Modern data science applications often involve complex relational data with dynamic structures. An abrupt change in such dynamic relational data is typically observed in systems that undergo regime changes due to interventions. In such a case, we consider a factorized fusion shrinkage model in which all decomposed factors are dynamically shrunk towards group-wise fusion structures, where the shrinkage is obtained by applying global-local shrinkage priors to the successive differences of the row vectors of the factorized matrices. The proposed priors enjoy many favorable properties in comparison and clustering of the estimated dynamic latent factors. Comparing estimated latent factors involves both adjacent and long-term comparisons, with the time range of comparison considered as a variable. Under certain conditions, we demonstrate that the posterior distribution attains the minimax optimal rate up to logarithmic factors. In terms of computation, we present a structured mean-field variational inference framework that balances optimal posterior inference with computational scalability, exploiting both the dependence among components and across time. The framework can accommodate a wide variety of models, including dynamic matrix factorization, latent space models for networks and low-rank tensors. The effectiveness of our methodology is demonstrated through extensive simulations and real-world data analysis.

One example is the Ethics Guidelines for Trustworthy AI, from the High-Level Expert Group on AI set up by the European Commission. According to the EC guidelines, trustworthy AI should be lawful, ethical and robust [6]. The security of AI models is essential to addressing many of its requirement areas, which are becoming codified into laws and regulations, e.g., the EU AI Act [5]. As we continue to develop and rely on AI, we must prioritize security and work to address the challenges of AI safety. The market for AI startups has exploded in recent years, with many companies working on new and innovative applications. Expertise in security is not a given among all those working in AI, which makes it essential to have a dedicated focus on it to ensure safe and secure AI systems. The other day we came across this article titled "Computer security checklist for non-security technology professionals."

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'The Big Sunday Show' highlights Elon Musk's upcoming interview with Tucker Carlson warning about the dangers of A.I.. The U.S. Patent and Trademark Office has launched a process that could determine whether artificial intelligence systems can get full or partial credit as inventors of new ideas that win patent protection. USPTO on Monday announced it would hold a "listening session" on this question in early May, and is accepting public comment on whether AI has now become so advanced that it should somehow be credited as an inventor when it produces an idea that has yet to be conceived by mankind. The question of whether and how to credit AI for new inventions is one that has emerged over the last few years. In 2019, USPTO asked for public comment on whether AI is now so advanced that federal laws need to be rewritten in order to protect inventions from "entities other than natural persons."

After describing ChatGPT's left-wing bias as'concerning', Elon Musk is now working on a new AI chatbot of his own. The Twitter, Telsa and SpaceX boss has registered a company with the name of'X.AI', a subsidiary under his new conglomerate X Holdings Corp. According to the Financial Times, the new subsidiary will be the home of efforts to build a tool just like the hugely successful ChatGPT, owned by OpenAI. Musk is assembling a team of AI researchers and engineers and is in discussions with some investors in SpaceX and Tesla about putting money into his new venture. Due to Musk's belief in free speech, the new bot product could have less of a left-wing bias than ChatGPT, which has already been criticised for'woke' responses. Twitter, Telsa and SpaceX boss Elon Musk (pictured) has registered an artificial intelligence (AI) company with the name of'X.AI' Mr Musk has been critical of AI-powered chatbot ChatGPT in the past.

Google's chief executive has said concerns about artificial intelligence keep him awake at night and that the technology can be "very harmful" if deployed wrongly. Sundar Pichai also called for a global regulatory framework for AI similar to the treaties used to regulate nuclear arms use, as he warned that the competition to produce advances in the technology could lead to concerns about safety being pushed aside. In an interview on CBS's 60 minutes programme, Pichai said the negative side to AI gave him restless nights. "It can be very harmful if deployed wrongly and we don't have all the answers there yet – and the technology is moving fast. So does that keep me up at night? Google's parent, Alphabet, owns the UK-based AI company DeepMind and has launched an AI-powered chatbot, Bard, in response to ChatGPT, a chatbot developed by the US tech firm OpenAI, which has become a phenomenon since its release in November. Pichai said governments would need to figure out global frameworks for regulating AI as it developed. Last month thousands of artificial intelligence experts, researchers and backers – including the Twitter owner Elon Musk – signed a letter calling for a pause in the creation of "giant" AIs for at least six months, amid concerns that development of the technology could get out of control. Asked if nuclear arms-style frameworks could be needed, Pichai said: "We would need that." The AI technology behind ChatGPT and Bard, known as a Large Language Model, is trained on a vast trove of data taken from the internet and is able to produce plausible responses to prompts from users in a range of formats, from poems to academic essays and software coding. The image-generating equivalent, in systems such as Dall-E and Midjourney, has also triggered a mixture of astonishment and alarm by producing realistic images such as the Pope sporting a puffer jacket. Pichai added that AI could cause harm through its ability to produce disinformation "It will be possible with AI to create, you know, a video easily.

PARIS--European Union lawmakers want to give regulators new powers to govern the development of technologies like those behind ChatGPT, the biggest push so far in the West to curb one of the hottest areas in artificial intelligence. The breakneck pace of AI development in recent months requires a new set of rules tailored to powerful, general-purpose AI tools, a group of influential EU lawmakers say in an open letter they plan to publish Monday.

A new joint report entitled A New National Purpose, which explores how the UK can harness innovative technologies to meet future challenges, has recently been published by Tony Blair and William Hague. The "cross-party" report argues that we are currently undergoing a new form of Industrial Revolution "as developments in artificial intelligence (AI), biotech, climate tech and other fields begin to change our economic and social systems". It calls for policymakers to mitigate the consequent threats whilst embracing opportunities. Several of its proposals touch upon the convergence of law and technology, and we will consider some of these aspects below. Perhaps unsurprisingly, given Tony Blair's foiled aspirations to introduce digital ID during his premiership, much of the press attention has focused on the report's call for the government to "provide a secure, private, decentralised digital-ID system for the benefit of both citizens and businesses".

The ongoing artificial intelligence (AI) revolution has the potential to change almost every line of work. As AI capabilities continue to improve in accuracy, robustness, and reach, AI may outperform and even replace human experts across many valuable tasks. Despite enormous efforts devoted to understanding AI's impact on labor and the economy and its recent success in accelerating scientific discovery and progress, we lack a systematic understanding of how advances in AI may benefit scientific research across disciplines and fields. Here we develop a measurement framework to estimate both the direct use of AI and the potential benefit of AI in scientific research by applying natural language processing techniques to 87.6 million publications and 7.1 million patents. We find that the use of AI in research appears widespread throughout the sciences, growing especially rapidly since 2015, and papers that use AI exhibit an impact premium, more likely to be highly cited both within and outside their disciplines. While almost every discipline contains some subfields that benefit substantially from AI, analyzing 4.6 million course syllabi across various educational disciplines, we find a systematic misalignment between the education of AI and its impact on research, suggesting the supply of AI talents in scientific disciplines is not commensurate with AI research demands. Lastly, examining who benefits from AI within the scientific workforce, we find that disciplines with a higher proportion of women or black scientists tend to be associated with less benefit, suggesting that AI's growing impact on research may further exacerbate existing inequalities in science. As the connection between AI and scientific research deepens, our findings may have an increasing value, with important implications for the equity and sustainability of the research enterprise.

Access to highly detailed models of heterogeneous forests from the near surface to above the tree canopy at varying scales is of increasing demand as it enables more advanced computational tools for analysis, planning, and ecosystem management. LiDAR sensors available through different scanning platforms including terrestrial, mobile and aerial have become established as one of the primary technologies for forest mapping due to their inherited capability to collect direct, precise and rapid 3D information of a scene. However, their scalability to large forest areas is highly dependent upon use of effective and efficient methods of co-registration of multiple scan sources. Surprisingly, work in forestry in GPS denied areas has mostly resorted to methods of co-registration that use reference based targets (e.g., reflective, marked trees), a process far from scalable in practice. In this work, we propose an effective, targetless and fully automatic method based on an incremental co-registration strategy matching and grouping points according to levels of structural complexity. Empirical evidence shows the method's effectiveness in aligning both TLS-to-TLS and TLS-to-ALS scans under a variety of ecosystem conditions including pre/post fire treatment effects, of interest to forest inventory surveyors.