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Robots master skills with ‘deep learning’ technique Robot learns to use hammer. What could go wrong? (credit: UC Berkeley) UC Berkeley researchers have developed new algorithms that enable robots to learn motor tasks by trial and error, using a process that more closely approximates the way humans learn. They demonstrated their technique, a type of reinforcement learning, by having a robot complete various tasks — putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more — without pre-programmed details about its surroundings. A new AI approach “What we’re reporting on here is a new approach to empowering a robot to learn,” said Professor Pieter Abbeel of UC Berkeley’s Department of Electrical Engineering and Computer Sciences. The work is part of a new People and Robots Initiative at UC’s Center for Information Technology Research in the Interest of Society (CITRIS). Neural-inspired learning Coat-hanger training (no wire hangers!) BRETT masters human tasks on its own A little nightcap?

Special Issue on Cloud Robotics and Automation - IEEE Robotics and Automation Society A special issue of the IEEE Transactions on Automation Science and Engineering. Introduction Cloud Robotics and Automation is attracting increased interest from academia, governments, and industry worldwide. General Electric’s “Industrial Internet” aims to create a “convergence of machine and intelligent data” across industries. Scope, Description, and More Information Scalable parallelization: How can parallel grid-based computing on demand change the current paradigm in automation science? Important Dates Guest Editors Dr. Lead Guest EditorUniversity of Zaragoza Zaragoza, Spain Dr. Guest Editor California, United States mateiciocarlie@gmail.comThis email address is being protected from spambots. Dr. Alper.O.Aydemir@jpl.nasa.govThis email address is being protected from spambots. Dr. New Jersey, United States Dr. Massachusetts, United States

Computer chatbot 'Eugene Goostman' passes the Turing test A computer program that pretends to be a 13-year-old Ukrainian boy called Eugene Goostman passed a Turing test at the Royal Society in London yesterday (Saturday 6 June) by convincing 33 percent of the judges that it was human during a five-minute typed conversation. The test was suggested by computer scientist Alan Turing in 1950, and the competition was held on the 60th anniversary of his death. The judges included Robert Llewellyn, who played the android Kryten in Red Dwarf, and Lord Sharkey, who led the campaign for Turing's posthumous pardon last year. Eugene Goostman's success was not a surprise. Kevin Warwick, a visiting professor at the University of Reading, which organised both tests, said it was the first time a chatbot had passed an open-ended test, rather than one where topics or questions were set in advance. The fictional Eugene has a father who is a gynaecologist, and has a pet guinea pig. Of course, bots are really not hard to spot, if you're trying. Further reading

20 lines of code that beat A/B testing every time Zwibbler.com is a drop-in solution that lets users draw on your web site. A/B testing is used far too often, for something that performs so badly. It is defective by design: Segment users into two groups. In recent years, hundreds of the brightest minds of modern civilization have been hard at work not curing cancer. With a simple 20-line change to how A/B testing works, that you can implement today, you can always do better than A/B testing -- sometimes, two or three times better. It can reasonably handle more than two options at once.. The Multi-armed bandit problem The multi-armed bandit problem takes its terminology from a casino. Like many techniques in machine learning, the simplest strategy is hard to beat. def choose(): if math.random() < 0.1: # exploration! Why does this work? Let's say we are choosing a colour for the "Buy now!" Then a web site visitor comes along and we have to show them a button. Another visitor comes along. But suddenly, someone clicks on the orange button!

SENNA SENNA is a software distributed under a non-commercial license, which outputs a host of Natural Language Processing (NLP) predictions: part-of-speech (POS) tags, chunking (CHK), name entity recognition (NER), semantic role labeling (SRL) and syntactic parsing (PSG). SENNA is fast because it uses a simple architecture, self-contained because it does not rely on the output of existing NLP system, and accurate because it offers state-of-the-art or near state-of-the-art performance. SENNA is written in ANSI C, with about 3500 lines of code. Proceed to the download page. New in SENNA v3.0 (August 2011) Here are the main changes compared to SENNA v2.0: Syntactic parsing.We now include our original word embeddings, used to trained each task.Bug correction: now outputs correctly tokens made of numbers (instead of replacing numbers by "0").Option -offsettags, which outputs start/end offsets (in the sentence) of each token. Details R. R. Download Compilation Linux gcc -o senna -O3 -ffast-math *.c Mac OS X

Brains, Minds and Machines | MIT150 | Massachusetts Institute of Technology 150th anniversary Watch videos of this symposium This symposium was inspired by the old dream of understanding the mind and the brain, which was at the core of several new fields created at MIT during the ‘50s and ‘60s. The same dream is now the main motivation for a new Intelligence Initiative (I2). Beyond being a great intellectual mission, this research helps to develop an understanding of the origins of intelligence, build more intelligent artifacts and systems, and improve the mechanisms for collective decisions. These advances will be critical to the future prosperity, education, health, and security of our society. Key question: Over the past 50 years, research in Artificial Intelligence (AI) and Machine Learning (ML) has led to the current development of remarkably successful applications such as Deep Blue, Google search, Kinect, Shazam, Watson, and MobilEye. a) that a new effort in curiosity-driven research is needed in order to understand intelligence and the brain Faculty leads Irene R. MIT News

Behavioral Targeting: the most underused technique in today’s marketing Posted in How To on May 30th, 2012 We recently launched geo-behavioral targeting feature in Visual Website Optimizer. (We also launched usability testing module; our vision is to offer all tools and techniques a marketer would need for conversion rate optimization). People use A/B testing, multivariate testing, analytics and usability studies for improving sales and conversions. However, I feel behavioral targeting is massively underused. Part of the reason could be due to difficulty of implementation, but with tools like ours (and others in the market), it is becoming easier by the day to get started with all sorts of targeting and personalization campaigns. What is behavioral targeting? Different visitors behave differently on your website. In my landing page optimization tips article, I recommended knowing who your target customer is and having only one clear call to action button (and thereby neglecting other visitors). Def. So, behavioral targeting must be hard? Actually, no!

Fast Cochlea Transform | Audience Fast Cochlea Transform™ The cochlea is the most complex part of the ear and central to the human auditory system. It is responsible for transforming sound pressure waves into electrical information that the brain can interpret as a sound. Sound captured by the microphones is sent to the voice processor and digitized before entering the FCT. The FCT is similar to the “Fast Fourier Transform” (FFT) that is commonly used in digital signal processing. Log-Frequency Scale: The FFT transforms the audio signal into the frequency domain on a linear scale, while the FCT performs its transformation on a logarithmic-frequency scale.

The Myth Of AI That mythology, in turn, has spurred a reactionary, perpetual spasm from people who are horrified by what they hear. You'll have a figure say, "The computers will take over the Earth, but that's a good thing, because people had their chance and now we should give it to the machines." Then you'll have other people say, "Oh, that's horrible, we must stop these computers." Most recently, some of the most beloved and respected figures in the tech and science world, including Stephen Hawking and Elon Musk, have taken that position of: "Oh my God, these things are an existential threat. They must be stopped." In the past, all kinds of different figures have proposed that this kind of thing will happen, using different terminology. A good starting point might be the latest round of anxiety about artificial intelligence, which has been stoked by some figures who I respect tremendously, including Stephen Hawking and Elon Musk. What do I mean by AI being a fake thing? This is not one of those.

Tom's Hardware US Researchers at the Imperial College in London believes that magnets could be used to develop future processors with far greater processing capacity than today's CPUs. According to a study published in the journal Science, a honeycomb-pattern of tiny, nano-sized magnets that are submerged in a material known as spin ice could solve a complex computational problem in a single step. In fact, clusters of such magnet arrays function similar to a neural network: It is more "similar to how our brains work than to the way in which traditional computers process information," the researchers said. Exploiting the potential of magnets gets more difficult the closer they are located to each other as they interfere with their magnetic fields, the scientists found that their honeycomb patterns create competition between magnets and "reduces the problems caused by these interactions by two-thirds." Honeycomb magnet processors are very much science fiction at this point.

Textos infantiles Grillo, grillo, quien se lo encuentre, va a su bolsillo. Santa Elena, que a la mar fuiste, luz y gracia a buscar, ayúdame a encontrar lo que no puedo hallar. San Cucufato, las barbas de gato, si no lo encuentro, no te las desato. —El que se fue a Sevilla perdió su silla. —El que se fue a Barranquilla perdió su silla. —Quien fue a Sevilla perdió su silla. —Abre los ojos, Salomé, que te viene el Papi a ver. —Venga el Papi o venga el rey, déjenme dormir. —Abre los ojos, Salomé, que te viene el Papi a ver y trae dulcecitos de café. —¡A verlos, a verlos, que me los quiero comer! Santa Rita, Rita, Rita, lo que se da no se quita, con papel y agua bendita en el cielo ya está escrita. El que fue a Sevilla perdió su silla. El que fue a Aragón perdió su sillón. Calabaza, calabaza, cada uno a su casa. Agua, San Marcos, rey de los charcos, para mi arbolito que ya está bonito; para mi cebada que ya está granada; para mi melón que ya tiene flor. Una cosa me encontré, pero no te la diré.

Edge.org "Dahlia" by Katinka Matson | Click to Expand | www.katinkamatson.com "Another year, and some of the most important thinkers and scientists of the world have accepted the intellectual challenge." —El Mundo, 2015 "Deliciously creative, the variety astonishes. Intellectual skyrockets of stunning brilliance. _________________________________________________________________Dedicated to the memory of Frank Schirrmacher (1959-2014) In recent years, the 1980s-era philosophical discussions about artificial intelligence (AI)—whether computers can "really" think, refer, be conscious, and so on—have led to new conversations about how we should deal with the forms that many argue actually are implemented. But wait! Numerous Edgies have been at the forefront of the science behind the various flavors of AI, either in their research or writings. Is AI becoming increasingly real? John Brockman Publisher & Editor, Edge [186 Responses:] Pamela McCorduck, George Church, James J. By Joshua E. ... Tania Lombrozo