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The CIO's Guide to Artificial Intelligence Gartner 20180102
The CIO’s Guide to Artificial Intelligence January 2, 2018 Contributor: Kasey Panetta DIGITAL BUSINESS CIOs can separate AI hype from reality by considering these areas of risk and opportunity. When a company realized that up to 30% of calls it received were from customers asking about order status, its leadership wanted to know if artificial intelligence (AI) would be able to help manage the interactions. The short answer was yes, a virtual customer assistant could answer questions ranging from “Where is my order” to “How long will I have to wait?” But the bigger question was if AI could help the company in even more impactful ways. “Look at how you are using technology today during critical interactions with customers — business moments — and consider how the value of that moment could be increased,” says Whit Andrews, vice president and distinguished analyst at Gartner. “Then apply AI to those points for additional business value.” “AI allows companies to collect data from a wide variety of places and apply self-improving analysis that can take action” For example, the interaction between company and consumer provides data about the customer. When combining information with other data about that particular customer (i.e., they order X amount of Y products every Z weeks), the company can use AI to further enrich the relationship beyond that interactio
#ai  #advice  #applications  #strategy  #2018  #CIO  @Gartner 
6 days ago by phil_hendrix
The First Frontier for Medical AI Is the Pathology Lab IEEE Spectrum 20181129
The First Frontier for Medical AI Is the Pathology Lab But before adopting startup PathAI’s tools, doctors must see if they are worth the cost. But before adopting startup PathAI’s tools, doctors must see if they are worth the cost
By Elie Dolgin
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Illustration: Carl De Torres
This is how a pathologist could save your life.

Imagine you’re coughing up blood, and a chest scan reveals a suspicious mass in your lungs. A surgeon removes a small cylindrical sample from the potential tumor, and the pathologist places very thin slices of the tissue on glass slides. After preserving and staining the tissue, the pathologist peers through a microscope and sees that the cells have the telltale signs of lung cancer. You start treatment before the tumor spreads and grows.

And this is how a pathologist could kill you: The expert physician would just have to miss the cancer. Or, more likely, misclassify the cells viewed on the slides as the wrong cancer subtype. Rather than getting a targeted therapy that beats your cancer into remission, you receive conventional chemo that buys you a few more months of life.

An artificially intelligent pathologist probably wouldn’t make that mistake. Trained on vast troves of digitized slides showing an enormous variety of tumors, artificial-intelligence (AI) systems will likely provide more accurate diagnoses than human pathologists, at least on fairly rote diagnostic tasks. They may even pick up on subtle features that the best-trained human eyes could never see. In this crucial, high-stakes branch of medicine, AI tools may soon offer diagnoses—and treatment recommendations—that are as close to infallible as we’re likely to get in the foreseeable future. And they’ll do so in a matter of seconds.

Lately, dazzlingly high success rates for AI-based systems in recognizing the presence of certain specific illnesses have prompted speculation that such tools will replace doctors. But the developments in pathology show us a more likely outcome: that machines will make the ever-increasing complexity of modern medicine manageable for human beings. This human-machine combination will outperform what either could do individually. At first, the improvement will be small. But eventually, it will be great.

“The promise of machine learning is to augment what a pathologist can do alone,” says Ulysses Balis, director of the division of informatics at the University of Michigan’s pathology department and chief strategy officer of a digital pathology company called Inspirata. “These technologies allow the profession to scale with increased demand.”
#ai  #hc  #applications  #automation  #augmentation  #casestudy  #innovation  #vendor  #pathology  #A+  +PathAI 
12 days ago by phil_hendrix
Machine Learning Use Cases for Optimized Customer Engagement ZineOne 201811
Machine Learning Use Cases for Optimized Customer Engagement While it may seem contradictory, advancements in technology are giving customer engagement a human touch—and it’s all thanks to Machine Learning (ML) capabilities. ML applies Artificial Intelligence (AI) to computers in order to give them the ability to learn from experience instead of being explicitly programmed. By analyzing customer data to predictively enhance and personalize engagements with increasing accuracy over time, ML models can drive both acquisition and loyalty; in fact, McKinsey reports that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and 6 times as likely to retain these customers. In this blog, we will explore the potential use cases for ML models in Customer Engagement Hubs (CEH), evaluating the personalization benefits of data-driven intelligence across industries.
#ml  #applications  #customerengagement  @ZineOne 
16 days ago by phil_hendrix
A Framework for Building Artificial Intelligence Capabilities WSJ 20180302
A Framework for Building Artificial Intelligence Capabilities Companies should look at AI through the lens of business opportunities, rather than technologies By Irving Wladawsky-Berger Mar 2, 2018 12:29 pm ET 2 COMMENTS A line of automated transport robots sit before operating to move shelving units containing goods at Amazon.com Inc.'s new fulfillment center in Kolbaskowo, Poland, Feb. 28, 2018. PHOTO: BARTEK SADOWSKI/BLOOMBERG After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance. AI is seemingly everywhere. Every day we can read about the latest AI advances and applications from startups and large companies. But, despite its market acceptance, a recent McKinsey report found that AI adoption is still at an early, experimental stage, especially outside the tech sector. Based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors, the report found that 20 percent of respondents had adopted AI at scale in a core part of their business, 40 percent were partial adopters or experimenters, while another 40 percent were still waiting to take their first steps. The report adds that the gap between the early AI adopters and everyone else is growing. While many companies have yet to be convinced of AI’s benefits, leading edge firms are charging ahead. Companies need to start experimenting with AI and get on the learning curve, or they risk falling further behind.
#ai  #applications  #capabilities  #fluency  #deployment  #prioritization  @WSJ 
18 days ago by phil_hendrix
Augmented human intelligence is changing health care for the better, experts say Mayo Clinic 20181126
Augmented human intelligence is changing health care for the better, experts say November 26, 2018 ROCHESTER, Minn. — Artificial intelligence (AI) is going to change health care, including the practice of radiology, profoundly. But rather than machines taking over, clinicians and researchers will use them to improve patient care. “If somebody puts their head in the sand, and wakes up and pulls their head out five years later, the practice will be very different,” says Bradley Erickson, M.D., Ph.D., a Mayo Clinic diagnostic radiologist. To help with this transitional time, Dr. Erickson and colleagues in the Radiological Society of North America (RSNA) and Nvidia Corp., a computer chip manufacturer and technology company, developed a course for radiologists interested in acquiring or developing the skills needed to navigate AI advancements. This course, called the “Deep Learning Institute,” will be held throughout the RSNA Scientific Assembly and Annual Meeting Nov. 25–30 in Chicago.
#ai  #healthcare  #applications  #radiology  #augmentation  @MayoClinic 
19 days ago by phil_hendrix
Beyond Automation Tom Davenport HBR 201506
Beyond Automation Thomas H. DavenportJulia Kirby FROM THE JUNE 2015 ISSUE SUMMARY SAVE SHARE COMMENT TEXT SIZE PRINT PDF 8.95 BUY COPIES VIEW MORE FROM THE June 2015 Issue EXPLORE THE ARCHIVE After hearing of a recent Oxford University study on advancing automation and its potential to displace workers, Yuh-Mei Hutt, of Tallahassee, Florida, wrote, “The idea that half of today’s jobs may vanish has changed my view of my children’s future.” Hutt was reacting not only as a mother; she heads a business and occasionally blogs about emerging technologies. Familiar as she is with the upside of computerization, the downside looms large. “How will they compete against AI?” she asked. “How will they compete against a much older and experienced workforce vying for even fewer positions?” Suddenly, it seems, people in all walks of life are becoming very concerned about advancing automation. And they should be: Unless we find as many tasks to give humans as we find to take away from them, all the social and psychological ills of joblessness will grow, from economic recession to youth unemployment to individual crises of identity. That’s especially true now that automation is coming to knowledge work, in the form of artificial intelligence. Knowledge work—which we’ll define loosely as work that is more mental than manual, involves consequential decision making, and has traditionally required a college education—accounts for a large proportion of jobs in today’s mature economies. It is the high ground to which humanity has retreated as machines have taken over less cognitively challenging work. But in the very foreseeable future, as the Gartner analyst Nigel Rayner says, “many of the things executives do today will be automated.”
#AI  #cognitive  #applications  #automation  #augmentation  @TomDavenport 
19 days ago by phil_hendrix
Contextual Walk Guide GumGum 2018
Contextual targeting, where ads online are targeted to people based on the context of what they’re looking at on page, is looking not just appealing but also safer. Triggered not only by the European privacy law GDPR but also increasing privacy concerns due to behavioral tracking globally, a soaring demand for personalized marketing strategies and the rise of mobile usage, contextual advertising is making a resurgence. And long gone are the days when contextual targeting was based on a simplistic reliance on a single keyword. With advances in computer vision, it’s now possible to decipher the visual content of a publisher’s page. Contextual targeting is the only targeting left.
#contextual  #advertising  #ai  #applications  #cv  #computervision  @GumGum 
20 days ago by phil_hendrix
Quantiphi - Capabilities
Natural Language Understanding A majority of the classic  natural language processing techniques attempt to process text without understanding the meaning of words. Deep learning enables machines to overcome this problem by training large neural networks in an environment with similar objects, relationships, and dynamics as our own making these models far more powerful. Our Natural Language models go beyond the traditional topic and sentiment analysis and give you the ability to build custom chat-bots, fraud detection agents, auto response systems and other powerful natural language understanding systems at unprecedented accuracy levels.

Speech Recognition
While conversational speech recognition systems have largely reached human parity with Google Home and Alexa showing less than 6% word error rate, the same can not be said about domain specific speech which is typically laden with vernacular terminology and slang. Our domain specific speech recognition models overcome this problem through careful customization of acoustic and language components of the model pipeline to offer world class accuracy in domain specific speech. They also perform at near human levels in speech emotion detection and other specialized voice recognition tasks.
#ai  #applications  #nlp  #nlu  #nlg  #speech  #vendor  @Quantiphi 
20 days ago by phil_hendrix
Transforming the contact center with AI Google Cloud Blog 20180724
Transforming the contact center with AI Contact Center AI Dan Aharon Product Manager, Dialogflow Daryush Laqab Product Manager, Contact Center AI July 24, 2018 Whether providing useful self-service tools, or offering detailed help through live agents, call centers make effortless customer experience one of their highest priorities. And AI can help. AI technology and tools today have the ability to make high quality customer service accessible to all customers, 24/7, and without wait times, all while helping live agents better anticipate and respond to customer needs. Today we’re announcing new features and solutions that will greatly benefit contact centers.  These include: New features in Dialogflow Enterprise Edition to help you build AI-powered virtual agents for the contact center, including phone-based conversational agents known as interactive voice response (IVR). Google Cloud Contact Center AI solution that includes these new Dialogflow features alongside other tools to assist live agents and perform analytics.
#ai  #applications  #automation  #contactcenter  #platform  @Google 
21 days ago by phil_hendrix
Adoption of AI advances, but foundational barriers remain McKinsey 201811
AI adoption advances, but foundational barriers remain Article Actions Share this article on LinkedIn Share this article on Twitter Share this article on Facebook Email this article Print this article Download this article Survey respondents report the rapid adoption of AI and expect only a minimal effect on head count. Yet few companies have in place the foundational building blocks that enable AI to generate value at scale. The adoption of artificial intelligence (AI) is rapidly taking hold across global business, according to a new McKinsey Global Survey on the topic.1 AI, typically defined as the ability of a machine to perform cognitive functions associated with human minds (such as perceiving, reasoning, learning, and problem solving), includes a range of capabilities that enable AI to solve business problems. The survey asked about nine in particular,2 and nearly half of respondents say their organizations have embedded at least one into their standard business processes, while another 30 percent report piloting the use of AI. Yet overall, the business world is just beginning to harness these technologies and their benefits. Most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions. Indeed, many organizations still lack the foundational practices to create value from AI at scale—for example, mapping where their AI opportunities lie and having clear strategies for sourcing the data that AI requires. One critical factor of using AI effectively, the results confirm, is an organization’s progress on transforming the core parts of its business through digitization. At the most digitized firms,3 respondents report higher rates of AI usage in more business functions than their peers, along with greater investment in AI and greater overall value from using AI. Another foundational challenge with AI is finding skilled people to implement it effectively. Many respondents say their organizations are addressing the issue by taking a diversified approach to sourcing talent. On the whole, despite reasonable concerns about AI being used to automate existing work, respondents tend to believe that AI will have only a minor effect on overall company head count in the coming years.
#ai  #adoption  #status  #barriers  #vertical  #applications  +McKinsey  #A+ 
21 days ago by phil_hendrix
Adoption of AI advances, but foundational barriers remain McKinsey 201811
AI adoption advances, but foundational barriers remain Article Actions Share this article on LinkedIn Share this article on Twitter Share this article on Facebook Email this article Print this article Download this article Survey respondents report the rapid adoption of AI and expect only a minimal effect on head count. Yet few companies have in place the foundational building blocks that enable AI to generate value at scale. The adoption of artificial intelligence (AI) is rapidly taking hold across global business, according to a new McKinsey Global Survey on the topic.1 AI, typically defined as the ability of a machine to perform cognitive functions associated with human minds (such as perceiving, reasoning, learning, and problem solving), includes a range of capabilities that enable AI to solve business problems. The survey asked about nine in particular,2 and nearly half of respondents say their organizations have embedded at least one into their standard business processes, while another 30 percent report piloting the use of AI. Yet overall, the business world is just beginning to harness these technologies and their benefits. Most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions. Indeed, many organizations still lack the foundational practices to create value from AI at scale—for example, mapping where their AI opportunities lie and having clear strategies for sourcing the data that AI requires. One critical factor of using AI effectively, the results confirm, is an organization’s progress on transforming the core parts of its business through digitization. At the most digitized firms,3 respondents report higher rates of AI usage in more business functions than their peers, along with greater investment in AI and greater overall value from using AI. Another foundational challenge with AI is finding skilled people to implement it effectively. Many respondents say their organizations are addressing the issue by taking a diversified approach to sourcing talent. On the whole, despite reasonable concerns about AI being used to automate existing work, respondents tend to believe that AI will have only a minor effect on overall company head count in the coming years.
#ai  #adoption  #status  #barriers  #vertical  #applications  +McKinsey  #A+ 
21 days ago by phil_hendrix
Wanted: The ‘perfect babysitter.’ Must pass AI scan for respect and attitude 20181116
The tech firm Fama says it uses AI to police workers' social media for “toxic behavior” and alert their bosses. And the recruitment-technology firm HireVue, which works with companies such as Geico, Hilton and Unilever, offers a system that automatically analyzes applicants' tone, word choice and facial movements during video interviews to predict their skill and demeanor on the job. (Candidates are encouraged to smile for best results.)


But critics say Predictim and similar systems present their own dangers by making automated and possibly life-altering decisions virtually unchecked.
#ai  #applications  #startup  #socialmedia  #HR  #assessment  #profiling 
21 days ago by phil_hendrix
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making Patanjali Kashyap 2018
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making Kindle Edition by Patanjali Kashyap (Author)

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other.

This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making.

The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business.


What You Will Learn
Discover the machine learning, big data, and cloud and cognitive computing technology stack
Gain insights into machine learning concepts and practices
Understand business and enterprise decision-making using machine learning
Absorb machine-learning best practices

Who This Book Is For

Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.
#ml  #book  #applications 
23 days ago by phil_hendrix
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making Patanjali Kashyap
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making Kindle Edition by Patanjali Kashyap (Author)

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other.

This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making.

The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business.


What You Will Learn
Discover the machine learning, big data, and cloud and cognitive computing technology stack
Gain insights into machine learning concepts and practices
Understand business and enterprise decision-making using machine learning
Absorb machine-learning best practices

Who This Book Is For

Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.
#ml  #book  #applications 
23 days ago by phil_hendrix
Artificial Intelligence Marketing in 2018 In-depth Guide appliedai 20180216
Artificial Intelligence Marketing in 2018: In-depth Guide POSTED ONFEBRUARY 16, 20184 MINUTE READ 8 SHARES 8 While launching appliedAI.com we interviewed corporate leaders and all sizes of AI vendors, searched news articles, patents, venture capital financing and more to identify established and emerging AI use cases. We have identified about a dozen fundamental artificial intelligence use cases in marketing. We focused on core marketing activities such as optimizing pricing and placement, optimizing advertising/marketing, personalizing recommendations, collecting and leveraging customer feedback. We are always improving our structure and would love to hear your any comments and suggestions. Primary marketing activities and AI use cases in these activities are listed below. To get more information on each, please visit the relevant page to see references, videos and detailed explanations:
#ai  #marketing  #guide  #vendors  #applications  #graphic  @appliedai 
4 weeks ago by phil_hendrix
How Deloitte Assist innovated healthcare through voice control video YouTube20181111
Nick White - How Deloitte Assist innovated healthcare through voice control 1 view 1 0 SHARE SAVE Nick Skillicorn - Creativity and Innovation Published on Nov 11, 2018 SUBSCRIBE 104 In today's episode of the Idea to Value podcast, we speak with Nick White from Deloitte Sydney about a new innovation which has been receiving a lot of attention, called "Deloitte Assist". Deloitte Assist is a way for patients in a hospital to ask for help using their voice, so that nurses and doctors know exactly what the patient needs before arriving at their bedside. Historically, and still the case in most hospitals, the only way that a patient has to ask for help or attention is a button to press next to their bed. This could be help for anything from being in pain, to having fallen and being seriously injured, to wanting something to drink or asking for the TV channel to be changed. Not all requests are as urgent as others. Deloitte Assist was conceived after a Deloitte partner saw how his father suffered while he was in a critical condition in hospital, and asked himself if there was a better way to help both the patients and those attending to them. The solution uses Amazon's voice recognition features built into Alexa and its Echo devices, to allow the patients to speak with the system, letting it know what they need, and alerting the relevant nurses allowing them to prioritise their work much more effectively. The solution has already won several innovation awards, like the AMY Grand Prix (https://www.dtcollective.org.au/award...) and helping Deloitte be listed as one of Australia's most Innovative companies in 2018 (https://mostinnovative.com.au/).
#ai  #applications  #hc  #hospital  #interview  #video  @Deloitte 
4 weeks ago by phil_hendrix
These 100 Companies Are Leading the Way in A.I. | Fortune 20180108
These 100 Companies Are Leading the Way in A.I. CLICK TO ENLARGE THE GRAPHIC By NICOLAS RAPP and BRIAN O'KEEFE January 8, 2018 Whether you fear it or embrace it, the A.I. revolution is coming—and it promises to have an enormous impact on the world economy. PwC estimates that artificial intelligence could add $15.7 trillion to global GDP by 2030. That’s a gargantuan opportunity. To identify which private companies are set to make the most of it, research firm CB Insights recently released its 2018 “A.I. 100,” a list of the most promising A.I. startups globally (grouped by sector in the graphic above). They were chosen, from a pool of over 1,000 candidates, by CB Insights’ Mosaic algorithm, based on factors like investor quality and momentum. China’s Bytedance leads in funding with $3.1 billion, but 76 of the 100 startups are U.S.-based.
#ai  #applications  #verticals  #startups  #list  @CBI  #funding  #vc 
5 weeks ago by phil_hendrix
Vertical Beats Horizontal in Machine Learning Zetta Venture Partners 2016
Vertical Beats Horizontal in Machine Learning Signup for our newsletter here The best products in the world are made by vertically integrated businesses: Apple’s hardware to software; Amazon’s warehouses to websites; and Carnegie’s mines to mills [1]. Zetta is completely focused on investing in data and machine learning startups. We see lots of horizontal platforms and APIs that anyone can use to add some machine learning models to their application. However, machine learning has advanced to the point where customers expect better than commodity performance. We like to see startups vertically integrating their technical skills with the skills of domain experts and unique data acquisition to build applications with the level of accuracy required in commercial and industrial settings. This article will describe the state of machine learning, focusing on the importance of domain expertise in feature development and labeling data when building high accuracy models. We will then explore ways in which startups can get the requisite domain expertise and labeled data to build such models. Finally, we will consider some of the challenges in working with customers to develop software based on such models. We won’t teach anyone in the field anything (we recommend that ML practitioners skip the next section) but do hope that you benefit from our perspective having seen thousands of startups and talked to hundreds of customers, and understand a little more about how we think.
#ml  #advice  #data  #applications  #A+ 
5 weeks ago by phil_hendrix
AI Use Cases Comprehensive Guide appliedAI 2018
AI Use Cases Comprehensive Guide by appliedAI Artificial Intelligence is a constantly evolving technology with huge implications on business. Download our FREE whitepaper to understand the top use cases of AI in business. Table of contents: Introduction AI Use Cases Marketing Optimize Product, Pricing & Placement Optimize marketing spend Personalize recommendations Connect & leverage customer feedback Analytics Sales Forecast Sales Enable Sales Reps Automate Sales Activities Sales Analytics Customer Service Data Analytics Generalist solutions Specialized solutions FinTech HealthTech HR IT Operations Back office automation Industrials Other Self-Driving Cars Conclusion Additional resources
#ai  #usecases  #applications  #marketing  #sales 
5 weeks ago by phil_hendrix
The Machine on Your Team - How Marketers Are Adapting in the Age of AI Ampero Forrester 2018
The Machine On Your Team New Forrester Research Shows How Marketers Are Adapting in the Age of AI Do marketers understand the full impact of integrating artificial into their org structure? How are marketing leaders applying core AI technologies to toward mission-critical metrics? What kind of roadmap should marketers apply toward AI marketing platforms? Download this commissioned study, "The Machine on Your Team: How Marketers Are Adapting in the Age of AI," conducted by Forrester Consulting on behalf of Amplero, to learn more about how marketing leaders are evaluating and leveraging AI in 2018. + 63% of respondents believe they have too much data to process in order to gain actionable insights for their campaigns. + 84% of marketing leaders surveyed plan to adopt or expand their AI marketing platform initiatives within the next year. + 87% of C-level marketing execs believe human intervention with AI is necessary.
#ai  #marketing  #applications  #assessment  #study  @Forrester  @Amplero  #2018  #A+ 
5 weeks ago by phil_hendrix

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