Machine Learning • Artificial Intelligence
Arlindo Oliveira is a Singularity University expert in Artificial Intelligence and Machine Learning. He is also a Distinguished Professor at the Department of Computer Science and Engineering of Instituto Superior Técnico (IST) in Lisbon, Portugal.
Arlindo is the author of three books and hundreds of articles in the areas of algorithms, machine learning, bioinformatics and computer architecture. He was a researcher at CERN, Electronics Research Labs of UC Berkeley, Cadence Labs and INESC-ID.
Arlindo Oliveira obtained a PhD degree from UC Berkeley, and his BSc and MSc degrees in EECS from IST. He is a member of the Portuguese Academy of Engineering, a senior member of IEEE and a past president of IST, INESC-ID and the Portuguese Association for Artificial Intelligence.
With many years of experience in this area, Arlindo Oliveira is the perfect person to provide the answer to your questions about AI and its potential and, most importantly, to analyze how AI can impact your business and your life!
Recent advances in the fields of Artificial Intelligence (AI) and Machine Learning are revolutionizing our economy and our society.
AI-based systems are finding numerous applications in marketing, sales, healthcare, finances, education, transportation, logistics and even in scientific research. In the near future, AI-based systems may replace a significant fraction of human workers in many jobs and functions.
Machine Learning, a technology that is at the core of recent AI developments, enables computers to learn from experience and opens the way to even more radical changes in the way we interact with machines.
In the distant future, AI research may even open the door to Artificial General Intelligence (AGI), enabling us to create digital minds, systems as intelligent and powerful as the human brain, either by direct emulation or by some other approach. If they come into existence, what will be the social, legal, and ethical implications?
Artificial Intelligence, and its diverse subfields, has been the subject of intense study for more than half a century. Recent advances in machine learning, jointly known as deep learning, have partially closed the gap that exists between the abilities of naturally intelligent systems (i.e., brains) and artificially intelligent ones, particularly in problems related with perception.
Some problems that have been deemed very hard to solve, like learning to play the game of Go or other complex strategy games, from scratch, have fallen to approaches that combine deep reinforcement learning with efficient computation methods.
However, the depth of understanding of these systems is still very limited, and DCNNs (deep convolutional neural networks) are still very far from approaching human abilities even in simple perception problems. In this seminar, I will lead a discussion about the power and limitations of DCNNs, with practical examples and challenges.