In another example of disruption through AI, travel companies have begun using behavioral data and predictive analytics to customize brand experiences based on individuals' preferences and patterns. Automating IT functions alone reduces expenses by 14 to 28 percent, so companies that launch using automated services quickly establish a financial advantage over larger, legacy-burdened competitors. Some tech experts believe that the current generation of applied AI systems, such as predictive analytics, will give small businesses advantages through increased automation and efficiency. New BI platforms offer data visualization, customer relationship management programs, and other critical BI services.
Machine Learning is most often considered a branch of the broad pursuit of Artificial Intelligence in which it is used to process unstructured data, such as text. But there is an even greater potential for its application in enhancing analytics of structured numerical data. In this domain, we predict Machine Learning capabilities will continue to offer further insights by discovering patterns in our extensive data set of more than 4.2 billion observations of software development revisions. Machine Learning offers an extension of the sophistication of data analytics, from automating analyses that our statisticians carry out, to discovering patterns that humans cannot. For example, our data scientists recognise that a software application that is no longer being worked on is likely to be no longer in use and can be retired.
BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters. Modeled after Torch, BigDL provides comprehensive support for deep learning, including numeric computing (via Tensor) and high level neural networks; in addition, users can load pre-trained Caffe or Torch models into Spark programs using BigDL. To achieve high performance, BigDL uses Intel MKL and multi-threaded programming in each Spark task. Consequently, it is orders of magnitude faster than out-of-box open source Caffe, Torch or TensorFlow on a single-node Xeon. BigDL can efficiently scale out to perform data analytics at "Big Data scale", by leveraging Apache Spark (a lightning fast distributed data processing framework), as well as efficient implementations of synchronous SGD and all-reduce communications on Spark.
No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. That movement revolved around three trends: new and improved tool kits for machine learning, better hardware (and easier access to it), and more cloud-hosted, as-a-service variants of machine learning that provided both open source and proprietary tools. Once upon a time, if you wanted to implement machine learning in an app, you had to roll the algorithms yourself.
Deep learning has continued to drive the computing industry's agenda in 2016. But come 2017, experts say the Artificial Intelligence community will intensify its demand for higher performance and more power efficient "inference" engines for deep neural networks. The current deep learning system leverages advances in large computation power to define network, big data sets for training, and access to the large computing system to accomplish its goal. Unfortunately, the efficient execution of this learning is not so easy on embedded systems (i.e. This problem leaves wide open the possibility for innovation of technologies that can put deep neural network power into end devices.
This article gives an introduction to the Intel's optimized machine learning and deep learning tools and frameworks and also gives a description of the Intel's libraries that have been integrated into them so they can take full advantage and run fastest on Intel architecture. This information will be useful to first-time users, data scientists, and machine learning practitioners, for getting started with Intel optimized tools and frameworks. Machine learning (ML) is a subset of the more general field of artificial intelligence (AI). ML is based on a set of algorithms that learn from data. Deep learning (DL) is a specialized ML technique that is based on a set of algorithms that attempt to model high-level abstractions in data by using a graph with multiple processing layers (https://en.wikipedia.org/wiki/Deep_learning).
Updated 11th November 2016 with the latest artificial intelligence (AI) software from Sentient to undertake complex multivariate testing. A/B and multivariate testing tools are essential for digital marketers as they enable you to deliver and measure the relative performance of different user experiences through robust online controlled experiments. Increasingly they also allow you to personalise your customer experience and allow you to discover new customer segments based upon behaviour rather than just demographics. A/B testing allows you to run an online controlled experiment to measure the difference in performance between an existing webpage (e.g. A/B testing tools randomly select visitors for each design and uses robust statistical analysis to measure the performance between the control and the variant.
Moore's Law says that the number of transistors per square inch will double approximately every 18 months. This article will show how many technologies are providing us with a new Virtual Moore's Law that proves computer performance will at least double every 18 months for the foreseeable future thanks to many new technological developments. This Virtual Moore's Law is propelling us towards the Singularity where the invention of artificial superintelligence will abruptly trigger runaway technological growth, resulting in unfathomable changes to human civilization. In the first of my "proof" articles two years ago, I described how it has become harder to miniaturize transistors, causing computing to go vertical instead. 2 years ago, Samsung was mass producing 24-layer 3D NAND chips and had announced 32-layer chips. As I write this, Samsung is mass producing 48-layer 3D NAND chips with 64-layer chips rumored to appear within a month or so.
Previous articles in ProgrammableWeb's microservices series look at what microservices are and explain differences between monolithic and microservices architectures. Once a distributed application is built and deployed, it is crucial to monitor and visualize it to make sure the software is reliable, available, and performs as expected. The heterogeneous and distributed nature of applications driven by a microservices architecture make monitoring, visualization, and analysis a difficult prospect. Traditional application monitoring and performance management (APM) solutions are not suited for today's complex distributed applications. Fortunately, several new APM solutions have been launched within the past few years to address these issues.