Google's Artificial Brain Learns to Find Cat Videos
wired.comGoogle’s X Lab designed a particular neural network to discern whether an algorithm could learn to recognize faces without being fed information on exactly what a face was. It did great at that task.
It also found cat videos quite well:
The “brain” simulation was exposed to 10 million randomly selected YouTube video thumbnails over the course of three days and, after being presented with a list of 20,000 different items, it began to recognize pictures of cats using a “deep learning” algorithm. This was despite being fed no information on distinguishing features that might help identify one.
On the surface, it’s a joke about the impending cat video singularity. But it’s a pretty awesome example of machine learning, too. It means that with careful programming, a synthetic network can learn fairly advanced patterns in completely novel data, like cat faces, or what a cat even IS.
The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI
wired.com
There’s a theory that human intelligence stems from a single algorithm.
The idea arises from experiments suggesting that the portion of your brain dedicated to processing sound from your ears could also handle sight for your eyes. This is possible only while your brain is in the earliest stages of development, but it implies that the brain is — at its core — a general-purpose machine that can be tuned to specific tasks.
robots-figuring-out-how-to-figure-things-out
spectrum.ieee.orgthissss issss tooo coooool!
DARPA envisions the future of machine learning | KurzweilAI
kurzweilai.netDARPA has launched a new programming paradigm for managing uncertain information called “Probabilistic Programming for Advanced Machine Learning”(PPAML).
Machine learning — the ability of computers to understand data, manage results, and infer insights from uncertain information — is the force behind many recent revolutions in computing.
Unfortunately, every new machine-learning application requires a Herculean effort. Even a team of specially trained machine learning experts makes only painfully slow progress, due to the lack of tools to build these systems.
PPAML seeks to greatly increase the number of people who can successfully build machine learning applications and make machine learning experts radically more effective. It also seeks to create more economical, robust and powerful applications that need less data to produce more accurate results — features inconceivable with today’s technology.
“We want to do for machine learning what the advent of high-level program languages 50 years ago did for the software development community as a whole,” said Kathleen Fisher, DARPA program manager.
Stanford is offering courses online: FREE
The Stanford School of Engineering Everywhere currently offers courses for free online. This fall the following courses will also be available to take online for free:
The amount of knowledge that can be acquired online is amazing. This is just another great example. So go sign up now and learn something new this fall.
Scientists Plan To Upload Bee Consciousness To Robots
Klint Finley
George Dvorsky writes:
A new project has been announced in which scientists at the Universities of Sheffield and Sussex are hoping to create the first accurate computer simulation of a honey bee brain — and then upload it into an autonomous flying robot.
This is obviously a huge win for science — but it could also save the world. The researchers hope a robotic insect could supplement or replace the shrinking population of honey bees that pollinate essential plant life.
io9: New project aims to upload a honey bee’s brain into a flying insectobot by 2015
Previously: Can You Imagine a Future Where London Police Bees Conduct Genetic Surveillance?
Photo by Steve Jurvetson / CC
List of 40+ Machine Learning APIs
Wikipedia defines Machine Learning as “a branch of artificial intelligence that deals with the construction and study of systems that can learn from data.”
Below is a compilation of APIs that have benefited from Machine Learning in one way or another, we truly are living in the future so strap into your rocketship and prepare for blastoff.
Google Puts Its Virtual Brain Technology to Work - Technology Review
technologyreview.comThis summer Google set a new landmark in the field of artificial intelligence with software that learned how to recognize cats, people, and other things simply by watching YouTube videos (see “Self-Taught Software”). That technology, modeled on how brain cells operate, is now being put to work making Google’s products smarter, with speech recognition being the first service to benefit.
Google’s learning software is based on simulating groups of connected brain cells that communicate and influence one another. When such a neural network, as it’s called, is exposed to data, the relationships between different neurons can change. That causes the network to develop the ability to react in certain ways to incoming data of a particular kind—and the network is said to have learned something.
Neural networks have been used for decades in areas where machine learning is applied, such as chess-playing software or face detection. Google’s engineers have found ways to put more computing power behind the approach than was previously possible, creating neural networks that can learn without human assistance and are robust enough to be used commercially, not just as research demonstrations.
Machine Learning Cheatsheets
Created by Andreas Mueller:
Then you can head to this Quora thread to read a bit more about the pros and cons of the different classification algorithms.
Original title and link: Machine Learning Cheatsheets (NoSQL database©myNoSQL)
