THE ARTIFICIAL INTELLIGENCE STARTER GUIDE

Transcription

1 THE ARTIFICIAL INTELLIGENCE STARTER GUIDE FOR IT LEADERS HOW DELL TECHNOLOGIES HELPS ENSURE SUCCESSFUL MACHINE LEARNING AND DEEP LEARNING PROJECTS EXECUTIVE SUMMARY Artificial intelligence (AI) is a transformative technology that will change the way organizations interact and will add intelligence to many products and services through new insights currently hidden in vast pools of data. In 2017 alone, venture capitalists invested more than $11.7 billion in the top 100 Artificial Intelligence startups, according to CB Insights 1, and the breadth of Artificial Intelligence applications continues to grow. While human-like intelligence will remain the stuff of fantasy novels and movies for the near future, most organizations can and should explore practical Artificial Intelligence projects. This technology has the real potential to: improve productivity of internal applications, increase revenue through enhanced customer interaction and improved customer acquisition, reduce costs by optimizing operations, and enhance products and services with smart functionality such as vision and voice interaction and control. This paper provides executives with an overview of Artificial Intelligence and the underlying technologies of Machine Learning (ML) and Deep Learning (DL). We cover the business drivers for the deployment of Artificial Intelligence in enterprises and outline common applications, pointing out when and where Machine Learning and Deep Learning are typically used. Finally, we examine the technologies and assistance that are available from Dell Technologies to enable successful Artificial Intelligence projects. 1 CB Insights, AI 100: The Artificial Intelligence Startups Redefining Industries, Dec. 12, Page 1 The Artificial Intelligence Starter Guide for IT Leaders May 2018

2 BUSINESS DRIVERS FOR ARTIFICIAL INTELLIGENCE The many possible benefits of Artificial Intelligence increases in productivity, revenues, product improvements, etc. are creating surging demand for Artificial Intelligence technologies. In fact, IDC forecasts total spending in Artificial Intelligence will ramp to tens of billions of dollars by the early 2020s. 2 Many drivers fuel this interest which we believe comes down to two primary motivations: 1) to improve operational efficiencies and 2) to enhance products and services through data-driven insights and use of unstructured data types such as voice and images to enhance human-machine interaction. For simplicity, we call these Smart Operations and Smart Products and Services, respectively. Smart Operations includes e-commerce product recommendation engines, cyber security, customer sales and support chatbots, financial trading, fraud detection, enhanced public safety services, and supply chain optimization. Smart Products and Services includes medical diagnosis and treatment, drug discovery, hospital clinical care management, autonomous vehicles, drones, consumer electronics, and threat intelligence and prevention. We believe Artificial Intelligence will become pervasive and impact virtually every product and business process over the next decade. GETTING STARTED SELECT THE RIGHT PROJECTS: CRAWL, WALK, RUN Getting started on the right foot with Artificial Intelligence and Machine Learning requires that you select a project that solves a pressing business problem for which you have access to the requisite data, and for which you have the right skills to tackle. Most organizations already have a list of projects that could benefit from Machine Learning. Start by compiling a list of these projects and prioritize them based on ROI, risk, and scope. It can be tempting to jump into the deep end of the pool, replete with racks of servers and high-end Graphic Processor Units, to analyze such data as photo images for plant security, or translate speech into text for sales force or customer support automation. Instead, take time to learn about a) what problems are most appropriate to solve with ML for your organization, b) what shape your organization s data is in, and c) 2 IDC, Worldwide Spending on Cognitive and Artificial Intelligence Systems Forecast to Reach $12.5 Billion This Year, According to New IDC Spending Guides, April 3, 2017, Page 2 The Artificial Intelligence Starter Guide for IT Leaders May 2018

3 what projects the team can handle. The old mantra of Crawl, Walk, Run! is good to keep in mind. We recommend starting small, perhaps extending Big Data analytics to include one or two Machine Learning capabilities where the ROI is relatively easy to achieve and measure. Subsequent projects can then harness Deep Learning for voice, image, or text processing for smart offerings or smart operations. Here are a few ideas that may help point the way: 1. Analyze customer service call length and topic recurrence to identify products that may have quality or documentation shortcomings. 2. Improve direct mail marketing with subject lines based on specific customer historical buying behavior. 3. Explore how your CRM or ERP vendor is enhancing their products with Machine Learning features. 4. If you sell products online, consider deploying a Machine Learning-based product recommendation engine to increase sales to existing customers. 5. Analyze your product pricing and forecasting methodologies and explore whether you have sufficient data to improve these with Machine Learning. 6. Look into your customer communications processes for areas where Machine Learning can automate more mundane tasks, such as analyzing s for common issues and resolution routing by using Natural Language Processing. 7. Work with your product definition teams to uncover areas where the addition of language, image, or vision-based control and interfaces could yield a competitive edge or improve customer satisfaction. TALENT, SKILLS, AND RESOURCES Getting the right team and expertise in place is critical for early success. We recommend providing the team with training for SPARK MLlib or SciKit-Learn if they are not already familiar with these tools. This training is critical for projects that build on existing Big Data infrastructure with classical Machine Learning. For those preferring ISVs solutions (see the section on the Machine Learning Software Ecosystem), leading software vendors have a rich portfolio of training and documentation available. Page 3 The Artificial Intelligence Starter Guide for IT Leaders May 2018

4 For teams embarking on Deep Learning projects, adding Deep Learning expertise to your existing staff will be needed. First, identify internal talent who possess the aptitude and motivation for learning about Artificial Intelligence, especially those with backgrounds in statistics and math. Consider the many online courses that are available from providers such as Coursera, Udemy, and Udacity. Leading Artificial Intelligence practitioners such as Andrew Ng, Geoffrey Hinton, and Yoshua Bengio teach some of these highly technical classes. ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING Artificial Intelligence is a branch of computer science dealing with the simulation of intelligent behavior in computers and the capability of a machine to imitate human intelligence. This broad term can be used to characterize something as simple as recognizing an object in an image to applying reason and ethical values in problem solving, something far beyond current capabilities. Machine Learning is a specific field of Artificial Intelligence that evolved from the study of pattern recognition and computational learning theory while Deep Learning is a branch of Machine Learning. Today, there are two approaches to Machine Learning. The first and perhaps most widely understood approach uses statistical analysis modeling, now often called classical Machine Learning. Typical use cases include resource allocation, predictive analytics, predictive maintenance, text classification, bioinformatics, trend discovery, forecasting, face detection, and pricing. Statistical approaches are generally well suited for uncovering trends and categories in numerical data and do not require massive training datasets or hardware accelerators. The second approach is Deep Learning which has stoked much publicity and hype in recent years. Deep Learning is now widely used for computer vision, speech recognition, natural language processing, social network processing, autonomous driving, image processing and classification, and financial market modeling. Page 4 The Artificial Intelligence Starter Guide for IT Leaders May 2018

5 FIGURE 1: THE RELATIONSHIP BETWEEN ARTIFICAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING Source: Moor Insights & Strategy In general, Deep Learning is used to solve multi-variant problems and queries that are beyond the scope of the traditional Machine Learning techniques. While Deep Learning is a powerful tool, it s a black box methodology, as one cannot easily determine exactly why a neural network produced a certain result. Therefore, it may not be appropriate for domains where the decision process must be transparent and auditable. THE MACHINE LEARNING SOFTWARE ECOSYSTEM There are three sources of Machine Learning software to build and manage your Machine Learning project: Open Source code, ML API cloud services (MLaaS), and traditional ISV solutions. Software selection should be based on the type of data you have, existing contracts/services, and the problem you want to solve. For projects working with classical Machine Learning, open source tools such as Python R, SciKits-Learn, and Apache SPARK MLib libraries provide state-of-the-art open source solutions and come complete with examples, documentation, tutorials, and large Page 5 The Artificial Intelligence Starter Guide for IT Leaders May 2018

6 user support communities. These tools do not require a great deal of training data and do not demand hardware acceleration. For organizations with ISV licenses and support agreements, robust classical Machine Learning solutions from enterprise-grade software companies like SAP, SAS, and Oracle provide the needed tools and training as well as support and implementation services. For Deep Learning projects, there are cloud services and Deep Learning frameworks for on-premises solutions. Google, Amazon AWS, and Microsoft provide Deep Learning tools and pre-trained neural networks as Application Programming Interfaces (APIs). Note that these cloud-based delivery models are inappropriate for projects requiring onpremises solutions, perhaps due to security concerns or data transfer costs. Nearly all production projects building deep neural networks in-house now use frameworks which are open source tools sponsored by universities and large search, e-commerce, and social networking companies. Each framework has its adherents and supporters and often favors a data type or use model as it was originally developed for internal use. Microsoft s CNTK, Google s TensorFlow, Amazon s MXNet, and the newer high-level Keras framework are a few of the most important frameworks. For more information on the software ecosystem, refer your technical team to Dell s companion technical Artificial Intelligence paper. DELL TECHNOLOGIES PLATFORMS AND ASSISTANCE FOR ARTIFICIAL INTELLIGENCE PROJECTS Dell gained much more than the leading enterprise storage vendor when the company acquired EMC along with the Virtustream cloud provider. Dell found a rich vein of Machine Learning expertise in EMC that we believe could improve their ability to service this fast-growing market and enable them to offer technologies and services that span from the edge to the datacenter. This section will briefly cover the Dell EMC server and workstation hardware for classical Machine Learning and Deep Learning, the new Ready Bundles that we believe make it easier to deploy medium to large-scale Machine Learning training and applications, solutions for cloud and hybrid off-premises/on-premises via Virtustream, and lastly the services capabilities of the combined company. Page 6 The Artificial Intelligence Starter Guide for IT Leaders May 2018

7 HARDWARE FOR CLASSICAL MACHINE LEARNING Standard two-socket servers or workstations will meet most projects needs here as tools such as SPARK MLlib and Intel BigDL do not require GPU acceleration. The Dell PowerEdge R740 or R740xd could be ideal Machine Learning platforms. The general purpose R740xd provides lowers investment risk while future proofing for potential Deep Learning projects, supporting accelerators and plenty of mass storage in 2U chassis. For a desk-side platform, the Dell Precision 5820 and 7920 towers offer cost effective workstation solutions for development and moderate size deployments with one to two high performance Intel Xeon CPUs, one to three GPUs, up to ten 3.5 inch storage devices, and 3TB of memory. Both R740 and Precisions loaded with accelerators can also be used for smaller Deep Learning jobs. HARDWARE FOR DEEP LEARNING To develop and train larger deep neural production networks, a system like the new Dell EMC PowerEdge C4140 supports the needs for scalable server solutions. Each C4140 supports up to four NVIDIA Tesla V100 (Volta) GPUs interconnected on the NVIDIA NVLINK 2.0 fabric. Eight or more C4140s can be clustered for larger scale product models, delivering up to 500 PetaFlops of Machine Learning performance. For developers, the PowerEdge T640 provides a desk-side or rackable server that supports up to four state-of-the-art GPUs for performance-demanding applications. MACHINE LEARNING AND DEEP LEARNING READY BUNDLES Dell EMC has developed pre-configured Ready Bundles for Machine Learning and Deep Learning at scale to simplify the configuration process, lower costs, and speed deployment of distributed multi-node Machine Learning/Deep Learning clusters. These offerings are relatively new in the Machine Learning market which we believe helps Dell EMC stand out as a vendor who understands the science and practice of Machine Learning and who is making life easier for the Artificial Intelligence practitioner. These bundles are targeted for customers with limited Artificial Intelligence expertise and who seek a competitive advantage by developing in-house Artificial Intelligence applications that will scale with their needs and capability demand. Page 7 The Artificial Intelligence Starter Guide for IT Leaders May 2018

8 ENTERPRISE CLOUD SERVICES Virtustream combines enterprise managed private cloud solutions with their cloud provider infrastructure and managed services capabilities, providing a highly secure, highly efficient managed Artificial/Machine Learning service for enterprise customers needing this in the cloud. Virtustream leverages a suite of Dell EMC hardware and ancillary components to provide this best-of-class offering. This gives the option to customers who want to attain an enhanced Artificial Intelligence/Machine Learning experience in an off-premises manner. CONSULTING SERVICES AND TRAINING Dell EMC offers fixed scope and project-based services to help their customers get up to speed in Machine Learning. The company has developed and deployed state-of-theart solutions in almost every industry vertical, particularly in healthcare, financial services, telecommunications, utilities, and media entertainment. The company s practice is comprised of experienced data scientists who can help design models and algorithms and prepare data to be processed in popular frameworks. CONCLUSIONS AND NEXT STEPS Many industry leaders proclaim we are entering the era of Artificial Intelligence, where machines can be trained to do tasks which traditional procedural programming models are unable to tackle. While the technology can seem daunting, a stepwise, practical approach to embracing Artificial Intelligence in the enterprise does not have to be intimidating. Solid ROIs can be achieved with relatively straight-forward extensions to existing Big Data infrastructure already in place in most enterprises. We believe enterprises and government agencies would be well served to examine how Artificial Intelligence can transform their products and services with Smart Products and Services and their internal business processes with Smart Operations. These areas can drive significant new sources of revenue and cost savings or improve public service and safety. With the combined expertise, technology, and best practices that resulted from the merger of Dell and EMC, we believe the new company is well positioned to help Page 8 The Artificial Intelligence Starter Guide for IT Leaders May 2018

9 their clients on this journey. In particular, the new Dell Ready Bundles are a notable step to establish the company as a leader in this new phase of IT. Enterprise and government IT staff should embrace Machine Learning and prepare themselves to plan and implement these new learning computing models to not only further their organizations best interests, but their own careers as IT, data science, and computer science professionals. Page 9 The Artificial Intelligence Starter Guide for IT Leaders May 2018

10 IMPORTANT INFORMATION ABOUT THIS PAPER AUTHOR Karl Freund, Senior Analyst, Moor Insights & Strategy PUBLISHER Patrick Moorhead, Founder, President, and Principal Analyst, Moor Insights & Strategy INQUIRIES Contact us if you would like to discuss this report, and Moor Insights & Strategy will respond promptly. CITATIONS This paper can be cited by accredited press and analysts but must be cited in-context, displaying author s name, author s title, and Moor Insights & Strategy. Non-press and non-analysts must receive prior written permission by Moor Insights & Strategy for any citations. LICENSING This document, including any supporting materials, is owned by Moor Insights & Strategy. This publication may not be reproduced, distributed, or shared in any form without Moor Insights & Strategy's prior written permission. DISCLOSURES This paper was commissioned by DELL EMC, Inc. Moor Insights & Strategy provides research, analysis, advising, and consulting to many high-tech companies mentioned in this paper. No employees at the firm hold any equity positions with any companies cited in this document. DISCLAIMER The information presented in this document is for informational purposes only and may contain technical inaccuracies, omissions, and typographical errors. Moor Insights & Strategy disclaims all warranties as to the accuracy, completeness, or adequacy of such information and shall have no liability for errors, omissions, or inadequacies in such information. This document consists of the opinions of Moor Insights & Strategy and should not be construed as statements of fact. The opinions expressed herein are subject to change without notice. Moor Insights & Strategy provides forecasts and forward-looking statements as directional indicators and not as precise predictions of future events. While our forecasts and forward-looking statements represent our current judgment on what the future holds, they are subject to risks and uncertainties that could cause actual results to differ materially. You are cautioned not to place undue reliance on these forecasts and forward-looking statements, which reflect our opinions only as of the date of this publication. Please keep in mind that we are not obligating ourselves to revise or publicly release the results of any revision to these forecasts and forward-looking statements in light of new information or future events Moor Insights & Strategy. Company and product names are used for informational purposes only and may be trademarks of their respective owners. Page 10 The Artificial Intelligence Starter Guide for IT Leaders May 2018