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Geoffrey Hinton and Yann LeCun, 2018 ACM A.M. Turing Award Lecture "The Deep Learning Revolution"
Geoffrey Hinton and Yann LeCun, 2018 ACM A.M. Turing Award Lecture "The Deep Learning Revolution"
Geoffrey Hinton and Yann LeCun won the 2018 ACM A.M. Turing Award and delivered a lecture on the deep learning revolution.
In the lecture, they discussed how deep learning has revolutionized computer science and how it can be used to benefit various aspects of life. They also talked about the challenges of deep learning and the future of the field.
They noted that while theoretical understanding of deep learning is important, it is still up to humans to make decisions in complex situations. They also discussed the potential for evolutionary computation and other forms of artificial intelligence in autonomous driving.
This Canadian Genius Created Modern AI
This Canadian Genius Created Modern AI
Geoff Hinton, an AI pioneer, has been working on getting computers to learn like humans for almost 40 years, and he revolutionized the field of Artificial Intelligence. Hinton was inspired by Frank Rosenblatt's perceptron, a neural network that mimics the brain, which was developed in the 1950s. Hinton's determination led to a breakthrough in the field of AI. In the mid-80s, Hinton and his collaborators created a multi-layered neural network, a deep neural network, which started to work in a lot of ways. However, they lacked necessary data and compute power until about 2006, when super-fast chips and massive amounts of data produced on the internet gave Hinton's algorithms a magical boost – computers could identify what was in an image, recognize speech, and translate languages. By 2012, Canada became an AI superpower, and neural nets and machine learning were featured on the front page of the New York Times.
Geoffrey Hinton: The Foundations of Deep Learning
Geoffrey Hinton: The Foundations of Deep Learning
Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. In this talk, Hinton breaks down the advances of neural networks, as applied to speech and object recognition, image segmentation and reading or generating natural written language.
Geoffrey Hinton discusses the foundations of deep learning, particularly the backpropagation algorithm and its evolution. Hinton explains how deep learning impacted early handwriting recognition and eventually led to winning the 2012 ImageNet competition. He also emphasizes the superiority of deep learning using vectors of neural activity over the traditional symbolic AI that used the same symbols in input, output, and the middle. The improvements in machine translation systems, image recognition, and their combination for natural reasoning are discussed, along with the potential for deep learning in interpreting medical images. Hinton concludes by highlighting the need for neural networks with parameters comparable to the human brain for achieving true natural language processing.
Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton
Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton
Geoffrey Hinton, a leading figure in deep learning, discussed his journey and contributions to the field in an interview with Andrew Ng. He talks about the origins of word embeddings, restricted Boltzmann machines' developments, and his recent work on fast weights and capsules. Hinton notes the crucial role of unsupervised learning in deep learning advancements and advises learners to read widely, work on large-scale projects, and find advisors with similar interests. Hinton believes there is a significant change occurring in computing, where computers learn by showing, and cautions that universities must catch up with industry in training researchers for this new approach.
Heroes of Deep Learning: Andrew Ng interviews Yann LeCun
Heroes of Deep Learning: Andrew Ng interviews Yann LeCun
In this interview between Andrew Ng and Yann LeCun, LeCun discusses his early interest in AI and the discovery of neural nets. He also describes his work on convolutional neural networks and the history behind CNNs. LeCun talks about how he persisted in the field, despite lack of interest in neural networks in the mid-90s, and eventually his work on CNNs took over the field of computer vision. He also discusses the defining moment in computer vision when the AlexNet team won the 2012 ImageNet competition, and advises those seeking a career in AI and machine learning to make themselves useful by contributing to open-source projects or implementing algorithms.
Heroes of Deep Learning: Andrew Ng interviews Ian Goodfellow
Heroes of Deep Learning: Andrew Ng interviews Ian Goodfellow
In an interview with Andrew Ng, Ian Goodfellow talks about his passion for deep learning and how he got interested in the field while studying at Stanford. Goodfellow discusses his invention of generative adversarial networks (GANs) and their potential in deep learning, while also emphasizing the need to make GANs more reliable. He reflects on how his thinking about AI and deep learning has evolved over the years, from simply getting the technology to work for AI-related tasks to exploring the full potential of deep learning models. Goodfellow also shares advice for those wanting to get involved in AI, stating that writing good code and building security into machine learning algorithms from the beginning are crucial.
Heroes of Deep Learning: Andrew Ng interviews Andrej Karpathy
Heroes of Deep Learning: Andrew Ng interviews Andrej Karpathy
In an interview with Andrew Ng, Andrej Karpathy discusses his introduction to deep learning through a class with Geoff Hinton and how he became the human benchmark for the ImageNet image classification competition. He talks about the surprising results when software deep nets surpassed his performance and decided to teach others about it through the creation of an online course. Karpathy also discusses the future of AI and how the field will likely split into two trajectories: applied AI and AGI. He advises those who want to enter the field of deep learning to build a full understanding of the whole stack by implementing everything from scratch.
Heroes of Deep Learning: Andrew Ng interviews Director of AI Research at Apple, Ruslan Salakhutdinov
Heroes of Deep Learning: Andrew Ng interviews Director of AI Research at Apple, Ruslan Salakhutdinov
Ruslan Salakhutdinov, the Director of AI Research at Apple, discusses the evolution of deep learning, the challenges in training generative models and unsupervised learning, and the exciting frontiers in deep learning research. He also encourages researchers to explore different methods and not be afraid to innovate.
Salakhutdinov emphasizes the importance of building dialogue-based systems and ones that can read text intelligently, and the ultimate goal of achieving more human-like learning abilities.
Heroes of Deep Learning: Andrew Ng interviews Yoshua Bengio
Heroes of Deep Learning: Andrew Ng interviews Yoshua Bengio
Andrew Ng interviews Yoshua Bengio, and they discuss various topics related to deep learning. Bengio expresses how he got into deep learning and how his thinking about neural networks has evolved. He also discusses his contributions to developing word embeddings for sequences of words and deep learning with stacks of autoencoders. Additionally, Bengio emphasizes the importance of unsupervised learning and his interest in understanding the relationship between deep learning and the brain.
Bengio highlights the need for understanding the science of deep learning and proper research to tackle big challenges. Finally, they focus on the need for a strong foundational knowledge of mathematics for a career in deep learning and the importance of continued education.
Heroes of Deep Learning: Andrew Ng interviews Pieter Abbeel
Pieter Abbeel discusses the challenges and potential of deep reinforcement learning in this interview with Andrew Ng. He notes the need for further work in exploration, credit assignment, and generating negative examples. Abbeel also highlights safety concerns and the importance of collecting safe learning data when teaching robots to live autonomously. He advises individuals to pursue hands-on practice with popular frameworks and suggests the benefits of receiving mentorship from experienced professionals. Additionally, he suggests the need for reinforcement learning in giving machines objectives of achievement and notes the importance of behavioral cloning and supervised learning before adding the reinforcement learning component.