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Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence Hillary Clinton at the Iowa State Fair on August 15, 2015 in Des Moines, Iowa. (Photo by Win McNamee/Getty Images) The meme that now seems to be dominating much of the media coverage of the Democratic Primary is that pundits and experts are underestimating Bernie Sanders’s chances of winning the Democratic nomination for president. Mr. Recency bias is essentially the tendency to predict upcoming events based too heavily on recent history, rather than a broader sample. Selection bias is a more general tendency to pick cases to support an argument rather than looking at the broader universe of cases or to make a random sample. Is Al Gore Hillary circa 2000? A related point here is that while new media has had an impact on political communication, strategy and campaigns that has permanently changed politics, it also has contributed to a polity where people increasingly communicate with people who share their own political views. While Ms.

Always be creative :: Deep learning Deep Learning이라는 것은 간단하게 이야기해서, Raw데이터의 feature들을 hierarchical하게 representation한다는 것을 의미한다. higher level일수록 concept과 같은 뭔가 abstraction된 feature가 representation된다는 것이다. 1. Boltzmann machine 이전에 쓴 글을 참고( Boltzmann machine의 특성을 정리하면 (1) A type of stochastic recurrent neural network >> 에너지 모델을 확률로 해석하였고, 아래의 좌측 그림에서 보듯이 v(visible layer)와 h(hidden layer)가 학습과정에서 에너지 함수를 가지고 재귀적으로 학습되기 때문에 recurrent neural network라고 한다. (2) weights are symmetric >> 그림처럼 각 layer사이의 edge들은 bi-direction(양방향성)이기때문에 노드들 사이의 가중치 값은 symmetric하다. (3) weights adjusted through stochastic update rule based on simulated annealing >> 노드간 연결된 edge들의 weight들은 확률에 기반하여 학습된다. (4) a network of units with an 'energy' defined for the network >>boltzmann machine은 하나의 visible layer와 하나의 hidden layer로 구성되어 있는데, 이 네트워크를 하나의 에너지 모델로 본다. 2. 위 그림처럼 같은 layer내에서는 각 노드간의 커넥션을 없앤 BM을 RBM이라고 한다. (1) Restrict the connectivity to make learning easier - only one layer of hidden units hidden 노드도 그렇고, visible 노드도 그렇고..

karpathy/convnetjs Practical Deep Learning For Coders—18 hours of lessons for free Guy Hoffman Deep Learning Tutorials — DeepLearning 0.1 documentation Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. For more about deep learning algorithms, see for example: The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU. The algorithm tutorials have some prerequisites. The code is available on the Deep Learning Tutorial repositories. The purely supervised learning algorithms are meant to be read in order: LSTM network

Python Introduction  |  Python Education  |  Google Developers Prelude Welcome to Google's Python online tutorial. It is based on the introductory Python course offered internally. Originally created during the Python 2.4 days, we've tried to keep the content universal and exercises relevant, even for newer releases. As mentioned on the setup page, this material covers Python 2. This course material was created for Python 2 and has not yet been updated for modern Python 3. We strongly recommend you follow along with the companion videos throughout the course, starting with the first one. Language Introduction Python is a dynamic, interpreted (bytecode-compiled) language. An excellent way to see how Python code works is to run the Python interpreter and type code right into it. As you can see above, it's easy to experiment with variables and operators. Python source code Python source files use the ".py" extension and are called "modules." #! # import modules used here -- sys is a very standard oneimport sys $ python Guido Hello there Guido $ .

The AI Revolution: Road to Superintelligence PDF: We made a fancy PDF of this post for printing and offline viewing. Buy it here. (Or see a preview.) Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. We are on the edge of change comparable to the rise of human life on Earth. — Vernor Vinge What does it feel like to stand here? It seems like a pretty intense place to be standing—but then you have to remember something about what it’s like to stand on a time graph: you can’t see what’s to your right. Which probably feels pretty normal… The Far Future—Coming Soon Imagine taking a time machine back to 1750—a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. This experience for him wouldn’t be surprising or shocking or even mind-blowing—those words aren’t big enough. This works on smaller scales too. 1. What Is AI?

Deeplearning4j - Open-source, distributed deep learning for the JVM Engineering Everywhere | CS229 - Machine Learning Ng's research is in the areas of machine learning and artificial intelligence. He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI.

Digital version of piece of rat brain fires like the real thing The brain is going digital. A tiny piece of a rat’s brain has been reconstructed in minute detail in a computer. The digital piece of brain, which includes 31,000 neurons and their 37 million synapses, fires like the real thing, and is already revealing fresh clues as to how the brain works. The simulation is the first significant achievement of the Blue Brain project, which was launched 10 years ago by Henry Markram at the Swiss Federal Institute of Technology in Lausanne, Switzerland. So far, the digital brain recreates a piece of tissue about one third of a millimetre cubed. “As one of the first concrete outputs from the billion-euro Human Brain Project this had to be a substantial piece of work, and it is,” says Anil Seth at the University of Sussex in the UK. Microcircuit brain Markram and his many colleagues – a team comprising 82 people across 12 institutions – created their model using data they have been collecting for the last two decades. Choristers and soloists