How smart can our machines make us? Tom Gruber, co-creator of Siri, wants to make “humanistic AI” that augments and collaborates with us instead of competing with (or replacing) us. He shares his vision for [More]
Daniel Dewey is a research fellow in the Oxford Martin Programme on the Impacts of Future Technology at the Future of Humanity Institute, University of Oxford. His research includes paths and timelines to machine superintelligence, [More]
Educator and entrepreneur Sebastian Thrun wants us to use AI to free humanity of repetitive work and unleash our creativity. In an inspiring, informative conversation with TED Curator Chris Anderson, Thrun discusses the progress of [More]
Fabian’s TEDx Talk dived into the challenges and opportunities of a world in which intelligent machines and humans coexist together. Artificial intelligence is and will be integrated more into vital parts of human life. Not [More]
New tech spawns new anxieties, says scientist and philosopher Grady Booch, but we don’t need to be afraid an all-powerful, unfeeling AI. Booch allays our worst (sci-fi induced) fears about superintelligent computers by explaining how [More]
After launching an internationally-recognized social media campaign, Noor is determined to achieve her dream of becoming the first hijabi anchor on commercial television in the United States. As she breaks down barriers, Noor Tagouri inspires [More]
The robots are coming. We (as in the people who attend TED talks and things) tend to think we will probably be fine. You know, knowledge folks and all. We might not be. What will [More]
In the last five years, significant advances were made in the fields of computer vision, speech recognition, and language understanding. In this talk, Jeff Dean discusses why and how these advances have come about, what [More]
“The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable,” says digital visionary Kevin Kelly — and technology is much the same, driven by patterns [More]
http://www.ted.com The news that Chinese artist Ai Weiwei has been detained by authorities has prompted significant concern here at TED-HQ. We had shown a film of him at last month’s conference, an unexpected and courageous [More]
Machine learning isn’t just for simple tasks like assessing credit risk and sorting mail anymore — today, it’s capable of far more complex applications, like grading essays and diagnosing diseases. With these advances comes an [More]
L’Ecole Dynamique est une micro-société démocratique où les enfants sont libres de faire leurs propres choix concernant leurs apprentissages et tous les autres domaines de la vie. Elle est ainsi libérée des programmes scolaires, des [More]
We’re on the edge of a new frontier in art and creativity — and it’s not human. Blaise Agüera y Arcas, principal scientist at Google, works with deep neural networks for machine perception and distributed [More]
Tanmay Bakshi wishes and works towards changing the lives of those who are living with disabilities; those who are living, yet NOT living as we are, and those who are NOT able to even communicate [More]
This talk was given at a local TEDx event, produced independently of the TED Conferences. In his talk, Andre will explain the current and future impacts of Artificial Intelligence on industry, science, and how it [More]
We’re building an artificial intelligence-powered dystopia, one click at a time, says technosociologist Zeynep Tufecki. In an eye-opening talk, she details how the same algorithms companies like Facebook, Google and Amazon use to get you [More]
Imagine a global “Hive Mind” that can tap the knowledge, wisdom, insights, and intuitions of millions of people, and produce a super-intelligence that is much smarter than any individual person. A new technology called Artificial [More]
View full lesson: http://ed.ted.com/lessons/the-turing-test-can-a-computer-pass-for-a-human-alex-gendler What is consciousness? Can an artificial machine really think? For many, these have been vital considerations for the future of artificial intelligence. But British computer scientist Alan Turing decided to disregard [More]
When a very young child looks at a picture, she can identify simple elements: “cat,” “book,” “chair.” Now, computers are getting smart enough to do that too. What’s next? In a thrilling talk, computer vision [More]
Watch the talks we loved in 2016 and discover the year’s most powerful ideas. To watch the full talks, check out the playlist: http://go.ted.com/2016 Can we build AI without losing control over it? | Sam [More]
Salut a tous les amis ! On se retrouve aujourd’hui pour une nouvelle map nommé Burning Hotel with Ted ! L’objectif : Survivre le plus longtemps possible dans un hôtel qui prend feu à cause [More]
What do you get when you give a design tool a digital nervous system? Computers that improve our ability to think and imagine, and robotic systems that come up with (and build) radical new designs [More]
Maurice Conti, Director of Applied Research and Innovation, shares Autodesk’s perspective on how humans and robots will work together in the future.
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, Prof. Winston introduces artificial intelligence and provides a brief history of the field. The last ten minutes are [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter This mega-recitation covers the boosting problem from Quiz 4, Fall 2009. We determine which classifiers to use, then perform three rounds [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter We begin by discussing neural net formulas, including the sigmoid and performance functions and their derivatives. We then work Problem 2 [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture begins with a brief discussion of cross-modal coupling. Prof. Winston then reviews big ideas of the course, suggests possible [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter In this mega-recitation, we cover Problem 1 from Quiz 1, Fall 2009. We begin with the rules and assertions, then spend [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston We begin with a review of inference nets, then discuss how to use experimental data to develop a model, which can [More]
MIT 6.868J The Society of Mind, Fall 2011 View the complete course: http://ocw.mit.edu/6-868JF11 Instructor: Marvin Minsky In this lecture, students use readings of M.A. Bozarth and Carl Sagan to discuss pleasure systems in the brain [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we consider the nature of human intelligence, including our ability to tell and understand stories. We discuss the [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we consider cognitive architectures, including General Problem Solver, SOAR, Emotion Machine, Subsumption, and Genesis. Each is based on [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter We start by discussing what a support vector is, using two-dimensional graphs as an example. We work Problem 1 of Quiz [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston To determine whether three blocks form an arch, we use a model which evolves through examples and near misses; this is [More]
* Please note: Lecture 20, which focuses on the AI business, is not available. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston We begin this lecture with basic probability [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston Why do “cats” and “dogs” end with different plural sounds, and how do we learn this? We can represent this problem [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter This mega-recitation covers Problem 1 from Quiz 2, Fall 2007. We start with a minimax search of the game tree, and [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we build an identification tree based on yes/no tests. We start by arranging the tree based on tests [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture covers map coloring and related scheduling problems. We develop pseudocode for the domain reduction algorithm and consider how much [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston How can we recognize the number of objects in a line drawing? We consider how Guzman, Huffman, and Waltz approached this [More]
*NOTE: These videos were recorded in Fall 2015 to update the Neural Nets portion of the class. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, Prof. [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston We consider how object recognition has evolved over the past 30 years. In alignment theory, 2-D projections are used to determine [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture covers strategies for finding the shortest path. We discuss branch and bound, which can be refined by using an [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture begins with a high-level view of learning, then covers nearest neighbors using several graphical examples. We then discuss how [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston Can multiple weak classifiers be used to make a strong one? We examine the boosting algorithm, which adjusts the weight of [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston We consider a block-stacking program, which can answer questions about its own behavior, and then identify an animal given a list [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture covers algorithms for depth-first and breadth-first search, followed by several refinements: keeping track of nodes already considered, hill climbing, [More]
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature [More]
For the past 6 months or so, I have been teaching myself about artificial intelligence. In this video, I describe some of the places I learned from and a few of the things I’ve done [More]
*NOTE: These videos were recorded in Fall 2015 to update the Neural Nets portion of the class. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this video, Prof. [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how alpha-beta pruning [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture covers a symbolic integration program from the early days of AI. We use safe and heuristic transformations to simplify [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture explores genetic algorithms at a conceptual level. We consider three approaches to how a population evolves towards desirable traits, [More]
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we explore support vector machines in some mathematical detail. We use Lagrange multipliers to maximize the width of [More]
Learn more about the DARPA Robotics Challenge at http://www.theroboticschallenge.org/
Last minute Xmas shopping? Give a FREE Netflix trial at http://netflix.com/wtbd DARPA held its latest Robotics Challenge this weekend, bringing tegether some of the top robots in the world to see which fares best in [More]
Robots army of america [DARPA ROBOTS] – Full Documentary
I see a few people have made silly versions of the hilarious footage of the robots failing. Here’s my take on it.
SCHAFTチームの階段登り.独特のステップでさくさく登っていった.実際に登り始めるのは3:00くらいから. I’m not a member of SCHAFT. But, I heard some informations about their robot at site. This robot is remotely controlled. Communication speed is limited to 100 kbps in the provisions of the challenge. [More]