness and accuracy, offering precise outputs with well-matched confidence levels across various contexts.

Smartphones, PC Lightning-quick matches, collectible superheroes and enticing simplicity make this the smartphone card game of dreams. Free of convoluted rules and sprawling decks, this is card-battling boiled down to the elegant essentials. What we said: A wonderful combination of nostalgia, fun and challenge. If the seemingly unstoppable Marvelisation of popular culture must continue, let it at least occasionally throw up gems like this. PC, Mac A puzzle game that draws upon the human talent for pattern recognition to turn us all into neat-freaks, rearranging books and cutlery and stationery by size, colour, shape, or whatever else feels right.

Did a student write that essay or a robot? Did a teacher provide that six trait feedback or was it an automated feedback system? Did that student understand that Mandarin dialog or did they translate it on the fly? Is that student talking to a mental health professional or a therapy-bot? If you are worried about plagiarism, things just got a lot more complicated with developments in Natural Language Processing (NLP), a branch of artificial intelligence that enables computers to understand, process, and generate language.

Many of Amazon's gadgets went on sale at the start of this week and continue to be discounted today. Key among them are both new Kindle Paperwhites -- the standard version and the Signature Edition -- along with the second-generation Echo Show 8 and the Fire TV Cube. On top of that, Apple's AirPods Pro are on sale for $175 and the 2021 Apple TV 4K remains discounted to $160. Here are the best tech deals from this week that you can still get today. The Kindle Paperwhite Signature Edition is on sale for $145, which is an all-time low.

With the Super Bowl around the corner and the weather still freezing in most of the northern hermisphere, now is a great time to snag some home entertainment products to help you make it through until the grass starts growing again. This week we've found excellent discounts on the best TVs, soundbars, headphones, and some other favorite tech. Check out our list of great camera gear on sale right now. Special offer for Gear readers: Get a 1-year subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like).

It's hard enough for people to categorize or discuss art, but it's even more difficult for artificial intelligence. Several research groups have recently tried to apply machine learning to large databases of artworks to sort and describe them in a meaningful way. First, researchers from Zhejiang University of Technology in Hangzhou, China, compared different neural networks to find out how well they perform at art classification. They used images from WikiArt and other digital collections to train the neural networks to learn what images of a certain art style look like. Then they asked the different neural network models to identify the art style of other images.

While it has been a challenging week on the general news front, but for those interested in the PC business, it's been an incredible one. While the overall amount of detailed product news from this year's CES show is lower in most categories than in years past, the PC-related introductions have been coming out at a torrential pace – likely due to the category's recent success. On top of that, several impressive debuts from big PC chip suppliers like AMD, Intel, and Nvidia mean significantly faster and more power efficient machines are on the near horizon. Finally, to put the cherry on top of it all, we've started to see the PC industry really embrace 5G as a critical new technology for its latest laptops. To put it succinctly, it's a good time for PCs.

In the wake of the COVID-19 pandemic, there has been a rapid increase in the deployment of artificial intelligence (AI) systems due to an increased need for automation, advanced analytics, and remote work. In fact, machine learning and other forms of AI are being applied to address the increasing scale of the pandemic itself. Now, say experts, is a good time to step back and consider the ethics of these (and all) AI applications. "These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivaled pace has left the internet and technology watchers aghast," Abhishek Gupta, founder of the Montreal AI Ethics Institute, said in the introduction to that organization's inaugural State of AI Ethics report this June. "It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other."

Times have changed and caught most of us unprepared. It is always a part of Bolt's culture to move quickly and adapt -- and the crisis situation that is unfolding due to a pandemic definitely requires significant adaptation. This is a look from inside Bolt's data team -- data analysts, data engineers, data scientists -- as we share our experience and advice for times of crisis with all the similar teams out there. Most of the resources are thrown into surviving and, for some, even on seizing new opportunities. Data teams definitely have a role to play in this.

For Gunnar, that means getting more women and girls excited about AI from a young age. "We should expose girls to AI, math and science at a much earlier age so they have a support system in place," said Gunnar. Indeed, IBM's report noted that although more women have been drawn to the industry over recent years, they did not consider AI a viable career path until later in life due to a lack of support during early education. A plurality of men (46%) said they became interested in a tech career in high school or earlier, while a majority of women (53%) only considered it a possible path during their undergraduate degree or grad school. But Bouari said she's hopeful that the surge in demand for AI currently can help drive the industry forward.