5 Good Reads in Big Open Data: March 13 2015

1. Jürgen Schmidhuber- Ask Me Anything– via Reddit:  Jürgen has pioneered self-improving general problem solvers and Deep Learning Neural Networks for decades. He is the recipient of the 2013 Helmholtz Award of the International Neural Networks Society.
2. Many think that intelligence is this awesome, infinitely complex thing. I think it is just the product of a few principles that will be considered very simple in hindsight, so simple that even kids will be able to understand and build intelligent, continually learning, more and more general problem solvers. Partial justification of this belief: (a) there already exist blueprints of universal problem solvers developed in my lab, in the new millennium, which are theoretically optimal in some abstract sense although they consist of just a few formulas.  (b) The principles of our less universal, but still rather general, very practical, program-learning recurrent neural networks can also be described by just a few lines of pseudo-code.
3. An abridged list of Machine Learning topics -via Startup.ML: great presentation of research, software, talks and more on Deep Learning, Graphical Models, Structured Predictions, Hadoop/Spark, Natural Language Processing and all things Machine Learning.
4. Deeper Content Analysis with Aspects: Interest Graph Grows Beyond Topics– via Prismatic Blog:  Prismatic opens up their Interest Graph with an aspect tagging API to classify URLS by aspect (structural content) and not just topic

1. Wikipedia’s open letter to the NSA- stop spying on our users! via New York Times:  The NSA tracks your every view and edit to a Wikipedia page, on top of your location and (if they can) who you are. Open knowledge collaboration shouldn’t come at the cost of losing privacy over your very private identity, especially when the cost can be as high as prosecution.
2. Generational Performance of Amazon EC2’s C3 and C4 families– via GigaOm:
3. The results presented here indicate that the C4 virtual machines had 10 to 20 percent higher vCPU performance and approximately 6 GB/s more memory throughput than the C3 VMs across different machine sizes. However, after factoring in the price increases, the price-performance values of the C4 VMs averaged the same as the C3 VMs. Both vCPU performance levels and network throughput displayed high stability over time and across all tested machines. The results highlight Amazon’s effort to provide highly predictable performance outputs and to match its C4 family’s price-performance with that of its earlier generation C3 family.

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