The ability of large language models to solve complex mathematical problems has progressed significantly, particularly for tasks requiring advanced reasoning. However, the scarcity of sufficiently challenging problems, particularly at the Olympiad level, hinders further advancements. In this work, we introduce PromptCoT, a novel approach for automatically generating high-quality Olympiad-level math problems. The proposed method synthesizes complex problems based on mathematical concepts and the rationale behind problem construction, emulating the thought processes of experienced problem designers. We provide a theoretical analysis demonstrating that an optimal rationale should maximize both the likelihood of rationale generation given the associated concepts and the likelihood of problem generation conditioned on both the rationale and the concepts. Our method is evaluated on standard benchmarks including GSM8K, MATH-500, and AIME2024, where it consistently outperforms existing problem generation methods. Furthermore, we demonstrate that PromptCoT exhibits superior data scalability, consistently maintaining high performance as the dataset size increases, outperforming the baselines. The implementation is available at https://github.com/zhaoxlpku/PromptCoT.

So now let's study gradient descent. So we're standing somewhere in Mount ABC and we need to go down. So now the inputs of the functions are W1 and W2 and the error function is given by E. Then the gradient of E is given by the vector sum of the partial derivatives of E with respect to W1 and W2. This gradient actually tells us the direction we want to move if we want to increase the error function the most. Thus, if we take the negative of the gradient, this will tell us how to decrease the error function the most.

Python is the most powerful language you can still read. Python is actively being used in various domains such as Data Science, Machine Learning, Web Applications, and much more. In this section, we'll cover more than ten must-have skills for python developers that would help you master the art of working with Python -- Before jumping into a framework or a development environment, it is crucial to first master the core concepts of any programming language. The same is the case with Python or any programming language for that matter. If you don't know where to start, you can find some good and useful resources on the internet.

A survey of youth 14-18 finds that they are interested in working with emerging technology, but feel unprepared to do so. As a response, IBM has launched three new AI-focused online tools to teach young people about the future of artificial intelligence. IBM's study of the cohort in 13 countries found that 68% of them think that AI will have a major impact on their lives, but half of that number (34%) said they don't feel properly equipped to use the technology that will make a large difference in their futures. "As a company bringing advanced technologies into the marketplace, we have a deep responsibility to ensure that learners have the skills required to participate in the digital economy," IBM said in its announcement of the new educational tools. More than half, 56%, of young people surveyed said they were interested in tech careers, and 60% of those were interested in emerging tech areas like cybersecurity and the cloud. When it comes to any one area of interest, AI dominates with 59% wanting to learn more about it.

EconSight finds that Microsoft leads the AI patent race going into 2019 with 697 world class patents that the firm classifies as having a significant competitive impact as of November 2018. Out of the top 30 companies and research institutions as defined by EconSight in their recent analysis, Microsoft has created 20% of all patents in the global group of patent-producing companies and institutions. The following graphic provides a comparison of the top 3o in the group. Please click on the graphic to expand it for easier reading. Machine learning dominates the AI patent landscape today, leading all categories of AI patents including deep learning and neural networks.

AI, or Artificial Intelligence, is cropping up more and more in eLearning conversations--who's using it and how, and what it means for the future of corporate digital learning. As Learning Solutions prepares to explore AI from many angles, an overview of foundational aspects of AI might come in handy. Here, we'll introduce concepts and trends that are likely to appear in any deep discussion of using AI in eLearning. Artificial intelligence refers broadly to technologies that can learn and perform specific tasks. More complex tasks entail machine learning, a next-level technology that takes an AI machine or technology and teaches it to make "decisions" based on algorithms, learn from those decisions, and refine its own performance.

The paper develops a branching-time ontology that maintains the classical restriction of forward movement through a temporal tree structure, but permits the representation of paths in which one can perform inferences about time-travel scenarios. Central to the ontology is the notion of an agent embodiment whose beliefs are equivalent to those of an agent who has time-traveled from the future.