Large Language Models (LLMs) are commonly used to generate solutions for mathematical reasoning problems in the following formats: natural language, code, or a combination of both. In this paper, we explore fundamental questions related to solving mathematical reasoning problems using natural language and code with state-of-the-art LLMs, including GPT-4o-mini and LLama-3.1-8b-Turbo. Our findings show that LLMs are better at reasoning in natural language compared to code. Additionally, although natural language and code serve as complementary forms of reasoning, they can affect each other in a negative way in certain scenarios. These insights motivate our development of a new prompting method, INC-Math, which leverages an LLM to dynamically select the most appropriate reasoning form, resulting in improved performance over comparable baselines with GPT-4o-mini.

This tutorial addresses the challenge of incorporating large language models (LLMs), such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by AI. For example, instructors can parallelize instruction by having students interact with different custom-made GPTs to learn different parts of an analysis and then teach each other what they learned from their AIs. For another example, instructors can turn problem sets into AI tutoring sessions, whereby a custom-made GPT guides a student through the problems, and the student uploads the chatlog for their homework submission. For a third example, you can assign different labs to each section of your class and have each section create AI assistants to help the other sections work through their labs. This tutorial advocates the programming in the English paradigm, in which students express the desired data transformations in prose and then use AI to generate the corresponding code. Students can wrangle data more effectively by programming in English than by manipulating in Excel. However, some students will program in English better than others, so you will still derive a robust grade distribution (at least with current LLMs).

Detecting when text has been generated by tools like ChatGPT is a difficult task. Popular artificial- intelligence-detection tools, like GPTZero, may provide some guidance for users by telling them when something was written by a bot and not a human, but even specialized software is not foolproof and can spit out false positives. As a journalist who started covering AI detection over a year ago, I wanted to curate some of WIRED's best articles on the topic to help readers like you better understand this complicated issue. Have even more questions about spotting outputs from ChatGPT and other chatbot tools? Sign up for my AI Unlocked newsletter, and reach out to me directly with anything AI-related that you would like answered or want WIRED to explore more.

Andy Rachleff, an entrepreneur and investor, famously said that you know you have a strong product-market fit when your customers are so attached to your product that they scream when you try to take it away from them. ChatGPT is a product that seems to have this kind of attachment, even while it is still in the research preview phase. ChatGPT has generated significant buzz and been put to various tests, including taking a bar exam and answering questions. People have also used it to compose songs and respond to emails. Some believe it poses a threat to search engines like Google. In this article, we will evaluate the pros and cons of ChatGPT, examine how it was trained, and suggest potential ways for improvement.

ChatGPT, which the company OpenAI recently released, generates text and can even write essays. The artificial intelligence, or AI, chatbot has already been reported to be a coauthor on four papers and preprints. What does this mean for the future of education? How can universities best address the challenges ChatGPT, or "Chat Generative Pre-trained Transformer," poses? Could the bot enhance education?

This course is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios. We cover deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments.

OpenAI has ruined my favorite assignment from when I was (briefly) a high school English teacher: Come up with a two-sentence horror story. All you really need to do the assignment is a topic and an idea of what's frightening. It seems so… human and creative, and yet, OpenAI does it better than most of my high school students ever did. What does OpenAI, which launched ChatGPT to much fanfare last week, offer developers? I fell down a rabbit hole exploring the private artificial intelligence company's playground options.

Artificial intelligence is quickly becoming an indispensable tool in the classroom. From helping students with homework to collaborating with classmates on projects, teachers are finding uses for AI beyond just checking homework assignments and reading texts aloud. With AI, teachers can tailor lessons to students' interests, create engaging activities, and even create virtual lessons to help students learn in more challenging aspects of a subject. We explore the pros and cons of the AI-only classroom, explore the pros and cons of teaching students with AI, and explore the best ways to integrate AI into education. Artificial intelligence has been around since the mid-twentieth century when it was used to help with tasks like word searching and chess playing.

If you are in tech, you know what Stack Overflow is. More than likely, you might have a love-hate relationship with the service -- while incredibly useful, users can be quite confrontational about a post they don't deem "worthy", asks a previously answered question or simply uses the word thanks. Nonetheless, finding the right SO post can be much faster than scraping through pages of documentation just to remember if you should be using a concat() or a merge(). So what is one to do? Brave the world of Stack Overflow, toxicity and all, or take a stand and try to live without it? And despite the barriers to entry to get one's post read/not downvoted into oblivion, there are a few guidelines that one can follow to increase the odds of being well received.

Alexa can read you the latest headlines, give you the weather forecast, and help you find a recipe to make for dinner, but did you know that your Echo device can also assist your student with homework? If you have an Amazon Echo smart speaker at home, like our favorite Echo device, the Echo (2nd Generation), your student has access to a wealth of brain-sharpening, knowledge-enhancing activities to help tackle homework assignments. While kids probably don't need more time interacting with electronics, Alexa offers a variety of skills that can help students with their homework when parents aren't available. To enable a skill on your Echo device say, "Alexa, enable [exact name of skill]." Or, open the Alexa app, tap the menu button, and select "Skills."