We accelerate the iterative hard thresholding (IHT) method, which finds (k) important elements from a parameter vector in a linear regression model. Although the plain IHT repeatedly updates the parameter vector during the optimization, computing gradients is the main bottleneck. Our method safely prunes unnecessary gradient computations to reduce the processing time.The main idea is to efficiently construct a candidate set, which contains (k) important elements in the parameter vector, for each iteration. Specifically, before computing the gradients, we prune unnecessary elements in the parameter vector for the candidate set by utilizing upper bounds on absolute values of the parameters. Our method guarantees the same optimization results as the plain IHT because our pruning is safe. Experiments show that our method is up to 73 times faster than the plain IHT without degrading accuracy.

We accelerate the iterative hard thresholding (IHT) method, which finds (k) important elements from a parameter vector in a linear regression model. Although the plain IHT repeatedly updates the parameter vector during the optimization, computing gradients is the main bottleneck. Our method safely prunes unnecessary gradient computations to reduce the processing time.The main idea is to efficiently construct a candidate set, which contains (k) important elements in the parameter vector, for each iteration. Specifically, before computing the gradients, we prune unnecessary elements in the parameter vector for the candidate set by utilizing upper bounds on absolute values of the parameters. Our method guarantees the same optimization results as the plain IHT because our pruning is safe.

It's a meal that many of us look forward to all year. But what exactly are the best items in a British Christmas Dinner? While many of us see the Roast Turkey, Goose or Ham as the main event, others prefer the trimmings, whether it's pigs in blankets, stuffing, or even Brussels Sprouts. With just 10 days to go before we get to devour our Christmas Dinner, MailOnline asked ChatGPT to rank the elements on the meal. So, do you agree with the AI chatbot's ranking? To get to the bottom of the Christmas Dinner ranking, MailOnline simply asked ChatGPT: 'How do you rank the elements of a British Christmas dinner?' Within seconds, the AI bot began to reply, diplomatically stating that'the ranking of elements can vary based on personal preferences and regional traditions.'

Large language models (LLMs) that have been trained on a corpus that includes large amount of code exhibit a remarkable ability to understand HTML code. As web interfaces are primarily constructed using HTML, we design an in-depth study to see how LLMs can be used to retrieve and locate important elements for a user given query (i.e. task description) in a web interface. In contrast with prior works, which primarily focused on autonomous web navigation, we decompose the problem as an even atomic operation - Can LLMs identify the important information in the web page for a user given query? This decomposition enables us to scrutinize the current capabilities of LLMs and uncover the opportunities and challenges they present. Our empirical experiments show that while LLMs exhibit a reasonable level of performance in retrieving important UI elements, there is still a substantial room for improvement. We hope our investigation will inspire follow-up works in overcoming the current challenges in this domain.

This exploratory study invesBgates the potenBal of the arBficial intelligence tool, ChatGPT, to support systems thinking (ST) in various subjects. Using both general and subject-specific prompts, the study assesses the accuracy, helpfulness, and reliability of ChatGPT's responses across different versions of the tool. The results indicate that ChatGPT can provide largely correct and very helpful responses in various subjects, demonstraBng its potenBal as a tool for enhancing ST skills. However, occasional inaccuracies highlight the need for users to remain criBcal of ChatGPT's responses. Despite some limitaBons, this study suggests that with careful use and aRenBon to its idiosyncrasies, ChatGPT can be a valuable tool for teaching and learning ST. In today's increasingly complex world, systems thinking (ST) emerges as an invaluable skill to equip our students with. It fosters a broader perspecBve, encouraging individuals to recognize the interconnectedness and complexity of various phenomena, thereby enhancing their understanding of the world and enabling more effecBve acBons (Binkley et al., 2012; Yoon et al., 2017). Complex situaBons, ranging from ecological problems to economic issues, social relaBonships, and health concerns, o]en confront even children. Successfully navigaBng these situaBons requires managing various aspects: percepBon, evaluaBon, understanding, consideraBon of alternaBves, decision-making, taking acBon, and reflecBon. Children o]en develop their own explanaBons and build knowledge from real-life experiences, even in the absence of formal educaBon (Arndt & Kopp, 2017).

When you think of Machine Learning, you think about models. These models need effective management to ensure that they are producing the outputs required to solve a specific problem or task. Machine Learning Model Management is used to help Data Scientists, Machine Learning engineers, and more to keep track and on top of all their experiments and the results produced by the model. Machine Learning Model Management sole responsibility is ensuring that the development, training, versioning and deployment of ML models is managed at an effective level. The tools used in the development cycle for Machine Learning and the managing of the models require MLOps - Machine Learning Operations. To recap and for those of you who may be unsure, MLOps is a core function to the engineering of Machine Learning.

Finding the right framing is key. As an educator, Alejandro Rodríguez knows that even the most complex concepts can become approachable if you choose your communication method wisely. This post is ostensibly an introduction to confusion matrices and classification metrics, but it's also a masterclass on the power of a simple, well-chosen example. Data visualization is about making massive amounts of information accessible and interpretable. Its success depends on a series of design decisions, both small and big; Weronika Gawarska-Tywonek's excellent primer will help you understand how color palettes work, and how to go about choosing the one that's most appropriate for the task at hand.

Dog puppies understand humans in a way that their relatives like wolves just don't thanks to undergoing some 14,000 years of domestication, a study has concluded. Researchers from Duke University tested both dog and wolf puppies in a series of tests that involved locating hidden food by picking up on human clues. They found that dog puppies have similar social cognition abilities as human babies -- and are able, for example, to instinctively recognise pointing as communication. This superficially simple understanding is, in fact, rare in the animal kingdom. Not only did the wolf pups in the study lack it, but so do chimps, our closest relatives.

To realize autonomous collaborative robots, it is important to increase the trust that users have in them. Toward this goal, this paper proposes an algorithm which endows an autonomous agent with the ability to explain the transition from the current state to the target state in a Markov decision process (MDP). According to cognitive science, to generate an explanation that is acceptable to humans, it is important to present the minimum information necessary to sufficiently understand an event. To meet this requirement, this study proposes a framework for identifying important elements in the decision-making process using a prediction model for the world and generating explanations based on these elements. To verify the ability of the proposed method to generate explanations, we conducted an experiment using a grid environment. It was inferred from the result of a simulation experiment that the explanation generated using the proposed method was composed of the minimum elements important for understanding the transition from the current state to the target state. Furthermore, subject experiments showed that the generated explanation was a good summary of the process of state transition, and that a high evaluation was obtained for the explanation of the reason for an action.

At the 2021 NVIDIA GTC conference Jensen Huang spoke from his kitchen to say that we need a metaverse (the Omniverse, with attribution to SF author Neil Stephenson), a digital twin of the real world and that NVIDIA is building the tools to make this possible. He spoke about democratizing high performance computing and of course, his talk included cool videos of special effects and showing how the Omniverse can be used to teach robots how to be robots and to design better factories. An important element in the Omniverse is creating ever more realistic models with "digital twins" that can accurately describe the real world and can be used for various optimization and failure analysis applications. Jensen talked about 4 stacks of technology and their applications. These are RTX visual computing platform that is being used to create the Onmiverse; computing and accelerator platforms (DGX, Grace, BlueField and DOCA); the EGX accelerated computing platform applied to Jarvis, Merlin, Maxine, Morpheus and NVIDIA AI; and autonomous vehicle products Hyperion, Atlan and Orlin. NVDIA said that AI model sizes are getting bigger in order to make more accurate models.