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- 1. Exploring opportunities in cognitive technologies November 2015
- 2. Agenda What is AI? What are cognitive technologies? Focus on machine learning The catalysts of progress Types of applications Investment and trends Where and whether to apply these technologies Cog tech, automation, and work Conclusion
- 3. What is artificial intelligence? DEFINING AI “… may lack an agreed-upon definition” − AI pioneer Nils Nilsson1 Leading AI textbook offers 8 definitions2 1 Nils Nilsson, The Quest for Artificial Intelligence 2 Stuart Russell and Peter Norvig, Artificial Intelligence
- 4. A useful definition of AI DEFINING AI The theory and development of computer systems able to perform tasks that normally require human intelligence.
- 5. The AI Effect DEFINING AI “As soon as it works, no one calls it AI anymore.”1 “AI is whatever hasn’t been done yet.”2 1 John McCarthy, quoted in Bostrom 2 Attributed to Larry Tesler in Douglas R. Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid
- 6. Cognitive technologies Cognitive technologies simulate perceptual and cognitive skills to perform tasks only humans used to be able to do
- 8. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Supervised learning Supervised learning is like learning by example “Learning a model from labeled training data” Used for • Classification - output is one of set of discrete values (e.g. spam, not spam) • Regression - output is a number (e.g., a price) - prediction Source: http://faculty.chicagobooth.edu/drew.creal/teaching/basiccoursematerial/lectures/lecture9.pdf
- 9. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Unsupervised learning Learning by discovering patterns – “There are two types of people ….” Applications - customer segmentation, product basket discovery, topic analysis
- 10. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Reinforcement learning Learning by trial and error – how a baby learns to crawl Applications: - Mechanical control - elevators, robots - Game playing
- 11. Catalysts of progress in AI
- 12. Moore’s law benefited all types of computing CATALYSTS OF PROGRESS Current generation of microprocessors are 4,000,000X more powerful than first single-chip microprocessor of 19711 1 Andrew Danowitz et al., “CPU DB: Recording microprocessor history,” ACMQueue, volume 10, issue 4 (April 6, 2014), http://queue.acm.org/detail.cfm?id=2181798, accessed October 11, 2014.
- 13. Big data and new techniques advance work in AI CATALYSTS OF PROGRESS • Volume of data doubles every 2 years1 • 44 trillion gigabytes annually by 20202 • New techniques for managing and analyzing data • AI models improved with “training” 1, 2 IDC 2014, http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm
- 14. Internet and cloud support AI with access to big data and collaborators CATALYSTS OF PROGRESS Access to vast data resources Crowd-sourcing to train machine learning models Implicit collaboration, e.g., Web search, translation
- 15. Advances in algorithms broke performance barriers CATALYSTS OF PROGRESS New algorithms dramatically improve performance of machine learning Over 500,000 scholarly papers on neural networks since 20061 New distributed computing breakthroughs 1 Google Scholar
- 16. Performance is improving…continually Facial recognition: 2014: 97% accuracy (Facebook)1; 2015: 100% accuracy (Google)2 Google speech recognition: 2013: 23% error rate 2015: 8% error rate3 IBM Watson 2400% smarter than when it won Jeopardy!4 1 Facebook, “DeepFace: Closing the gap to human-level performance in face verification,” https://www.facebook.com/publications/546316888800776/, accessed October 3, 2014; 2 http://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf; 3 Jordan Novet, Venture Beat, “Google says its speech recognition technology now has only an 8% word error rate,” May 28, 2015, http://venturebeat.com/2015/05/28/google-says-its-speech-recognition-technology-now-has- only-an-8-word-error-rate/, accessed September 16, 2015; 4 IBM, “IBM Watson,” http://www-03.ibm.com/press/us/en/presskit/27297.wss, accessed October 3, 2014.
- 17. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Three main applications of cognitive technologies: Product, process and insight
- 18. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Product: Embedding cognitive technologies in a product or service Embed cognitive technologies to help increase the value of products or services by making them more effective, convenient, safer, faster, distinctive, or otherwise more valuable. eBayNetflix GM Domino’s Pizza AudiVuCOMP
- 19. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Process: automate tasks or processes humans used to do Using computer systems to do work that people used to do. The work gets done faster, cheaper, better, or some combination of the three. Organization benefits. Automate scheduling engineering works Clinical trials eligibility Process handwritten forms
- 20. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Insight: Discerning patterns, making predictions, to improve operations or guide strategy Generate insights that can help reduce costs, improve efficiency, increase revenues, improve effectiveness, or enhance customer service • Intel • BBVA Compass • Aetna
- 21. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business New applications of machine learning daily Public sector • Smartphone app to reduce urban congestion • Flag parking abuses • Detect misbehavior by prisoners Automotive • Detected driver absent-mindedness Enterprise information management • Classify and route business documents • Setting rules for accessing and manipulating documents Health • Detect signs of gambling addiction • Predict cancer remission or drug resistance • Drug discovery • Predict development of psychosis • Predict air pollution days in advance Sales • Predicting which deals will close Announced in the last few months
- 22. Billions in investment capital aimed mostly at traditional business problems and industries $281.3 $855.1 $1,037.4 $2,000.5 $2,464.0 $0 $1,000 $2,000 $3,000 Supporting Technologies Rethinking Humans / HCI Core Technologies Rethinking Industries Rethinking Enterprise Millions VC investment in cognitive technology companies that have raised at least $10M, (Jan. 2011 – Sep. 2015, US only)
- 23. Commercialization and improving performance expand applications Improving performance and commercialization fueled by surging investment expand the applications for cognitive technologies GRAPHIC: DELOITTE UNIVERSITY PRESS
- 24. Applications are broadening As performance improves, applications of speech recognition, computer vision, natural language processing and understanding are growing conclusion
- 25. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Deciding where to apply cognitive technologies in an organization
- 26. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Viable: Where is it possible to apply cognitive technologies Types of tasks Examples Perceptual tasks involving vision, speech, reading handwriting Forms processing, first-tier customer service, warehouse operation Analytical tasks, involving large data sets Document review; finding patterns, making predictions Decision-making tasks where expertise can be expressed as rules Planning maintenance operations Planning tasks in a constrained domain Scheduling
- 27. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Valuable: Where it is worth applying cognitive technologies It may be worth using cognitive technologies where • Workers’ cognitive abilities or training are underutilized • Business process has high labor costs • The value of improved performance is high Opportunities Examples Worker’s cognitive abilities or training are under utilized Writing company earnings reports; e-discovery Business process has high labor costs Medical utilization management Expertise is scarce Medical diagnosis— especially rare conditions Value of improved performance high Decision-making in financial services Create new features customers care about Natural interfaces, automation, “intelligence”
- 28. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Consumer benefits of cognitive technologies Anupam Narula, David Schatsky, Ben Stiller, & Robert Libbey, "The thinker and the shopper: Four ways cognitive technologies can add value to consumer products," Deloitte University Press (June 3, 2015)
- 29. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Vital: Where it is necessary to apply cognitive technologies It may be necessary to use cognitive technologies where • Industry-standard performance requires it (e.g. Online retail product recommendations) • Cannot scale relying on human labor alone (e.g. media sentiment analysis, fraud detection) Types of tasks Examples Industry-standard performance requires cognitive tech Online retail product recommendations Service cannot scale relying on human labor alone Fraud detection; social media sentiment analysis
- 30. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business The unintended consequences of automation People are flawed; automated systems can have flaws too Humans are bad at monitoring automated processes—paying attention to things that hardly change People lose skills if they don’t practice them— the autopilot irony Cognitive “underload” can reduce performance Automated systems can undermine worker motivation, cause alienation, and reduce satisfaction, productivity, and innovation Ill-conceived automation strategies may diminish our sense of self-worth
- 31. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business What to automate, and to what extent? High 10. The computer decides everything, acts autonomously, ignoring the human, 9. informs the human only if it, the computer, decides to 8. informs the human only if asked, or 7. executes automatically, then necessarily informs the human, and 6. allows the human a restricted time to veto before automatic execution, or 5. executes that suggestion if the human approves, or 4. suggests one alternative 3. narrows the selection down to a few, or 2. the computer offers a complete set of decision/action alternatives, or Low 1. the computer offers no assistance: humans take all decisions and actions Information acquisition Information analysis Decision and action selection Action implementation Adapted from: Raja Parasuraman et al., “A Model for Types and Levels of Human Interaction with Automation,” IEEE Transactions on Systems, Man, and Cybernetics 30, no. 3 (2000): 286–297.
- 32. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Organizations have automation choices Automation approach What is automated Examples Replace Everything ATM; first-tier customer support Atomize/automate As much as possible Machine translation plus human cleanup Relieve Dull, dirty, or dangerous jobs Routine earnings stories at AP; caller authentication at Barclays Empower What wasn’t even being done before IBM Watson for Oncology; oil & gas drilling problem resolution
- 33. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Maximizing the value of workers and machines A COST STRATEGY USES TECHNOLOGY TO REDUCE COSTS, ESPECIALLY BY REDUCING LABOR A VALUE STRATEGY AIMS TO INCREASE VALUE BY COMPLEMENTING LABOR WITH TECHNOLOGY OR REASSIGNING LABOR TO HIGHER-VALUE WORK Besides automation choices, organizations must choose between a cost strategy and a value strategy A cost strategy uses technology to reduce costs, especially by reducing labor A value strategy aims to increase value by complementing labor with technology or reassigning labor to higher-value work
- 34. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Automation choices under different strategies NEITHER THE TASK NOR THE TECHNOLOGY DICTATE THE STRATEGY TO BE FOLLOWED Automation Choice Cost strategy Value strategy Replace Eliminate worker Reassign worker; use tech to provide superior performance Atomize/ automate Accelerate work, reduce staff, possibly alienate creative workers and artisans Create new low-cost offers, employ lower-skilled, less-experienced workers Relieve Eliminate routine tasks, increase productivity, reduce staff Redeploy people to higher-value tasks; create more value for customers Empower Increase performance of workers Increase workers’ performance and use to enhance their skills
- 35. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Some skills will become more valuable TAKING A FRESH LOOK AT WHAT SKILLS WILL BE NEEDED Tasks that cannot be substituted by computerization are generally complemented by it. Technology increases productivity, raises earnings, and augments demand for skilled labor The skills required for routine work to become less valuable The skills required to perform broadly-defined jobs and those required for successful interpersonal interactions to become relatively more valuable: Flexibility General problem solving Creativity Tolerance of ambiguity Empathy Drive Emotional intelligence Resourcefulness Critical thinking Openness to serendipity
- 36. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business Organizations must plan for cognitive technologies THERE ARE CHOICES TO BE MADE Cognitive technologies will change the employment landscape in the coming years Some jobs will disappear; others will be redesigned; new kinds of work will arise Workers whose skills are complemented by cognitive technologies will thrive; those whose skills are being supplanted by smart machines may struggle Leaders face choices about how to apply cognitive technologies: • Will their workers be marginalized or empowered? • Will their organizations use the technology to create value or cutting costs? Talent strategies must start to account for impact of cognitive technologies
- 37. Some take aways Understand how these technologies enable new, better ways of working. Prepare to adopt when appropriate, or risk being sidelined. Begin today to explore cognitive technologies But killer robots are not around the corner Something new and important is happening Their impact on business is increasing The technologies are getting better An opportunity to differentiate The use of cognitive technologies can confer competitive advantage today. It will become table stakes tomorrow.
- 38. New online course on artificial intelligence and cognitive technologies Free course. Register today: http://novoed.com/cognitive-technology
- 39. Now available on: Signals for Strategists: Sensing Emerging Trends in Business and Technology This book is for strategists—leaders, managers, entrepreneurs—who are so caught up in the daily pressures of the business that they’re missing key signals of their future reality. Signals for Strategists identifies the emerging trends on the horizon. The sooner we see them, the better our response.
- 40. As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. dschatsky@deloitte.com Twitter: dschatsky Copyright © 2015 Deloitte Development LLC. All rights reserved. Member of Deloitte Touche Tohmatsu Limited. This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.
Notas del editor
- About me
- Includes perceptual such as recognizing speech, handwriting Understanding language Vision, recognizing faces Planning, reasoning under uncertainty, learning Moving around an unstructured environment autonomously (animals can do this too) Not machines that think Not computers that work the way a brain works Definition is: what can they do Not: how a machine gets it done
- “As soon as it works, no one calls it AI anymore” – John McCarthy Quoted in Bostrom, Loc 477 “AI is whatever hasn’t been done yet”: Douglas R. Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid, (Harmondsworth, Middlesex: Penguin, 1980), p. 597, accessed October 9, 2014 at http://www.physixfan.com/wp-content/files/GEBen.pdf. Hofstadter called this Tesler’s Theorm. Tesler says Hofstadter misquoted him and that what he really said was “Intelligence is whatever machines haven't done yet.” Definition will change. Rules-based systems maybe not called AI anymore. But they used to be. The key point: AI enables computers to do things they couldn’t do before—begin to encroach on the domain that was reserved solely for humans.
- To start, it’s useful to define terms…. Artificial intelligence lacks a precise definition. Most experts in the field agree that it’s not about machines that think. It’s about what machines can do. it’s not concerned specifically with machines as smart as people; It’s concerned with making machines that can do tasks that used to require human intelligence. The technologies that enable machines to do these tasks I call cognitive technologies. Useful to distinguish the technologies companies are applying from the field that gave rise to them. [next slide]
- To understand the applications of AI it is useful to understand a bit about the specific cognitive technologies that have emerged from the field. This graphic depicts many of the cognitive technologies in growing use today, which are bringing powerful new capabilities to enterprises and products. They include: Computer vision: The ability of computers to identify objects, scenes, and activities in unconstrained (i.e., naturalistic) visual environments Machine learning: The ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions Natural language processing (NLP): The ability of computers to work with text the way humans do—for instance, extracting meaning from text or even generating text that is readable, stylistically natural, and grammatically correct Speech recognition: The ability to automatically and accurately transcribe human speech Optimization: The ability to automate complex decisions and trade-offs about limited resources Planning and scheduling: The ability to automatically devise a sequence of actions to meet goals and observe constraints Rules-based systems: The ability to use databases of knowledge and rules to automate the process of making inferences about information When you hear about artificial intelligence or cognitive computing, see if you can identify which specific cognitive technology is being described.
- http://faculty.chicagobooth.edu/drew.creal/teaching/basiccoursematerial/lectures/lecture9.pdf
- http://shabal.in/visuals/kmeans/4.html
- Advanced system designs that might have worked in principle were in practice off limits just a few years ago because they required computer power that was cost prohibitive or just didn’t exist Today, the power necessary to implement these designs is readily available. Current generation of microprocessors delivers 4 million times the performance of the first single-chip microprocessor introduced in 1971
- Volume of data in the world is increasing rapidly, thanks in part to Internet, social media, mobile devices, and low-cost sensors Development of new techniques for managing and analyzing very large data sets. Big data has been a boon to the development of AI: some AI techniques use statistical models for reasoning probabilistically about data such as images, text, or speech. These models can be improved, or “trained,” by exposing them to large sets of data, which are now more readily available than ever. Volume of data doubles every two years. By 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes. http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm (IDC 2014)
- Supported progress in AI for two reasons: access to data and fostering collaboration make available vast amounts of data and information to any Internet-connected computing device, propelling work on AI approaches that require large data sets. provided a way for humans to collaborate—sometimes explicitly and at other times implicitly—in helping to train AI systems. E.g. some researchers have used cloud-based crowdsourcing services like Mechanical Turk to enlist thousands of humans to describe digital images, enabling image classification algorithms to learn from these descriptions. Google’s language translation project analyzes feedback and freely offered contributions from its users to improve the quality of automated translation. For discussion of “cognitive analytics,” including the role of cloud computing, see Rajeev Ronanki and David Steier, “Cognitive analytics,” Deloitte University Press, February 21, 2014, http://dupress.com/articles/2014-tech-trends-cognitive-analytics/, accessed October 9, 2014. Catherine Wah, “Crowdsourcing and its applications in computer vision,” U.C. San Diego, May 26, 2011, http://vision.ucsd.edu/~cwah/files/re_cwah.pdf, accessed October 8, 2014. Google Inc., “Google Translate Community FAQ,” https://docs.google.com/document/d/1dwS4CZzgZwmvoB9pAx4A6Yytmv7itk_XE968RMiqpMY/pub, accessed October 8, 2014.
- An algorithm is a routine process for solving a program or performing a task In recent years, new algorithms have been developed that dramatically improve the performance of machine learning, an important technology in its own right and an enabler of other technologies such as computer vision. (These technologies are described below.) The fact that machine learning algorithms are now available on an open-source basis is likely to foster further improvements as developers contribute enhancements to each other’s work. Multiple researchers have devised algorithms that have improved the performance of machine learning. Google Scholar finds some 500,000 scholarly papers on the topic of neural networks, for example, published since 2006. Geoffrey Hinton is a widely published and cited researcher in this area credited with several important innovations. See: Geoffrey Hinton, “Home Page of Geoffrey Hinton,” http://www.cs.toronto.edu/~hinton/, accessed October 6, 2014. Other researchers who are widely recognized for contributions in this area include Yann LeCun (See Yann LeCunn, “Yann LeCun’s Home Page,” http://yann.lecun.com/, accessed October 9, 2014), and Yoshua Bengio (See Joshua Bengio, “Yoshua Bengio’s Research,” http://www.iro.umontreal.ca/~bengioy/yoshua_en/research.html, accessed October 9, 2014. Recently, Microsoft demonstrated a new machine learning architecture that dramatically accelerates the machine learning process, improving precision and accuracy. See, Microsoft Research, “On Welsh Corgis, computer vision, and the power of deep learning,” http://research.microsoft.com/en-us/news/features/dnnvision-071414.aspx?0hp=002c, accessed October 6, 2014. The Apache Software Foundation sponsors Apache Mahout, an open source machine learning library. Startup PredictionIO is offering an open-source machine learning server and recently received $2.5 million in venture funding. See Steve O’Hear, “PredictionIO raises $2.5M for its open source machine learning server,” TechCrunch, July 17, 2014, accessed October 6, 2014.
- Google’s Facenet results – trained on massive 260-million-image dataset Driven by clever engineering, access to data sets for training, algorithm improvements (to a lesser degree) Voice recognition: Accuracy of Google’s voice recognition technology improved from 84 percent in 2012 to 98 percent less than two years later, according to an assessment by investment bank Piper Jaffray Computer vision: Facebook reported in a peer-reviewed paper that its DeepFace technology can now recognize faces with 97 percent accuracy – about as well as people can msft dog breed; A standard benchmark used by computer vision researchers has shown a four-fold improvement in image classification accuracy from 2010 to 2014 IBM Watson precision: IBM doubled the precision of Watson’s answers in the few years leading up to its famous Jeopardy! victory in 2011. The company now reports its technology is 2,400 percent “smarter” today than on the day of that triumph. Not everything is improving so fast. One benchmark found a 13 percent improvement in the accuracy of Arabic to English translations between 2009 and 2012, for instance. NIST Information Technology Laboratory, “OpenMT12 Evaluation Results,” August 28, 2012, http://www.nist.gov/itl/iad/mig/openmt12results.cfm, accessed October 8, 2014. BBN’s system, BBN_ara2eng_primary_cn, performed better than all competitors in both years but improved just 13 percent. Apple Insider, “Tests find Apple's Siri improving, but Google Now voice search slightly better,” http://appleinsider.com/articles/14/07/22/tests-find-apples-siri-improving-but-google-now-voice-search-slightly-better, accessed October 3, 2014. Facebook, “DeepFace: Closing the gap to human-level performance in face verification,” https://www.facebook.com/publications/546316888800776/, accessed October 3, 2014. Olga Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” arXiv:1409.0575v1 [cs.CV] (September 1, 2014), http://arxiv.org/pdf/1409.0575v1.pdf, accessed October 3, 2014.
- We looked at over 100 examples of companies that had deployed applications of cognitive technologies and found that most applications fall into one of three main categories, which I call product, process, and insight: Product: Embedding cognitive technologies in a product or service in a way that touches and delivers a benefit to the end customer Process: Automate or enhance tasks or business processes internal to an organization Insight Analyzing data, often large amounts of data, unstructured data such as text, images, etc. to discern patterns or make predictions Let’s look at some examples of each type of application. [next slide] We reviewed over 100 examples of organizations that implemented or piloted cognitive technologies examples spanned 17 industry sectors, including aerospace and defense, agriculture, automotive, banking, consumer products, health care, life sciences, media and entertainment , oil and gas, power and utilities, the public sector, real estate, retail, technology, and travel, hospitality and leisure. Application areas were broad and included research and development, manufacturing, logistics, sales, marketing, and customer service.
- Netflix uses machine learning to predict which movies a customer will like Now accounts for as much as 75 percent of Netflix usage eBay uses machine translation to enable users who search in Russian to discover English-language listings that match GM Will make some of its vehicles safer by equipping them with computer vision to determine whether the driver is distracted or not spending enough time looking in certain areas such as the road ahead or the rear-view mirror [possible image from seeingmachines.com here: http://www.cbsnews.com/news/gm-takes-aim-at-distracted-driving-with-head-eye-trackers/] Audi integrating speech recognition technology into some cars to enable drivers to engage in a more convenient, natural communication with infotainment and navigation systems VuCOMP A maker of medical imaging technology make radiologists more effective by using computer vision algorithms to identify and outline areas of mammograms consistent with breast cancer Clinical study: radiologists were significantly more effective in finding cancer and in differentiating cancer from non-cancer when using the system [** Image: VuCOMP?] Dominos function on its mobile app that lets customers place orders by speaking with a computer-generated voice named "Dom." Not for cost cutting. Instead, to increase convenience and sales Customers say they prefer to order online or mobile, and spend more when they do Associated Press To scale and improve the quality of its business news coverage implemented natural language generation software that automatically writes corporate earnings stories Rather than reduce staffing levels, AP using the technology to increase by 10X the number of such stories it publishes AP to cover companies of local or regional importance it did not have the resources to cover before Freeing journalists from writing formulaic earnings stories so they can focus on more analytical and exclusive stories New categories like Roomba, Google Now Sallie Davies, “GM to launch cars that can pick up on distracted driving ,” Financial Times, September 1, 2014, http://www.ft.com/intl/cms/s/0/e5787fea-30e9-11e4-8313-00144feabdc0.html#axzz3CMkdphUC, accessed October 14, 2014. Nabanita Singha Roy, “Audi TT’s new voice and natural language understanding (NLU) technology,” Rushlane, October 2, 2014, http://www.rushlane.com/audi-tt-nlu-tech-paris-2014-12132560.html, accessed October 14, 2014. VuCOMP, “The M-Vu System,” http://www.vucomp.com/products/m-vu-system, accessed October 14, 2014. Candice Choi, “Domino's introduces a 'Siri' to take mobile orders,” Associated Press, June 16, 2014, http://bigstory.ap.org/article/dominos-introduces-siri-take-mobile-orders, accessed October 14, 2014.
- Hong Kong subway - It carries over 5 million passengers daily and boasts a 99.9 percent on-time record To improve quality and efficiency Automate and optimize the planning of 2600 weekly these engineering works performed by 10,000 people a “genetic algorithm” that pits many solutions to the same problem against each other to find the best one, producing an optimal engineering schedule automatically and saving two days of planning work per week State of Georgia Government Transparency and Campaign Finance Commission Has to process 40,000 pages of campaign finance disclosures per month, many of which are handwritten Solution uses automated handwriting recognition to keep up with the workload coupled with crowdsourced human review to ensure quality Cincinnati children’s hospital automatically identifying patients eligible for clinical trials using natural language processing to read free-form clinical notes, and machine learning to refine the list of terms extracted from them reduced the workload by 92 and increased efficiency by 450 percent Hal Hodson, “The AI boss that deploys Hong Kong's subway engineers,” NewScientist, July 4, 2014, http://www.newscientist.com/article/mg22329764.000-the-ai-boss-that-deploys-hong-kongs-subway-engineers.html#.U9efI_ldWSo, accessed October 14, 2014. Richard W. Walker, “Georgia Solves Campaign Finance Data Challenge Via OCR,” InformationWeek, April 15, 2014, http://www.informationweek.com/government/cloud-computing/georgia-solves-campaign-finance-data-challenge-via-ocr/d/d-id/1204471, accessed October 14, 2014. Yizhao Ni et al., “Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department,” JAMIA, July 16, 2014, doi:10.1136/amiajnl-2014-002887, http://jamia.bmj.com/content/early/2014/07/16/amiajnl-2014-002887.full, accessed October 14, 2014.
- Insight applications represent a great opportunity for a many companies. On example is Intel, which is using machine learning to improve sales effectiveness and boost revenue. One approach: automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns. This enables them to automatically prioritize sales efforts and tailor promotions. The company expects this strategy to result in tens of millions of dollars of additional revenue when rolled out globally. Another example is Aetna, which has used machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them. They did an analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke and diabetes Using claims and biometric data for a population of 37,000 Aetna members, they developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with it. They were also able to determine which medical interventions are most likely to improve an individual’s health outlook. Wherever there is large amounts of data, it may be possible to apply machine learning to discover useful patterns and make valuable predictions. [next slide] Stevia First Optimizing its industrial processes. Rather than explore various production approaches by trial and error, the company uses what it calls “smart search” cognitive algorithms to determine the optimal parameters for the volume of raw materials and process time, for instance, to boost the cost efficiency of their production process. Company is evaluating other applications from using NLP to automatically read and summarize findings from thousands of academic papers, to using machine learning to reanalyze data sets from old biotechnology research in search of a new gene or a new drug. [**Image See http://www.steviafirst.com/ for image ideas] Intel Using machine learning to improve sales effectiveness and boost revenue. One approach: automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns. Prioritize sales efforts and tailor promotions company expects this strategy to result in $20 million in additional revenue when rolled out globally BBVA Compass To improve marketing and customer service uses a social media sentiment monitoring tool with NLP to track and understand what consumers are saying about itself and its competitors. automatically identifies salient topics of consumer chatter and the sentiments surrounding those topics. Insights influence the bank’s decisions on setting fees and offering consumer perks, and how customer service representatives should response to certain customer inquiries about services and fees. Aetna With GNS Health, use machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke and diabetes Using claims and biometric data for a population of 37,000 Aetna members, the companies developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with the disorder. It also is able to determine which medical interventions are most likely to improve an individual’s health outlook Robert Brooke, “Stevia First – A New Era is Beginning,” http://www.thechairmansblog.com/stevia-first/robert-brooke/stevia-first-new-era-beginning/#, accessed October 14, 2014. Derrick Harris, GigaOm, November 18, 2013, https://gigaom.com/2013/11/18/how-intel-is-betting-on-big-data-to-add-tens-of-millions-to-its-bottom-line/, accessed October 14, 2014.
- Insight applications represent a great opportunity for a many companies. On example is Intel, which is using machine learning to improve sales effectiveness and boost revenue. One approach: automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns. This enables them to automatically prioritize sales efforts and tailor promotions. The company expects this strategy to result in tens of millions of dollars of additional revenue when rolled out globally. Another example is Aetna, which has used machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them. They did an analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke and diabetes Using claims and biometric data for a population of 37,000 Aetna members, they developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with it. They were also able to determine which medical interventions are most likely to improve an individual’s health outlook. Wherever there is large amounts of data, it may be possible to apply machine learning to discover useful patterns and make valuable predictions. [next slide] Stevia First Optimizing its industrial processes. Rather than explore various production approaches by trial and error, the company uses what it calls “smart search” cognitive algorithms to determine the optimal parameters for the volume of raw materials and process time, for instance, to boost the cost efficiency of their production process. Company is evaluating other applications from using NLP to automatically read and summarize findings from thousands of academic papers, to using machine learning to reanalyze data sets from old biotechnology research in search of a new gene or a new drug. [**Image See http://www.steviafirst.com/ for image ideas] Intel Using machine learning to improve sales effectiveness and boost revenue. One approach: automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns. Prioritize sales efforts and tailor promotions company expects this strategy to result in $20 million in additional revenue when rolled out globally BBVA Compass To improve marketing and customer service uses a social media sentiment monitoring tool with NLP to track and understand what consumers are saying about itself and its competitors. automatically identifies salient topics of consumer chatter and the sentiments surrounding those topics. Insights influence the bank’s decisions on setting fees and offering consumer perks, and how customer service representatives should response to certain customer inquiries about services and fees. Aetna With GNS Health, use machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke and diabetes Using claims and biometric data for a population of 37,000 Aetna members, the companies developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with the disorder. It also is able to determine which medical interventions are most likely to improve an individual’s health outlook Robert Brooke, “Stevia First – A New Era is Beginning,” http://www.thechairmansblog.com/stevia-first/robert-brooke/stevia-first-new-era-beginning/#, accessed October 14, 2014. Derrick Harris, GigaOm, November 18, 2013, https://gigaom.com/2013/11/18/how-intel-is-betting-on-big-data-to-add-tens-of-millions-to-its-bottom-line/, accessed October 14, 2014.
- Top enterprise categories: marketing, intelligence (including analytics solutions), security and authentication, and sales. Top industry sectors: Retail, adtech, medical and diagnostics, and education. Top industries: The biggest funding category by far is those companies building applications for traditional enterprise functions such as marketing and sales. Startups like these have raised nearly $2.5 billion since 2011 (see figure 1), suggesting that the biggest near-term opportunity for cognitive technologies is in using them to enhance current business practices. Indeed, startups are using cognitive technologies to develop valuable features and capabilities such as intelligent automation, ease of use, and insightful analytics that are superior to what can readily be achieved with conventional information technologies. In the Rethinking Enterprise category, for instance, marketing-focused startups have used machine learning to improve customer targeting and website personalization, natural language processing to understand what consumers are saying about television content on social media, and speech recognition to gauge the quality of inbound telephone leads. The top segments in the enterprise category are marketing, intelligence (including analytics solutions), security and authentication, and sales. Companies developing applications tailored for specific sectors such as retail, advertising (“adtech”), education, medical/diagnostics, and media have also received major investments—over $2 billion during the same period. In the Rethinking Industry category, startups providing solutions aimed at the medical and diagnostics sectors are using natural language processing to automate the coding of medical charts for insurance reimbursement, machine learning to power mobile care-management apps that tailor their content to better engage patients in their care regimen, and computer vision and machine learning to power a simple, low-cost ultrasound device that can automatically diagnose disorders. Top segments in this category include retail, adtech, medical and diagnostics, and education. This analysis suggests that the applications of cognitive technologies are broad; they can often resemble traditional enterprise applications—with advanced capabilities and performance—rather than specialized cognitive computing products. A principal way that cognitive technologies can create value for companies is by intelligently automating tasks and surfacing insights that augment human decision making. The opportunities for this are huge, spanning all sectors and business functions. Derrick Harris, “Exclusive: Causata raises $7.5M and steps up its game in targeted ads,” GigaOm, February 6, 2013, https://gigaom.com/2013/02/06/exclusive-causata-raises-7-5m-and-steps-up-its-game-in-targeted-ads/, accessed September 19, 2015. Jolie Katz, “Better recommendations are worth $500M,” Rich Relevance, March 31, 2015, www.richrelevance.com/blog/2015/03/better-recommendations-worth-500m/, accessed September 19, 2015. Mike Isaac, “Why Twitter dropped close to $90 million on Bluefin Labs,” All Things D, February 12, 2013, http://allthingsd.com/20130212/why-twitter-dropped-close-to-90-million-on-bluefin-labs/, accessed September 19, 2015. Convirza, “Convirza closes $20M of Series B funding for call analytics and automation,” www.convirza.com/press-releases/convirza-closes-20m-of-series-b-funding-for-call-analytics-and-automation/, accessed September 19, 2015. Marketing vendors have attracted $590 million; analytics and intelligence vendors: $570 million; security and authentication startups: $480 million; sales technology vendors: $350 million. Data from Capital IQ and Quid Inc., as of September 10, 2015. Investments in US-based companies that have raised at least $10 million. Apixio, www.apixio.com/solutions/#, accessed September 19, 2015. PRWeb, “Wellframe closes $8.5 million in Series A financing,” September 8, 2014, www.prweb.com/releases/2014/09/prweb12149420.htm, accessed September 19, 2015. Davey Alba, “The startup that’s bringing AI to ultrasounds and MRIs,” Wired, November 4, 2014, www.wired.com/2014/11/butterfly-network/, accessed September 19, 2015. Startups with solutions aimed at the retail sector have raised $520 million; adtech startups have raised $510 million; medical + diagnostics: $440 million; education: $270 million. CALLOUT
- Speech: From medical dictation to millions of web searches [** Image: clerk with headset; someone doing voice search on a phone: like this.] Vision: from industrial automation to consumer applications (pictures) [**Image: image of “machine vision”, to amazon Flow Watson: from Jeopardy to medicine, financial services, recipe design Intelligent automation [**image: automated process flow?]
- Speech: From medical dictation to millions of web searches [** Image: clerk with headset; someone doing voice search on a phone: like this.] Vision: from industrial automation to consumer applications (pictures) [**Image: image of “machine vision”, to amazon Flow Watson: from Jeopardy to medicine, financial services, recipe design Intelligent automation [**image: automated process flow?]
- All or part of a task, job, or workflow requires low or moderate level of skill plus human perception: Forms processing, first-tier customer service, warehouse operation Large data sets: Investment advice, medical diagnosis, oil exploration Expertise can be expressed as rules: Scheduling maintenance operations
- Workers’ cognitive abilities or training are underutilized: Writing company earnings reports; e-discovery; driving/piloting Business process has high labor costs: Medical insurance utilization management Expertise is scarce; value of improved performance is high: Medical diagnosis; aerial surveillance; trading If you have a business process performned by abundant, low-cost people, little benefit
- Kraft iFood Assistant (SR) - voice control L'oreal Diagnost my hair (NLP) - natural text response Sharp cororobo vaccum (SR, Rob) - voice control Aether cone (SR, ML) - simplicity - machine learning and no nobs Guess True Fit (ML) L'Oreal Makeup Genius (CV) Pepper robot (CV, SR, R)
- Industry-standard performance requires use of cognitive technologies: Online retail product recommendations A service cannot scale relying on human labor alone: Fraud detection; Media sentiment analytics – what are people saying about us? Do they tend to experss positive, negative, or neutral sentiment. How is this changing over time?
- Translation example: Do away with human translators Machine translation plus human cleanup Give routine translation to machines; focus on higher end stuff like marketing copy Translation assistant – scan corpus; recommend phrases
- Replace. With the cost strategy, organizations replace workers with cognitive computing systems that perform equivalent work. The financial appeal of this choice is clear, but limited to the cost savings that it might achieve. Organizations may produce greater value by reassigning workers to new roles, or expanding their roles. Or they might seek to deploy cognitive systems that not only substitute for human workers but provide superior performance, measured in speed or quality, for instance. These are examples of the value strategy. Automize. Automizing work to reduce labor costs is an example of the cost strategy. But automizing can be disempowering and alienating to creative people, the highly skilled, or artisans. A value strategy might automize to create new lower-cost offerings that serve the needs of a new market segments. For instance, translation service providers could offer a range of qualities at different prices by varying by the level of automation used in the translation and using less experienced translators to perform post-editing. Relieve. A cost strategy might realize the benefits of efficiency with this automation choice by reducing headcount. An example is call centers that automate first-tier customer support in order to reduce staffing levels. A value strategy, on the other hand, might expand or shift the focus of the workers to higher-value tasks. For instance, when a new automated engineering planning system saved the expert engineers of the Hong Kong subway system two days of work per week, they reallocated their time to harder problems that require human interaction and negotiation. Empower. A cognitive system may empower lower-skilled workers to perform tasks that were formerly performed by higher-skilled workers. This is an example of the cost strategy at work. A value strategy might employ a system not only to empower lower-skilled workers but also to train them and build their skills. It might also be designed to enhance the performance of even highly skilled workers. It should be noted that cognitive automation, even in systems intended to empower workers, may meet with resistance. An illustration of this can be found at Intel, which, as mentioned earlier, developed a cognitive system to improve sales productivity. The system used machine learning to classify customers and guide sales people on what to offer different customers. Some members of the sales team were initially resistant to following the advice of the machine learning system, possibly because they resented that their salesmanship was being subordinated to a machine. But after an initial group of sales people adopted the system and saw a dramatic improvement in sales productivity, the rest of the sales team was quick to follow. If the essence of a sales person’s work is building and maintaining relationships with customers, a little automated assistance that prioritizes customer calls and recommends offers may be an empowering use of technology. David Schatsky, Craig Muraskin, & Ragu Gurumurthy, “Cognitive technologies: The real opportunities for business.” Rachel King, "How Intel’s CIO Helped the Company Make $351 Million."
- Workers with spreadsheet skills likely receive higher pay than clerks working with pencil and paper before them, for instance. Construction workers skilled with power tools and sophisticated machinery command higher wages than unskilled manual laborers.