This paper presents a comprehensive performance evaluation of Large Language Models (LLMs) in solving programming challenges from Leetcode, a widely used platform for algorithm practice and technical interviews. We began by crawling the Leetcode website to collect a diverse set of problems encompassing various difficulty levels and topics. Using this dataset, we generated solutions with multiple LLMs, including GPT-4 and GPT-3.5-turbo (ChatGPT-turbo). The generated solutions were systematically evaluated for correctness and efficiency. We employed the pass@k metric to assess the success rates within a given number of attempts and analyzed the runtime performance of the solutions. Our results highlight the strengths and limitations of current LLMs [10] in code generation and problem-solving tasks, providing insights into their potential applications and areas for improvement in automated programming assistance.

In the last week, I've been experimenting with the hot new version of ChatGPT to discover how it might conserve a leader's scarcest resource: time. When OpenAI launched the AI chatbot at the end of November, it instantly attracted millions of users, with breathless predictions of its potential to disrupt business models and jobs. It certainly promises to deliver on a prediction I made in 2019 in my book The Human Edge, which explores the skills needed in a world of artificial intelligence and digitization. I forecasted: "…AI can offer us more free time by automating the stupid stuff we currently have to do, thereby reducing our cognitive burden." The prize is clear for leaders.

Artificial intelligence (AI) is one of the current advancements in the modern era. Together with machine learning, they have revolutionized how businesses interact and engage with their production line, other organizations, and customers. Many experts consider these advanced solutions as game changes, with the ability to make life better for everyone. Over the last few years, all manufacturing, healthcare, retail, and more sectors have adopted AI. This adoption has transformed their core processes and business models, improving their competitive advantage.

When Katica Roy returned to work after the birth of her daughter, her supervisor asked her to take on two new teams, tripling her workload in a matter of two weeks without additional pay or a promotion. Meanwhile, management asked a male colleague to take on one extra team. With his new responsibilities came a promotion and more pay. In order to get the pay equity due her, Roy notified her human resources team about the Lilly Ledbetter Act, a federal law that helps pay practices are non-discriminatory and fair, without gender or other bias, by making it easier to file equal-pay lawsuits. While she ended up succeeding in her gender bias protest, the process led Roy to found and become CEO of Pipeline Equity, a SaaS vendor that uses cloud-based AI, machine learning and natural language processing (NLP) technology to improve the financial performance of its users by trying to close the gender equity gap.

AI is a technology that has been around for a long time already and has opened up many different avenues in the business world. However, despite the benefits, a recent Gartner study found that many organisations remain reluctant to apply AI, particularly in HR. In fact, only 17% of organisations are using AI-based solutions in their HR function. While the study suggests this trend will rise, with an additional 30% of organisations exploring AI in HR by 2022, it's clear that HR remains behind on the use of this technology, missing out on the key benefits that AI can bring, not only to the department operations but also to their employees and the wider business too. Choosing an AI-based solution for your organisation can be tough, especially when it is unclear how the technology can truly impact the business or what is required for the solution to be as effective as possible.

Allan Jones has seen the challenges of running a small business firsthand. When he was 14, his father was sued for wrongful termination by a former employee of his Compton, California mini-market. Without the guidance of a human resources department or the finances to fight the suit, he was forced to hire an attorney and dip into Jones' college savings to pay the fees. This experience stuck with Jones, and in 2016 inspired him to found Bambee, a Los Angeles-based company that pairs HR managers with small and midsize businesses on a monthly basis. "I knew that small businesses did not have HR, and the primary reason was price," says Jones, 34.

The hit TV anthology'Black Mirror' has captivated viewers with speculative tales of how emerging technologies like artificial intelligence, machine learning and intelligent automation could go horribly awry. It makes for great television, but do similar futures await learning leaders who are looking to strategically leverage these technologies? We have seen where AI-powered digital technologies are steadily and increasingly becoming part of our daily lives. Amazon's Alexa, Apple's Siri, Google's Assistant and Microsoft's Cortana are accessible in many of our homes and through our digital devices. These and other AI agents are evolving and becoming more capable of completing processes humans are traditionally tasked with.

Which robo advisor is best? This is a key question that investors must ponder as we begin 2020. The top robo advisors are beginning to assert themselves and disrupt the financial space. Fortunately, we've done the heavy lifting for you and compiled a list of robo advisors for the coming year. Each of these companies has established itself as a player in the growing robo advisor market, and Insider Intelligence predicts that robo-advisors will be managing $4.6 trillion by 2022.

Reskilling is going to define the return to work. There is untapped value in building an internal pipeline of talent. Internal promotions and referrals are the preferred source to fill open jobs. The cost to hire, time to hire, and performance benefits are well documented. Their domain knowledge comes with them into the new role.

Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justice theory, three different fairness types (distributive, procedural, interactional), and the frameworks for redressing instances of unfairness (retributive justice, restorative justice). Second, we review the design literature that specifically focuses on issues of AI fairness in organizations. Third, we propose a design agenda for AI fairness in organizations that applies each of the fairness types to organizational scenarios. Then, the paper concludes with implications for future research.