Silk Road: The Untold Story In October 2013, a young entrepreneur named Ross Ulbricht was arrested at the Glen Park branch of the San Francisco Public library. It was the culmination of a two-year investigation into a vast online drug market called Silk Road. The authorities charged that Ulbricht, an idealistic 29-year-old Eagle Scout from Austin, Texas, was the kingpin of the operation. The story of how Ulbricht founded Silk Road, how it grew into a $1.2 billion operation, and how federal law enforcement shut it down is complicated, dark, and utterly fascinating. Go Back to Top.
Silk Road Creator Ross Ulbricht Sentenced to Life in Prison Ross Ulbricht conceived of his Silk Road black market as an online utopia beyond law enforcement’s reach. Now he’ll spend the rest of his life firmly in its grasp, locked inside a federal penitentiary. On Friday Ulbricht was sentenced to life in prison without the possibility of parole for his role in creating and running Silk Road’s billion-dollar, anonymous black market for drugs. Judge Katherine Forrest gave Ulbricht the most severe sentence possible, beyond what even the prosecution had explicitly requested. The minimum Ulbricht could have served was 20 years. “The stated purpose [of the Silk Road] was to be beyond the law. In addition to his prison sentence, Ulbricht was also ordered to pay a massive restitution of more than $183 million, what the prosecution had estimated to be the total sales of illegal drugs and counterfeit IDs through the Silk Road—at a certain bitcoin exchange rate—over the course of its time online. Go Back to Top.
Demis Hassabis Demis Hassabis (born 27 July 1976) is a British computer game designer, artificial intelligence programmer, neuroscientist and world-class games player.[4][3][5][6][7][1][8][9][10][11] Education[edit] Career[edit] Recently some of Hassabis' findings and interpretations have been challenged by other researchers. A paper by Larry R. In 2011, he left academia to co-found DeepMind Technologies, a London-based machine learning startup. In January 2014 DeepMind was acquired by Google for a reported £400 million, where Hassabis is now an Engineering Director leading their general AI projects.[12][23][24][25] Awards and honours[edit] Hassabis was elected as a Fellow of the Royal Society of Arts (FRSA) in 2009 for his game design work.[26] Personal life[edit] Hassabis lives in North London with his wife and two sons. References[edit]
Google DeepMind Artificial intelligence division DeepMind Technologies Limited,[4] doing business as Google DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Google. Founded in the UK in 2010, it was acquired by Google in 2014,[5] The company is based in London, with research centres in Canada,[6] France,[7] Germany and the United States. Google DeepMind has created neural network models that learn how to play video games in a fashion similar to that of humans,[8] as well as Neural Turing machines (neural networks that can access external memory like a conventional Turing machine),[9] resulting in a computer that loosely resembles short-term memory in the human brain.[10][11] History[edit] The start-up was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman in September 2010.[20][21] Hassabis and Legg first met at the Gatsby Computational Neuroscience Unit at University College London (UCL).[22] Logo from 2015–2016 Logo from 2016–2019
Q-learning Model-free reinforcement learning algorithm For any finite Markov decision process, Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.[2] Q-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy.[2] "Q" refers to the function that the algorithm computes – the expected rewards for an action taken in a given state.[3] Reinforcement learning[edit] Reinforcement learning involves an agent, a set of states , and a set of actions per state. , the agent transitions from state to state. The goal of the agent is to maximize its total reward. As an example, consider the process of boarding a train, in which the reward is measured by the negative of the total time spent boarding (alternatively, the cost of boarding the train is equal to the boarding time). Algorithm[edit] After ).
Deep learning Branch of machine learning Deep learning is the subset of machine learning methods based on artificial neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.[2] Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Definition[edit] Deep learning is a class of machine learning algorithms that[9]: 199–200 uses multiple layers to progressively extract higher-level features from the raw input. From another angle to view deep learning, deep learning refers to "computer-simulate" or "automate" human learning processes from a source (e.g., an image of dogs) to a learned object (dogs). Overview[edit] The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. Interpretations[edit]
The Deep Mind of Demis Hassabis — Backchannel In the race to recruit the best AI talent, Google scored a coup by getting the team led by a former video game guru and chess prodigy From the day in 2011 that Demis Hassabis co-founded DeepMind—with funding by the likes of Elon Musk—the UK-based artificial intelligence startup became the most coveted target of major tech companies. In June 2014, Hassabis and his co-founders, Shane Legg and Mustafa Suleyman, agreed to Google’s purchase offer of $400 million. Late last year, Hassabis sat down with Backchannel to discuss why his team went with Google—and why DeepMind is uniquely poised to push the frontiers of AI. The interview has been edited for length and clarity. [Steven Levy] Google is an AI company, right? [Hassabis] Yes, right. Were your interactions with Larry Page a big factor in your decision to sell to Google? Yes, a really big factor. So even though Facebook may have super intelligent leadership, Mark [Zuckerberg] might see AI as more of a tool than a mission in a larger sense?
Google's DeepMind uses Daily Mail to teach computers how to read human language British-based unit analysed almost 400,000 articles from the siteWebsite used because of its unique style of bullets, text and captionsArtificial intelligence was able to learn key facts from the news articles Could lead to robot 'brains' that read documents and respond to questions By Jonathan O'Callaghan for MailOnline Published: 12:26 GMT, 18 June 2015 | Updated: 14:04 GMT, 18 June 2015 Google’s DeepMind division is using Daily Mail and CNN articles to teach its artificial intelligence programs to read. Using the unique style of articles on the sites - with concise bullet points summarising a story at the top of a page - artificial intelligence was able to learn key facts about articles to answer queries. Ultimately, scientists hope that the study could lead to complex artificial 'brains' that can read entire documents and respond to questions put to them by a human. The British-based DeepMind unit analysed almost 400,000 articles from the sites (language process shown).
Google DeepMind artificial intelligence can beat humans at 31 video games but can't master Pac-Man Google-owned artificial intelligence start-up DeepMind has revealed that its deep learning software is now able to outperform humans in 31 different video games. The algorithm, which uses reinforcement learning to master the games, has been described as the "first significant rung of the ladder" towards proving such a system can work, and a significant step towards use in real-world applications. Speaking at the Re.Work Deep Learning Summit in London on Thursday (24 September), DeepMind research scientist Koray Kavukcuoglu described how the same algorithm could be used to master dozens of different games. Without having the games' rules programmed into its software, the AI was able to improve by analysing the pixels on the screen and learning which patterns produce the optimum score. Games that the AI algorithm has mastered include the classic arcade game Pong, and Atari titles like Bank Heist, Enduro, River Raid and Battlezone.
Google DeepMind: What is it, how it works and should you be scared? | Personal Tech What is DeepMind? Google DeepMind is an artificial intelligence division within Google that was created after Google bought Oxford University spinout, DeepMind, for a reported £400 million in January 2014. The division, which employs around 140 researchers at its lab in a new building at Kings Cross, London, is on a mission to solve general intelligence and make machines capable of learning things for themselves. It plans to do this by creating a set of powerful general-purpose learning algorithms that can be combined to make an AI system or “agent”. Suleyman explains These are systems that learn automatically. The systems we design are inherently general. That’s why we’ve started as we have with the Atari games. Instead we’ve taken the principle approach of starting on tools that are inherently general. We characterise AGI as systems and tools which are flexible and adaptive and that learn. How does it work? Suleyman explains You’ve probably heard quite a bit about deep learning.
Google DeepMind Teaches Artificial Intelligence Machines to Read A revolution in artificial intelligence is currently sweeping through computer science. The technique is called deep learning and it’s affecting everything from facial and voice to fashion and economics. But one area that has not yet benefitted is natural language processing—the ability to read a document and then answer questions about it. That’s partly because deep learning machines must first learn their trade from vast databases that are carefully annotated for the purpose. Today, that changes thanks to the work of Karl Moritz Hermann at Google DeepMind in London and a few pals. The deep learning revolution has come about largely because of two breakthroughs. But a neural network is of little use without a database to learn from. That’s recently become possible thanks to crowdsourcing services like Amazon’s Mechanical Turk. But creating a similarly annotated database for the written word is much harder. Creating such a database is easier said than done.
DeepMind: inside Google's super-brain This article was first published in the July 2015 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online The future of artificial intelligence begins with a game of Space Invaders. From the start, the enemy aliens are making kills -- three times they destroy the defending laser cannon within seconds. Half an hour in, and the hesitant player starts to feel the game's rhythm, learning when to fire back or hide. This player, it should be mentioned, is not human, but an algorithm on a graphics processing unit programmed by a company called DeepMind. In February, Hassabis and colleagues including Volodymyr Mnih, Koray Kavukcuoglu and David Silver published a Nature paper on the work. DeepMind has not, admittedly, launched any products -- nor found a way to turn its machine gameplay into a revenue stream.
Networking Upload Eli the Computer Guy Website Loading... Working... Eli the Computer Guy ► Play all Networking by Eli the Computer Guy29 videos910,800 viewsLast updated on Jul 2, 2014 Play all Sign in to YouTube Sign in History Sign in to add this to Watch Later Add to Loading playlists... In a Big Network of Computers, Evidence of Machine Learning Photo MOUNTAIN VIEW, Calif. — Inside ’s secretive X laboratory, known for inventing self-driving cars and augmented reality glasses, a small group of researchers began working several years ago on a simulation of the human brain. There Google scientists created one of the largest neural networks for machine learning by connecting 16,000 computer processors, which they turned loose on the Internet to learn on its own. Presented with 10 million digital images found in YouTube videos, what did Google’s brain do? What millions of humans do with YouTube: looked for cats. The neural network taught itself to recognize cats, which is actually no frivolous activity. The research is representative of a new generation of computer science that is exploiting the falling cost of computing and the availability of huge clusters of computers in giant data centers. And then, of course, there are the cats. To find them, the Google research team, led by the computer scientist Andrew Y.