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.
Marketing automation platforms save time, improve efficiency and increase productivity; but, they do not provide deep insight into the 2.5 quintillion bytes of data being created every day as people move from screen to screen consuming information and making buying decisions. In November 2013, IBM introduced the Watson Ecosystem Program, opening up Watson as a development platform and giving companies the ability to build applications powered by Watson's cognitive computing intelligence. Watson is a cognitive technology that processes information more like a human than a computer -- by understanding natural language, generating hypotheses based on evidence, and learning as it goes. Rather than simply automating manual tasks, artificial intelligence adds a cognitive layer that infinitely expands marketers' ability to process data, identify patterns, and build intelligent strategies and content faster, cheaper and more effectively than humans.
And one must not underestimate China's Google equivalent, Baidu, which has launched its intelligent virtual assistant Duer. Quill 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.
These intelligent RPA systems, which use the latest cognitive computing technology, have huge potential to step up management effectiveness. Our goal was to assess the potential impact of cognitive computing on their jobs and to understand their perceptions of the how their work would change as a result of this new technology. The vast majority of managers, 84 percent, believe intelligent machines will make them more effective and make their work more interesting. In my next blog post I'll highlight two further obstacles that might hamper business leaders' efforts to boost management performance with intelligent machines.
The objective of the Kaggle competition was to predict the 2016 NCAA Basketball Tournament, called March Madness. Predicting performance through machine learning algorithms is a crucial aspect for HR Analytics. I learnt about a Bayesian skill rating system called TrueSkill used in large-scale commercial online gaming platforms, for example Xbox Live developed by Microsoft. Kaggle is a community of data scientists who come to compete in machine learning competitions.
Cambridge University, which runs the events, said the festival's main theme, artificial intelligence, sparked "considerable interest". The James Dyson Foundation gave people engineering challenges, including making a functioning chair out of nothing but cardboard, and crowds flocked to the Whittle Laboratory to see how jet engines produce such awesome power. Festival co-ordinator Dr Lucinda Spokes said: "On the final day, and to mark Addenbrooke's 250th anniversary, thousands of people visited the Cambridge Biomedical Campus to attend talks, demonstrations and exhibitions showcasing the medical research taking place in Cambridge. "The success of these events is also due to the thousands of visitors who attended the talks, debates and performances and those who got involved with the hands-on, interactive experiences – they all make the Science Festival what it is."
In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). In terms of operational intelligence, some of the relevant AI techniques to address problems like fleet monitoring and asset maintenance in the Rail industry include Knowledge Based Systems, Case Based Reasoning, Genetic Algorithms, Neural Networks and Fuzzy Logic etc. They eliminate the need for lengthy root cause identification and arrive at the required repair action more quickly, leading to faster repair, reduced maintenance cost and increased fleet availability and customer satisfaction. When it comes to asset intelligence, the continuous data streams produced from various sub-systems in trains help OEMs build digital twins that represent physical systems in real-time.