With the recent evolution of large language models (LLMs), concerns about aligning such models with human values have grown. Previous research has primarily focused on assessing LLMs' performance in terms of the Helpful, Honest, Harmless (3H) basic principles, while often overlooking their alignment with educational values in the Chinese context. To fill this gap, we present Edu-Values, the first Chinese education values evaluation benchmark designed to measure LLMs' alignment ability across seven dimensions: professional ideology, cultural literacy, educational knowledge and skills, education laws and regulations, teachers' professional ethics, basic competencies, and subject knowledge. We meticulously design and compile 1,418 questions, including multiple-choice, multi-modal question answering, subjective analysis, adversarial prompts, and questions on traditional Chinese culture. We conduct both human evaluation and automatic evaluation over 11 state-of-the-art (SoTA) LLMs, and highlight three main findings: (1) due to differences in educational culture, Chinese LLMs significantly outperform English LLMs, with Qwen 2 ranking the first with a score of 81.37; (2) LLMs perform well in subject knowledge and teaching skills but struggle with teachers' professional ethics and basic competencies; (3) LLMs excel at multiple-choice questions but perform poorly on subjective analysis and multi-modal tasks. This demonstrates the effectiveness and potential of the proposed benchmark. Our dataset is available at https://github.com/zhangpeii/Edu-Values.git.

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previously been used to reliably 'screen' articles for review - that is, identify relevant articles based on reviewers' inclusion criteria. The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We therefore set out to develop a series of tools that would assist in the profiling and analysis of 1,952 publications on the theme of 'outcomes-based contracting'. Tools were developed for the following tasks: assign publications into 'policy area' categories; identify and extract key information for evidence mapping, such as organisations, laws, and geographical information; connect the evidence base to an existing dataset on the same topic; and identify subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of ML techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While ML techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analysing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.

Advances in artificial intelligence (AI) will transform many aspects of our lives and society, bringing immense opportunities but also posing significant risks and challenges. The next several decades may well be a turning point for humanity, comparable to the industrial revolution. If so, future historians will judge how well we harnessed the benefits of AI for humanity, while protecting against potential harms. In this column, we share a set of recommendations for moving forward from the perspective of a founder and leaders of the One Hundred Year Study on AI.3 Launched 10 years ago with a dedicated endowment, the project is committed to a perpetual series of studies by multidisciplinary experts to evaluate the immediate, longer-term, and far-reaching effects of AI on people and society,1 and to make recommendations about AI research, policy, and practice.2,4 Beyond these recurrent studies and reports, our initiatives have included related efforts aimed at providing a diverse audience with insights about the trajectory of AI, including the creation of the AI Index, an annual benchmarking of AI progress.5

In 2024, we will see courts and regulators around the world demonstrate that tech exceptionalism, when it comes to the applicability of legal rules, is magical thinking. The tide has already started to turn on the assumption that law and regulation cannot keep up with technological innovation. But, in 2024, the sea change will come: not through new rules, but by old rules being applied aggressively to new problems. In the United States, in the absence of federal privacy legislation, regulators have already started to repurpose laws and rules they do have at their disposal to address some of the most egregious examples of Big Tech playing fast and loose with our rights and personal data. In 2023, the US Federal Trade Commission (FTC) continued to expand the regulatory heft of consumer protection regulations.

Americans shared whether or not they believe the government should regulate Artificial Intelligence amid the technology's rapid, and ongoing, advancement. AUSTIN, Texas – The majority of Americans who spoke with Fox News said the government should stay out of regulating artificial intelligence technologies. "Keep the government out of regulating things," a Fort Worth resident told Fox News. "They regulate too many things already." Brian similarly opposed state regulation of the technology.

Hollywood has colourful notions about artificial intelligence (AI). The popular image is a future where robot armies spontaneously turn to malevolence, pitching humanity in a battle against extinction. In reality, the risks posed by AI today are more insidious and harder to unpick. They are often a by-product of the technology's seemingly endless application in modern society and increasing role in everyday life, perhaps best highlighted by Microsoft's latest multi-billion-dollar investment into ChatGPT-maker OpenAI. Either way, it's unsurprising that AI generates so much debate, not least in how we can build regulatory safeguards to ensure we master the technology, rather than surrender control to the machines. Right now, we tackle AI using a patchwork of laws and regulations, as well as guidance that doesn't have the force of law. Against this backdrop, it's clear that current frameworks are likely to change – perhaps significantly.

The use of artificial intelligence (AI) is expanding rapidly. These technological breakthroughs present both opportunity and potential peril. AI technology offers great hope for increasing economic opportunity, boosting incomes, speeding life science research at reduced costs, and simplifying the lives of consumers. With so much potential for innovation, organizations investing in AI-oriented practices are already ramping up initiatives that boost productivity to remain competitive. Like most disruptive technologies, these investments can both create and displace jobs.

I recently took a look at ChatGPT and how it deals with charity law questions. Sometimes it did a good job, and other times, it failed miserably. For example, I asked ChatGPT, "How to explain charity law to a kid." Charity law is a set of rules that govern how organizations that help others, called charities, can operate. These laws help make sure that charities use their resources, such as money and property, in the best way to help people in need.

We've all heard them: 24 carat gold, 100% Florida orange juice, 100% all natural, 100% made in the USA. Much of what we consume--from food to data--is qualified in some way to help us gain insights into what we're consuming. Sometimes, it's directly related to things like ingredients and other times, it's more about the social and political implications. But the rise of machine learning and natural language processing has led to the development of advanced language models such as GPT (Generative Pre-trained Transformer) and raised important questions about creativity and ownership, to name just a few. These models are amazing and are capable of generating human-like text, making it difficult to distinguish between text written by a human and text generated by a machine.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! You are a walking data repository. While outside your residence or vehicle, walking down a street, shopping in a store, or visiting any type of public event or meeting -- you potentially lose your personal privacy and cross the boundary from being a private individual to a virtual public figure. You can be filmed or photographed, your image can be transported to a storage silo anywhere in the world, your voice can be recorded, and your time in public view can be noted.