Will the start of 2026 see a change of fortune for BBC Sport football expert Chris Sutton, whose title hopes are fading fast? After Sutton made a strong start to the season, AI has taken charge of the predictions title race, clinching another win last week. It's been a bad start to the new year for me, with AI top of the table, Sutton said. I also lost to my daugher Sophia at cards over Christmas. We played rummy and I think she was cheating - a lot like AI, she was definitely getting some help from somewhere. Sutton is making predictions for all 380 Premier League games this season, against AI, BBC Sport readers and a variety of guests. For week 20, he takes on singer-songwriter Andrew Cushin, who is a Newcastle fan.

Our study introduces an automated system leveraging large language models (LLMs) to assess the effectiveness of five key tutoring strategies: 1. giving effective praise, 2. reacting to errors, 3. determining what students know, 4. helping students manage inequity, and 5. responding to negative self-talk. Using a public dataset from the Teacher-Student Chatroom Corpus, our system classifies each tutoring strategy as either being employed as desired or undesired. Our study utilizes GPT-3.5 with few-shot prompting to assess the use of these strategies and analyze tutoring dialogues. The results show that for the five tutoring strategies, True Negative Rates (TNR) range from 0.655 to 0.738, and Recall ranges from 0.327 to 0.432, indicating that the model is effective at excluding incorrect classifications but struggles to consistently identify the correct strategy. The strategy \textit{helping students manage inequity} showed the highest performance with a TNR of 0.738 and Recall of 0.432. The study highlights the potential of LLMs in tutoring strategy analysis and outlines directions for future improvements, including incorporating more advanced models for more nuanced feedback.

What is GPT-3 and how can writers use it responsibly as part of their creative process? How can we approach AI tools with curiosity, rather than fear? In the intro, I mention the discussion about whether Google's language model, LaMDA, could be sentient [The Verge]; and the Alliance of Independent Authors Ethical Usage of AI tools. If you'd like to know more about using AI for writing, images, marketing, voice, translation, and more, check out my course, The AI-Assisted Author. Andrew Mayne is the multi-award-nominated and internationally best-selling author of thrillers. He invented an underwater stealth suit for shark diving, and he works with OpenAI as a science communicator. He also has books for authors, including, 'How to Write a Novella in 24 hours,' and a co-hosts the podcast'Weird Things.' You can find Andrew at www.AndrewMayne.com You can find GPT-3 on OpenAI.com. There are many tools built on top of GPT-3. I use and recommend Sudowrite for fiction, in particular. Joanna: Andrew Mayne is the multi-award-nominated and internationally best-selling author of thrillers. He invented an underwater stealth suit for shark diving, and he works with OpenAI as a science communicator. He also has books for authors, including, 'How to Write a Novella in 24 hours,' and a co-hosts the podcast'Weird Things.' Andrew: Hey, thank you for having me. Joanna: Oh, you do so many things. But we are actually going to talk about AI today. Andrew: Well, ever since I was a little boy, I was really interested in science, and entertainment, and everything in between. And I loved robots when I was a kid. And I'd build robots from science fairs and stuff, and I would use coffee cans, and little motors and things I pulled from toys to do that.

A man has told his story of going from living on a council estate to founding one of Britain's largest biotech companies which floated on New York's Nasdaq stock exchange for $2.9bn. The Welsh scientist's company - which uses artificial intelligence to cut the time and money being spent on discovering new drugs - has earned him a whopping €470 million, but he says he is nowhere near finished. Andrew Hopkins grew up on a council estate in the UK but described how he has since swapped that life for one in the prestigious city of Oxford after setting up his company, Exscientia. The 50-year-old founder retains 18.6 million shares, giving him a 15.8% stake of the company. On paper, he's worth around €470m since the flotation in October 2021, but in real life, Andrew, or Professor Hopkins as he's known in the field, is only just beginning.

David Yunger is CEO of AI and software development firm Vaital. We were days away from IPO. We had raised $100 million in funding and exploded from a team of 50 in a garage to 600 in 18 months. One million technologists joined our platform. We were the next big deal.

As Editor-in-Chief, it is my pleasure to introduce a new program: Communications' digital initiative that connects leading-edge research and technology insights and breakthroughs from ACM's conferences to a much larger audience. The idea is to select compelling topics of broad interest and highlight them in a vibrant conversation with key leaders in an interactive digital event--one you can participate with live or view later via the ACM Digital Library. We held our first digital initiative in February. Below are some details about the event and link to watch it. Communications' first Digital Event was an exciting discussion with AI research leaders from academia and industry who explored how science and AI are transforming each other.

In this article, we summarise two of the invited talks from the AAAI Conference on Artificial Intelligence. We hear from Andrew Ng and Marta Kwiatkowska, who talked about data-centric AI and robust deep learning respectively. Andrew began with a definition of data-centric AI – "the discipline of systematically engineering the data used to build an AI system". AI systems tend to consist of two parts: data and code. The conventional approach for developing such systems, and one which many researchers take, is to download a dataset and then work on the code.

Inside of the development studios of San Francisco-based Dexterity, Inc. there is a robot arm that stands as tall as a human. It is placed between a conveyor belt and several wooden pallets, all of which are typical of most warehouse packing facilities. But this is no typical warehouse facility. This is the location where most warehouse packing facilities would have teams of people manually picking up boxes from the conveyor belt and carefully placing them onto the pallets for wrapping and shipping, but there are no such people here. Instead, as the packages come down the belt, this robot recognizes them, picks them up, and then deposits them onto the target pallet with a gentle touch.

社内勉強会でAndrew Ng先生が提唱するData-Centric AIについて紹介した際の資料です。

The Sparsest Permutation (SP) algorithm is accurate but limited to about 9 variables in practice; the Greedy Sparest Permutation (GSP) algorithm is faster but less weak theoretically. A compromise can be given, the Best Order Score Search, which gives results as accurate as SP but for much larger and denser graphs. BOSS (Best Order Score Search) is more accurate for two reason: (a) It assumes the "brute faithfuness" assumption, which is weaker than faithfulness, and (b) it uses a different traversal of permutations than the depth first traversal used by GSP, obtained by taking each variable in turn and moving it to the position in the permutation that optimizes the model score. Results are given comparing BOSS to several related papers in the literature in terms of performance, for linear, Gaussian data. In all cases, with the proper parameter settings, accuracy of BOSS is lifted considerably with respect to competing approaches. In configurations tested, models with 60 variables are feasible with large samples out to about an average degree of 12 in reasonable time, with near-perfect accuracy, and sparse models with an average degree of 4 are feasible out to about 300 variables on a laptop, again with near-perfect accuracy. Mixed continuous discrete and all-discrete datasets were also tested. The mixed data analysis showed advantage for BOSS over GES more apparent at higher depths with the same score; the discrete data analysis showed a very small advantage for BOSS over GES with the same score, perhaps not enough to prefer it.