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Augmented Intelligence Story
The Best Way To Leverage Artificial Intelligence Is To Combine Its Superhuman Abilities With The Super-AI Abilities Of Humans
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by Kjell Carlsson, Ph.D. December 4, 2018
What do fully autonomous vehicles, virtual agents, and human-like robots have in common? They’re terrible role models for driving outcomes with AI , as they require big investments, take years to develop, and fail to meet expectations. In contrast, augmenting human intelligence with AI leverages both AI ’s superhuman advantages and humans’ advantages over AI to quickly create substantial business value. This report describes augmented intelligence to help application development and delivery (AD&D) pros design AI projects with the greatest nearterm transformative potential.
Key Takeaways
AI Is Terrible At Replacing Human Intelligence
AI struggles with tasks where there is a long tail of potential outcomes or alternatives, uncertainty, and insufficient data — i.e., essential aspects of every human job. And there is no sign that this will change in the foreseeable future.
Augmented Intelligence Empowers Humans With AI And Vice Versa
Augmented intelligence solutions combine the best of humans and machines. They use machine learning to analyze data and detect patterns at superhuman scale and leverage automation to act at superhuman speed. They leverage human expertise to go beyond the confines of the existing data, source additional data, reason, make judgement calls, and engage with other humans.
The Augmented Are Already Among Us
Thanks to easier stakeholder buy-in, quantifiable ROI s, lower R&D costs, fewer technological hurdles, and less change management, AI is currently augmenting jobs ranging from data center technicians and oncology nurses to sales executives and customer service agents. And a host of new augmented jobs is on the way.
Augmented Intelligence Unlocks The Intelligence In AI The Best Way To Leverage Artificial Intelligence Is To Combine Its Superhuman Abilities With The Super-AI Abilities Of Humans
by Kjell Carlsson, Ph.D. with Srividya Sridharan, J. P. Gownder, and Jeremy Vale December 4, 2018
Table Of Contents
2
4 Replace Human Intelligence With AI — And Brace For Failure
AI Is An Excellent Complement To Human Intelligence But A Lousy Substitute For It
Combine Human And Artificial Intelligence For Augmented Intelligence
Meet The Superhumans Using Augmented Intelligence Today
Augmented Intelligence Delivers Value Faster
8 Recommendations
Rethink AI For Augmented Intelligence
Related Research Documents
Automation, AI , And Robotics Aren’t Quick Wins
The Forrester Tech Tide™: Artificial Intelligence For Business Insights, Q3 2018
The Future Of Work: Intelligent Machines Whispering To Your Employees
The Technology-Augmented Employee
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What do fully autonomous vehicles, human-like robots, and virtual agents have in common? They try to use AI technologies to replace human intelligence — but they’re terrible at it (see Figure 1). While these examples may be extraordinary displays of technological progress, they’re also a cautionary tale of how hard it is to use AI to fully automate any job that requires a decently paid human. 1 That’s why Forrester thinks that job losses due to AI and automation will be just one-seventh of what some influential studies predict. 2 Unfortunately, thanks to movies, literature, overly optimistic marketing, and a general lack of AI understanding, these use cases to automate human intelligence are exactly the AI projects that enterprises think they should focus on — but they’re wrong. 3 These projects are:
Expensive, slow, and risky. At best, projects that look to replace human intelligence require scarce talent; at worst, they require years of expensive R&D with uncertain prospects. It has taken Google almost a decade to bring its first autonomous vehicle offerings to market, even though the tech sector’s combined recent investment in these projects is larger than the GDP of the poorest 40 developing countries put together. 4 And while human-like robots have been in development since the 1970s, it’s still uncertain when — or even if — the technology will be mature enough for widespread commercial applications.
Hard to operationalize. Yes, employees are likely to push back on new AI systems that they think might replace them — but this is actually less of a challenge than having to redesign and rebuild your end-to-end business processes in order to replace human intelligence. We could implement fully autonomous vehicles today — if we were willing to design and build a separate road network just for them. Switching exclusively to virtual agents for customer service could also work well — if you’re willing to re-engineer your entire business model to make your offerings so cheap that the higher demand and lower expectations outweigh the blowback from the decline in your customer service. 5
“AI is currently very, very stupid. It is really good at doing things our brains can’t handle, but not things that we can do very easily as humans.” (Andrew Moore, VP of AI , Google Cloud)
FIGURE 1 The Most Commonly Cited Examples Of AI Are Terrible
Human-like robots Self-driving cars Virtual agents
Extremely long time-to-value
Staggering development costs
Limited functionality
AI Is An Excellent Complement To Human Intelligence But A Lousy Substitute For It
AI fails at replacing human intelligence because no technology replicates the general-purpose nature
of human intelligence. Yes, deep learning involves neural networks, where each artificial neuron is a mathematical representation of a biological one, but the similarities end there. If you could scale a neural network to match the 100 billion neurons in a human brain, it would be no more intelligent than an insect. So is AI doomed? Far from it. Instead of a general-purpose intelligence that operates on a wide range of tasks, AI consists of a vast array of technologies that deliver superhuman performance on specific tasks (see Figure 2). 6 AI technologies:
Have superhuman abilities to analyze data . . . Machine learning (ML) technologies that are part of AI can analyze a breadth of data types and sources at a scale and speed far beyond the abilities of any person or team of people. Given enough data and computational horsepower, AI could analyze all of the data from every machine on your assembly line, each of your surveillance cameras, or every social network or stock market in the world — in near real time. With sufficient training, AI can detect patterns that are far too complex for humans to detect. AI regularly outperforms oncologists at evaluating medical images to determine whether a patient’s tumor is benign or malignant and has been used to predict the likelihood of heart disease from variations in vocal patterns that no human can detect. 7
. . . and enable actions based on data at scale. AI automation technologies like decision
management, robotic process automation, and chatbots excel at executing standard processes that are deterministic or relatively invariable at a speed, scale, and efficiency that dwarf human abilities. 8 This enables these technologies to convey the results of ML analyses at the right moment and in a form that enables humans to make a decision and partially or fully automate the resulting action. Swift Medical enables nurses to measure and classify the wounds they treat using computer vision and automatically generate the required Medicare documentation; nurses can focus on validating the documentation and treating patients. 9
› Lack humans’ ability to make decisions involving variety and uncertainty. Human jobs involve making a constant stream of decisions based on incomplete data. We have ways to overcome this; we gather additional information, reassess based on the specifics of a situation, and reason our way toward conclusions. But when AI solutions face situations for which they lack the right data — because it wasn’t available at the time of training or isn’t visible to the system — they make abysmal decisions. This inability to make accurate decisions when fed insufficient or biased training data has led to high-profile and often inexcusable AI failures: Uber’s self-driving car killed a pedestrian; Google’s Vision API labeled two black people as gorillas; and early versions of IBM’s Watson for Oncology suggested inaccurate cancer diagnoses and inappropriate treatment.
FIGURE 2 AI ’s Superhuman Abilities And Less-Than-Human Failings
Characteristic
AI delivers superhuman insight
AI automates standard processes
AI struggles with uncertainty and judgment Strengths and weaknesses
• Processes data beyond humans’ abilities to do so • Performs analyses more powerful than any mind • Finds patterns too complex to perceive • Lightning speed
• Effectively tackles tasks you feel bad asking a human to do • Takes rapid action based on human direction
• Struggles with the long tail of possible situations • Cannot ll gaps in context • Can neither reason nor explain • Struggles to instill trust and build engagement
Superhuman ability
So how do enterprises use AI to rapidly create substantial and sustainable value? They combine the strengths of human and artificial intelligence into augmented intelligence. Specifically, they use AI to augment the intelligence and performance of employees undertaking difficult, valuable jobs in the organization. In the process, they boost not just individual outcomes and productivity, but also the employee experience. Forrester defines augmented intelligence as:
The use of AI to improve a human’s ability to do their job combining machine learning technologies for processing and analyzing data at scale; technologies for automating and orchestrating standard processes; and human input, decision making, and action.
Meet The Superhumans Using Augmented Intelligence Today
A rapidly increasing number of jobs are starting to leverage augmented intelligence to make better decisions and dramatically boost their productivity (see Figure 3). With augmented intelligence:
› Oncology nurses spend more time with patients. Tasked with identifying patients at risk for cancer, nurse navigators at HCA, the largest for-profit hospital operator in the US , spent most of their time combing through documentation, shadowing physicians, and entering data. HCA worked with Digital Reasoning to develop a solution using natural language processing to continuously mine test results to identify patients with a high likelihood of cancer, trigger workflows, and
What do fully autonomous vehicles, virtual agents, and human-like robots have in common? They’re terrible role models for driving outcomes with AI, as they require big investments, take years to develop, and fail to meet expectations. In contrast, augmenting human intelligence with AI leverages both AI’s superhuman advantages and humans’ advantages over AI to quickly create substantial business value. This report describes augmented intelligence to help application development and delivery (AD&D) pros design AI projects with the greatest nearterm transformative potential.
