Machine Learning Takes On Sepsis

By Deborah Borfitz, Senior Science Writer An early-stage startup is endeavoring to help clinical teams deliver high-value critical care with precision, and its first target is sepsis—a life-threatening complication of infection that kills one in three stricken adults and one in 10 kids. Its chief tactics are to “organize behavior at the bedside” and make […]

By Deborah Borfitz, Senior Science Writer

An early-stage startup is endeavoring to help clinical teams deliver high-value critical care with precision, and its first target is sepsis—a life-threatening complication of infection that kills one in three stricken adults and one in 10 kids. Its chief tactics are to “organize behavior at the bedside” and make machine learning a member of the team, according to James Courtney Fackler, founder of Rubicon Health as well as director of pediatric critical care medicine and associate professor of anesthesiology and critical medicine at John Hopkins Medicine.

Fackler led a session at the 2019 Next Generation Dx Summit on integrating artificial intelligence into clinical care for infectious diseases. He works full time in a pediatric intensive care unit (ICU) but is also an avid reader who believes in human intuition, he says, referencing the books Sources of Power (Gary Klein) and The Book of Why (Judea Pearl).

As a physician, it’s difficult to be precise when faced with 350 clinical data elements on a child coming out of surgery, Fackler says. The human brain can generally handle five to seven pieces of information at a time and, if it’s well-trained, perhaps 12 to 15.

In a 40-bed pediatric ICU, such as the one at Johns Hopkins, a physician might be looking at 14,000 data streams and “some of it is analog and some of it is wrong,” says Fackler. How well a diagnostic device improves patient care involves all 150 people on the team.

Machine learning could be playing a pivotal role to help solve some of the more perplexing problems, including earlier diagnosis of sepsis, he adds. “Mortality increases 8% per hour if antibiotics are delivered late.” Better patient outcomes also require care coordination.

“Goal-directed therapy is bad,” Fackler says. “It does not work; the goals are wrong.” The central problems are that sepsis is a complex condition and needed treatment is delivered late.

Rubicon Health is currently working on a machine learning algorithm for predicting sepsis, which has an 8% to 10% prevalence rate in the pediatric ICU, says Fackler. The goal is to do better than current methodologies that typically have a positive predictive value of around 19%—up to one in three cases “at best.” Asking additional questions not in the electronic health record (EHR) will save a life one in five times, he adds.

The idea behind the algorithm is to help the computer-clinician dyad know when a child is sick and chart out what the next 10-12 steps should be, including administration of an antibiotic within one hour, says Fackler. Rubicon is using a version of collaboration hub Slack to create a place for care teams to hold virtual huddles.

The MITRE Corporation was sent a dataset and, based on prescribed criteria, came up with a positive predictive value for sepsis at different times slices in the 24-hour period preceding its onset, says Fackler. “We’re still only in the 20% range. That’s not good enough, but it’s a decent place to start.”

In an article published earlier this year in Scientific Reports, Fackler and his colleagues discussed how they used publicly available retrospective data to make an earlier prediction of impending septic shock by applying three different machine learning techniques to the EHR data of 15,930 adult patients, producing a median early warning time of seven hours.

The paper introduces the notion of patient-specific positive predictive value. The detection method estimates the confidence with which a positive prediction is made, providing more reliable and actionable information to clinicians than would an alert alone, Fackler says.

Of the eight potential paths a clinician might take—based on machine-generated true and false positives and true and false negatives together with the doctor’s clinical judgement to treat or not to treat in each of those cases—Rubicon hopes to push systems toward the two where diseased people get treated. “The false negatives are what keep me awake at night,” says Fackler.

“Early diagnosis, if it leads to early therapy, is the only thing that will bend the cost curve,” Fackler says, noting he is making a case for team intelligence. “As a clinician you need to know when to turn the machine off and do things by hand.”

How SparkBeyond Is Using AI To Ask The Right Questions

By Benjamin Ross Six years ago, Sagie Davidovich and Ron Karidi wanted to see if they could find a way to harness all of humanity’s collective intelligence using artificial intelligence (AI). Now, their co-founded startup, SparkBeyond, is making waves in AI, using their platform to ask the questions about data we’ve never thought of asking […]

By Benjamin Ross

Six years ago, Sagie Davidovich and Ron Karidi wanted to see if they could find a way to harness all of humanity’s collective intelligence using artificial intelligence (AI). Now, their co-founded startup, SparkBeyond, is making waves in AI, using their platform to ask the questions about data we’ve never thought of asking before.

SparkBeyond’s problem-solving platform takes data provided by its customers and partners, and analyzes it, using open-source algorithms to look for unique patterns and identifiers.

The company is “vertically agnostic,” SparkBeyond’s Senior Data Scientist, Ryan Grosso, PhD, told AI Trends. The company has partnered with experts in insurance, financial services, pharmaceutical, and retail sectors among others. SparkBeyond’s platform is available through a standard software as a service (SaaS) licensing model.

Dr. Grosso says once the right questions are asked, companies can then find actionable insights in their data.

“Traditionally one would pull data in, clean up that data, do some [exploratory data analysis] to visualize that data, determine what kind of data they were interested in, and then build a machine learning model around it,” says Dr. Grosso. “This approach leaves companies in a state where they don’t really know what’s going on, but they’re able to predict A or B. “We focus on turning that on its head and start in the middle, asking the right questions, which makes it very easy to build a model around. If you don’t have good questions and good insights to plug into a model, it’s basically garbage in garbage out. There’s only so much model optimization you can do with garbage.”

The platform is built to answer clear, defined questions about data. Will customers buy this product? Is a client about to withdraw from an account?

“Many businesses have problems that are very clear and straight forward,” Dr. Grosso says. “SparkBeyond’s approach was to say, let’s ask millions of questions to see if the problem you’re trying to fix can be fixed by the questions we ask.”

SparkBeyond’s platform is a new way to ask those questions in an automatic fashion, with a key emphasis on transparency, says Dr. Grosso. The company takes data and shares transparent insights on them, creating what Dr. Grosso calls a “glass box” of interpretability.

SparkBeyond wanted to build a client-empowering platform, Dr. Grosso says. SparkBeyond is not a consultancy, but a software company that provides its platform to companies who then use their own resources to tackle their specific issue. For SparkBeyond, that means having the ability to deploy in all clouds, as well as supporting clients who don’t want data leaving their premises. “We see that many people want to use this tech and we want to support that.”

Dr. Grosso says the company’s results speak for themselves, whether it’s a major transaction company finding an additional $140 million in fraudulent charges in six weeks or an insurance company increasing automation from 9-33%. The platform was even able to generate new insights to old questions, recently calculating the likelihood of an individual surviving the Titanic sinking.

As a data scientist, Dr. Grosso points to the reduction of data necessary for these tasks as a major component to the platform.

“In these cases, we were able to reduce the amount of data [our clients] needed to purchase,” said Dr. Grosso. “We were able to find signal by interacting and testing and asking the right types of questions with a smaller data universe. To me, that’s incredible.”

SparkBeyond has a good foundation, Dr. Grosso says, with a good team that understands that their problem-solving platform has put them ahead of everyone else by six-seven years. This gives them confidence as they look to the future, rolling out additional capabilities in the next year focusing on knowledge management and understanding across different mediums.

“We’re reshaping the way people do data science and AI,” Dr. Grosso said. “[That creates] a space to be creative, collaborate, and come up with new ideas.”

Learn more at SparkBeyond.

Modzy Aims to Accelerate Enterprise AI Adoption with Pre-trained Models

By AI Trends Staff The consulting firm Booz Allen has announced Modzy, a product aimed at accelerating the deployment of AI applications in the enterprise. The company has assembled AI models from its own experience, from open source communities, and technology partners. Modzy provides an environment for the models to be uploaded, managed, and deployed. […]

By AI Trends Staff

The consulting firm Booz Allen has announced Modzy, a product aimed at accelerating the deployment of AI applications in the enterprise. The company has assembled AI models from its own experience, from open source communities, and technology partners. Modzy provides an environment for the models to be uploaded, managed, and deployed.

This is said to address barriers related to scaling AI to the enterprise by: providing a marketplace of pre-trained AI models; enabling data scientists and software engineers to more quickly integrate AI into applications; abstracting data pipelines and machine learning development frameworks from the models; giving administrators control of how models are deployed and governed; providing model transparency and early-stage explainability; being able to deploy AI on any on-premise or cloud-based infrastructure; employing patent-pending adversarial defensive techniques that filer poisoned data; and evaluating model vulnerabilities.

The product is to be ready for general availability in the spring of 2020. Customers can request access to the Modzy Early Access Program today.

Dr. Josh Sullivan, Senior VP of Booz Allen and a Modzy executive leader, said in the release, “Achieving the promise of AI is much more than training the next algorithm. It’s about giving organizations choice and having a predictable and repeatable way to rapidly deploy, manage, and secure AI models at enterprise scale. With Modzy, Booz Allen is challenging the idea that AI has to be custom-built for each department, project or purpose. By combining our deep domain and technical expertise with that of other leading AI providers, we’re helping the US government and companies deploy AI at a fraction of the price and time required to build models from scratch.”

A Briefing Note from Cognilytica Research on Modzy cited these challenges to scaling AI in the enterprise: lack of trust in models; limited consistency in model creation; lack of governance for model usage and best practices; and security threats to model usage. AI and Machine Learning Operations, or ML Ops, it described as being focused on the consumption-centric aspects of machine learning model usage and deployment, as opposed to model development.

The core aspects of ML Ops Cognilytica describes as: model discovery, model governance, model versioning, model monitoring and management and model security.

Cognilytica stated, “The market for ML ops tools and solutions is just now starting to emerge, and in this space, Booz Allen’s Modzy offering is filling this gap.”

Analysts at IDC maintains that the market for pre-trained, domain-optimized and “ready to use” AI and ML models is growing rapidly. In a market note on Modzy, IDC wrote, “Booz Allen is taking advantage of this trend in the market where companies want to transform and differentiate their businesses with AI-based offerings but don’t want to spend years developing and training their own algorithms.”

IDC credited Booz Allen with furthering an AI models market. “Modzy is one of the first AI/ML model marketplaces to emerge in the AI software platforms market. While Google, IBM, Microsoft, Amazon and others have pre-trained cloud-based AI/ML models available for sale, none of them have the concept of an open marketplace where other companies can also sell and offer their AI/ML models. The jury is out on how effective this approach may be, but it is a worthy goal to aim for.”

Hypergiant Industries, offering AI products and consulting services, has contributed models to the Modzy marketplace. Ben Lamm, CEO and founder of Hypergiant, is quoted in the Modzy release: “The Modzy platform is one step closer to ensuring that the government has the technology it needs to protect the American people. We are thrilled to apply our advanced AI capabilities to the marketplace.”

Learn more at Booz Allen.

Key Considerations in AI Vendor Selection, Deployment

The world of artificial intelligence is frightening. No, not the danger of an army of AI-powered robots taking over the world (though that is a bit concerning). The real fear is that the wrong vendor is chosen or the rollout handled poorly. After all, AI is complex, not fully mature, in some cases poorly understood, and […]

The world of artificial intelligence is frightening. No, not the danger of an army of AI-powered robots taking over the world (though that is a bit concerning). The real fear is that the wrong vendor is chosen or the rollout handled poorly. After all, AI is complex, not fully mature, in some cases poorly understood, and involves great changes to how an organization thinks and operates.

Much of the complexity stems from the fact that AI has no single meaning or definition. It is a combination of several elements (machine learning, natural language processing, computer vision and others). This means that use cases tend to be unique and complex. Companies not big enough to hire expertise rely deeply on consultants and vendors, likely more than in more familiar areas. AI is not for the corporate faint of heart.

So how should organizations approach AI?

The first step in any corporate initiative is to fully understand what is on the table. It seems almost needless to say that organizations must educate themselves about AI before taking the plunge. But, in this case, it’s so important that it is worth stating the obvious. They must assess what data they have to feed into the system and if remedial work is necessary to enable that data to be used.

Tractica Research Director Aditya Kaul suggests that organizations understand the difference between the AI platforms that process raw data to reach conclusions and perception-driven approaches that focus on the intricacies and nuances of language and vision. The next step is to experiment on a wide variety of use cases and settle on those that bring the greatest value to the organization. It is important to understand the metrics that will be used to gauge success, such as increased productivity or reduced costs.

Moving Ahead with AI

At that point, they are set to move ahead aggressively. “Once companies have a good understanding of the AI technologies and use cases, they can go [choose] a third-party enterprise-grade AI platform and build a robust framework around data and model warehousing that allows for efficient production-grade AI that can be swiftly deployed into client-facing products and services,” Kaul wrote to IT Business Edge in response to emailed questions.

This suggests deep changes, which makes choosing vendors an even more vital decision than better understood limited technology deployments. The stakes are high. It is a nascent field where some companies no doubt are selling vaporware and some perhaps haven’t figured out their own value proposition. It’s best to be very careful. “If your AI vendor won’t promise you real ROI, it’s because they can’t deliver,” wrote Ben Lamm, the co-founder and CEO of Hypergiant. “If a vendor is trying to skirt around a clear statement of value, you know they won’t serve you well in the long run.” Organizations should do the same block and tackling that is done for any other significant investment. Credentials should be checked, deep conversations conducted and a high comfort level achieved. “One of the most important things enterprises can look for in an AI vendor is understanding the success of their customer base,” wrote Peresh Kharya, the director of Accelerated Computing for NVIDIA. “Don’t be afraid to ask which of their customers are successful and how has their new AI solution benefited their business. Asking this question will help you gauge the tangible business value the vendor is promoting.”

Organizations can take steps to increase the odds that they will choose the right vendor. Dave Damer, the founder and CEO of Testfire Labs, offers three tips. The first two focus on precisely what the vendor will be providing. Companies should ask if the prospective vendor delivers packaged solutions, custom solutions or both, and if it has the necessary expertise in house or must outsource. Finally, the organization must understand what will happen after the deployment is done. “A lack of employee training or further customization of models can lead to unusable and/or ineffective technology,” Damer wrote.

Best of Breed or Single Vendor?

A longstanding debate in telecom and IT circles is whether platforms are better coming from a single vendor or “best in breed” arrangements in which the top elements are cherry picked and strung together. The single vendor platforms presumably are better integrated and have deeper and easier to use management functions, while the best in breed approach potentially offers better performance.

The pendulum is swinging toward multiple vendors, at least according to Tracy Malingo, the senior vice president of Product Strategy at Verint, which bought AI firm Next IT last December. “This is actually one of the biggest shifts that we’ve seen in AI,” Malingo wrote. “As major players have sought to lock in ecosystems and as companies have evolved in their understanding and needs for AI, we’ve seen the market begin to shift toward best of breed over single-source vendors. That trend will continue in the future.”

The bottom line is that AI is a slippery slope: That slope can arc toward more efficient operations and a healthier bottom line – or toward confusion, failed implementations and all the headaches that those results bring on. “Organizations should have a clear understanding of what business issues they’re trying to solve with AI,” wrote Guy Yehiav, the CEO of Profitect. “How will the technology they’re evaluating make an impact to both top and bottom line and what is the approach to roll it out across the business? If analytics and AI are done well, the impact should be quick and results tangible.”

Read the source article at IT Business Edge.

Here are 9 AI Use Cases Happening in Business Today

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it’s no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI. These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now […]

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it’s no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI.

These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now tapping advanced capabilities such as image recognition, speech recognition, the use of smart agents, and predictive analytics to reinvent the way organizations do business. Combined with other digital technologies, including the Internet of Things (IoT), a new era of AI promises to transform business.

Here’s a look at 10 leading AI use cases and how organizations can use them to gain a competitive advantage:

Marketing: AI for Real Time Data 

The use of real-time data, Web data, historical purchase data, app use data, unstructured data and geolocation information have introduced the ability to deliver information, product recommendations, coupons and incentives at the right time and place. AI allows companies to engage in personalized marketing and slide the dial closer to one-to-one relationships.

In addition, businesses gain competitive advantage by using machine learning and deep learning for sentiment analysis by analyzing e-mail and social media streams. More advanced systems can detect a person’s mood from photos and videos. This helps systems respond contextually and create more targeted marketing and interactions.

Retail Sales: AI for Voice and Image Search

Artificial intelligence in retail is transforming the way people shop and buy items ranging from clothes to cars. Voice search and image search are now widespread. Amazon and many other retailers now incorporate these tools in their apps. Next generation AI is also taking shape. For example, augmented reality (AR) lets shoppers view a sofa or paint color superimposed in their house or office. Virtual reality (VR) allows consumers to sit inside a vehicle and even test drive it without leaving home. Audi, BMW and others have developed VR systems for shoppers.

But the AI use cases don’t stop there. AI in retail extends to bots and virtual assistants that recommend products and provide information; algorithms that helps sales teams focus on high value customers and high probability transactions; and predictive analytics that factor in weather, the price of raw goods and components, or inventory levels to adjust pricing and promotions dynamically. Clothing retailer North Face, for instance, asks customers a series of questions related to a purchase at its website. Not only does this lead customers to the right product, it taps machine learning to gain insights that potentially lead to higher cart values and additional sales.

Customer Support: AI for Natural Language

AI in retail is emerging as a powerful force, but customer support is also harnessing the technology for competitive advantage. Bots and digital assistants are transforming the way support functions take place. These technologies increasingly rely on natural language processing to identify problems and engage in automated conversations. AI algorithms determine how to direct the conversation or route the call to the right human agent, who has the required information on hand. This helps shorten calls and it produces higher customer satisfaction rates. A Forrester study found that 73 percent of customers said that valuing their time is the most important thing a company can do to provide them with good online customer service.

Manufacturing: AI Powers Smart Robots

Robotics has already changed the face of manufacturing. However, robots are becoming far more intelligent and autonomous, thanks to AI. What is machine learning used for in factories? Many companies are building so-called “smart manufacturing” facilities that use AI to optimize labor, speed production and improve product quality. Companies are also turning to predictive analytics to understand when a piece of equipment is likely to require maintenance, repair or replacement.

For example, Siemens is now equipping gas turbine systems with more than 500 sensors that continuously monitor devices and machines. All this data is helping create the manufacturing facility of the future, sometimes referred to as Industry 4.0. Smart manufacturing–which merges the industrial IoT and AI–is projected to grow from $200 billion in 2018 to $320 billion by 2020, according to a study conducted by market research firm TrendForce.

Read the source article in Datamation.

A Strong Digital Base is Critical for Success with AI

By Jacques Bughin and Nicolas van Zeebroeck of McKinsey The diffusion of a new technology, whether ATMs in banking or radio-frequency identification tags in retailing, typically traces an S-curve. Early on, a few power users bet heavily on the innovation. Then, over time, as more companies rush to embrace the technology and capture the potential gains, the market […]

By Jacques Bughin and Nicolas van Zeebroeck of McKinsey

The diffusion of a new technology, whether ATMs in banking or radio-frequency identification tags in retailing, typically traces an S-curve. Early on, a few power users bet heavily on the innovation. Then, over time, as more companies rush to embrace the technology and capture the potential gains, the market opportunities for non-adopters dwindle. The cycle draws to a close with slow movers suffering damage.

Our research suggests that a technology race has started along the S-curve for artificial intelligence (AI), a set of new technologies now in the early stages of deployment. It appears that AI adopters can’t flourish without a solid base of core and advanced digital technologies. Companies that can assemble this bundle of capabilities are starting to pull away from the pack and will probably be AI’s ultimate winners.

Executives are becoming aware of what is at stake: our survey research shows that 45 percent of executives who have yet to invest in AI fear falling behind competitively. Our statistical analysis suggests that faced with AI-fueled competitive threats, companies are twice as likely to embrace AI as they were to adopt new technologies in past technology cycles.

AI builds on other technologies

To date, though, only a fraction of companies—about 10 percent—have tried to diffuse AI across the enterprise, and less than half of those companies are power users, diffusing a majority of the ten fundamental AI technologies. An additional quarter of companies have tested AI to a limited extent, while a long tail of two-thirds of companies have yet to adopt any AI technologies at all.

The adoption of AI, we found, is part of a continuum, the latest stage of investment beyond core and advanced digital technologies. To understand the relationship between a company’s digital capabilities and its ability to deploy the new tools, we looked at the specific technologies at the heart of AI. Our model tested the extent to which underlying clusters of core digital technologies (cloud computing, mobile, and the web) and of more advanced technologies (big data and advanced analytics) affected the likelihood that a company would adopt AI. As Exhibit 1 shows, companies with a strong base in these core areas were statistically more likely to have adopted each of the AI tools—about 30 percent more likely when the two clusters of technologies are combined. These companies presumably were better able to integrate AI with existing digital technologies, and that gave them a head start. This result is in keeping with what we have learned from our survey work. Seventy-five percent of the companies that adopted AI depended on knowledge gained from applying and mastering existing digital capabilities to do so.

Companies with a strong base in core digital technologies and big data analytics are more likely to have adopted an array of AI tools.

This digital substructure is still lacking in many companies, and that may be slowing the diffusion of AI. We estimate that only one in three companies had fully diffused the underlying digital technologies and that the biggest gaps were in more recent tools, such as big data, analytics, and the cloud. This weak base, according to our estimates, has put AI out of reach for a fifth of the companies we studied.

Leaders and laggards

Beyond the capability gap, there’s another explanation for the slower adoption of AI among some companies: they may believe that the case for it remains unproved or that it is a moving target and that advances in the offing will give them the chance to leapfrog to leadership positions without a need for early investments.

Read the source study at McKinsey.com.

AI Tiptoes Into the Workplace, in the Beginning of a Wave

There is no shortage of predictions about how artificial intelligence is going to reshape where, how and if people work in the future. But the grand work-changing projects of A.I., like self-driving cars and humanoid robots, are not yet commercial products. A more humble version of the technology, instead, is making its presence felt in […]

There is no shortage of predictions about how artificial intelligence is going to reshape where, how and if people work in the future.

But the grand work-changing projects of A.I., like self-driving cars and humanoid robots, are not yet commercial products.

A more humble version of the technology, instead, is making its presence felt in a less glamorous place: the back office.

New software is automating mundane office tasks in operations like accounting, billing, payments and customer service.

The programs can scan documents, enter numbers into spreadsheets, check the accuracy of customer records and make payments with a few automated computer keystrokes.

The technology is still in its infancy, but it will get better, learning as it goes. So far, often in pilot projects focused on menial tasks, artificial intelligence is freeing workers from drudgery far more often than it is eliminating jobs.

The bots are mainly observing, following simple rules and making yes-or-no decisions, not making higher-level choices that require judgment and experience. “This is the least intelligent form of A.I.,” said Thomas Davenport, a professor of information technology and management at Babson College.

But all the signs point to much more to come. Big tech companies like IBM, Oracle and Microsoft are starting to enter the business, often in partnership with robotic automation start-ups. Two of the leading start-ups, UiPath and Automation Anywhere, are already valued at more than $1 billion. The market for the robotlike software will nearly triple by 2021, by one forecast.

“This is the beginning of a wave of A.I. technologies that will proliferate across the economy in the next decade,” said Rich Wong, a general partner at Accel, a Silicon Valley venture capital firm, and an investor in UiPath.

The emerging field has a klutzy name, “robotic process automation.” The programs — often called bots — fit into the broad definition of artificial intelligence because they use ingredients of A.I. technology, like computer vision, to do simple chores.

For many businesses, that is plenty. Nearly 60 percent of the companies with more than $1 billion in revenue have at least pilot programs underway using robotic automation, according to research from McKinsey & Company, the consulting firm.

The companies and government agencies that have begun enlisting the automation software run the gamut. They include General Motors, BMW, General Electric, Unilever, Mastercard, Manpower, FedEx, Cisco, Google, the Defense Department and NASA.

State Auto Insurance in Ohio Ahead of the Curve

State Auto Insurance Companies in Columbus, Ohio, started its first automation pilot project two years ago. Today, it has 30 software programs handling back-office tasks, with an estimated savings of 25,000 hours of human work — or the equivalent of about a dozen full-time workers — on an annualized basis, assuming a standard 2,000-hour work year.

Holly Uhl, a technology manager who leads the automation program, estimated that within two years the company’s bot population would double to 60 and its hours saved would perhaps triple to 75,000, nearly all in year-after-year savings rather than one-time projects.

Cutting jobs, Ms. Uhl said, is not the plan. The goal for the company, whose insurance offerings include auto, commercial and workers’ compensation, is to increase productivity and State Auto’s revenue with limited additions to its head count, she said.

Ms. Uhl said her message to workers is: “We’re here to partner with you to find those tasks that drive you crazy.”

Rebekah Moore, a premium auditor at the company, had one in mind. Premium auditors scrutinize insurance policies and make recommendations for changing rates. They audit less than half of the policies, Ms. Moore said.

The policies that will not be audited then have to be set aside and documented. That step, she explained, is a routine data entry task that involves fiddling with two computer programs, plugging in codes and navigating drop-down menus. It takes a minute or two. But because auditors handle many thousands of policies, the time adds up, to about an hour a day, she estimated.

Starting in May, a bot took over that chore. “No one misses that work,” Ms. Moore said.

Is she worried about the bots climbing up the task ladder to someday replace her? Not at all, she said. “We’ll find things to do with our time, higher-value work,” said Ms. Moore, 37.

On State Auto’s current path, her confidence seems justified. If the company hits its target of 75,000 hours in savings by 2020, that would be the equivalent of fewer than 40 full-time workers, compared with State Auto’s work force of 1,900. The company plans to grow in the next two years. If so, State Auto would most likely be hiring a few dozen people fewer than it would otherwise.

UiPath Envisions One Bot Per Employee

Automation companies are eager to promote the bots as helpful assistants instead of job killers. The technology, they say, will get smarter and more useful, liberating workers rather than replacing them.

“The long-term vision is to have one bot for every employee,” said Bobby Patrick, chief market officer for UiPath. The company, which is based in New York, recently reported that its revenue more than tripled in the first half of 2018, to a yearly rate of more than $100 million.

Mihir Shukla, chief executive of Automation Anywhere, refers to his company’s bots as “digital colleagues.” In July, the company announced it had raised a $250 million round of venture funding, valuing the company at $1.8 billion.

The market for A.I.-enhanced software automation is poised for rapid growth, but that expansion, analysts say, will ultimately bring job losses.

Forrester Research estimated that revenue would nearly triple to $2.9 billion over the next three years. And by 2021, robotic automation technology will be doing the equivalent work of nearly 4.3 million humans worldwide, Forrester predicted.

In a dynamic global labor market, that is not a clear-cut forecast of 4.3 million layoffs. The bots may do work not previously done by humans, and people may move onto new jobs.

“But these initial bots will get better, and the task harvesting will accelerate,” said Craig Le Clair, an analyst for Forrester. “For workers, there will be a mix of automation dividends and pain.”

The recent research has examined jobs as bundles of tasks, some of which seem ripe for replacement and others not. So the technology’s immediate impact will resemble the experience to date with robotic software, changing work more than destroying jobs.

For Ms. Uhl of State Auto, the most persistent pushback has come not at the company but at home, from her two young sons, Christian, 9, and Elijah, 7, who are eager to glimpse the future.

Hearing their mother talk about robots at work, they keep asking her to bring one home. “It’s not the kind of robot you can see,” Ms. Uhl said she has told her disappointed sons.

Read the source article in The New York Times.

Overcome the Inertia That Keeps Businesses From Deploying AI – Here is How

By Harry Kabadaian, CEO of Fancy Lab, a digital marketing agency Artificial intelligence (AI) isn’t merely “important” to innovation and basic processes at the organization of the future, it’s indispensable. To thrive in that future, businesses already are in early-stage explorations to transform into AI-driven workplaces. But despite the high interest level in leveraging AI in business, […]

By Harry Kabadaian, CEO of Fancy Lab, a digital marketing agency

Artificial intelligence (AI) isn’t merely “important” to innovation and basic processes at the organization of the future, it’s indispensable.

To thrive in that future, businesses already are in early-stage explorations to transform into AI-driven workplaces. But despite the high interest level in leveraging AI in business, implementation remains quite low. According to Gartner’s 2018 CIO Agenda Survey, only four percent of Chief Information Officers (CIOs) have implemented AI. The survey report is careful to note we’re about to see more growth in “meaningful” deployments: 46 percent more CIOs had made plans for AI implementation by February, when the report was published.

t won’t happen instantly. First, you must understand your business in terms of goals, technology needs and the impact its adoption will have on employees and customers. Plenty can go wrong as you address any of those points. Here are a few tips to help achieve minimum resistance.

1. Treat AI as a business initiative, not a technical specialty.

Many organizations view AI’s implementation as a task for the IT department. That mistake alone could give rise to most of your future challenges.

AI is a business initiative in the sense that successful adoption calls for active participation throughout the process — not simply when it’s deployed. The same people currently responsible for running daily business processes must have real roles to help build and maintain the AI-driven model.

Here’s how it looks in real life:

  • The organization requires collaboration and support from data scientists and the IT team.
  • IT is responsible for deploying machine-learning models that are trained on historical information, demanding a prediction-data pipeline. (Creating that pipeline is a process unto itself, with specific requirements for each of the multiple tasks.)

The odds of finding success with AI implementation increase when the whole team is on board to acquire data, analyze it and develop complex systems to work with the information.

2. Teach staff to identify problems that AI can solve.

AI-driven enterprises often search out data scientists with deep knowledge of their business. A better approach would be teaching employees to identify problems that AI can solve and then guiding workers to create their own models. Your team members already understand how your business operates. In fact, they even know the factors that trigger specific responses from partners, customers and prospects.

IT can help businesses analyze and understand the context of each model. It also can plan its deployment using supported systems. Specifically, IT should be able to obtain answers on topics such as:

  • The usage pattern required by a particular business process.
  • The optimal latency period between a prediction request and its service.
  • Models that need to be monitored for update, latency and accuracy.
  • The tolerance of a business process to predictions delayed or not made.

Employees who tackle problems with an AI mindset can monitor business processes and learn to ask the right questions when it matters.

3. Allow business professionals to build machine-learning models.

A company trying to transform its complete scope of operations with AI might view the timeline as a bit slow. The current approach hinges on manually building machine-learning models. When asked, businesses managers ranked time to value among the biggest challenges. Respondents in the Gartner survey revealed their teams took an average of 52 days to build a predictive model and even longer to deploy it into production. Management teams often have little means to determine the model’s quality, even after months of development by data scientists.

An automated platform could transform AI’s economics, producing machine-learning models in hours or even minutes — not months. Such a platform also should allow business leaders to compare multiple models for accuracy, latency and analysis so they can select the most suitable model for any given task.

Equipping your staff with the right tools and skills empowers them to contribute to a system that’s optimized for your business. What’s more, automated platforms can help them create the models they need to transform processes.

Considering the many challenges businesses face when deploying AI, it’s understandable so many still lag behind. Organizations that have overcome these barriers can attest to AI’s power to revolutionalize business through process improvement and increased employee productivity.

End-use technologies require human participation as an input. Without human creators, technology can’t successfully morph into human roles.

Read the source article in Entrepreneur.

Fintech: Sector Can Benefit From Up To $512 Billion via Intelligent Automation

A new report from Capgemini’s Digital Transformation Institute reveals that, by 2020, the financial services industry could reap up to $512 billion in new global revenues through intelligent automation. The report, Growth in the Machine, demonstrates the advantages of applying the right combination of robotic process automation (RPA), artificial intelligence (AI), and business process optimisation in the sector […]

A new report from Capgemini’s Digital Transformation Institute reveals that, by 2020, the financial services industry could reap up to $512 billion in new global revenues through intelligent automation.

The report, Growth in the Machine, demonstrates the advantages of applying the right combination of robotic process automation (RPA), artificial intelligence (AI), and business process optimisation in the sector to achieve what Capgemini terms “intelligent automation”.

Capgemini surveyed 1,500 senior executives from 750 global organisations in retail and commercial banking, capital markets, and insurance. The survey covered organisations in the UK, US, France, Germany, Italy, the Netherlands, Spain, Sweden, and India, 42 percent of which had global revenues greater than $10 billion.

A business could already realise a 10-25 percent uptick in cost savings by applying RPA, according to Capgemini. However, that could potentially scale up to 30-50 percent with the application of AI-enhanced automation.

To date, automation technologies, such as RPA, have been implemented by the financial services industry to drive down costs and create new business efficiencies – mirroring how AI is being adopted by most industries, according to another Capgemini report this week.

Revenues or savings?

But leaders in the sector don’t see AI and automation in simple cost-savings terms, cautions the consultancy.

Leaders in the financial services industry have already begun taking automation directly to their customers, says Capgemini, adding that they are using it as a revenue generator rather than just a means to slash internal costs.

The report says that, on average, over one-third (35 percent) of financial services firms have seen a two to five percent increase in top-line growth from automation, with faster time to market and improved cross-selling being the key factors that influence gains.

Anirban Bose, head of Capgemini’s Financial Services Global Business Unit said, “The most visionary financial services firms have leaders with a sophisticated view of the potential impact that automation can have throughout their business. And they’re already reaping the rewards.

“Hundreds of billions of dollars in automation-generated revenue is up for grabs in the coming years. Only those companies that deploy this technology in a way that looks beyond cost-cutting and focuses on creating value for customers and shareholders will be able to win in the marketplace.”

Slow to adopt

With substantial gains within reach thanks to intelligent automation, it’s no surprise that an increasing number of financial services firms are considering deploying the technology on the front line.

However, despite the obvious opportunities, the adoption of intelligent automation has been slow to date. Only 10 percent of companies have implemented the technology at scale, says the report, with the majority struggling with business, technology, and staffing challenges.

The study finds that several factors are preventing organisations from moving beyond proof of concept to live deployment of intelligent automation systems.

For example, around four in 10 organisations (43 percent) are struggling to establish a clear business case. Many are also struggling to persuade leadership to commit to a cohesive intelligent automation strategy (41 percent.)

More, the successful deployment and scaling of automation programmes requires expert staff with a deep understanding of RPA and AI technologies. However, almost half of businesses (48 percent) say they struggle to find the right resources to implement intelligent automation effectively.

Meanwhile, 46 percent say that the lack of an adequate data management strategy is hampering progress, as AI-based automation algorithms require the right data to be available at sufficient volumes.

Capgemini reveals that only around one in four organisations has the technological maturity to implement cognitive automation technologies (comprising machine learning, computer vision, and biometrics). Most organisations still have traditional RPA, or – at best – natural language processing (NLP) in the backbone of their automation programmes.

Internet of Business says

Capgemini warns that exploring intelligent automation could be critical for the long-term health of the financial services sector, because of the growing threat from non-traditional players.

The study says that nearly half (45 percent) of organisations believe that so-called ‘BigTech’ players, such as Amazon and Alphabet/Google, will be their competitors in the next five years.

That much is certain. However, the report comes in the wake of two others this week: one from Capgemini on AI adoption in the enterprise, and another from Riot Research, suggesting that the AI bubble is set to burst.

Put the three reports together, and a granular picture emerges of AI adoption opportunities over the next five years, as a component of an overall trend towards automation.

Read the source article at Internet of Business.

Accenture: Most Health Organizations Can’t Ensure Responsible AI Use

Despite a growing interest in artificial intelligence, most healthcare organizations still lack the tools necessary to ensure responsible use of such technologies, finds a report from Accenture Health. According to the report, Digital Health Technology Vision 2018, 81% of healthcare executives said they are not yet prepared to face the societal and liability issues needed to explain […]

Despite a growing interest in artificial intelligence, most healthcare organizations still lack the tools necessary to ensure responsible use of such technologies, finds a report from Accenture Health.

According to the report, Digital Health Technology Vision 2018, 81% of healthcare executives said they are not yet prepared to face the societal and liability issues needed to explain their AI systems’ decisions. Additionally, while 86% of respondents said that their organizations are using data to drive automated decision-making, the same proportion (86%) report they have not invested in the capabilities needed to verify data sources across their most critical systems.

Kaveh Safavi, head of Accenture’s health practice, observed that the current lack of AI data verification investment activity is exposing healthcare organizations to inaccurate, manipulated and biased data that can lead to corrupted insights and skewed results. “The 86% figure is critical,” he stated, “given that 24% of executives also said that they have been the target of adversarial AI behaviors, such as falsified location data or bot fraud on more than one occasion.” On a positive note, the study found that 73% of respondents plan to develop internal ethical standards for AI to ensure that their systems act responsibly.

Kaveh Safavi of Accenture

As a growing number of AI-powered healthcare tools enter the market, hospitals, clinics and other healthcare organizations are using intelligent technologies in various ways to become more agile, productive and collaborative. “Until recently, AI was mainly used as a back-end tool but is increasingly becoming part of the everyday consumer and clinician experience,” Safavi noted.

AI’s ability to sense, understand, act and learn enables it to augment human activity by supporting, or even taking over, tasks that bridge administrative and clinical healthcare functions — from risk analysis to medical imaging to supporting human judgment. “In terms of value and cost-savings, there are many ways in which AI can improve and change healthcare,” Safavi observed. Accenture estimates that key clinical health AI applications can create $150 billion in annual savings for the U.S. healthcare economy by 2026.

Moving toward greater compliance

Healthcare organizations recognize the value of AI not only for its potential cost savings, but also for its ability to tackle entrenched issues related to sustainability and access. Adopters are also hoping that IT will help them address the growing healthcare workforce shortage and the increasing dissatisfaction of healthcare consumers. “AI can help increase productivity and personalization in healthcare in ways that few other technologies can,” Safavi explained.

Ultimately, healthcare organizations will need to turn to AI-powered automation to improve a wide range of services. “Because of this, the market will help drive compliance over time as trust both from consumers and clinicians is the only way to truly foster adoption,” Safavi predicted.

Read the source article in Information Week.