wrrn + ai   265

Unsupervised Sentiment Neuron
While training the linear model with L1 regularization, we noticed it used surprisingly few of the learned units. Digging in, we realized there actually existed a single “sentiment neuron” that’s highly predictive of the sentiment value.
machinelearning  language  ai  SentimentAnalysis  linguistics  computing 
27 days ago by wrrn
Perspective
Perspective was created by Jigsaw and Google’s Counter Abuse Technology team in a collaborative research project called Conversation-AI. We are also open sourcing experiments, models, and research data to explore the strengths and weaknesses (e.g. potential unintended biases) of using machine learning as a tool for online discussion.
api  google  ai  MachineLearning  language  conversation  connectionmachine 
february 2017 by wrrn
Machine-learning boffins 'summon demons' in AI to find exploitable bugs • The Register
People could fiddle with cost function algorithms to crank up the price of insurance bonuses, or criminals could evade facial recognition on CCTV cameras, assuming they know the program’s source code and can control input. It’s a realistic possibility, granted that a lot of machine learning software is open source.
Ai  MachineLearning  sousveillance  surveillance  data  society  social-software 
january 2017 by wrrn
Top 9 ethical issues in artificial intelligence | World Economic Forum
In many ways, this is just as much a new frontier for ethics and risk assessment as it is for emerging technology. So which issues and conversations keep AI experts up at night?
ai  MachineLearning  ethics  philosophy  computing  humancomputer  future 
january 2017 by wrrn
AI and Unreliable Electronics (*batteries not included) « Pete Warden's blog
our only hope for long-lived smart sensors is driving down the energy used by local compute to the point at which harvesting gives enough power to run useful applications.
energy  power  ai  hardware  future  sensor  senses  supersenses 
december 2016 by wrrn
Keras Documentation
Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
ai  MachineLearning  tensorflow  python  tools  grand-unified-theory 
december 2016 by wrrn
Twitter bots | botwiki 🤖
Twitter remains a popular network for bot makers and enthusiasts, which can be easily proved by the variety of bots hosted on it:
bots  twitter  ai  software  tools 
november 2016 by wrrn
[1610.08401] Universal adversarial perturbations
The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.
ai  computing  MachineLearning  security  surveillance  activism 
november 2016 by wrrn
The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe
There is no mathematical reason why networks arranged in layers should be so good at these challenges. Mathematicians are flummoxed. Despite the huge success of deep neural networks, nobody is quite sure how they achieve their success.
physics  DeepLearning  MachineLearning  networks  AI 
october 2016 by wrrn
AI program able to predict human rights trials with 79 percent accuracy - The Verge
Computer scientists have created an AI program capable of predicting the outcome of human rights trials. The program was trained on data from nearly 600 cases brought before the European Court of Human Rights (ECHR), and was able to predict the court's final judgement with 79 percent accuracy. Its creators say it could be useful in identifying common patterns in court cases, but stress that they do not believe AI will be able to replace human judgement
AI  MachineLearning  law  knowledge  information 
october 2016 by wrrn
Artificial intelligence is hard to see – Medium
there are no agreed-upon methods to assess the human effects and longitudinal impacts of AI as it is applied across social systems. This knowledge gap is widening as the use of AI is proliferating, which heightens the risk of serious unintended consequences.
AI  MachineLearning  activism  critical_thinking  human  society 
september 2016 by wrrn
How to Get a Job In Deep Learning
Getting involved in deep learning may seem a bit daunting at first, but the good news is that there are more resources out there now than ever before.
ai  deep-learning  MachineLearning  learning 
september 2016 by wrrn
Society-in-the-Loop – MIT MEDIA LAB – Medium
MIT Media Lab director Joi Ito recently published a thoughtful essay titled “Society-in-the-Loop Artificial Intelligence,” and has kindly credited me with coining the term. Now that it is out there, I wanted to elaborate a little on what I mean by “society in the loop,” and to highlight the gap that it bridges between the humanities and computing.
society  ai  human-computation  systems  grand-unified-theory  mind 
september 2016 by wrrn
WaveNet: A Generative Model for Raw Audio | DeepMind
We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems, reducing the gap with human performance by over 50%.
music  ai  audio  speech  synthesis  DeepLearning  MachineLearning  dsp 
september 2016 by wrrn
Chatbot lawyer that overturned 170,000 parking tickets now helps fight homelessness | Ars Technica UK
The bot's creator, Joshua Browder, a 19-year-old Brit studying at Stanford University in California, told Ars that since the update launched last Wednesday "almost every local government in the UK has signed up for the website."
bot  ai  law  computing 
august 2016 by wrrn
AutoRally
The AutoRally platform is a high-performance testbed for advanced perception and control research. The robot, developed at Georgia Tech, is integrated with ROS and designed as a self contained system that requires no external sensing or computing. The robot is a robust, cost-effective, and safe platform that opens the space of agressive autonomous off-road driving to all researchers.
ai  autonomous  automation  transport  UAV  MachineLearning 
june 2016 by wrrn
Welcome to Magenta!
Magenta has two goals. First, it’s a research project to advance the state of the art in machine intelligence for music and art generation. Machine learning has already been used extensively to understand content, as in speech recognition or translation. With Magenta, we want to explore the other side—developing algorithms that can learn how to generate art and music, potentially creating compelling and artistic content on their own.
art  creativity  ai  code  tensorflow  google 
june 2016 by wrrn
Conscious-Robots.com | ConsScale - A Machine Consciousness Scale
ConsScale has been specifically designed for the evaluation of Machine Consciousness implementations. Each level is characterized by architectural and behavioral criteria. Try the ConsScale Generic Calculator to discover how creatures are rated in the scale.
ai  consciousness  MachineLearning  human  intelligence  mind 
may 2016 by wrrn
Could machines have become self-aware without our knowing it? | Aeon Essays
The most systematic effort to piece all the tests together is ‘ConsScale’, a rating procedure developed in 2008 by the Spanish AI researcher Raúl Arrabales Moreno and his colleagues. You fill in a checklist, beginning with anatomical features, on the assumption that human-like consciousness arises only in systems with the right components. Does the system have a body? Memory? Attentional control? Then you look for behaviours and communicativeness: Can it recognise itself in a mirror? Can it empathise? Can it lie?
ai  consciousness  MachineLearning  intelligence  machines  mind 
may 2016 by wrrn
A Neural Network Playground
a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure
MachineLearning  tensorflow  google  opensource  AI  neuroscience 
april 2016 by wrrn
TensorFlow for Poets « Pete Warden's blog
When I first started investigating the world of deep learning, I found it very hard to get started. There wasn’t much documentation, and what existed was aimed at academic researchers who already knew a lot of the jargon and background. Thankfully that has changed over the last few years, with a lot more guides and tutorials appearing.
ai  google  tensorflow  MachineLearning 
april 2016 by wrrn
Adventures in Narrated Reality — Medium
New forms & interfaces for written language, enabled by machine intelligence
ai  MachineLearning  narrative  linguistics  NLP  language  beinghuman 
april 2016 by wrrn
How to Make a Bot That Isn't Racist | Motherboard
Thricedotted and others belong to an established community of botmakers on Twitter that have been creating and experimenting for years. There’s a Bot Summit. There’s a hashtag (#botALLY).
ai  microsoft  racism  bots  NLP  linguistics  language 
april 2016 by wrrn
Deep or Shallow, NLP is Breaking Out | March 2016 | Communications of the ACM
In domains as varied as finding pertinent news for a company's potential investors to making hyper-personalized recommendations for online shopping to making music recommendations on streaming radio services, NLP is enabling everyday human-computer interaction in an ever-increasing range of venues. In the process, some of these advances are not only redefining what computers and humans can accomplish together, but also the very concept of what deep learning is.
MachineLearning  NLP  AI  computational-science  language  semantic 
march 2016 by wrrn
cantino/huginn
Build agents that monitor and act on your behalf. Your agents are standing by!
opensource  ai  autonomous  bot  tools  humancomputer 
february 2016 by wrrn
Berkeley AI Materials
The Pac-Man projects were developed for UC Berkeley's introductory artificial intelligence course, CS 188. They apply an array of AI techniques to playing Pac-Man. However, these projects don't focus on building AI for video games. Instead, they teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning. These concepts underly real-world application areas such as natural language processing, computer vision, and robotics.
ai  games  programming 
january 2016 by wrrn
Human-level concept learning through probabilistic program induction | GitXiv
On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches
AI  MachineLearning  ComputerVision  probabilistic-programming  statistics 
december 2015 by wrrn
Software better than humans at guessing how you feel from speech | New Scientist
Chalk up another win for computers. Software developed at the University of Rochester in New York has outstripped humans in its ability to identify emotions in speech. The researchers plan to use it to understand the effects of emotion in parent-child interactions.
AI  human  emotion  beinghuman  MachineLearning  humancomputer 
november 2015 by wrrn
Peter | Your AI-Based Business Lawyer
Our mission is to enable frictionless agreements between humans.
AI  work  Law  computing  humancomputer 
november 2015 by wrrn
Google Geo Developers Blog: Predicting the Future with Google Maps APIs
For example, one of our customers, Redfin, plan to use the Google Maps APIs to predict how long it will take to drive between homes, so they will use the pessimistic traffic model to ensure there’s enough travel time taking traffic into account. On the other hand, a developer building thermostat app wants their user’s house to be warm by the time they get home from work, so they would use the optimistic travel time estimate to calculate when their user is likely to get home, and when their thermostat needs to turn on.
geo  maps  MachineLearning  prediction  google  ai 
november 2015 by wrrn
The Sentient Surveillance Camera | Motherboard
"The sentient surveillance camera presents one possible implementation of such an entity, albeit a bizarre one, but it's designed to raise questions about the places these technologies could take us, and the possibilities for technology that actively judges us and forms its own conclusions about its environment.”
ai  art  technology  surveillance  sousveillance  MachineLearning 
september 2015 by wrrn
AI surveillance camera tells you what it sees when it recognises you | Naked Security
There are two masters students at New York University who have invented a surveillance camera that's designed to answer the question, quite literally, reading aloud in real-time its interpretation of what it sees when it captures a facial image.
ai  surveillance  sousveillance  MachineLearning  video 
september 2015 by wrrn
The WTF Economy — What’s The Future of Work? — Medium
What’s the future of business when technology-enabled networks and marketplaces are better at deploying talent than traditional companies? What’s the future of education when on-demand learning outperforms traditional universities in keeping skills up to date?
work  ai  future  human  employment 
september 2015 by wrrn
‘Thought vectors’ could revolutionize artificial intelligence - Magng
The underlying idea is that by ascribing every word a set of numbers (or vector), a computer can be trained to understand the actual meaning of these words.
ai  MachineLearning  future  mathematics  statistics 
may 2015 by wrrn
Damage Recovery Algorithm Could Make All Robots Unstoppable - IEEE Spectrum
Using an exceptionally clever algorithm, the robots have demonstrated that they can shrug off absurd amounts of damage, adapting within minutes to recover their mobility even if you chop a third of their legs off. 
ai  robotics  MachineLearning  hardware 
may 2015 by wrrn
This is What Happens When You Teach Machines the Power of Natural Selection - The Daily Beast
We’re at that point analogous to when single-celled organisms were turning into multi-celled organisms. We are amoebas and we can’t figure out what the hell this thing is that we’re creating.” —Danny Hillis, founder of Thinking Machines, Inc.
ai  singularity  MachineLearning  future  technology  thinktank 
may 2015 by wrrn
Self-Aware Systems
A system’s goals are orthogonal to its intelligence and may lead to either harm or good. So goal choices we make today will have a big impact on future outcomes. Simplistic goals give rise to unintended drives that may be anti-social. But even “superintelligences” will be limited by the laws of mathematics, physics, and cryptography. Mathematical proof can be used to create constrained systems with a high confidence of safety.
ai  singularity  blockchain  thinktank  systems  MachineLearning 
may 2015 by wrrn
DeepDive
DeepDive is a new type of system that enables developers to analyze data on a deeper level than ever before. DeepDive is a trained system: it uses machine learning techniques to leverage on domain-specific knowledge and incorporates user feedback to improve the quality of its analysis.
ai  learning  bigdata  MachineLearning  knowledge  information-retrieval 
april 2015 by wrrn
A Global Arms Race to Create a Superintelligent AI is Looming | Motherboard
The launch of the first truly autonomous, self-aware artificial intelligence—one that has the potential to become far smarter than human beings—is a matter of the highest national and global security. Its creation could change the landscape of international politics in a matter of weeks—maybe even days, depending on how fast the intelligence learns to upgrade itself, hack and rewrite the world's best codes, and utilize weaponry.
AI  future  politics  corporation  4thGenWar  war  power  MachineLearning 
march 2015 by wrrn
FLI - Future of Life Institute
The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI.
ai  future  research  MachineLearning  statistics  humancomputer  society 
january 2015 by wrrn
Sterling Crispin
This is meant as a theoretical and technical investigation into the form and function of biometric surveillance technology, which is the mathematical analysis of biological data.
algorithms  3d  ai  facial-recognition  MachineLearning  art  surveillance  beinghuman 
december 2014 by wrrn
Neglected machine learning ideas | Locklin on science
This post is inspired by the “metacademy” suggestions for “leveling up your machine learning.” They make some halfway decent suggestions for beginners.  The problem is, these suggestions won’t give you a view of machine learning as a field
ai  MachineLearning  tools  theory  learning 
july 2014 by wrrn
There's a Bit of a Flaw in the Way Artificial Intelligence Is Being Developed | Motherboard
researchers from Google, Facebook, and academia found that if they first presented the computer-brain with an image it could recognize and then modified the pixels ever so slightly, the algorithm could be tricked into misclassifying the second image, even though if placed side by side, the images would look identical to the human eye.
AI  MachineLearning  ComputerVision  image  data  data-mining  data-science 
june 2014 by wrrn
Conscientious Redux: new-aesthetic: Introducing AISight: The slightly...
autonomously building an ever-changing knowledgebase of activity seen through every camera on your video network. The result: AISight delivers accurate, real-time alerts on any suspicious behavior
surveillance  ai  ComputerVision  panopticon  security 
april 2014 by wrrn
Can we design machines to automate ethics? – Tom Chatfield – Aeon
When is it ethical to hand our decisions over to machines? And when is external automation a step too far?
philosophy  ai  algorithms  technology  human  ethics 
april 2014 by wrrn
Jetpac City Guides
We’ve Analyzed Every Pixel of Public Instagram Photos to Bring You Jetpac City Guides.
DeepLearning  ai  MachineLearning  media  society  panopticon 
march 2014 by wrrn
BVLC/caffe · GitHub
Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. Network structure is easily specified in separate config files, with no mess of hard-coded parameters in the code. Python and Matlab wrappers are provided.
ComputerVision  ai  DeepLearning  tools  library  python 
march 2014 by wrrn
Improving Photo Search: A Step Across the Semantic Gap
Ten years ago, running neural networks of this complexity would have been a momentous task even on a single image -- now we are able to run them on billions of images. Second, new training techniques have made it possible to train the large deep neural networks necessary for successful image recognition.
DeepLearning  ai  ComputerVision  google  research  image  MachineLearning 
march 2014 by wrrn
Why deep belief matters so much - O'Reilly Radar
It’s traditionally been almost impossible to extract meaning from images, but deep belief networks are able to crack them open and say something significant about what’s in them. They bridge the gap between the real world and CPUs in a way we’ve never been able to do before.
ai  image  DeepLearning  MachineLearning  vision 
march 2014 by wrrn
Real-life 'Doc Ock' arm learns like a baby | The Verge
The arm, which was designed to be as dexterous as an elephant's trunk, can be taught to reproduce certain positions on command through a process known as "goal babbling," in which the robot remembers small changes in the pressure of its pneumatic "muscles." 
ai  MachineLearning  robotics  learning  embodied  cognition 
march 2014 by wrrn
More on DeepMind: AI Startup to Work Directly With Google’s Search Team | Re/code
“If anyone builds something remotely resembling artificial general intelligence, this will be the team,” one early investor in DeepMind told Re/code today. “Think Manhattan Project for AI.”
AI  google  research  future  search 
january 2014 by wrrn
2H2K Lawyer: Science Fiction Design, Artificial Labor, and Ubiquitous Interactive Machine Learning | Ideas For Dozens
Due to our focus on urbanism and the built-environment, John’s stories so far have mainly explored the impact of artificial labor on physical work: building construction, forestry, etc. For this project, I wanted to look at how automation will affect white collar work.
AI  labor  politics  technology  MachineLearning  class 
january 2014 by wrrn
Neural networks and deep learning
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you all the core concepts behind neural networks and deep learning.
neuralnetwork  DeepLearning  ai  algorithms  computing  MachineLearning 
november 2013 by wrrn
Deep Learning 101
My goal is to give you a layman understanding of what deep learning actually is so you can follow some of my thesis research this year as well as mentally filter out news articles that sensationalize these buzzwords.
ai  MachineLearning  DeepLearning  statistics  algorithms  learning 
november 2013 by wrrn
Content-based image retrieval - Wikipedia, the free encyclopedia
the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey[1] for a recent scientific overview of the CBIR field). Content-based image retrieval is opposed to concept-based approaches
search  image  image-processing  MachineLearning  media  ai 
october 2013 by wrrn
Python extensions to do machine learning
I started to compare the functionalities of some Python extensions
python  libraries  MachineLearning  algorithms  frameworks  programming  ai 
october 2013 by wrrn
Stanford researchers to open-source model they say has nailed sentiment analysis — Tech News and Analysis
What makes the Sentiment Treebank so novel is that the team split those nearly 11,000 sentences into more than 215,000 individual phrases and then used human workers — via Amazon Mechanical Turk — to classify each phrase on a scale from “very negative” to “very positive.”
NLP  sentiment  analysis  ai  language  MachineLearning 
october 2013 by wrrn
Gamasutra: Matthew Klingensmith's Blog - Overview of Motion Planning
In this article I wanted to discuss the common techniques of motion planning in context, and explain some of the advantages and disadvantages of each. I also want to present some of the basic techniques beyond those commonly used in video games, and hopefully shed light on how these might be useful in game development.
AI  games  algorithms  robotics  programming  hardware  diy 
september 2013 by wrrn
Chips that mimic the brain in real time | KurzweilAI
The scientists thus demonstrate how a real-time hardware neural-processing system, where the user dictates the behavior, can be constructed. “Thanks to our method, neuromorphic chips can be configured for a large class of behavior modes. Our results are pivotal for the development of new brain-inspired technologies,”
ai  computing  brain  hardware  research 
july 2013 by wrrn
simpleai-team/simpleai · GitHub
This lib implements many of the artificial intelligence algorithms described on the book "Artificial Intelligence, a Modern Approach", from Stuart Russel and Peter Norvig. We strongly recommend you to read the book, or at least the introductory chapters and the ones related to the components you want to use, because we won't explain the algorithms here.
ai  python  NLP  MachineLearning  library  frameworks 
july 2013 by wrrn
One of the latest artificial intelligence systems from MIT is as smart as a 4-year-old — Tech News and Analysis
“All of us know a huge number of things. As babies, we crawled around and yanked on things and learned that things fall. We yanked on other things and learned that dogs and cats don’t appreciate having their tails pulled,” computer science professor and study lead Robert Sloan said in a release. “We’re still very far from programs with commonsense – AI that can answer comprehension questions with the skill of a child of 8.”
AI  cognition  information  knowledge  human 
july 2013 by wrrn
The Rise of Artificial Intelligence | Off Book | PBS Digital Studios - YouTube
Contemporary attempts to create AI have us looking more at how our own brains work to see how a computer could simulate the core activities that create our intelligence. No matter how we get there, it is certain that artificial intelligence will have tremendous impact on our society and economy, and lead us down a path towards evolving our own definitions of humanity.
AI  computing  future  video  documentary 
july 2013 by wrrn
The New Aesthetic — “Quake 3 bots left to work out tactics for 3 years...
“Quake 3 bots left to work out tactics for 3 years decided to Just Get Along but Frag Humans’,
quake  bots  ai  games 
july 2013 by wrrn
Church Wiki
Church is a probabilistic programming language designed for expressive description of generative models (Goodman, Mansinghka, Roy, Bonawitz and Tenenbaum, 2008). Church is a derivative of the programming language Scheme with probabilistic semantics.
programming  ai  church  probability  bayesian  statistics  probabilistic-programming  MachineLearning 
july 2013 by wrrn
The Long View | When Computers Know How You Feel
There are a lot of channels from which we can distil a range of emotional states. The face is one of the most powerful social and emotional communication channels. It can communicate everything from joy to interest to disgust to confusion to worry. Our gestures are also important — both head gestures and body movements. Similarly our voice carries information on our emotional states. There are also physiological measures. You can look at skin conductance, which is the level of sweat on your skin — and that gives you a measure of arousal, how activated or calm you are.
emotion  computing  affective-computing  ai  media  technology  future  from readability
july 2013 by wrrn
Improving Photo Search: A Step Across the Semantic Gap
We feel it would be interesting to the research community to discuss some of the unique aspects of the system we built and some qualitative observations we had while testing the system.
computervision  MarchineLearning  google  research  ai 
june 2013 by wrrn
Weekend reads and videos on Deep Learning - Raw thoughts from Alex Dong
Deep learning is set of algorithms in machine learning that attempt to learn layered models of inputs, commonly neural networks. The layers in such models correspond to distinct levels of concepts, where higher-level concepts are defined from lower-level ones, and the same lower-level concepts can help to define many higher-level concepts.
DeepLearning  MachineLearning  ai  computing  research  video  algorithms 
june 2013 by wrrn
[1306.0239] Deep Learning using Support Vector Machines
In almost all of the previous works, hidden representation of deep networks are first learned using supervised or unsupervised techniques, and then are fed into SVMs as inputs. In contrast to those models, we are proposing to train all layers of the deep networks by backpropagating gradients through the top level SVM, learning features of all layers. Our experiments show that simply replacing softmax with linear SVMs gives significant gains
MachineLearning  DeepLearning  statistics  ai 
june 2013 by wrrn
SimpleAI, Artificial Intelligence with python | aima | Machinalis
There is a special focus in decision tree learning, where three different
methods for decision tree learning are added, with one of them following
strictly the aima pseudo-code, being particularly useful for teaching.
The other classifiers added are Naive Bayes and K-Nearest Neighbors.
MachineLearning  tools  libraries  python  AI 
march 2013 by wrrn
Predicting the Future By Mining Online News and Other Web Data | MIT Technology Review
Horvitz says the performance is good enough to suggest that a more refined version could be used in real settings, to assist experts at, for example, government aid agencies involved in planning humanitarian response and readiness.
AI  MachineLearning  computing  connectionmachine 
february 2013 by wrrn
Automated Storytelling - Future of StoryTelling
As artificial-intelligence pioneer Kris Hammond explains in this film, the great challenge in the Big Data era is understanding the stories those numbers tell and, just as important, connecting the right people with the right stories. This is what Hammond and his company, Narrative Science, do: create fluidly written, micro-targeted news stories from massive amounts of raw data—and do it hundreds of thousands of times, and slightly differently for each reader or listener
narrative  storytelling  AI  MachineLearning  writing 
january 2013 by wrrn
Deep Learning How I Did It: Merck 1st place interview
What was your background prior to entering this challenge? We are a team of computer science and statistics academics. Ruslan Salakhutdinov and Geoff Hinton are professors at the University of…
code  models  engineering  speech  learning  networks  MachineLearning  statistics  AI  DeepLearning 
january 2013 by wrrn
Project Halo Update — Progress Toward Digital Aristotle
we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base.
knowledge  ai  information-retrieval  information  textbook  humancomputer  beinghuman 
january 2013 by wrrn
McGraw-Hill’s new adaptive ebooks aim to adjust to students’ learning needs — Tech News and Analysis
SmartBook aims to provide an adaptive reading experience that adjusts to students with a good deal of granularity, using dynamic text and voice instructions to literally talk them through the program and point out the areas on which they should focus.
education  interactive  interface  ai  information-retrieval  connectionmachine  textbook  humancomputer 
january 2013 by wrrn
Deep Learning
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.

This website is intended to host a variety of resources and pointers to information about Deep Learning.
AI  MachineLearning  DeepLearning  computing  statistics  beinghuman 
november 2012 by wrrn
Neural Networks for Machine Learning | Coursera
Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
AI  neuralnetwork  study  coursera  Online-Courses  MachineLearning  DeepLearning 
november 2012 by wrrn
Advances in artificial intelligence: deep learning « Mind Hacks
The innovation of deep learning is that it not only arranges these properties into hierarchies – with properties and sub-properties – but it works out how many levels of hierarchy best fit the data.
AI  computing  deep-learning  MachineLearning 
november 2012 by wrrn
Deep learning - Wikipedia, the free encyclopedia
Deep learning refers to a sub-field of machine learning that is based on learning several levels of representations, corresponding to a hierarchy of features or factors or concepts, where higher-level concepts are defined from lower-level ones, and the same lower-level concepts can help to define many higher-level concepts
AI  learning  computing  DeepLearning  MachineLearning 
november 2012 by wrrn
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