第8章データ管理

The technology that tries to simulate a human’s way of solving problems (e.g., Siri and Alexa) is called cognitive computing. Artificial intelligence (AI) is a system that creates intelligent ways to solve problems that previously required human interaction.

Three components must interact to achieve AI (Figure 8.28): syntax (structure), semantics (meaning), and inference (reasoning/planning). Human intelligence requires that these three components rely on some form of data management and relate to deep learning of other brain functions such as restricted Boltzmann machines, stacked autoencoder, and deep belief networks. Restricted Boltzmann machines are artificial networks that can learn a probability distribution using a set of input. Stacked autoencoder is an artificial network used to learn unlabeled data through efficient coding. Deep belief networks are intelligent networks used to invent a solution for a specific problem when the traditional intelligent network could not solve it.

Amazon provides various ML services. They allow developers to integrate Cloud ML into mobile and other applications. Amazon Lex allows users to incorporate voice input into applications (extension of Amazon’s Echo product) so that users can ask questions about everything from the weather to news and streaming music. Amazon Polly is the opposite of Lex; Polly turns text into speech in 27 languages. Amazon Rekognition, which is at the cutting edge of deep learning applications, takes an image as input and returns a textual description of the items that it sees in that image by performing detailed facial analysis and comparisons.

The machine learning method based on encouraging desired behaviors and removing undesired behaviors is called reinforcement. The machine learning method based on reusing the result of a specific task to start a new task is called transfer learning. A deep learning network is a type of machine learning method based on artificial neural networks (Figure 8.29). Artificial neural networks simulate the network of neurons to make a computer learn and make decisions like the human brain does (e.g., recurrent neural networks or RNNs, which are an artificial neural network, uses time series data). There are various classes of deep learning networks such as:

• Cloud-based deep learning frameworks such as Microsoft Cognitive Toolkit, which is an open-source toolkit for commercial-grade distributed deep learning

• Google TensorFlow system applications for neural network computing and deep learning such as handwritten digit recognition and cognitive services

• Predictive software libraries for cognitive applications such as Keras, which is an open-source software library that provides a Python interface for artificial neural networks

In this section, we introduce examples of Cognitive analytics frameworks for deep machine learning applications such as Spark MLlib, Amazon Machine Learning Platform and MXNet, Google TensorFlow, Azure Machine Learning Platform, and Microsoft Cognitive Toolkit.

Spark ML includes a High-level API (Spark Machine Learning Library of MLlib) for creating ML pipelines. Data can be fed into data frames, and the library enables the quick creation of a machine learning processing pipeline by combining transformers and estimators.

MXNet is an open-source library for distributed parallel machine learning that was developed at Carnegie Mellon University, the University of Washington, and Stanford University. MXNet can be programmed with Python, Julia, R, Go, Matlab, or C++ and runs on many different platforms including clusters and GPUs; it is also now the deep learning framework of choice for Amazon.

Google’s TensorFlow is a frequently discussed and used deep learning toolkit; if you have installed the Amazon Deep Learning AMI, you already have TensorFlow installed, and you can begin experimenting right away.

The Microsoft Cognitive Toolkit software is available for download in a variety of formats so that deep learning examples can be run on Azure as clusters of Docker containers (i.e., multiple nodes joined using a special configuration).