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.

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.

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.

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.

AI should be saving you time, boosting your productivity, and even helping you think more creatively. But if you're stuck rewriting prompts, dealing with bad responses, or wondering why it feels so basic, here's a hard truth: it's not ChatGPT … it's you. But getting your skills up to snuff is simple if you enroll in our best-selling e-degree program. It doesn't matter if you're a complete beginner, an aspiring master, or somewhere in between, you'll learn how to use ChatGPT like an expert for just 19.97 (reg. Don't worry about fitting time into your schedule--these courses are completely self-paced.

Artificial Intelligence (AI) has achieved new levels of performance and spread in public usage with the rise of deep neural networks (DNNs). Initially inspired by human neurons and their connections, NNs have become the foundation of AI models for many advanced architectures. However, some of the most integral processes in the human brain, particularly neurogenesis and neuroplasticity in addition to the more spread neuroapoptosis have largely been ignored in DNN architecture design. Instead, contemporary AI development predominantly focuses on constructing advanced frameworks, such as large language models, which retain a static structure of neural connections during training and inference. In this light, we explore how neurogenesis, neuroapoptosis, and neuroplasticity can inspire future AI advances. Specifically, we examine analogous activities in artificial NNs, introducing the concepts of ``dropin'' for neurogenesis and revisiting ``dropout'' and structural pruning for neuroapoptosis. We additionally suggest neuroplasticity combining the two for future large NNs in ``life-long learning'' settings following the biological inspiration. We conclude by advocating for greater research efforts in this interdisciplinary domain and identifying promising directions for future exploration.

Image restoration has experienced significant advancements due to the development of deep learning. Nevertheless, it encounters challenges related to ill-posed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning, as a powerful machine learning technique, aims to address these deviations by combining the predictions of multiple base models. Most existing works adopt ensemble learning during the design of restoration models, while only limited research focuses on the inference-stage ensemble of pre-trained restoration models. Regression-based methods fail to enable efficient inference, leading researchers in academia and industry to prefer averaging as their choice for post-training ensemble.