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In this lecture, Prof. Sifakis will discuss the relevance of existing criteria for comparing human and machine intelligence and show some notable analogies and differences between scientific knowledge and that produced by neural networks. Emphasising that autonomy is an important step towards Artificial General Intelligence (AGI), he will present a characterisation of autonomous systems, and showing key differences with mental systems equipped with common sense knowledge and reasoning, and advocate challenging work directions, including the development of a new foundation for systems engineering and scientific knowledge, and the joint exploration of physical and mental phenomena that embody human intelligence.
Even date: 3/3/2023
Speaker: Prof. Joseph Sifakis
Hosted by: PolyU Academy for Interdisciplinary Research
The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Brilliant helps you see concepts visually and interact with them, and poses questions that get you to think. Our courses show you that math, science, and computer science are – at their core – a way of thinking. All of our courses are crafted by award-winning teachers, researchers, and professionals from MIT, Caltech, Duke, Microsoft, Google, and more, with these principles of learning in mind. Get started as a beginner with the fundamentals, or dive right into the intermediate and advanced courses for professionals. Brilliant is for ambitious and curious people ages 10 to 110.
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).