This is very cool, iOS video image recognition.
I am totally convinced that deep learning approaches to hard AI are going to change our world, especially when they’re running on cheap networked devices scattered everywhere. I’m a believer because I’ve seen how good the results can be on image recognition, but I understand why so many experienced engineers are skeptical. It sounds too good to be true, and we’ve all been let down by AI promises in the past.
That’s why I’ve decided to release DeepBeliefSDK, an iOS version of the deep learning approach that has taken the computer vision world by storm. In technical terms it’s a framework that implements the full Krizhevsky stack of 60 million neural network connections, with a customizable top layer inspired by the Decaf approach. It does all this in under 300ms on an iPhone 5S, and in less than 20MB of memory. Here’s a video of me of me using the sample app to detect our cat!
More at Pete Warden’s Blog
Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the Apache 2.0 license, and it’s FREE for you to use it.
Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks.
If you are beginner with neural networks, and you just want to try how they work without going into complicated theory and implementation, or you need them quickly for your research project the Neuroph is good choice for you. It is small, well documented, easy to use, and very flexible neural network framework.
The number of online education opportunities continues to grow. I’ll post them here as I hear about them.
Firm start/stop date, no certificates
Right now computer science, including machine learning, artificial intelligence and math make up most of the classes.
It is hard to resist a project called ‘Sam I Am’.
Sam I Am (Sensitivity Analysis Modeling Inference and More) provides GUI Java tools for designing and experimenting with Bayesian Networks.
They do not appear to be open source, but they are free for you to download and use.
SamIam is a comprehensive tool for modeling and reasoning with Bayesian networks, developed in Java by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA.
Samiam includes two main components: a graphical user interface and a reasoning engine. The graphical interface lets users develop Bayesian network models and save them in a variety of formats. The reasoning engine supports many tasks including: classical inference; parameter estimation; time-space tradeoffs; sensitivity analysis; and explanation-generation based on MAP and MPE.
The Awareness project researching self awareness in systems. There’s a pretty good amount of interesting articles online along with interviews with several people in the field.
Awareness is a Coordination Action (CA), supporting research under the FP7: FET Proactive Intiative:
Self-Awareness in Autonomic Systems (Awareness). The CA is a 3 year project: 2010 – 2013.
Awareness provide a supportive environment for research into self-awareness in autonomic systems, helping to create a well-connected community of researchers and conveying a coherent prospect to a wider scientific and technological audience.
We reach out to a diverse, multidisciplinary scientific community that researches the domain of Self-awarness in Autonomic Systems. 6 FET funded projects that we support are:
ASCENS: Autonomic Service-Component Ensembles
EPICS: Engineering Proprioception in Computing Systems
RECOGNITION: Relevance and cognition for self-awareness in a content-centric Internet
SAPERE: Self-aware Pervasive Service Ecosystems
SYMBRION: Symbiotic Evolutionary Robot Organisms (funded by PerAda)
CoCoRo: Collective Cognitive Robots