Trending in 2016: Next Machina. Augmented Intelligence.

Trending in 2016: Next Machina. Augmented Intelligence.


From business disruptors to innovative thinkers, machine learning specialists to data linguists, healthcare advocates to wealth management gurus, e-commerce thinkers to customer experience futurists, we have quite the variety of thought leaders at CognitiveScale. All of us are intrigued by the potential of cognitive computing and augmented intelligence to transform business. In the tech world, it’s an end-of-year tradition to speculate about the year ahead. This holiday season, we launch our new blog by exploring the key trends driving our industry and revamping the business world. Read on as CognitiveScale’s multi-disciplinary team explores the technology innovations that will have tremendous impact on all professions in 2016.

Paul Sparta, Board Member, @paulsparta

“Digital exhaust” is an unusual term that means exactly what is says — the leftover data by-product of any digital transaction. Digital exhaust can be generated by a 30-year-old mainframe, a mobile phone, or even a home appliance. Unlike automobile exhaust, digital exhaust does not diffuse into the environment; while sometimes deleted, it most often gets stored in logs, legacy data structures, and, increasingly, virtual warehouses called “data lakes.” Just as a turbocharger harvests automobile engine exhaust to extract energy for increasing power and fuel efficiency, harvesting digital exhaust can provide hidden insights and context — value that is otherwise difficult or impossible to obtain in any other way. As it turns out, there is much value in digital detritus that can be applied to a wide range of problems, offering a huge time-to-value improvement over traditional business intelligence or analytics methods. There’s gold in those fumes, and someone’s going to extract it.

Forget HAL. Think JARVIS.

Manoj Saxena, Executive Chairman, @manojsaxena

As a species, we are going through the fourth most significant inflection point in the 12,000 years of human history — from Sumerian cuneiform writing around 3500 BC, to the printing press by Gutenberg in 1440, to the World Wide Web by Tim Berners-Lee in 1990, to the rise of present-day digital networks. The evolution of Homo sapiens into Homo digitas will happen through the massive convergence of mobile, cloud, social, big data, and artificial intelligence technologies. Unlike Homo sapiens, who relied on local resources and knowledge centered around their body and brain, Homo digitas will tap into digital networks through mobile, wearable, and soon embedded devices that will augment thinking, learning, working, and everyday living. Think of it as your own personal JARVIS. The legal profession, for example, will transition from humans consulting bookshelves to humans tapping legal databases managed by “deep learning” algorithms. Everything from how banks provide advice to clients, to how insurance companies gauge and underwrite risk, to how healthcare providers deliver care and process claims, will be shaped by a new emphasis on digital networks and augmented intelligence.

The System Evolution To Insights

Akshay Sabhikhi, CEO, @sabhikhi

When we use a search engine or otherwise tap into an information storage system, we’re relying on technologies that presume we know what we want to do, or what questions to ask. We’re often inundated with so much information that we don’t even know what we don’t know. Playing off the IT terms “Systems of Record” and “Systems of Engagement,” Forrester has coined “Systems of Insight,” which aptly describes how cognitive computing is shaping the next generation of human engagement. Systems of Insight understand consumers and employees deeply through every interaction and touch point, learning continuously to generate highly contextual insights that engage users to realize very specific outcomes, such as reducing emergency room visits, decreasing call center volume, and improving shopper experience. As with the advent of the Internet — where the specification for served content was driven through the HTML standard — a specification is emerging for the world of Insights that will dictate how an Insight is constructed, experienced, and delivered. To stay ahead, businesses or enterprises need to determine how Systems of Insight can help them offer greater value to customers as well as employees.

Exponential Performance Through Augmented Intelligence

Matt Sanchez, CTO, @MattSanchez

Over the past 50 years, we’ve experienced an exponential increase in digital information. Even with the best tools in the world, humans cannot understand, process, or derive all possible insights from digital data by themselves. Machine intelligence can help, but in order to be useful it must drive exponentially better outcomes for the people and businesses involved. What if augmented intelligence could achieve exponential patient outcomes through contextualized and deeply personalized insights for patients to nudge them towards optimal care outcomes? Or demonstrate an exponentially improved shopping experience by unlocking the unique DNA of every shopper and learning how to tailor individual mobile, e-commerce, and in-store experiences? Or drive exponential workforce productivity by guiding and advising each employee towards optimal productivity in core business processes, reducing cost and lost time? These are just a few examples of exponential performance we will see unfold in 2016 as augmented intelligence is increasingly applied to deep domain and industry problems.

Measure Your Point of Influence

Shaku Selvakumar, VP, Marketing and Communications, @ShakuS

The always on, always sharing consumer will continue to challenge the already beleaguered CMO, who faces an infinite flood of data about consumer behavior, and finite time to understand and act on it. With social, mobile, and device data cluttering the channels, point of influence will become as critical as point of purchase. Fresh and contextual customer insights surfaced at the point of influence will be highly sought after by business leaders. According to Gartner, 89 percent of marketing leaders expect to compete primarily on the basis of customer experience by 2016. The CIO, CMO, and CDO (Chief Design Officer) trifecta will become a key imperative with a strong sales alignment to positively impact the experience continuum. Expect more blurring lines on who owns the technology budget, as success will depend on the ease and speed of collaboration. Finally, expect more companies to adopt machine learning technologies to help develop an intimate understanding in order to provide greater and more consistent value and utility.

Retailers Capturing Share of Wallet and Share of Time

Nij Chawla, GM, Commerce Group and Market Development, @neerajc77

Aditya Rustgi, Director of Product Management, @arustgi

There are over 800,000 digital retailers and growing. With digital now ubiquitous, but delivering spotty usage patterns, retailers must focus on providing real value to customers. Brands must strive to connect through deep human engagement. Building this relationship starts by doing more than just putting up a digital storefront. Retailers must focus on delivering curated insights in the form of hyper-personalized shopping advice and micro experiences that cater to each individual’s unique interests. However, with an explosion of knowledge, curating billions of data points into valuable insights has been nearly impossible. We predict an increased investment in cognitive technologies by retailers in order to capture shoppers’ share of wallet and time.

Making a Cognitive System Speak Your Language

Peter Nuernberg, Director of Engineering

A cognitive system, like any system that works probabilistically, can occasionally produce surprising results. One particularly difficult challenge is presenting non-intuitive and even sub-optimal results in a consumable way. The UI and UX challenges surrounding cognitive computing are receiving increasing attention. How can a cognitive system “explain” its reasoning in a way that engenders both understanding and confidence in users, even when those users don’t accept the results of that reasoning? In 2016, innovators will be looking to build systems that address this issue successfully, as they will have a huge competitive advantage.

Automation (Not Curation)

Dr. Joydeep Ghosh, Board Member

Ayan Acharya, Research Engineer in Machine Learning

The confluence of deep learning and cognitive computing will yield insights from diverse sources, including images/video, text, speech, and databases. A decision-theoretic framework will emerge to collate and reconcile information distilled from this multitude of sources, to produce results that are more robust, accurate, and actionable. Furthermore, instead of humans curating features for a given prediction or analysis problem, 2016 will allow for learning richer representations automatically from the multitude of data in social media and the web. Google, MSR, Metamind, and Baidu are all marching toward a merger between Bayesian non-parametrics — a tool for automated model selection — and deep learning, a form of representation learning.

Weeding Out Big Data Problems With Learning and Enrichment

Jacy Legault, VP, Products, @JacyLegault

Daniel Morris, Director of Product Management, @demorris

In 2000, you could buy the fastest supercomputer on the planet, Ascii White, for $110 Million. In 2016, $1000 is all you need get the same computing power in the Cloud. These economics will make enterprise-scale cognitive computing applications come alive. The current market leaders in every industry will be disrupted by sensing and learning applications that forever change how businesses engage their customers and employees. Solving the big data problem will extend beyond the acquisition, storage, and analysis of data with big data platforms. With augmented intelligence, success lies at the nexus of people, process, and technology. In 2016, applications will need to understand not only the data they are storing, but also predict what will happen in the future, engage with customers and employees, surface hidden insights, and continuously learn and improve over time.

From Healthcare Volume to Value: The Continuous Care Imperative

Charles Barnett, President, Healthcare Group

Andrew Chen, Director of Product Management, @AndrewChenGGL

In recent months, multiple reports project that healthcare costs will continue to outpace general inflation, driving up out-of-pocket healthcare expenses. The Commonwealth Fund reports that nearly one quarter of primary care physicians in the U.S. say they are unprepared to care for the sickest and frailest patients. At the same time, the CDC projects that by 2020, 157 million Americans will have one or more chronic conditions. In 2016, many U.S. healthcare providers will adopt the imperative of “volume to value,” using augmented intelligence from a confluence of digital tools to provide continuous care to their patients. Increasing economic pressure on healthcare organizations to drive better quality outcomes will see more consumer health devices connect with professional health systems to drive marked improvements in a patient’s chronic condition over time. These technologies will be marketed to relatively healthy people or patients pre-disposed to become healthy, in effect shifting more control and responsibility to the consumer and encouraging new personal health behaviors enabled by digital technology.

Workforce Productivity — Eating Your Own Dog Food

Pankaj Vaish, GM and VP, Solutions Delivery, @vaishpankaj

We’re seeing an explosion of startups that provide digital insights and amplified intelligence to improve organizational efficiency, employee engagement, customer relations, service delivery, and more. These new companies and business models draw upon a complex mix of mobility, big data, analytics, cloud, and cognitive computing technologies, but they are still largely designed and engineered through traditional methods. Competitive pressures and speed-to-market will force startups to continuously evolve the way they deliver products and solutions. In 2016 we’ll see the rise of development platforms that improve collaboration by surfacing timely information each individual needs to know to do their job better, as well as processes and methodologies that automatically improve over time, identify skill gaps, or track status by looking at the code repositories.

No Screen in ‘16

Josh Segars, Creative Director, @creatium

The user experience revolution will not be televised in 2016. Traditional visual interfaces navigated by clicks, taps, and swipes will have to evolve with improving voice-activated software, the myriad of wearable and Internet connected tech, and highly anticipated improvements in virtual and augmented reality (VR and AR). Fortune Magazine estimates that AR alone will be a $150 billion market by 2018. The change isn’t just coming, it’s coming in force. We wager that in 2016 more and more people will be moving away from the screen.

Domain Experts Becoming Less Integral To Schema Design

Hannah Lindsley, Data Linguist, @hlindsley

In 2016, rules-based systems will converge with statistical approaches in data analysis, manifesting in several key ways. First, the role of the human domain expert will move away from from traditional activities like rule generation and schema design, and toward more curatorial activities like data cultivation. Second, neural nets and machine learning models will increasingly resemble features of common language, rather than the features of specific industries, and we’ll see a shift toward abstract representations for storage of pre-computed or learned information. Finally, feedback mechanisms will become a hallmark of top-performing and responsive digital services.

Equipping, Training, and Enabling an Adaptive Workforce

Darrell Walker, VP, Customer Success and Enablement

Companies are increasingly challenged by digital transformation of roles, skills, and technologies in the workplace. Employees in every industry face increasing pressure to work faster and produce measurable business outcomes. In 2016 the most competitive companies will equip, train, and enable employees to streamline processes and track ROI. Instead of “boiling the ocean,” companies will attempt to limit scope and prove value quickly, create repeatable processes, and treat pilot projects as the first step of multi-phased solutions. This approach supports organizational transformation by improving the ability of employees and customers to adapt, one of the most valuable and strategic activities for investment today.

Your Customer Has New Rules

Bob Horn, VP, Worldwide Sales, @bobhorn90

Whether it’s customers, patients, members, or employees, the old set of rules of engagement is no longer relevant. Your users require a new level of responsiveness, contextual awareness, and learning. The customer of 2016 simply won’t care about your application, your hours of service, your process, or how your back office works. But your audience will care that you get to know them and learn about them. They have given you permission to give them value. In 2016, companies must adapt and completely reimagine engagement throughout all aspects of company involvement.


February 6, 2016 / by / in , , , , ,

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