As AI becomes an increasingly used tool by organizations across all industries, studies show that employees' expectations of being knowledgeable about AI are only increasing. Now, Google is presenting business leaders with a new AI literacy opportunity. On Wednesday, Google Cloud announced a "first-of-its-kind" generative AI certification geared toward non-technical learners, such as managers and business leaders, who want to learn about AI's impacts beyond coding. According to Google, the course focuses on how to strategically adopt, discuss, and lead generative AI efforts. The Google Cloud Generative AI Leader certification exam, which costs 99 and lasts 90 minutes, is available starting May 14.

These free online courses don't include certificates of completion or direct instructor messaging, but you still get unrestricted access to all the video content. You can learn at a pace that suits you, so there are no stressful deadlines. Find the best free AI courses on Udemy.

This post contains a list of the AI-related seminars that are scheduled to take place between 5 May and 30 June 2025. All events detailed here are free and open for anyone to attend virtually. Gurobi Machine Learning Speaker: Roland Wunderling (Gurobi Optimisation) Organised by: Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list. Beyond Returns: A Candlestick-Based Approach to Covariance Estimation Speaker: Yasin Simsek (Duke University) Organised by: Statistics and Machine Learning in Finance, University of Oxford Join the mailing list to receive notifications about the seminar series. Robust and Conjugate Gaussian Processes Regression Speaker: François-Xavier Briol (University College London) Organised by: Finnish Center for Artificial Intelligence Zoom link is here.

There's a catch: the author, genre-defining mystery writer Agatha Christie, died 50 years ago, and was thus unavailable to participate. Instead, BBC Maestro used an actress and artificial intelligence to recreate Christie, drawing from the author's own novels, interviews, and letters for the course material. The creators describe the effort as a "world-first," and the "Agatha Christie On Writing" masterclass is available now. Almost as soon as the course launched, critics accused the BBC of making an Agatha Christie "deepfake." Meanwhile, BBC Maestro wants to emphasize the participation of the Christie estate and their high esteem for the late author.

But AI is a tool; like any tool, it is only as good as the person wielding it. Now's the time to get the upper hand on AI and learn how to use tools like ChatGPT and automation platforms to work for you. The ChatGPT & Automation E-Degree from Eduonix Learning Solutions gives you the knowledge to stay on top for just 29.99 (MSRP 790) The course includes 12 modules and 25 hours of content you can move through at your own pace, and they never expire. You'll learn how to automate workflows, streamline repetitive tasks, and get AI to handle the boring stuff while you take credit for the results. It also dives into prompt engineering, real-world use cases, and customizing ChatGPT to fit your job, industry, or hustle.

Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the distinct aspects called archetypes in observations with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction and dimensionality reduction, facilitating the understanding of the structure of high-dimensional data with wide applications throughout the sciences. However, AA also faces challenges, particularly as the associated optimization problem is non-convex. This survey provides researchers and data mining practitioners an overview of methodologies and opportunities that AA has to offer surveying the many applications of AA across disparate fields of science, as well as best practices for modeling data using AA and limitations. The survey concludes by explaining important future research directions concerning AA.

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For better or worse, AI tools have steadily become a reality of the academic landscape since ChatGPT launched in late 2022. Anthropic is studying what that looks like in real time. On Tuesday, shortly after launching Claude for Education, the company released data on which tasks university students use its AI chatbot Claude for and which majors use it the most. Using Clio, the company's data analysis tool, to maintain user privacy, Anthropic analyzed 574,740 anonymized conversations between Claude and users at the Free and Pro tiers with higher education email addresses. All conversations appeared to relate to coursework.

In this series of articles, we're publishing summaries with some of the key takeaways from a few of workshops held at the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025). The first ever workshop on "Open Source AI for Mainstream Use" was held on March 4, 2025 at the Pennsylvania Convention Center in Philadelphia. The goal of this workshop was to bring the researchers and practitioners into a single forum to discuss topics at the intersection of AI and open source and demonstrate relevant technology. Overall, the participants appreciated the interdisciplinary nature of this workshop and are looking forward to repeating it next year. This first edition of the FLUID workshop focused on the emerging challenges and opportunities in federated learning and intelligent decentralization, bringing together a growing international community of researchers working across optimization, privacy, scalability, and practical deployment of decentralized learning systems.

There is great interest in agentic LLMs, large language models that act as agents. We review the growing body of work in this area and provide a research agenda. Agentic LLMs are LLMs that (1) reason, (2) act, and (3) interact. We organize the literature according to these three categories. The research in the first category focuses on reasoning, reflection, and retrieval, aiming to improve decision making; the second category focuses on action models, robots, and tools, aiming for agents that act as useful assistants; the third category focuses on multi-agent systems, aiming for collaborative task solving and simulating interaction to study emergent social behavior. We find that works mutually benefit from results in other categories: retrieval enables tool use, reflection improves multi-agent collaboration, and reasoning benefits all categories. We discuss applications of agentic LLMs and provide an agenda for further research. Important applications are in medical diagnosis, logistics and financial market analysis. Meanwhile, self-reflective agents playing roles and interacting with one another augment the process of scientific research itself. Further, agentic LLMs may provide a solution for the problem of LLMs running out of training data: inference-time behavior generates new training states, such that LLMs can keep learning without needing ever larger datasets. We note that there is risk associated with LLM assistants taking action in the real world, while agentic LLMs are also likely to benefit society.