Artificial intelligence (AI) will enable care that is more personalized, proactive and smarter than ever before – abating acute events through early, accurate detection. Veering from treatment to prevention will disrupt traditional business models. 

AI-based applications can now listen to one single heartbeat and detect congestive heart failure (CHF) with 100 percent accuracy. It’s not a stretch to imagine that the dreaded nightmare of ending up in an emergency room (ER) on a gurney has now morphed into a new reality – one where you can proactively detect CHF and then walk into your doctor’s office to discuss options for managing it. 

Prevention in this way is powerful. Yet, it has proved elusive for many reasons. None are more telling, perhaps, than the inability to continuously listen and harness our bodies’ voices and messages. 

Preventative services constitute 3 percent of overall U.S. healthcare spend. Almost 75 percent of all healthcare spend is tied to treatments for patients with chronic conditions. These figures trend even higher for publicly funded programs – as high as 83 percent for Medicaid and 96 percent for Medicare. 

We expect AI will significantly enhance our ability to proactively and accurately detect the likelihood of acute events for a patient with chronic conditions and minimize expensive and unnecessary ER visits or hospital admissions. We also expect it will transform chronic care, prevention and waste. 

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Artificial Intelligence (AI) promises to make the human race smarter. Raymond Kurzweil has made predicting the Singularity — when artificial intelligence exceeds human intelligence — a cottage industry. Is AI going to make us all smarter, or are we already as smart as we can handle?

REPETITIVE STUPIDITY SYNDROME

Some of our issues are cognitive, such as our inherent inability to estimate exponential functions. Many US political leaders, including the governor of Arizona, couldn’t understand how quickly COVID-19 could spread — pesky exponentials! — so infections and deaths are spiking here, while other states and countries have successfully contained the disease.

Human cognitive gaps, well documented in The Logic of Failure, explain why we keep making the same mistakes over and over.
There’s also our fast leaps-to-conclusions mind and our slow, analytical mind. The fast mind is usually correct on everyday issues, but complex problems baffle it.

COUNTERFIT INTELLIGENCE

This is why pundits love to offer simple, short, and irrelevant punchlines that inflame emotions rather than encourage thought. 

Collecting evidence, analyzing data, teasing out causality, making hypotheses, and testing their validity is hard work. That’s why we have highly trained people spending their lives doing just that. And they make mistakes too!

CULTURAL TRANSMISSION VS EVOLUTIONARY SELECTION

In the paper, “Why Aren’t We Smarter Already: Evolutionary Trade-Offs and Cognitive Enhancements” two scientists consider the potential of drugs for cognitive enhancement. They consider how evolution has shaped our cognitive functions, looking at two major problem examples.

The first type is “inverted U-shaped performance functions.” These are common in problems where the goal is to maximize an outcome, but with a cost involved. For instance, you’d like to get married. When do you stop searching and settle on one person?

The other cognitive problem reflects the limits of the human IT system: Our brains. Cross-functional dependencies can cripple a cognitive enhancement, whether it is natural, artificial, or drug-induced. 

UNLIMITED MEMORY

One example: A man with a seemingly unlimited memory who found it difficult to remember faces. Instead of generalizing a face, he remembered faces as an ever-changing sea of patterns. Maybe infinite storage isn’t a good idea, at least for people.

Or consider savants, who display extraordinary talents in some domains at the cost of huge deficits in other domains. Marvin Minsky’s Society of Mind proposed this sort of cooperative model of human intelligence back in the 1980s. It looks like he wasn’t too wide of the mark.

THE TAKE

Perhaps intelligence isn’t the unalloyed advantage smart people would like to believe it is. Likewise, maybe AI isn’t going to be as beneficial as we’d like to believe.

Intelligence isn’t an end in itself, but a tool that allows us to understand and shape our environment. If people willfully abandon facts, evidence, and logic, it doesn’t matter how “intelligent” they are. They’ll get the wrong answer and damage themselves, and, sometimes, the rest of us.

The two goals of AI should be to extend the fast-thinking part of our intelligence, so we can apply more intelligence to the areas where we are most likely to make mistakes. That is an ideal use case for embedded mobile device intelligence.

The second goal should be where we’ve made the most progress: Domain-specific expertise, such as interpreting medical imaging. This will put a lot of expensively trained professionals out of business, but improve the quality of life for the rest of us.

The idea of the “Singularity” where machine intelligence finally exceeds human capability and wonderful things start happening will fail because most people — and cultures — can only stand so much “intelligence.” But let’s take what we can get instead of expecting the impossible.

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Chatbots and virtual assistants that don’t provide the right answers to customer queries can result in a frustrating digital experiences for customers – particularly those chatbots programmed without deep learning capabilities.

Deep learning is essential for automated chatbots to understand natural language questions and to provide the right answers, which is something that AI-powered search firm Lucidworks has taken on board. The company recently launched Smart Answers for chatbots and virtual assistants – a solution the company says will help to avoid frustrating digital support experiences and long customer support wait times.

According to Lucidworks, companies rely on digital portals to provide information to users, whether digital commerce customers looking for product information before purchase, employees hunting for an HR document, or someone looking for an airline’s updated cancellation policies. Information is often scattered across disparate silos and is impossible for a user to locate using natural language questions.

Smart Answers on Lucidworks Fusion aims to help employees and customers resolve issues via chatbots or virtual assistants without the need for additional support or ‘irrelevant’ search results.

Lucidworks CEO Will Hayes explains that customers expect more conversational interactions with a chatbot or virtual assistant that understands natural language questions, and is quickly provides the correct answers. 

“In the current environment, it’s important to meet customers where they are, especially if they’re moving from a real life experience to digital,” he says.

“Our work over the years with Fusion has been focused on understanding a user’s intent. Digging for answers wears their patience, wastes their time, and can even motivate them to seek out a competitor. Being able to understand what your users are asking for in their own words and returning the best answer instantly, allows companies to provide a natural conversational experience digitally. With Smart Answers, we’re cutting down time-to-resolution, increasing customer retention, and powering conversational experiences for users.”

The company cites Red Hat as one of its customers.

“We conducted an A/B test where we introduced a self-solve based homepage to some customers. There was an increase in traffic that confirmed that customers are really motivated to self-solve and we saw a seven per cent decrease in support case creation for customers who were given the self-solve homepage,” comments Red Hat principal software engineer Manikandan Sivanesan.

Lucidworks states that Smart Answers on Lucidworks Fusion enhances conversational applications to help users help themselves by delivering immediate and contextual responses that drive engagement and satisfaction.

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Software robots designed to handle routine work around the office are undertaking more ambitious jobs.

By combining robotic process automation with machine-learning capabilities, software makers are developing robots that can tackle higher-level workplace functions, including many that require a degree of judgment.

Some of these new roles include verifying a signature on a check, assessing insurance claims and detecting fraud in paperwork.

Most software robots in use today are programmed to complete straightforward numerical tasks, such as processing weekly payrolls or reviewing employee expense reports, which can be based on a template.

“Organizations want to automate ever more complex, wide-ranging activities, so we’re pushing the boundaries of what RPA can do,” said Pat Geary, chief evangelist at Blue Prism Group PLC, a U.K.-based robotic process automation vendor with roughly 1,300 customers.

Blue Prism is testing an AI-enabled platform for insurers that can interpret the validity of claims and make recommendations for human examiners. With approval from the examiners, the robot can process the claim, significantly reducing the time and effort needed to handle a case from start to finish, Mr. Geary said.

UiPath Inc., an RPA maker based in New York, is working on an AI-enabled software robot that can verify signatures on checks deposited in ATMs.

“Checking a signature takes two seconds for a person,” said Daniel Dines, UiPath’s chief executive. But if you want to automate the entire banking process, he added, “this is the only missing piece.” Unlike many instant electronic-banking transactions, such as mobile payments or transfers, checks deposited in ATMs need to be manually verified, prompting some banks to withhold the funds.

The problem is that legitimate signatures—on checks, contracts or other documents—tend to vary in ways that are difficult for automated systems to recognize and clear, Mr. Dines said. Through machine learning, robots that continually repeat the task can be trained to disregard these minor differences without losing the ability to flag suspicious cases.

In early testing, these trained robots have had a success rate of 80%, where checks were cleared with absolute certainty. For the remaining 20%, the robots were less sure and sent the checks to human tellers.

In another initiative, UiPath is developing robots that can identify and extract the most valuable data from reports with a mix of unstructured formats, sources and information.

The Canadian unit of energy company Chevron Corp. is testing a version of the tool designed to extract critical operating data from drilling and completion reports. Such reports are produced for dozens of oil wells every day; they range from 75 to 300 pages.

Until now, the task had been handled by staff analysts who went through reports page by page, eating up time and resources.

The AI-powered robot taps a Microsoft Corp. tool designed to identify and gather desired data, and puts the data into a common format. The robot then assigns a value rating, based on its interpretation of the data, and stores it in back-end systems for analysts’ review.

The UiPath technology would allow Chevron employees “to analyze data faster and to gain deeper insights,” a Chevron spokesman said.

These and other smart capabilities are putting Blue Prism and UiPath at the front of a fast-growing market, according to Forrester Research Inc.

An estimated 60% of large companies world-wide deployed some form of RPA technology last year, lifting total annual spending on software robots by 57% to $680 million, according to tech-industry research firm Gartner Inc. It expects such spending to reach $2.4 billion by 2022.

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