As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. Machine learning can deliver benefits across telecom provider operations with financially-oriented applications - including fraud mitigation and revenue assurance - which currently make the most compelling use cases. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization.
In contrast to k-nearest neighbors, a simple example of a parametric method would be logistic regression, a generalized linear model with a fixed number of model parameters: a weight coefficient for each feature variable in the dataset plus a bias (or intercept) unit. While the learning algorithm optimizes an objective function on the training set (with exception to lazy learners), hyperparameter optimization is yet another task on top of it; here, we typically want to optimize a performance metric such as classification accuracy or the area under a Receiver Operating Characteristic curve. Thinking back of our discussion about learning curves and pessimistic biases in Part II, we noted that a machine learning algorithm often benefits from more labeled data; the smaller the dataset, the higher the pessimistic bias and the variance -- the sensitivity of our model towards the way we partition the data. We start by splitting our dataset into three parts, a training set for model fitting, a validation set for model selection, and a test set for the final evaluation of the selected model.
A positive label means that an utterance was an actual response to a context, and a negative label means that the utterance wasn't – it was picked randomly from somewhere in the corpus. Each record in the test/validation set consists of a context, a ground truth utterance (the real response) and 9 incorrect utterances called distractors. Before starting with fancy Neural Network models let's build some simple baseline models to help us understand what kind of performance we can expect. The Deep Learning model we will build in this post is called a Dual Encoder LSTM network.
Google, Baidu, and Microsoft have the resources to build dedicated deep learning clusters that give the deep learning algorithms a level of processing power that both accelerates training time as well as increases their model's accuracy. Yahoo, however, has taken a slightly different approach, by moving away from a dedicated deep learning cluster and combining Caffe with Spark. The ML Big Data team's CaffeOnSpark software has allowed them to run the entire process of building and deploying a deep learning model onto a single cluster. The MapR Converged Data Platform is the ideal platform for this project, giving you all the power of distributed Caffe on a cluster with enterprise-grade robustness, enabling you to take advantage of the MapR high performance file system.
Google, as it normally does, has organized I/O around three distinct categories: development, monetization and the future. The conference will have 190 sessions for developers to learn how to make fast and efficient Web apps, optimize Android development and learn about the tools and features that will progressively make the Internet a more intelligent place. The biggest news on the machine learning front at Google I/O will be around Project Tango, a machine vision framework that allows smartphones to sees what is in front of them and let software react to it. ARC will be at Google I/O 2016 covering everything that matters to people who build software for a living and people who make a living with software.
Extending potential AI applications beyond the personal level to the'social network' level, we are faced with another graph searching opportunity: the social graph. Many highly active groups showed no social cohesion, while several lower activity groups showed very high social cohesion. Clearly the "intelligence" provided by traditional top down activity measures provide only "artificial value" in trying to predict collaboration performance. What it will take however, is careful management of the solution search space, matched with appropriate relationship centered analytics and search, if real value is to be now achieved from AI.
More importantly, however, Google and its competitors are moving towards keying their search algorithms to understand natural speech as well, in anticipation of more and more voice search. But new machine learning algorithms are making more accurate, real-time translations possible. You might also be interested in my new big data case study collection, which you can download for free from here: Big Data Case Study Collection: 7 Amazing Companies That Really Get Big Data. My current book is Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance' and my new books (available to pre-order now) are Key Business Analytics: The 60 Business Analysis Tools Every Manager Needs To Know and Big Data in Practice.
Seeing the frequency of such language inspired Lewis years later to create a feature-length documentary to interrogate and explore race, desire and body image, and the ways in which they're informed by media, pop culture and capitalism. The Times spoke with Lewis, who's known online as Fat Femme, following his-her recent West Coast visit about the documentary -- which is slated for a 2017 completion date -- how people "fail gender" and how gender deviant and trans people fit into the Black Lives Matter movement. The "no fats, no femmes" ideology is often used by gay men [on dating sites] situating their desires within a framework that excludes particular kinds of bodies, mostly those fat, feminine, disabled, HIV positive and the list goes on. How do the topics you're raising in "No Fats, No Femmes" intersect with the Black Lives Matter movement, and what seems like the exclusion of black trans and gender nonconforming people in that movement?
That's the backlog of pre-orders that Tesla Motors tallied up in the days after announcing its latest car, the Tesla Model 3. Aside from a handful of parts that need routine replacement--think tires and wiper blades--the bulk of the vehicle's components and functions were designed to be upgraded, not by mechanics wielding wrenches, but by software engineers working in Tesla's Silicon Valley research and development labs. A fix, the message informed him, was automatically downloaded to Robert's car (and every other Tesla) overnight while it charged in his garage. And this is happening not just in transportation but virtually every industry, as I write in my latest book "The Digital Revolution: How Connected Digital Innovations Are Transforming Your Industry, Company and Career."
And one must not underestimate China's Google equivalent, Baidu, which has launched its intelligent virtual assistant Duer. Quill Engage aims to provide intelligent narratives that efficiently communicate the insights buried in big data that people can comprehend, act on and trust. Automated Insights also offers specific services aimed at the marketing industry: it can automatically generate campaign summaries, ad performance overviews and brand management reports. The Grid offers AI websites that design themselves by algorithmically generating website designs and improving them based on user behaviour.