Silvia Chiappa is a Senior Research Scientist at DeepMind, working at the intersection of probabilistic modeling and deep learning. Prior to DeepMind, she worked at Microsoft Research Cambridge, at the Statistical Laboratory University of Cambridge and the Max-Planck Institute for Biological Cybernetics. I spoke with Silvia to learn about her career in science, how we can overcome barriers for women in tech, and more. How did you begin your work in science and technology? At the age of 12 I started to appreciate the elegance of maths when learning about trigonometry.

To meet the growing demand for AI expertise, companies are offering online education courses to prepare the workforce for the future. Increasingly, computers and devices learn and act on their own using software algorithms, the building blocks for artificial intelligence (AI) and machine learning (ML). Getting smartphones to understand voice commands, smart home sprinkler systems to change with the weather and online services to predict what people want requires programmers skilled in AI and ML. Demand for these coding skills is skyrocketing. Making devices smart and proactive remains controversial to anyone who fears that automation will lead to human job loss.

Welcome to the Artificial Intelligence and Games book. This book aims to be the first comprehensive textbook on the application and use of artificial intelligence (AI) in, and for, games. Our hope is that the book will be used by educators and students of graduate or advanced undergraduate courses on game AI as well as game AI practitioners at large. The first draft of the book is available here! If you spot any typos or inaccurate information, disagree with parts of the text or you have suggestions for papers we should discuss or exercises (and readings) we should include please contact us via email at gameaibook [ at ] gmail [ dot ] com.

One in five people are concerned their jobs could come into competition with automated or artificial intelligence technologies, according to a survey commissioned by tech provider Ricoh Ireland. The study also revealed that just 29 per cent of people believe schools are equipping children with the necessary skills for the digital era. Only a third felt schools have access to the range of modern technologies to help students become digitally proficient. The survey found that confidence in Irish third-level education was higher, with 64 per cent believing graduates are digitally prepared to enter the workplace. Nevertheless, half of respondents said the Irish education system lags behind its European counterparts.

Google is using machine learning to teach computers to sketch and make music, but one engineer says it isn't ready to "generate" a new Beatles album just yet. IN the future, cars will drive themselves, fridges will order groceries, and doors will unlock automatically as you approach. But what happens when computers move beyond chores and take on creative endeavours? What happens when computers start making art? It's a question Google is investigating, not only investing money in making computers code the most efficient programs themselves, but asking them to learn how to draw, and make their own music based on our own.

Robotic hands can play drums and even twirl objects with aplomb, but they're still poor at picking up unfamiliar objects. That's why UC Berkeley's DexNet 2.0 bot is so impressive -- using deep learning, it can successfully grasp random, real-world objects 99 percent of the time. What's more, the tech, developed with the help of Amazon, Google and Toyota, is far enough along that it could be put to work in manufacturing and supply chains in the near future. Researchers trained the DexNet 2.0 deep learning system using a vast library of 3D shapes and suitable grasp positions to match those objects. Using virtual, rather than real objects made it possible to train the AI much more quickly.

We don't know how quickly machines will displace people's jobs, or how many they'll take, but we know it's happening -- not just to factory workers but also to money managers, dermatologists and retail workers. The logical response seems to be to educate people differently, so they're prepared to work alongside the robots or do the jobs that machines can't. But how to do that, and whether training can outpace automation, are open questions. Pew Research Center and Elon University surveyed 1,408 people who work in technology and education to find out if they think new schooling will emerge in the next decade to successfully train workers for the future. Two-thirds said yes; the rest said no.

While the standard approach to statistical learning theory is based on assumptions chosen largely for their convenience (e.g., i.i.d. or stationary ergodic), in this work we are interested in developing a theory of learning based only on the most fundamental and natural assumptions implicit in the requirements of the learning problem itself. We specifically study universally consistent function learning, where the objective is to obtain low long-run average loss for any target function, when the data follow a given stochastic process. We are then interested in the question of whether there exist learning rules guaranteed to be universally consistent given only the assumption that universally consistent learning is possible for the given data process. The reasoning that motivates this criterion emanates from a kind of optimist's decision theory, and so we refer to such learning rules as being optimistically universal. We study this question in three natural learning settings: inductive, self-adaptive, and online. Remarkably, as our strongest positive result, we find that optimistically universal learning rules do indeed exist in the self-adaptive learning setting. Establishing this fact requires us to develop new approaches to the design of learning algorithms. Along the way, we also identify concise characterizations of the family of processes under which universally consistent learning is possible in the inductive and self-adaptive settings. We additionally pose a number of enticing open problems, particularly for the online learning setting.

The vast majority of optimization and online learning algorithms today require some prior information about the data (often in the form of bounds on gradients or on the optimal parameter value). When this information is not available, these algorithms require laborious manual tuning of various hyperparameters, motivating the search for algorithms that can adapt to the data with no prior information. We describe a frontier of new lower bounds on the performance of such algorithms, reflecting a tradeoff between a term that depends on the optimal parameter value and a term that depends on the gradients' rate of growth. Further, we construct a family of algorithms whose performance matches any desired point on this frontier, which no previous algorithm reaches.

As with learning any new skills, the more you practice, the better you become. Practice different algorithms and work with different data sets to have a better understanding of machine learning, and to improve your overall problem-solving skills. Machine learning with Python is a great addition to your technical skillset, and there are lots of free and low-cost online resources available to help. How have you picked up machine learning skills? Leave a comment below, or submit an article proposal to share your story.