I know you've heard of gamification, but have you ever heard of festification? That's what Microsoft will be doing in April and May, with the Microsoft AI Skills Fest. It's a little odd, but it also looks like it might be a heck of a lot of fun. Microsoft's AI Skills Fest offers courses that are open for all skill levels. You can learn early stages of the lessons if you're new to AI, or work on deeper topics if you're more familiar with AI concepts.

I know you've heard of gamification, but have you ever heard of festification? That's what Microsoft will be doing in April and May, with the Microsoft AI Skills Fest. It's a little odd, but it also looks like it might be a heck of a lot of fun. I've written a lot about Microsoft over the years. I've mocked its product naming.

I know you've heard of gamification, but have you ever heard of festification? That's what Microsoft will be doing in April and May, with the Microsoft AI Skills Fest. It's a little odd, but it also looks like it might be a heck of a lot of fun. I've written a lot about Microsoft over the years. I've mocked its product naming.

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.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In the latest interview, we hear from Joseph Marvin Imperial, who is focussed on aligning generative AI with technical standards for regulatory and operational compliance. Standards are documents created by industry and/or academic experts that have been recognized to ensure the quality, accuracy, and interoperability of systems and processes (aka "the best way of doing things"). You'll see standards in almost all sectors and domains, including the sciences, healthcare, education, finance, journalism, law, and engineering.

The appearance of the Blue Screen of Death in Windows is never a good thing. But Microsoft is aiming to at least make the screen look better when it does appear. Also: Microsoft's free AI skills training'Fest' starts next week - anyone can sign up In a change currently rolling out to Windows 11 insiders, BSODs (or unexpected restarts, as Microsoft calls them) will appear simpler and more streamlined. The screens may display the same technical information as always, but with a refreshed design. "We're previewing a new, more streamlined UI for unexpected restarts, which better aligns with Windows 11 design principles and supports our goal of getting users back into productivity as quickly as possible," Microsoft said in an announcement about the latest Windows 11 insider build.

I know you've heard of gamification, but have you ever heard of festification? That's what Microsoft will be doing in April and May, with the Microsoft AI Skills Fest. It's a little odd, but it also looks like it might be a heck of a lot of fun. I've written a lot about Microsoft over the years. I've mocked its product naming.

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In classical information theory, the Doeblin coefficient of a classical channel provides an efficiently computable upper bound on the total-variation contraction coefficient of the channel, leading to what is known as a strong data-processing inequality. Here, we investigate quantum Doeblin coefficients as a generalization of the classical concept. In particular, we define various new quantum Doeblin coefficients, one of which has several desirable properties, including concatenation and multiplicativity, in addition to being efficiently computable. We also develop various interpretations of two of the quantum Doeblin coefficients, including representations as minimal singlet fractions, exclusion values, reverse max-mutual and oveloH informations, reverse robustnesses, and hypothesis testing reverse mutual and oveloH informations. Our interpretations of quantum Doeblin coefficients as either entanglement-assisted or unassisted exclusion values are particularly appealing, indicating that they are proportional to the best possible error probabilities one could achieve in state-exclusion tasks by making use of the channel. We also outline various applications of quantum Doeblin coefficients, ranging from limitations on quantum machine learning algorithms that use parameterized quantum circuits (noise-induced barren plateaus), on error mitigation protocols, on the sample complexity of noisy quantum hypothesis testing, on the fairness of noisy quantum models, and on mixing times of time-varying channels. All of these applications make use of the fact that quantum Doeblin coefficients appear in upper bounds on various trace-distance contraction coefficients of a channel. Furthermore, in all of these applications, our analysis using Doeblin coefficients provides improvements of various kinds over contributions from prior literature, both in terms of generality and being efficiently computable.