Munich Cyber Security Conference 2019: Keynote by Dr. Alexander Evans OBE (Director Cyber, Foreign & Commonwealth Office)
Tagged with “science & technology” (21)
Speaker: Dr. Paul Vixie
Due to pervasive unpreparedness of users, applications, operating systems, and protocols, DNS has become an essential control point for “cyber” security. Most networks have a mix of legacy, modern, safe, and unsafe devices attached to them, and this condition won’t change as quickly as the Beyondcorp initiative might suggest. However, DNS is also an important control point for authoritarian regimes, and so “bypass” innovation is continuous, rapid, and ambitious. Here, Dr. Vixie pays special attention to the "bypass" innovation called “DNS over HTTP” or “DoH” protocol, now being strongly pushed by Mozilla, Cloudflare, and others, and outlines its problems and risks. In addition, a brief mention is made of IRTF Resolverless DNS.
The 29th First Annual Ig Nobel Prize Ceremony is scheduled for Thursday, September 12th at Sanders Theatre at Harvard University. It will introduce ten new Ig Nobel Prize winners. Each winner has done something that makes people laugh then think. Winners travel to the ceremony, at their own expense, from around the world to receive their prize from a group of genuine, genuinely bemused Nobel Laureates, in Harvard’s historic and largest theater. . More information about the ceremony: https://www.improbable.com/ig-about/2019-ceremony/ . More information about Improbable Research: https://www.improbable.com/
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Media Manipulation. What Is it? How does it work? Can you really make people see, buy and read things? Ryan is an experienced and talented media manipulator. Download Standard Podcasts […]
James Mickens, Harvard University
Q: Why Do Keynote Speakers Keep Suggesting That Improving Security Is Possible? A: Because Keynote Speakers Make Bad Life Decisions and Are Poor Role Models
Some people enter the technology industry to build newer, more exciting kinds of technology as quickly as possible. My keynote will savage these people and will burn important professional bridges, likely forcing me to join a monastery or another penance-focused organization. In my keynote, I will explain why the proliferation of ubiquitous technology is good in the same sense that ubiquitous Venus weather would be good, i.e., not good at all. Using case studies involving machine learning and other hastily-executed figments of Silicon Valley’s imagination, I will explain why computer security (and larger notions of ethical computing) are difficult to achieve if developers insist on literally not questioning anything that they do since even brief introspection would reduce the frequency of git commits. At some point, my microphone will be cut off, possibly by hotel management, but possibly by myself, because microphones are technology and we need to reclaim the stark purity that emerges from amplifying our voices using rams’ horns and sheets of papyrus rolled into cone shapes. I will explain why papyrus con…
Alex Stamos, Professor, Freeman-Spogli Institute, Stanford University — Alex Stamos is a cybersecurity expert, business leader and entrepreneur working to improve the security and safety of the Internet through his teaching and research at Stanford University. Stamos is an Adjunct Professor at Stanford’s Freeman-Spogli Institute, a William J. Perry Fellow at the Center for International Security and Cooperation, and a visiting scholar at the Hoover Institution.
Prior to joining Stanford, Alex served as the Chief Security Officer of Facebook. In this role, Stamos led a team of engineers, researchers, investigators and analysts charged with understanding and mitigating information security risks to the company and safety risks to the 2.5 billion people on Facebook, Instagram and WhatsApp. During his time at Facebook, he led the company’s investigation into manipulation of the 2016 US election and helped pioneer several successful protections against these new classes of abuse. As a senior executive, Alex represented Facebook and Silicon Valley to regulators, lawmakers and civil society on six continents, and has served as a bridge between the interests of the Internet policy community and the complicated reality of platforms operating at billion-user scale. In April 2017, he co-authored “Informat…
This is the opening lecture for course 6.S099: Artificial General Intelligence. This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world.
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First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
OUTLINE: 0:00 - Introduction 2:14 - Types of learning 6:35 - Reinforcement learning in humans 8:22 - What can be learned from data? 12:15 - Reinforcement learning framework 14:06 - Challenge for RL in real-world applications 15:40 - Component of an RL agent 17:42 - Example: robot in a room 23:05 - AI safety and unintended consequences 26:21 - Examples of RL systems 29:52 - Takeaways for real-world impact 31:25 - 3 types of RL: model-based, value-based, policy-based 35:28 - Q-learning 38:40 - Deep Q-Networks (DQN) 48:00 - Policy Gradient (PG) 50:36 - Advantage Actor-Critic (A2C & A3C) 52:52 - Deep Deterministic Policy Gradient (DDPG) 54:12 - Policy Optimization (TRPO and PPO) 56:03 - AlphaZero 1:00:50 - Deep RL in real-world applications 1:03:09 - Closing the RL simula…
Original video: https://www.youtube.com/watch?v=zR11FLZ-O9M&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf
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An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
INFO: Website: https://deeplearning.mit.edu GitHub: https://github.com/lexfridman/mit-deep-learning Slides: http://bit.ly/deep-learning-basics-slides Playlist: http://bit.ly/deep-learning-playlist Blog post: https://link.medium.com/TkE476jw2T
OUTLINE: 0:00 - Introduction 0:53 - Deep learning in one slide 4:55 - History of ideas and tools 9:43 - Simple example in TensorFlow 11:36 - TensorFlow in one slide 13:32 - Deep learning is representation learning 16:02 - Why deep learning (and why not) 22:00 - Challenges for supervised learning 38:27 - Key low-level concepts 46:15 - Higher-level methods 1:06:00 - Toward artificial general intelligence
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New lecture on recent developments in deep learning that are defining the state of the art in our field (algorithms, applications, and tools). This is not a complete list, but hopefully includes a good sampling of new exciting ideas. For more lecture videos visit our website or follow code tutorials on our GitHub repo.
OUTLINE: 0:00 - Introduction 2:00 - BERT and Natural Language Processing 14:00 - Tesla Autopilot Hardware v2+: Neural Networks at Scale 16:25 - AdaNet: AutoML with Ensembles 18:32 - AutoAugment: Deep RL Data Augmentation 22:53 - Training Deep Networks with Synthetic Data 24:37 - Segmentation Annotation with Polygon-RNN++ 26:39 - DAWNBench: Training Fast and Cheap 29:06 - BigGAN: State of the Art in Image Synthesis 30:14 - Video-to-Video Synthesis 32:12 - Semantic Segmentation 36:03 - AlphaZero & OpenAI Five 43:34 - Deep Learning Frameworks 44:40 - 2019 and beyond
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