Large Language Models (LLMs) have demonstrated exceptional proficiency in mathematical reasoning tasks due to their extensive parameter counts and training on vast datasets. Despite these capabilities, deploying LLMs is hindered by their computational demands. Distilling LLM mathematical reasoning into Smaller Language Models (SLMs) has emerged as a solution to this challenge, although these smaller models often suffer from errors in calculation and semantic understanding. Prior work has proposed Program-of-Thought Distillation (PoTD) to avoid calculation error. To further address semantic understanding errors, we propose Key-Point-Driven Mathematical Reasoning Distillation (KPDD). KPDD enhances the reasoning performance of SLMs by breaking down the problem-solving process into three stages: Core Question Extraction, Problem-Solving Information Extraction, and Step-by-Step Solution. This method is further divided into KPDD-CoT, which generates Chain-of-Thought rationales, and KPDD-PoT, which creates Program-of-Thought rationales. The experiment results show that KPDD-CoT significantly improves reasoning abilities, while KPDD-PoT achieves state-of-the-art performance in mathematical reasoning tasks. Our approach effectively mitigates misunderstanding errors, advancing the deployment of efficient and capable SLMs.

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex math word problems, as it usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors and step-missing errors. Prior studies involve addressing the calculation errors and step-missing errors, but neglect the semantic misunderstanding errors, which is the major factor limiting the LLMs' performance. To this end, we propose a simple-yet-effective method, namely Deeply Understanding the Problems (DUP), to improve the LLMs' math problem-solving ability by addressing semantic misunderstanding errors. The core of our method is to encourage the LLMs to deeply understand the problems and extract the key problem-solving information used for better reasoning. Extensive experiments on 10 diverse reasoning benchmarks show that our DUP method consistently outperforms the other counterparts by a large margin. More encouragingly, DUP achieves a new SOTA result on the GSM8K benchmark, with an accuracy of 97.1% under zero-shot setting.

AI technology is quickly creeping into every industry, prompting new questions about whether online content comes from a human or a computer. Hallow app CEO and co-creator Alex Jones says that while artificial intelligence (AI) can be used for both evil and good, there is one central question people must ask when it comes to emerging technologies. Jones said that a lot of friends and "fellow startup folks" who are working in AI are building "really mind-blowing tools that can do a lot of different things." AI is considered by many as one of the "scarier technologies," which can be used for "tremendous, tremendous evil," said Jones, naming the the internet's "massive proliferation of pornography" as just one example. AI GIVES GOOGLE POWER TO'DICTATE' THE NEWS PEOPLE SEE, WHAT THEY BUY, HOW THEY VOTE, ATTORNEY CLAIMS Technology can also be used for good, he continued, including advances in medical care and of connecting long-lost loved ones by acting as a vehicle "to allow God to reach into people's lives."

What I think is Agile-able or not in an ML project. Disclaimer: This post is not endorsed or sponsored by any of the firms I work for or by any of the tools or services I mentioned. I use the term AI, Data Science, and ML interchangeably. Follow me on Medium, LinkedIn, or Twitter. Also, do you want to learn business thinking and communication skills as a Data Scientist?

TLDR: Many people worry about another AI Winter. We don't lack ML pilots, but enterprises are only deploying about 10% of them. We must lower the cost of deployment with five tactical solutions. I hope this post can help ML Executives, Managers, and Practitioners to think deeper and act faster. We are the last line of defense against another AI Winter. Lastly, you can find a real-time survey to see how others think about this problem.

We have been experiencing an "AI Spring" (e.g. This was due to technological breakthroughs, commercialization of Deep Learning, and cheap computation. Such uptick in interest in AI was largely driven by the work from Alex Krizhevsky (a student of Geoff Hinton and co-worker of mine) and investment from firms like Google and Nvidia. We had similar AI Springs every decade since the 60s. However, AI Winters, defined by 1) skepticism and 2) cut in funding, followed every time.

The use of machine learning technology is spreading across all areas of modern organizations, and its predictive capabilities suit the finance function's forward-looking needs. Understanding how to work with machine learning models is crucial for making informed investment decisions. Yet, for many finance professionals, successfully employing them is the equivalent of navigating the Bermuda Triangle. Does this project match the characteristics of a typical machine learning problem? Is there a solid foundation of data and experienced analysts?

We need to discuss the systems in which it will be a critical component, Genevieve Bell, Katherine Daniell, and Amy McLennan write. Defining artificial intelligence (AI) is messy. Ask 10 experts to define AI and you will get 10 different answers. It is easy to think that the most important policy question, then, is a definitional one that tidies up the mess: what is AI? But AI is not a singular thing.

Samuel Kaski) - One of the core questions in machine learning at the moment is how to interact with humans. We turn this question into a probabilistic modelling problem, and model both the user and the task to drive the interaction. The solutions need combinations of probabilistic modelling, reinforcement learning and approximate Bayesian computation. We are looking for a postdoc who already masters some of these and offer an opportunity to learn the rest and work with us on this exciting bleeding-edge problem. Antti Oulasvirta) - The position offers an exciting opportunity to learn about and work on applications of machine learning methods and computational models of cognition, perception, and behavior in interactive systems.