If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As you may already know, the amount of data that we create, and store, as human beings has been growing immensely in the last few years. We start having more and more devices that can create, send, store and save data – we can just look at our mobile phones, and how powerful they have become in the last few years. One study is showing that the amount of data, on a global level, will reach 175 zettabytes (ZB) by year 2025 (just as a fun-fact, and for comparison: 1000 Terabytes 1 Petabyte; 1000 Petabytes 1 Exabyte; 1000 Exabytes 1 Zettabyte). Obviously, this is a lot of data. Data can be considered everything that one business creates, or even an individual (from basic stuff like pictures, documents to a more complex calculation, and similar).
Last year we were amazed by the level of dexterity achieved by OpenAI's Dactyl system which was able to learn how to manipulate a cube block to display any commanded side/face.If you missed that article, read about it here. OpenAI then set themselves a harder task of teaching the robotic hand to solve a Rubik's cube. Quite a daunting task made no easier by the fact that it would use one hand which most humans would find it hard to do. OpenAI harnessed the power of neural networks which are trained entirely in simulation. However, one of the main challenges faced was to make the simulations as realistic as possible because physical factors like friction, elasticity etc. are very hard to model.
Find all our Student Opinion questions here. Last week, a robotic hand successfully solved a Rubik's Cube. While that feat might seem like a fun parlor trick, it's a sign that robots are being programmed to learn and not just memorize. Robots are already playing important roles inside retail giants like Amazon and manufacturing companies like Foxconn by completing very specific, repetitive tasks. But many believe that machine learning will ultimately allow robots to master a much wider array of more complex functions.
U.S.-based artificial intelligence research organization OpenAI have rolled out a robot hand that can take and solve a Rubik's Cube. Joshua Gans is a professor of Strategic Management at the Rotman School of Management and the chief economist at the Creative Destruction Lab. Tiff Macklem is dean of Rotman School of Management at the University of Toronto. Last week, the U.S.-based artificial intelligence research organization, OpenAI, rolled out a robot hand that can take and solve a Rubik's Cube. Creating a robot with visual sense and complex touch and dexterity is an impressive achievement in AI.
Plastic Dinosaur's normal living space is a big warehouse with a lot of interesting features for it to clamber over and peer behind. But it's noticed that the bipeds he shares the space with come and go through parts of the barriers that contain him. He's noticed that they can swing part of the barrier open, pass through the opening and then the barrier closes behind them. He's seen space behind the barriers that he hasn't explored. Plastic Dinosaur wanders over to the door, peering at things as he goes to see if anything more interesting shows up.
Yesterday, artificial intelligence(AI) powerhouse OpenAI astonished the world by unveiling a prototype of a robotic arm that could solve a Rubik's cube with one hand. The prototype didn't only represent a milestone for the robotics ecosystem in solving high complexity tasks that actively require sensorial information but it also resulted on a major achievement for the AI community. The reason is that the OpenAI robot was completely trained using simulations based on the reinforcement learning models that the OpenAI Five system used to beat human players in Dota2. The research was discussed in a paper that accompanied the news. The importance of OpenAI's achievement was not about designing a robot that could solve a Rubik's cube.
Meta-Learning describes the abstraction to designing higher level components associated with training Deep Neural Networks. The term "Meta-Learning" is thrown around in Deep Learning literature frequently referencing "AutoML", "Few-Shot Learning", or "Neural Architecture Search" when in reference to the automated design of neural network architectures. Emerging from comically titled papers such as "Learning to learn by gradient descent by gradient descent", the success of OpenAI's rubik's cube robotic hand demonstrates the maturity of the idea. Meta-Learning is the most promising paradigm to advance the state-of-the-art of Deep Learning and Artificial Intelligence. OpenAI set the AI world on fire by demonstrating ground-breaking capabilities of a robotic hand trained with Reinforcement Learning.
Edward Snowden has finally laid it all out - documenting his memoires in a new 432-page book, Permanent Record, which will be published worldwide on Tuesday, September 17. Meeting with both The Guardian and Spiegel Online in Moscow as part of its promotion, the infamous whistleblower spent nearly five hours with the two media outlets - offering a taste of what's in the book, details on his background, and his thoughts on artificial intelligence, facial recognition, and other intelligence gathering tools coming to a dystopia near you. While The Guardian interview is'okay,' scroll down for the far more interesting Spiegel interview, where Snowden goes way deeper into his cloak-and-dagger life, including thoughts on getting suicided. Snowden describes in detail for the first time his background, and what led him to leak details of the secret programmes being run by the US National Security Agency (NSA) and the UK's secret communication headquarters, GCHQ. He describes the 18 years since the September 11 attacks as "a litany of American destruction by way of American self-destruction, with the promulgation of secret policies, secret laws, secret courts and secret wars".
Book a suite in a luxury hotel in Moscow, send the room number encrypted to a pre-determined mobile number and then wait for a return message indicating a precise time: Meeting Edward Snwoden is pretty much exactly how children imagine the grand game of espionage is played. But then, on Monday, there he was, standing in our room on the first floor of the Hotel Metropol, as pale and boyish-looking as the was when the world first saw him in June 2013. For the last six years, he has been living in Russian exile. The U.S. has considered him to be an enemy of the state, right up there with Julian Assange, ever since he revealed, with the help of journalists, the full scope of the surveillance system operated by the National Security Agency (NSA). For quite some time, though, he remained silent about how he smuggled the secrets out of the country and what his personal motivations were. Now, though, he has written a book about it. It will be published worldwide on September 17 under the title "Permanent Record." Ahead of publication, Snowden spent over two-and-a-half hours patiently responding to questions from DER SPIEGEL. DER SPIEGEL: Mr. Snowden, you always said: "I am not the story."
This article is a part of Artificial Neural Networks Serial, which you can check out here. In the previous blog posts, we covered some very interesting topics regarding Artificial Neural Networks (ANN). The basic structure of Artificial Neural Networks was presented, as well as some of the most commonly used activation functions. Nevertheless, we still haven't mentioned the most important aspect of the Artificial Neural Networks – learning. The biggest power of these systems is that they can be familiarized with some kind of problem in the process of training and are later able to solve problems of the same class – just like humans do!