Imperial College London's major new initiative I-X will use artificial intelligence and data science to tackle global challenges. I-X harnesses the College's long-standing excellence in artificial intelligence (AI), machine learning, data sciences, and the many fields in science, engineering, medicine and business where they are applied. Its projects include developing new computational tools for improving image-based detection and diagnosis of disease, using AI to direct the design and implementation of new biological systems, and intelligent systems and networks for monitoring, control, and security of critical infrastructure. Other fields of research include human-AI cooperation, robotics and automation, and machine learning systems that understand the real world, such as self-driving vehicles. The initiative is housed over two floors at the Translation & Innovation Hub (I-HUB) at Imperial's White City Campus.

SMASH is an innovative, intersectoral, career-development training program for outstanding postdoctoral researchers, co-funded by the Marie Skłodowska-Curie Actions COFUND program. SMASH is open to researchers around the world who are interested in developing cutting-edge machine learning applications for science and humanities. During the five years of the SMASH project (2023-28) and over three planned calls, SMASH aims to hire 50 individuals for 2-year full-time postdoctoral contracts with highly attractive conditions. Fellows will be hosted by five leading Slovenian research institutions: the University of Nova Gorica, the University of Ljubljana, the Jožef Stefan Institute, the Institute of Information Science*, and the Slovenian Environment Agency*. Applicants should propose ambitious research projects in one of SMASH's five key research areas, that are centered on the use of cutting-edge machine learning, or more broadly, artificial intelligence techniques, to address some of the world's most challenging questions in: Applicants should choose the SMASH host institution and supervisor with whom they will coordinate the project proposal preparation.

I-Chiao Lin is a computer vision specialist who builds AI applications for virtual reality headsets. Previously, she developed computer vision systems for baby monitors that detect a child's sleep quality. She spoke to us about how she prepares for job interviews, the lessons she has learned developing consumer products, and how a Pixar movie inspired her to pursue a career in AI. Tell me about your current job. What are your main responsibilities and what is your day-to-day work like?

I fully embraced AI for my classes this semester, requiring students to use AI tools in a number of ways. This policy attracted a lot of interest, and I thought it worthwhile to reflect on how it is going so far. The short answer is: great! But I have learned some early lessons that I think are worth passing on. First, as background, I required AI use in slightly different ways across three separate undergraduate and masters-level entrepreneurship and innovation classes.

Whether they're thick and fluffy or thin and crispy at the edges, every household will have a favourite style of pancake this Shrove Tuesday. But whatever your preference, it's not just a case of mixing flour, eggs and milk and pouring the mixture into a pan. Science tells us that several additions to the batter and a few important preparation tips will get the most delectable results. Adding both an acid and an alkali to your batter is essential if you want fluffy pancakes, while butter will help create a delicious browning reaction – but don't overbeat your batter or the results will be too tough. London experts have already used AI to identify the ultimate pancake recipe that lists seven ingredients – flour, sugar, baking powder, salt, milk, butter and eggs.

As the field of machine learning continues to grow, there is an increasing demand for professionals with the skills and knowledge to develop and implement machine learning solutions. Whether you're a data scientist, engineer, or business professional looking to expand your skillset, pursuing an online machine learning course can help you stand out in the job market and take your career to the next level. To help you get started, we've compiled a list of 15 top-ranked online machine-learning certificates that you can pursue in 2023. These certificates are offered by reputable institutions and are designed to provide you with the theoretical knowledge and practical skills needed to succeed in the field of machine learning. Check out the 15 best machine learning courses below that will help you cement a spot in this ever-growing industry!

Machine learning is a rapidly growing field that has gained a lot of attention in recent years.

This blog explores the pros and cons of Artificial Intelligence (AI) and suggests strategies to balance the advancements and risks of this technology. The pros of AI include increased efficiency, personalization, improved healthcare, and cost savings. On the other hand, the cons of AI include job losses, bias and discrimination, security risks, and a lack of accountability. To balance the advantages and risks of AI, the blog suggests focusing on transparency, investing in education and training, developing regulations and standards, considering the impact on society, and fostering collaboration.

Elon Musk recently stated that "AI is one of the greatest threats to the survival of humanity." ChatGPT, the newest chatbot from OpenAI, has undoubtedly generated a lot of buzz worldwide. For those with a college education and those whose careers depend on the creation of material, such as playwrights, professors, journalists, and programmers, it created a massive, never-before-seen threat. Musk co-founded OpenAI in 2015, partly because he believed Google was not paying enough attention to AI safety. But, a new corporate conflict is now brewing; with Microsoft staunchly supporting ChatGPT, Google perhaps felt forced to introduce a rival, Bard, in quite a hurry.

While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prior distribution. One idea is to employ a worst-case prior. However, this is not as easy to specify in sequential decision making as in simple statistical estimation problems. This paper studies (sometimes approximate) minimax-Bayes solutions for various reinforcement learning problems to gain insights into the properties of the corresponding priors and policies. We find that while the worst-case prior depends on the setting, the corresponding minimax policies are more robust than those that assume a standard (i.e. uniform) prior.