Intelligent Tutoring Systems (ITSs) have revolutionized education by offering personalized learning experiences. However, as goal-oriented learning, which emphasizes efficiently achieving specific objectives, becomes increasingly important in professional contexts, existing ITSs often struggle to deliver this type of targeted learning experience. In this paper, we propose GenMentor, an LLM-powered multi-agent framework designed to deliver goal-oriented, personalized learning within ITS. GenMentor begins by accurately mapping learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset. After identifying the skill gap, it schedules an efficient learning path using an evolving optimization approach, driven by a comprehensive and dynamic profile of learners' multifaceted status. Additionally, GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs. Extensive automated and human evaluations demonstrate GenMentor's effectiveness in learning guidance and content quality. Furthermore, we have deployed it in practice and also implemented it as an application. Practical human study with professional learners further highlights its effectiveness in goal alignment and resource targeting, leading to enhanced personalization. Supplementary resources are available at https://github.com/GeminiLight/gen-mentor.

Can human decision-makers learn from AI feedback? Using data on 52,000 decision-makers from a large online chess platform, we investigate how their AI use affects three interrelated long-term outcomes: Learning, skill gap, and diversity of decision strategies. First, we show that individuals are far more likely to seek AI feedback in situations in which they experienced success rather than failure. This AI feedback seeking strategy turns out to be detrimental to learning: Feedback on successes decreases future performance, while feedback on failures increases it. Second, higher-skilled decision-makers seek AI feedback more often and are far more likely to seek AI feedback after a failure, and benefit more from AI feedback than lower-skilled individuals. As a result, access to AI feedback increases, rather than decreases, the skill gap between high- and low-skilled individuals. Finally, we leverage 42 major platform updates as natural experiments to show that access to AI feedback causes a decrease in intellectual diversity of the population as individuals tend to specialize in the same areas. Together, those results indicate that learning from AI feedback is not automatic and using AI correctly seems to be a skill itself. Furthermore, despite its individual-level benefits, access to AI feedback can have significant population-level downsides including loss of intellectual diversity and an increasing skill gap.

As Artificial Intelligence (AI) changes how businesses work, there is a growing need for people who can work in this sector. This paper investigates how well universities in United Kingdom offering courses in AI, prepare students for jobs in the real world. To gain insight into the differences between university curricula and industry demands we review the contents of taught courses and job advertisement portals. By using custom data scraping tools to gather information from job advertisements and university curricula, and frequency and Naive Bayes classifier analysis, this study will show exactly what skills industry is looking for. In this study we identified 12 skill categories that were used for mapping. The study showed that the university curriculum in the AI domain is well balanced in most technical skills, including Programming and Machine learning subjects, but have a gap in Data Science and Maths and Statistics skill categories.

The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market. Existing efforts in this area either rely on domain-expert knowledge or regarding skill evolution as a simplified time series forecasting problem. However, both approaches overlook the sophisticated relationships among different skills and the inner-connection between skill demand and supply variations. In this paper, we propose a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework for joint skill demand-supply prediction. Specifically, CHGH is an encoder-decoder network consisting of i) a cross-view graph encoder to capture the interconnection between skill demand and supply, ii) a hierarchical graph encoder to model the co-evolution of skills from a cluster-wise perspective, and iii) a conditional hyper-decoder to jointly predict demand and supply variations by incorporating historical demand-supply gaps. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed framework compared to seven baselines and the effectiveness of the three modules.

This paper presents a framework for learning player embeddings in competitive games and events. Players and their win-loss relationships are modeled as a skill gap graph, which is an undirected weighted graph. The player embeddings are learned from the graph using a random walk-based graph embedding method and can reflect the relative skill levels among players. Embeddings are low-dimensional vector representations that can be conveniently applied to subsequent tasks while still preserving the topological relationships in a graph. In the latter part of this paper, Graphical Elo (GElo) is introduced as an application of player embeddings when rating player skills. GElo is an extension of the classic Elo rating system. It constructs a skill gap graph based on player match histories and learns player embeddings from it. Afterward, the rating scores that were calculated by Elo are adjusted according to player activeness and cosine similarities among player embeddings. GElo can be executed offline and in parallel, and it is non-intrusive to existing rating systems. Experiments on public datasets show that GElo makes a more reliable evaluation of player skill levels than vanilla Elo. The experimental results suggest potential applications of player embeddings in competitive games and events.

AI hands are reaching further into the tech industry. Microsoft has added Security Copilot, a natural language chatbot that can write and analyze code, to its suite of products enabled by OpenAI's GPT-4 generative AI model. Security Copilot, which was announced on Wednesday, is now in preview for select customers. Microsoft will release more information through its email updates about when Security Copilot might become generally available. Microsoft Security Copilot is a natural language artificial intelligence data set that will appear as a prompt bar.

The unprecedented changes witnessed globally in the recent past have transformed the way we think, interact, and work. Enterprises across the globe are going through cultural and structural shifts that requires them to reimagine and restructure their business processes. New Age technologies such as AI, ML, cloud, with their ability to connect processes, data, and people are revolutionizing the work culture. In such a scenario, enterprises need to reshape their operating models to accommodate this transition. Here are the five trends that are impacting the future of work globally.

Accelerated by the Covid pandemic, this digital transformation has created never-seen-before opportunities and significant workplace disruption. Fully realizing the new market opportunities demands a modernized workforce. A skills gap contributed to by several factors exist in today's labor market. Some of these factors are the increase in the number of people entering the workforce each year, lack of relevant education, and the rise in technology which needs workers to be equipped with new skills to help them keep up with advancements. Addressing this widening gap between the current workforce skills and those needed for tomorrow is front and center in the minds of every C-suite.

From 2001: A Space Odyssey and Ex Machina to Wall-E and Her, artificial intelligence has reliably been a subject of fascination in modern culture. But AI is no longer a thing of imagination, books or film scripts – it is already playing a pivotal role in both our professional and personal lives. And when it comes to the capability of this next-generation technology, we are now on the precipice of an exponential leap. The potential impact of AI on our lives cannot be understated, so the growing AI skills gap must be addressed if we are to ensure that businesses are prepared to take this jump. AI has already transformed the way we interact with banks, how we shop and how we manufacture.

As Machine Learning professional, navigating the diverse landscape of ML roles within the industry can be confusing. Job titles are usually not a big help because they change depending on the company and also depending on the organization within a company. Job titles tend to change over time as well, as we've seen in the rebranding of data analysts to data scientists. In order to navigate the job market and find potential roles for yourself, you therefore need to have a list of probing question. Let's dive a little bit deeper into each of these 3 probing questions, and why they should always be on your mind when surveying the ML job market.