In the search for happiness, having a good day every day is surely crucial. But when there are so many pursuits competing for our attention, sometimes it's difficult to know how much time to allocate for each one. Now, scientists in Canada claim to cracked the code for the perfect day – and surprisingly, it includes a short shift at work. According to the experts, the formula for the perfect day is six hours of family time, two hours spent with friends, 1.5 hour socialising, two hours exercising and one hour eating and drinking. Additionally, the perfect day should involve no more than six hours of work and less than 15 minutes commuting.

Large language models (LLMs) demonstrate substantial capabilities in solving math problems. However, they tend to produce hallucinations when given questions containing unreasonable errors. In this paper, we study the behavior of LLMs when faced with unreasonable math problems and further explore their potential to address these problems. We construct the Unreasonable Math Problem (UMP) benchmark to examine the error detection ability of LLMs. Experiments show that LLMs are able to detect unreasonable errors, but still fail in generating non-hallucinatory content. In order to improve their ability of error detection and correction, we further design a strategic prompt template called Critical Calculation and Conclusion(CCC). With CCC, LLMs can better self-evaluate and detect unreasonable errors in math questions, making them more reliable and safe in practical application scenarios.

Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be challenging. Instructing tuning, i.e. tuning models on instruction and sample responses generated by humans (Ouyang et al., 2022), has proven as an effective method to do so, yet requires a number of data samples that a) might not be available or b) costly to generate. Furthermore, this cost increases when the goal is to make the LLM follow a specific workflow within a dialogue instead of single instructions. Inspired by the self-play technique in reinforcement learning and the use of LLMs to simulate human agents, we propose a more effective method for data collection through LLMs engaging in a conversation in various roles. This approach generates a training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. We introduce an automated way to measure the (partial) success of a dialogue. This metric is used to filter the generated conversational data that is fed back in LLM for training. Based on our automated and human evaluations of conversation quality, we demonstrate that such self-talk data improves results. In addition, we examine the various characteristics that showcase the quality of generated dialogues and how they can be connected to their potential utility as training data.

AI has officially taken over the world. Depending on who you ask, ChatGPT and Midjourney are saviour of work, art, journalism, law and ethics – or the destroyer of them. Right now, consumer AI is in no man's land, with computer-generated art mostly showing us how Mr Blobby would fare in the Napoleonic Wars. But that hasn't stopped AI start-ups from securing big money investment, and websites using ChatGPT to create personalised content. Which got me thinking: if multi-million dollar companies can wrangle AI to lighten their workloads, why can't I? If'real' jobs will be made obsolete once the machines take over, why resist it?

Amazon is offering some solid deals on a multitude of games, with many popular titles available at or near their all-time-low prices. The most noteworthy of the bunch is Elden Ring, on sale for PlayStation 5 and PlayStation 4 at $50 for a savings of $10 over the regular price -- a solid deal on a relatively fresh release. You'll also find discounts on a host of other titles including Animal Crossing (Switch), Deathloop (PS5/Xbox One Series X) and Disco Elysium (PS4/Xbox One). FromSoftware's Elden Ring was a hit from the start, despite some early performance and other issues that have mostly been rectified. Critics raved about the perfectly conceived open world, mysterious story and challenging gameplay and users have generally liked it as well.

If you're in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he's here to help. Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process. On today's podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical! Tom Zimmermann: If you think of a typical software engineer at Microsoft, they spend about half of a day on development related activities, and the other half of the day is spent on other activities like coordinating with other people in meetings, sending emails… So, there's actually not that much time that they can spend on writing code, and the time they spend writing code, on a good day, it's actually only 96 minutes, and on a bad day it's, on average, 66 minutes. And half an hour writing code actually can make the difference between a bad and a good workday. Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: If you're in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he's here to help. Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process. On today's podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical! Host: You have a cool nickname. Why do people call you that? Tom Zimmermann: So, it goes back to when I started at Microsoft.

If you're in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he's here to help. Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process. On today's podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical! Tom Zimmermann: If you think of a typical software engineer at Microsoft, they spend about half of a day on development related activities, and the other half of the day is spent on other activities like coordinating with other people in meetings, sending emails… So, there's actually not that much time that they can spend on writing code, and the time they spend writing code, on a good day, it's actually only 96 minutes, and on a bad day it's, on average, 66 minutes. And half an hour writing code actually can make the difference between a bad and a good workday. Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: If you're in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he's here to help. Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process. On today's podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical! Host: You have a cool nickname. Why do people call you that? Tom Zimmermann: So, it goes back to when I started at Microsoft.

In this post, we will talk about natural language processing (NLP) using Python. This NLP tutorial will use Python NLTK library. NLTK is a popular Python library which is used for NLP. So what is NLP? and what are the benefits of learning NLP? Simply and in short, natural language processing (NLP) is about developing applications and services that are able to understand human languages. We are talking here about practical examples of natural language processing (NLP) like speech recognition, speech translation, understanding complete sentences, understanding synonyms of matching words, and writing complete grammatically correct sentences and paragraphs.