Data Science/Society: The AI Threat Isn't Skynet. It's the End of the Middle Class | WIRED

The AI Threat Isn't Skynet. It's the End of the Middle Class | WIRED: "That said, these researchers say they are intent on finding the answer. “People work through the concerns in different ways. But I haven’t met an AI researcher who doesn’t care,” Etzioni says. “People are mindful.” But they feel certain that preventing the rise of AI is not the answer. It’s also not really possible—a bit like bringing those old manufacturing jobs back."



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The Netflix Tech Blog: Distributed Time Travel for Feature Generation

The Netflix Tech Blog: Distributed Time Travel for Feature Generation:



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New ML frameworks: Google TensorFlow and Samsung VELES

TensorFlow: Google Open Sources Their Machine Learning Tool

TensorFlow is a machine learning library created by the Brain Team researchers at Google and now open sourced under the Apache License 2.0. TensorFlow is detailed in the whitepaperTensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. The source code can be found on Google Git.

TensorFlow is a tool for writing and executing machine learning algorithms. Computations are done in a data flow graph where the nodes are mathematical operations and the edges aretensors (multidimensional data arrays) that are exchanged between nodes. An user constructs the graph and writes the algorithms that executed on each node. TensorFlow takes care of executing the code asynchronously on different devices, cores, and threads.

TensorFlow runs on CPU and GPUs on the desktop, server or mobile devices. It can be containerized with Docker to be deploy in the cloud. The version that is open sourced runs on single machines, not on clusters.

TensorFlow has a complete Python API and C++ interface for building and executing graphs. It also has a C-based client API. Google invites the community to write interfaces in other languages, the most probably being Lua, R, Java, Go and JavaScript.

Google considers the library is not final and will continue to improve it. They will make public some of the actual implementations they have created.

TensorFlow is used by Google for GMail (SmartReply), Search (RankBrain), Pictures (Inception Image Classification Model), Translator (Character Recognition), and other products.

Google TensorFlow

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

What is a Data Flow Graph?

Data flow graphs describe mathematical computation with a directed graph of nodes & edges. Nodes typically implement mathematical operations, but can also represent endpoints to feed in data, push out results, or read/write persistent variables. Edges describe the input/output relationships between nodes. These data edges carry dynamically-sized multidimensional data arrays, or tensors. The flow of tensors through the graph is where TensorFlow gets its name. Nodes are assigned to computational devices and execute asynchronously and in parallel once all the tensors on their incoming edges becomes available.

Machine Learning links

A Visual Introduction to Machine Learning

10 more lessons learned from building Machine Learning systems


A Tour of Machine Learning Algorithms

There are only a few main learning styles or learning models that an algorithm can have and we’ll go through them here with a few examples of algorithms and problem types that they suit. This taxonomy or way of organizing machine learning algorithms is useful because it forces you to think about the the roles of the input data and the model preparation process and select one that is the most appropriate for your problem in order to get the best result.

• Supervised Learning: Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. A model is prepared through a training process where it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data. Example problems are classification and regression. Example algorithms are Logistic Regression and the Back Propagation Neural Network.

• Unsupervised Learning: Input data is not labelled and does not have a known result. A model is prepared by deducing structures present in the input data. Example problems are association rule learning and clustering. Example algorithms are the Apriori algorithm and k-means.

• Semi-Supervised Learning: Input data is a mixture of labelled and unlabelled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabelled data.

• Reinforcement Learning: Input data is provided as stimulus to a model from an environment to which the model must respond and react. Feedback is provided not from of a teaching process as in supervised learning, but as punishments and rewards in the environment. Example problems are systems and robot control. Example algorithms are Q-learning and Temporal difference learning.

When crunching data to model business decisions, you are most typically using supervised and unsupervised learning methods. A hot topic at the moment is semi-supervised learning methods in areas such as image classification where there are large datasets with very few labelled examples. Reinforcement learning is more likely to turn up in robotic control and other control systems development.

Palladium: Predictive Analytics, Machine Learning framework

https://github.com/ottogroup/palladium

Palladium provides means to easily set up predictive analytics services as web services. It is apluggable framework for developing real-world machine learning solutions. It provides generic implementations for things commonly needed in machine learning, such as dataset loading, model training with parameter search, a web service, and persistence capabilities, allowing you to concentrate on the core task of developing an accurate machine learning model. Having a well-tested core framework that is used for a number of different services can lead to a reduction of costs during development and maintenance due to harmonization of different services being based on the same code base and identical processes. Palladium has a web service overhead of a few milliseconds only, making it possible to set up services with low response times.....