Artificial Intelligence

How Artificial Intelligence Will Impact The Insurance Industry

Shutterstock

Shutterstock

Published in

forbes_1200x1200.jpg

If you’re like most people, calling an insurance company isn’t among your favorite activities. That’s because the insurance industry is one of the least innovative areas for customer experience, meaning that customers typically come away from their interactions disappointed and dissatisfied. However, things are definitely changing, and artificial intelligence is playing a large role. The fast-growing technology has the potential to disrupt the entire industry and greatly improve the insurance customer experience.

Artificial Intelligence In The Claims Process

The insurance agency is notorious for its outdated processes. Filing a claim often looks the same today as it did decades ago because the industry isn’t consistently leveraging new technologies that are available to them. If an employee is busy or on vacation, a claims request could sit still until the right person is back. The outdated processes make it harder for agents by increasing the workload and forcing them to work with antiquated systems and frustrated customers.

However, AI can be applied to improve the claims process. Claims currently are touched by multiple employees. However, a new process of “touchless” claims doesn’t require any human intervention. This process uses artificial intelligence and other technology to report the claim, capture damage, audit the system, and communicate with the customer. The potential here is huge, as the process could allow clients the chance to file claims without having to wade through red tape.

Companies that have already automated some aspects of their claims process have seen a significant reduction in processing times and quality. AI-powered claims could also fight against one of the most costly elements of the insurance industry: fraudulent claims, which cost the industry more than $40 billion a year. Instead of relying on humans to manually comb through reports to catch inaccurate claims, AI algorithms can identify patterns in the data and recognize when something is fraudulent.

Future Of AI And Insurance

The industry is definitely ripe for AI disruption. Customers expect to be able to interact with companies through modern technology; a recent survey found that 74% of consumers say they would be happy to get computer-generated insurance advice.

Many insurance companies are already using artificial intelligence to some degree, and the number of companies following in their footsteps is sure to increase dramatically over the coming years. Artificial Intelligence has never been less expensive or more accessible, which means most companies don’t have a reason not to adopt it in at least some form.

Chatbots

Chatbots work through messaging apps many customers already have on their phones, which makes them a natural next step in customer interaction. In order to truly be effective, chatbots must have natural language processing and sentiment analysis so they can understand what customers are really asking. Effective chatbots can process concerns that are either typed or spoken from customers and provide personalized service. In the insurance space, chatbots can be used to answer basic questions and resolve claims, as well as sell products, address leads, or make sure customers are properly covered by their insurance. (Read More...)

How AI Is Taking the Scut Work Out of Health Care

CSA IMAGES/PATTERN COLLECTION/GETTY IMAGES

CSA IMAGES/PATTERN COLLECTION/GETTY IMAGES

Published in

hbr.jpg

When we think of breakthroughs in healthcare, we often conjure images of heroic interventions — the first organ transplantation, robotic surgery, and so on. But in fact many of the greatest leaps in human health have come from far more prosaic interventions — the safe disposal of human excrement through sewage and sanitation, for example, or handwashing during births and caesarians. 

We have a similar opportunity in medicine now with the application of artificial intelligence and machine learning. Glamorous projects to do everything from curing cancer to helping paralyzed patients walk through AI have generated enormous expectations. But the greatest opportunity for AI in the near term may come not from headline-grabbing moonshots but from putting computers and algorithms to work on the most mundane drudgery possible. Excessive paperwork and red-tape is the sewage of modern medicine. An estimated 14% of wasted health care spending — $91 billion — is the result of inefficient administration. Let’s give AI the decidedly unsexy job of cleaning out the administrative muck that’s clogging up our medical organizations, sucking value out of our economy, and literally making doctors illwith stress.

Here’s just one example of the immediate opportunity: Each year, some 120 million faxes still flow into the practices of the more than 100,000 providers on the network of athenahealth, the healthcare technology company where I’m CEO. That’s right: faxes. Remember those?

In healthcare, faxes remain the most common method that practitioners use to communicate with each other, and therefore often contain important clinical information: lab results, specialist consult notes, prescriptions and so on. Because most healthcare fax numbers are public, doctors also receive scores of pizza menus, travel specials, and other “junk faxes.” Faxes don’t contain any structured text — so it takes medical practice staff an average of two minutes and 36 seconds to review each document and input relevant data into patient records. Through a combination of machine learning and business-process outsourcing that has automated the categorizing of faxes, we’ve reduced time-per-fax for our practices to one minute and 11 seconds. As a result, last year alone we managed to eliminate over 3 million hours of work from the healthcare system. (Read More...)

Getting personal: how AI-driven personalised marketing is the future of brand communications

Ai-branding.jpg

Published in

Yourstory-logo.jpg

Moreover, this phrase even encapsulates what personalised marketing is all about. Simply put, it is the art of creating and delivering communication tailored according to each individual consumer’s preferences. Fundamentally, personalised marketing is the process of communicating the different value of the same product or service to various consumer segments, be it students, working professionals, millennials, middle-aged consumers, digital or offline consumers, etc. Hence, as the communication channels for each of these segments varies greatly, marketers must also craft their messages for consumers in such a way that it is connected to everyone. This simply means only pitching the content, products, and services that appeal to them.

Why personalise?

Consumers today want to spend as little time as possible on choosing from the seemingly endless number of offerings in the market. Moreover, they are often looking to avoid the overload of messages from multiple brands across various channels. Hence, the success of personalised marketing hinges on short and precise messages that convey value and elicit a positive response from the consumer. Personalised marketing also delivers better measurable responses and can be leveraged considerably to not only acquire new customers, but also retain them in the long term.

Where does Artificial Intelligence (AI) fit into personalised marketing?

Today, Artificial Intelligence is that secret ingredient that’s enabling brands to win customers through hyper-personalisation of their products and services. AI has transformed the customer experience into something which, until a few years ago, was simply inconceivable, and several companies are already applying the technology for various digital marketing activities. For instance, AI is being deployed by businesses to create websites, social media posts, run email marketing campaigns, optimise content for different consumer segments, etc. Thus, it is helping brands become more agile in their communications, as well as more responsive to consumer demands, as and when they change.

Traditionally, marketing campaigns are designed around a single message or product. While the message may be a predefined one based on the customer lifecycle, these campaigns are usually targeted to a broad consumer segment. With AI, however, marketing campaigns can be made much more streamlined and targeted. The combination of AI, Machine Learning, and data analytics can enable marketers to do impressive things. For instance, insights from a customer’s behavioural traits, gathered through predictive analytics, can be an indicator of not only when they are likely to purchase a product, but can also help marketers create tailor-made messages based on specific data from the past. Customer profiles, purchase patterns and histories, brand interactions, and social data all create a detailed map of each customer’s mindset and preferences. (Read More...)

Machine Learning Is Revolutionizing Marketing

Thinkstock

Thinkstock

Published in

Forbes-logo.png

Measuring marketing’s many contributions to revenue growth is becoming more accurate and real-time thanks to analytics and machine learning. Knowing what’s driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing.

The best marketers are using machine learning to understand, anticipate and act on the problems their sales prospects are trying to solve faster and with more clarity than any competitor. Having the insight to tailor content while qualifying leads for sales to close quickly is being fueled by machine learning-based apps capable of learning what’s most effective for each prospect and customer. Machine learning is taking contextual content,  marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed.

The strongest marketing departments rely on a robust set of analytics and Key Performance Indicators (KPIs) to measure their progress towards revenue and customer growth goals. With machine learning, marketing departments will be able to deliver even more significant contributions to revenue growth, strengthening customer relationships in the process.

The following are 10 ways machine learning is revolutionizing marketing today and in the future:

  1. 57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support. 44% believe that AI and machine learning will provide the ability to improve on existing products and services. Marketing departments and the Chief Marketing Officers (CMOs) running them are the leaders devising and launching new strategies to deliver excellent customer experiences and are one of the earliest adopters of machine learning. Orchestrating every aspect of attracting, selling and serving customers is being improved by marketers using machine learning apps to more accurately predict outcomes. Source: Artificial Intelligence: What’s Possible for Enterprises In 2017 (PDF, 16 pp., no opt-in), Forrester, by Mike Gualtieri, November 1, 2016. Courtesy of The Stack.
  2. 58% of enterprises are tackling the most challenging marketing problems with AI and machine learning first, prioritizing personalized customer care, new product development. These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in).  (Read More...)

 

How AI Can Keep Accelerating After Moore’s Law

ANDREA CHRONOPOULOS

ANDREA CHRONOPOULOS

Published in

mit.png

Google CEO Sundar Pichai was obviously excited when he spoke to developers about a blockbuster result from his machine-learning lab earlier this month. Researchers had figured out how to automate some of the work of crafting machine-learning software, something that could make it much easier to deploy the technology in new situations and industries.

But the project had already gained a reputation among AI researchers for another reason: the way it illustrated the vast computing resources needed to compete at the cutting edge of machine learning.

A paper from Google’s researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see “10 Breakthrough Technologies 2013: Deep Learning”). They told MIT Technology Review that the project had tied up hundreds of the chips for two weeks solid—making the technique too resource-intensive to be more than a research project even at Google.

A coder without ready access to a giant collection of GPUs would need deep pockets to replicate the experiment. Renting 800 GPUs from Amazon’s cloud computing service for just a week would cost around $120,000 at the listed prices.

Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. “Computing power is a bottleneck right now for machine learning,” says Reza Zadeh, an adjunct professor at Stanford University and founder and CEO of Matroid, a startup that helps companies use software to identify objects like cars and people in security footage and other video.

The sudden thirst for new power to drive AI comes at a time when the computing industry is adjusting to the loss of two things it has relied on for 50 years to keep chips getting more powerful. One is Moore’s Law, which forecast that the number of transistors that could be fitted into a given area of a chip would double every two years. The other is a phenomenon called Dennard scaling, which describes how the amount of power that transistors use scales down as they shrink. (Read More...)

Why Big Pharma and biotech are betting big on AI

KaPe Schmidt / Cultura Exclusive/Getty Images

KaPe Schmidt / Cultura Exclusive/Getty Images

Published in

nbc.jpg

Developing new medicines isn’t for the faint of heart. On average, it takes about a decade of research — and an expenditure of $2.6 billion — to shepherd an experimental drug from lab to market. And because of concerns over safety and effectiveness, only about 5 percent of experimental drugs make it to market at all.

But drug makers and tech companies are investing billions of dollars in artificial intelligence with the hope that AI will make the drug discovery process faster and cheaper.

“I believe that AI is a sleeping giant for healthcare in general,” Eric Horvitz, director of Microsoft Research Labs in Redmond, Washington, said last month at the annual meeting of the American Association of the Advancement for Science in Austin, Texas. He said Microsoft was investing in AI for drug design and pharmacology, which studies how drugs act in the body, and called the technology a “tremendous opportunity.”

Microsoft is far from alone in its AI bet. As of late February, the Toronto-based biotech company BenchSci had counted 16 pharmaceutical companies and more than 60 startups using AI for drug discovery.

BIGGEST BOTTLENECKS

The biggest bottlenecks in drug development usually lie within the early stages of research, especially in the time needed to go from identifying a potential disease target (typically a protein within the body) to testing whether a drug candidate can hit that target.

The most ambitious AI groups, including a private-public consortium dubbed ATOM, are aiming to compress that process — which can take four to six years — into a single year. (Read More...)

 

Artificial Intelligence Gains Momentum

ml_icons.jpg

Published in

finextratweetbot.jpg

Artificial intelligence has quickly evolved from science fiction to digital assistants such as Alexa and Siri learning about our daily lives. A.I. applications are interpreting MRIs and will soon be operating self-driving cars. In personal finance, many of us interact with chat boxes on bank web sites.  And in the investing world, robo-advisors are managing portfolios for retail investors.

“But we haven’t seen the penetration of AI within institutional finance,” said Richard Johnson, vice president of market structure and technology at Greenwich Associates on a recent webinar about the evolution of A.I. and current levels of adoption on Wall Street.

In an audience poll taken during the Greenwich webinar, “Artificial Intelligence: The Coming Disruption on Wall Street, “37.5% of attendees said that artificial intelligence would have the biggest impact on research, 34.7% trading, 23.6% compliance and 4.2% cited sales.

Among the key benefits of artificial intelligence is that it can analyze large volumes of structured and unstructured data more quickly than humans do, which can boost their productivity.

Large banks, hedge funds and traditional asset managers have been hiring data scientists to develop machine-learning algorithms that scan billions of data points, news articles, blogs, social media posts, and images.

“Machine learning cannot only find alternative data but also measure the value of satellite images, predict how much oil is in the ground or how many cars are in a parking lot,” noted Johnson.

One of the world’s biggest hedge funds, Man Group, has used machine-learning techniques to find new strategies, “from start to finish,” noted Johnson.  Man runs $43 billion of its $96 billion in total assets quantitatively, mainly through algorithms, operating 21.5 hours a day, from the open in Asia to the close in the U.S., reports CNBC.

But Man Group’s AHL quantitative investment unit is now allowing the models to learn from the data and trade completely on their own, reported CNBC. The firm’s Sandy Rattray, Man’s London-based chief investment officer, told CNBC that the strategy is earning higher returns than traditional quant methods. (Read More...)

Machine Learning's Secret Sauce? Getting Your Data Right

Untitled.png

Published in

cmswire.png

When it comes to new marketing technology obsessions, it’s easy to see why the likes of artificial intelligence (AI), cognitive computing and machine learning generate such hype1. These evocative-sounding technologies conjure images of a brave new world where marketers can sit back, relax and let the machine do real-time, personalized, one-to-one marketing with effortless ease.

'Intelligent' Tech Needs Trustworthy Data

Few of the articles about those technologies ever seem to focus on the need for accurate, clean and trustworthy data to fuel such awesome capabilities. As close as we feel we are to some revolutionary future of AI-based marketing, many brands still do not even have access to their customer data, let alone the people or tools necessary to manipulate it in the way some forecasts have envisioned.

The data is the boring bit, though, so it doesn’t stop people from lusting after the promised advances, or wondering how the technologies could work for them.

Predictive analytics and machine learning are used for many things, and have been for some time. Meteorologists use those technologies to forecast the weather. Insurance companies use them to detect fraudulent activity and for underwriting. Email providers use them to power their spam filters. (Read More...)

 

The Democratization of AI Is Putting Powerful Tools in the Hands of Non-Experts

Shutterstock

Shutterstock

SingularitySummit1.png

The shortage of qualified data scientists is often highlighted as one of the major handbrakes on the adoption of big data and AI. But a growing number of tools are putting these capabilities in the hands of non-experts, for better and for worse.

There’s been an explosion in the breadth and quality of self-service analytics platforms in recent years, which let non-technical employees tap the huge amounts of data businesses are sitting on. They typically let users carry out simple, day-to-day analytic tasks—like creating reports or building data visualizations—rather than having to rely on the company’s data specialists.

Gartner recently predicted that workers using self-service analytics will output more analysis than professional data scientists. Given the perennial shortage of data specialists and the huge salaries they command these days, that’s probably music to the ears of most C-suite executives.

And increasingly, it’s not just simple analytic tasks that are being made more accessible. Driven in particular by large cloud computing providers like Amazon, Google, and Microsoft, there are a growing number of tools to help beginners start to build their own machine learning models.

These tools provide pre-built algorithms and intuitive interfaces that make it easy for someone with little experience to get started. They are aimed at developers rather than the everyday business users who use simpler self-service analytics platforms, but they mean it’s no longer necessary to have a PhD in advanced statistics to get started.

Most recently, Google released a service called Cloud AutoML that actually uses machine learning itself to automate the complex process of building and tweaking a deep neural network for image recognition.

They aren’t the only ones automating machine learning. Boston-based DataRobot lets users upload their data, highlight their target variables, and the system then automatically builds hundreds of models based on the platform’s collection of hundreds of open-source machine learning algorithms. The user can then choose from the best performing models and use it to analyze future data.

For the more adventurous developers, there are a growing number of open-source machine learning libraries that provide the basic sub-components needed to craft custom algorithms. (Read More...)

AI’s Hidden Patterns Transform Content, Marketing and Advertising on Facebook

iStock

iStock

adweek.png

Artificial intelligence finds and identifies the best parts about human behavior. It matches our desires with the exact information, product or service we need, at the right time. It is increasingly used to improve search results, and it will be the driving force behind the changes in content production, target marketing and advertising that we will see on Facebook.

Facebook has an abundance of data on its users that, when mined intelligently, can uncover hidden patterns. Such information—when implemented properly into content creation, marketing strategy or advertising targeting—creates a truly unique lifestyle experience. It gives users the most efficient information and access to knowledge, tailored and customized in a way that has never been done before.

AI is the marriage of technology and data, and it can be leveraged to produce personally tailored content based on online personas. For instance, when you search for restaurants in a certain city, AI can reveal personalized matches based on past searches, browser history, mobile application usage, Facebook check-ins, Facebook likes, groups and prior reservations. These points intersect and provide a wealth of information about your desires, behaviors and preferences that customize restaurant recommendations specifically for you—and this goes for anything you’re searching for, not just restaurants.

Why brands must personalize through data

Thanks to its wealth of data, Facebook has positioned itself as the place where marketers can reach consumers in the most cost-effective way. Consumers want to be empowered by content that is specific to their needs and interests, and tolerance for irrelevant advertisements is at an all-time low. Facebook will be key to companies that want to grow their customer-bases and retain current consumers because they can tap into the technology, data and machine learning that the social media giant has to offer.

When brands use AI in the development of their marketing tactics, it effectively increases conversions and decreases budget waste. Product design and marketing are driven by human behavior—the how and why people do things—and at the end of the day, consumers want brands that assert themselves into that human desire. Those that do so through the combination of Facebook and machine learning will be successful and grant the opportunity to connect and communicate with other people at the precise time they need, with custom content they are likely to care about.

Consumers continue to demand more personalized experiences with the added caveat that they function seamlessly—they want to see the ads they want. For instance, AI capabilities can detect what products you are searching for, and it will pull data on the times you often make purchases. AI lets marketers know that information so that they can hit each consumer at a likely purchase time.

Using AI and machine learning to map out and skillfully intrude in the customer life journey to increase conversions is one of the biggest opportunities for marketers on Facebook. (Read More...)

CIOs Plan to Invest More in AI, Predictive Analytics, Big Data Tools

photo credit: thinkstock

photo credit: thinkstock

Authored by

Jessica Kent (HealthIT Analytics)

Healthcare IT leaders are investing more time and money in predictive analytics tools because of their potential to improve population health and reduce care costs, but they will also have to invest in artificial intelligence and big data analytics solutions to generate truly accurate clinical predictions.

As the industry shifts to value-based care and organizations seek to extract more value from their data, it’s no wonder that health IT leaders are choosing to focus on predictive analytics in the coming year.

A cross-industry poll from the International Data Group (IDG) found that 47 percent of CIOs plan to increase their spending on predictive analytics in the next few months.

In addition, 37 percent of CIOs said they are actively researching predictive analytics or have it on their radar.

Thirteen percent said predictive analytics are the most important tool they’re working on right now.

Providers have long believed that predictive analytics are critical for successfully managing the changing healthcare landscape. In a 2017 Society of Actuaries (SOA) survey, 93 percent of respondents said that healthcare organizations will not be able to navigate future financial and clinical challenges if they do not invest in predictive analytics tools. (Read More...)

The Age of Artificial Intelligence – Can AI Really Transform the Future of Businesses?

lm.jpg

Authored By

livemint-logo.jpg

Think about this – today, there are millions of smartphones, internet users, connected devices, and the number is just increasing by the hour. There is a historic shift in the way digital is becoming a part of our lives – from digital payments, to the way we shop or even interact with each other. Fueled by technologies such as Artificial Intelligence (AI), Cloud, Internet of Things (IoT) and Blockchain – this new era of digitization is producing data which is outstripping human capacity to understand the meaning and value hidden within that data.

Data, in all forms, is expanding as a resource to be utilized. Industries and professions are exploring several new possibilities and the potential value of this data is boundless. The next wave of technology – Artificial Intelligence – is already helping us make sense of the deluge of data out there, providing systems that are able to adapt and learn.

Once the preserve of science fiction, Artificial Intelligence (AI) has now become ubiquitous. From transforming healthcare to improving public safety, from enhancing the quality of education to bringing us into a world of connected devices- Artificial Intelligence is helping businesses reinvent and rethink.

A Gartner report predicts that AI will create 2 million net new jobs by 2025. Advances in virtual assistants and deep learning will foster adoption of artificial intelligence, according to the market research firm.

As one of the technology pioneers that helped establish the industry, IBM has been exploring AI and Machine Learning technologies for close to a decade with the launch of Watson in 2011. Little did the world know that the technology would very soon be touching more than one billion lives across the world?

Today, early adopters are using AI to position themselves as innovators and those that haven’t yet leveraged AI - are missing out on insights and opportunities that could help transform their businesses. (Read More...)

AI is changing the Risk Management & Compliance

AI8.jpg

Authored By...

Piyush Srivastava (RiskCounts)

In this increasing debate over AI, in how it is good for mankind, and on the other side, how it is going to take away our jobs, what is the right answer?

I would take the analogy of the advent of cars, trains or airplanes, in the times when people used horse carriages, horses or bullock carts. What was the advantage of a motor car or the train? Well, it reduced the time of travel, brought efficiency in terms of speed, reliability and saved valuable hours. So did the creation of an airplane, which made traveling across the globe a viable and easier options for the masses. If we look at the AI and machine learning today, it seems that it is no different from graduating from the horse driven carriage to a car.

Now, Artificial Intelligence (AI) is affecting multiple areas of our lives, like AI driven cars, back office automations, machines learning to operate non-stop operations and doing tasks in minutes, what would take a person perhaps a week to do the same manually.

Risk Management is no exception to this. Fintechs and Banks are introducing Artificial Intelligence (AI) applications in risk management in a limited way, but these applications are also finding usage in the areas of investment decision making that is supported by huge amounts of data, Hedge Funds and Asset Managers are using high speed trading using complex models. At the same time, phone based market making is giving way to electronic execution. Market Makers and Asset Managers are now looking to use technology and Artificial Intelligence (AI) to assess the risk of the counterparties from the publicly and privately available data. (Read More...)

 

How Artificial Intelligence Can Influence Governance, Risk, and Compliance

risj.jpg

Authored By

nads.png

Artificial Intelligence (AI) offers enormous opportunities to businesses. Given the correlation between risk and an organization's objectives, one could easily extrapolate how AI can help bring insight to Governance, Risk, and Compliance (GRC) activities as well.

But, what is AI? As a working definition, AI is the science and engineering of making intelligent machines and computer programs to achieve a goal. It's about creating a computer mind that can think like a human. It's about machines taking action.

One of the most important technological advances of our time is artificial intelligence, and, in particular, machine learning, which is the ability for a machine to keep improving its performance without human involvement to accomplish tasks. Systems can now be taught to perform activities on their own.

The transformative effects of AI will be felt across nearly all industries. The impact on core processes and business models will be enormous, placing further strain on management and implementation.

There are similar implications for risk management. Probably one of the best cases is fraud detection. Algorithms can be written using various stochastic modeling techniques, coding, and data testing. Of course, for machine learning to be successful, it must have quality data. As a result, there is a premium on structuring risk data in such a way to use it as AI input. Conversely, a challenge implicit in machine learning is substantiating its outcomes. As machines "learn," their conclusions may not always yield the desired result. This conceivably makes it difficult for a risk manager to explain the machine's conclusions to executives or a regulator difficult. For example, there may be issues with multicollinearity, lack of data, as well as how the machine deals with outliers, which is common with many risk data, especially if the organization uses external data. (Read More...)

 

The Business of Artificial Intelligence

AI-conclusion-850x455.jpg

Authored By

Erik Brynjolfsson and Andrew McAfee

hbr.png

For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models.

The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own.

Why is this such a big deal? Two reasons. First, we humans know more than we can tell: We can’t explain exactly how we’re able to do a lot of things — from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn’t automate many tasks. Now we can.

Second, ML systems are often excellent learners. They can achieve superhuman performance in a wide range of activities, including detecting fraud and diagnosing disease. Excellent digital learners are being deployed across the economy, and their impact will be profound.

In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination. (Read More...)

How Big Data Is Empowering AI and Machine Learning at Scale

ai.png

Authored By...

Randy Bean

mit.jpeg

 

Big data is moving to a new stage of maturity — one that promises even greater business impact and industry disruption over the course of the coming decade. As big data initiatives mature, organizations are now combining the agility of big data processes with the scale of artificial intelligence (AI) capabilities to accelerate the delivery of business value.

The Convergence of Big Data and AI

The convergence of big data with AI has emerged as the single most important development that is shaping the future of how firms drive business value from their data and analytics capabilities. The availability of greater volumes and sources of data is, for the first time, enabling capabilities in AI and machine learning that remained dormant for decades due to lack of data availability, limited sample sizes, and an inability to analyze massive amounts of data in milliseconds. Digital capabilities have moved data from batch to real-time, on-line, always-available access.

Although many AI technologies have been in existence for several decades, only now are they able to take advantage of datasets of sufficient size to provide meaningful learning and results. The ability to access large volumes of data with agility and ready access is leading to a rapid evolution in the application of AI and machine-learning applications. Whereas statisticians and early data scientists were often limited to working with “sample” sets of data, big data has enabled data scientists to access and work with massive sets of data without restriction. Rather than relying on representative data samples, data scientists can now rely on the data itself, in all of its granularity, nuance, and detail. This is why many organizations have moved from a hypothesis-based approach to a “data first” approach. Organizations can now load all of the data and let the data itself point the direction and tell the story. Unnecessary or redundant data can be culled, and more indicative and predictive data can be analyzed using “analytical sandboxes” or big data “centers of excellence,” which take advantage of the flexibility and agility of data management approaches. Apostles of big data have often referred to their approach as “load and go.” Big data enables an environment that encourages data discovery through iteration. As a result, businesses can move faster, experiment more, and learn quickly. To put it differently, big data enables organizations to fail fast and learn faster. (Read More...)

What’s now and next in analytics, AI, and automation

mk1.jpeg

Authored by

mckinsey-logo-official.jpg

Innovations in digitization, analytics, artificial intelligence, and automation are creating performance and productivity opportunities for business and the economy, even as they reshape employment and the future of work.

Rapid technological advances in digitization and data and analytics have been reshaping the business landscape, supercharging performance, and enabling the emergence of new business innovations and new forms of competition. At the same time, the technology itself continues to evolve, bringing new waves of advances in robotics, analytics, and artificial intelligence (AI), and especially machine learning. Together they amount to a step change in technical capabilities that could have profound implications for business, for the economy, and more broadly, for society.