Forces Shaping Cognitive Computing

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Cognitive computing has the power to accelerate, enhance and scale human expertise.  According to IBM, these six factors – society, technology, perception, information, policy and skills – are shaping how the technology advances.  Below is a brief summary of each factor:

Society – consumers will continue to demand more intelligent machines and it is expected that mobile device use will continue to grow.  These two trends will allow society as a whole to gain more familiarity and comfort with cognitive systems.

Technology – leading experts agree that current architecture must advance to take full advantage of cognitive computing.  So far technology advances such as natural language processing and deep-learning coupled with more robust data from IoT devices has made strides in providing better input and output opportunities for cognitive systems.

Perception – continued education about the value of cognitive computing, grounded in reality, is necessary to keep interest and funding.

Information – With the explosion in digital data, it is getting harder and harder to keep pace with the volume and velocity without technology such as cognitive computing.  IDC predicts that the digital universe will reach 40 zettabytes by 2020 which will further drive the advancement of cognitive capabilities.

Policy – as more entities adopt the technology, policies around data sharing, data privacy, data security and decision making will also need to be revised and reapplied.

Skills – a skilled work-force is recognized as a top challenge by industry leaders.  Currently, the skills needed to advance the technology are limited.  The supply of skilled workers is low and more are needed to not only put cognitive systems in place but to also produce actionable results.

If you would like to read in more depth about the factors affecting cognitive computing growth, please check out this white paper by IBM: Your Cognitive Future, How Next-Gen Computing Changes the Way We Live and Work.

Adding Cognitive Computing to Your Business

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Many organizations are using cognitive computing, such as IBM Watson, to crunch data and produce insights to improve customer engagement, productivity and to grow their businesses.  But, for many of us adopting cognitive computing can seem daunting. IBM shares these best practices for adopting cognitive computing:

Start with a pilot project. Most businesses adopt cognitive computing by first testing the technology on a specific business issue.  From there these companies find success by embracing a test-and-learn mindset so that they are refining their model as they learn more.  Finally, your cognitive system and solution must be scalable, so you are ready when it is time to expand.

Data is everything. When approaching a cognitive project you must have a clear data strategy.  You must know what data sources you need, where the data is created and how it is managed. Your data set must also be big enough in order for your cognitive system to drive valid conclusions.

Build a team. Many early adopters of cognitive computing also agree that building a cross-disciplinary project team, whether the project is large or small, is important.  Allowing business to be involved helps make sure the insights derived from your cognitive system can be applied to the customer or company-specific challenge.  The team also looks for and relies on IT partners that bring to the table specific technology and industry expertise to augment critical skills.

At Evolving Solutions we can help you adopt cognitive computing through IBM Watson.  IBM Watson has the ability to:

  • Understand by analyzing and interpreting all of your data both structured and unstructured
  • Reason by providing personalized recommendations based on a user’s personality, tone and emotion
  • Learn by utilizing machine learning to grow expertise in a specific app or system
  • Interact by creating chat bots that can actually engage in dialogue with users

Our team works with you to first access your IT architecture, data and specific needs to determine which IBM Watson solution would best suit.  We then put together a recommendation on how to best implement the cognitive solution and work closely with IBM and your team to set it up.

Read more about what cognitive computing can do for your business.

How Artificial Intelligence is Boosting Productivity

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“Artificial intelligence (AI) is rapidly moving from nice-to-have into must-have territory for organizations. In fact, nearly six in ten AI early adopters think it will be a necessary element to remain competitive within the next few years. Two-thirds say AI is very important to their organization’s strategy and success”, reports Susan Hupfer for IBM. In fact, 62% of early adopters are reporting that their AI initiatives are exceeding their expectations.

What can AI systems do? AI technologies augment human intelligence to help improve decision making by crunching large amounts of data quickly and being able to generate predictions, relations and recommendations.  This helps managers make more informed decisions faster. Here are some examples of how AI is helping companies boost their productivity:

  • Search. For many companies offering an intelligent search option for employees and customers across fragmented systems and data sources is difficult.  AI search platforms have enabled companies to improve search offerings for both structured and unstructured data
  • Customer care. AI systems are working in a number of ways for customer care departments. In some case, AI evaluates natural language questions submitted via text, web chats or instant messaging platforms. In other cases, AI  allows customer care employees to find faster the right answers to customer questions
  • Workflows. Some companies are also using AI systems to compile “organizational wisdom” to cut down on the bottlenecks that can happen when the same subject matter experts are repeatedly sought out for information
  • Operations.  Equipment and operational based businesses are using the predictive powers of AI systems to identify potential risks for downtime and to better plan for equipment maintenance. These businesses can also use these predictive elements to better manage inventory and supply chains to save money

According to IBM, half of early adopters are already reporting workplace productivity and efficiency.  Think about how AI technology could combine with your business  strengths to improve operations, employee performance and ultimately customer satisfaction.

Early Adopters Report Cognitive Benefits

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IBM explains cognitive computing as follows, “cognitive technology augments human expertise to unlock new intelligence from vast quantities of data and to develop deep, predictive insights at scale.”  Cognitive computing is opening up more real-time opportunities  for many businesses across many industries. In fact, analysts expect investment in cognitive technology to grow from $2.5 billion in 2014 to $12.5 billion in 2019. IBM recently surveyed over 600 decision makers who are early adopters and asked about the benefits they are experiencing.

First, who are the early adopters?  Below is a great graphic from IBM outlining where these early adopters fall within their cognitive uses:

The cognitive early adopters

Here are also some of the reasons why they are investing in cognitive:

  • 65% of early adopters view cognitive technology as very important to their organization’s strategy
  • 58% feel that cognitive is essential for digital transformation
  • 58% reported that they feel strongly that cognitive will become a “must have” in just a few short years

62% of cognitive users are already reporting that their projects have exceeded expectations.  For the most part these early adopters are finding success in three categories: customer engagement, productivity and efficiency and business growth. Here is a break down from the survey:

  • 49% report more personalized customer/user experiences
  • 42% said they have been enabled to respond faster to customer/market demands
  • 46% have seen improved decision making and planning
  • 46% report improved security and compliance
  • 41% have used cognitive to expand the company into new markets
  • 45% report a reduction in costs due to productivity and efficiency gains

Business growth - IBM Cognitive Graphic

The road to adoption isn’t always easy. Many of the decision makers surveyed sited data management and an employee skills gap as challenges. Turning to outside expertise has been key to helping them overcome both challenges.

Cognitive computing is helping many early adopters unlock new opportunities. As more practical use cases develop, we can expect more companies to put cognitive technology to work for them.

Cognitive Computing: Improving X-Rays

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“What if your X-ray could predict a potential disease months in advance,” writes Laura Lorenzetti for Fortune. This past summer IBM Watson Health created a new partnership with leading medical providers and imaging tech companies to see if cognitive computing can take medical imaging one step further to actually predict the chance of diseases like cancer and heart failure.

Ms. Lorenzetti’s article points out that much of the data gathered from an x-ray or MRI is “unstructured.” It can be difficult for computers to connect the information to patient records in a meaningful way. IBM Watson Health is trying to change that and utilizing its power to connect unstructured data with its massive databases of patient medical history.  Ms. Lorenzetti writes, “the goal is to provide new offerings across various medical environments (a hospital ER or an everyday physician’s office) that can connect systems (medical records, picture archiving, lab results) and deliver cognitive insights to doctors on the spot for better diagnoses.”

One example from the article is the use of mammograms. Not only could Watson connect the image results to the patient’s medical history but it could also “cross-reference against the similar patients within the Watson database.”  These connections could improve a doctor’s  ability to identify early warning signs or risks.

Another example is the use of cognitive computing to help doctors predict which patients are more likely to have a heart attack after reporting chest pain.  Ms. Lorenzetti reports that 2% of patients who visit an ER with chest pain have the early signs of a heart attack missed. By connecting the data dots IBM Watson Health could help doctors identify these signs better.

IBM Watson Health’s shear power to process unstructured data such as medical imaging while also consuming vast amounts of patient data allows for it to draw cognitive insights that will one day improve patient diagnoses and treatments.

Industries Exceling with Big Data

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“Two and a half quintillion bytes or 2,500,000,000,000,000,000 bytes. That’s how much data humanity generates every single day. And the amount is increasing; we’ve created 90% of the world’s data in the last two years alone,” writes Michael Belfiore for IBM’s Watson Blog.

Many businesses struggle with this ever increasing data, especially growing unstructured data, but other businesses in an assortment of industries are succeeding by using cognitive computing technology to turn big data into big insights.  Mr. Beliofe provides a summary of several industries succeeding with big data:

Telecommunications – cognitive computing tools are being used to index documents, images and manuals giving call center agents access to more actionable data to better solve customer issues. Every second saved in call time is $1 in cost savings.

Finance – an auto finance firm is using big data to develop better customer insights that not only allow them to serve customers better but also to improve data security.

Healthcare – by being able to better capture and analyze unstructured data, one health system is using this data to help identify patients with risks for chronic diseases to improve treatments and reduce readmission.

Fitness – the rise of mobile and wearables has led to a fitness app that tracks your performance and progress and coaches you through your workouts to meet fitness goals.

Retail – a clothing retailer has used big data to drive a better in-store experience using sensors and Wi-Fi data to track customer behavior while in the store.

Travel – one airline is using mobile to access customer preferences, travel history and even allergies to provide more personalized service in-flight.

In each of these examples, cognitive computing technology allows companies to harness big data to create better customer service experiences.

AI – Practical Applications

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Last week’s blog post featured information on what augmented intelligence (AI) means and the industries using the technology from IBM Research.  Today, let’s review a couple real life examples of AI applications.

First, a quick definition, IBM prefers to refer to AI as augmented intelligence.  Their approach is to use cognitive computing capabilities, such as machine learning, reasoning and decision tech, language, speech and visual tech and human interface tech, to create practical applications that enhance and scale human expertise.

IBM’s Watson Health – Partnering with New York’s Memorial Sloan Kettering Cancer Center (MSK), IBM’s application helps to consume and process the massive amounts of medical research while also “learning” from cancer experts, working to ultimately expand access to cancer treatment options and expertise. Laura Lorenzetti of Fortune explains, “Some MSK oncologists have a highly specific expertise in certain cancers. By training Watson to think like they do, that knowledge expands from one specialist to any doctor who is querying Watson. That means that a patient can get the same top-tier care as if they traveled directly to the center’s offices in Manhattan. IBM’s Watson provides the framework to learn, connect, and store the data, while MSK is imparting its knowledge to train the computer.”

Financial Services – cognitive computing is assisting financial advisors so  they can better serve their  clients.  By ingesting financial information and client data, Watson can answer the everyday client questions while also using its processing power to help identify potential options for the advisor to evaluate.  Many believe that by integrating with Watson financial advisors will be able to expand their practices and serve more clients. William Sprouse of Financial Planning further explains, “In practice, such cognitive computing power would work with an adviser just like a helpful Star Wars droid: virtually present during a meeting with a client, gathering data, and ready to instantly assist with queries and projections, along with its own suggestions based on client data.”

These two examples both demonstrate not only the processing power of augmented intelligence systems like Watson but also the ability to “learn”.  This ability to learn can provide access to critical expertise to more people than ever before in healthcare and financial services.

IBM: AI 101

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A recent article from Tech Crunch by Devin Coldewey highlights an RFI response from IBM regarding artificial intelligence (AI). Mr. Coldewey writes, “The field of artificial intelligence is so huge, and the potential applications so numerous, that it would be folly to try to explain it all in one — no, wait, IBM just did.” Today we will look at some of the highlights from IBM’s response.

First, artificial intelligence vs. augmented intelligence. IBM prefers to speak to augmented intelligence which is the process of creating systems that enhance and scale human expertise rather than systems that attempt to replicate human intelligence.  IBM further describes their approach as cognitive computing or “a comprehensive set of capabilities based on technologies such as machine learning, reasoning and decision technologies; language, speech and vision technologies; human interface technologies; distributed and high-performance computing; and new computing architectures and devices. When purposefully integrated, these capabilities are designed to solve a wide range of practical problems, boost productivity, and foster new discoveries across many industries.”

How is AI currently being used?  IBM provides the follow highlights by industry:

  • Healthcare – AI is advancing precision medicine through its ability to “ingest” patient information and run it against vast stores of medical research
  • Social Services – AI can be used to predict resource needs from specific population groups
  • Education – AI provides new capabilities to design true personalized learning plans
  • Financial Services – AI is being used to ensure financial resources are utilized well. This can come from the advancement of the applicant approval process or through efficient weighing and processing of insurance needs against risk, costs and regulations

In particular for IBM what started as a contestant on Jeopardy, IBM Watson, is now full blown cognitive computing that can be applied to practical problems in a variety of industries.

Next week, we will feature more on the blog from IBM’s AI 101. Be sure to check back. Until then you can also read more on AI and cognitive computing here.

The Importance of Hardware to Cognitive Computing

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“Back when the world shifted from the horse and buggy to the automobile, paved roads were needed to enable people to enjoy the full benefits of the internal combustion engine. So roads were paved and, eventually, the highway was invented. The same will be true in today’s transition from conventional computing to cognitive computing,” writes Scott Crowder of IBM Power Systems.  In a recent article Mr. Crowder breaks down the role that hardware is playing in cognitive computing. Today, let’s take a  look at some of his thoughts.

Cognitive computing hitting the mainstream

Mr. Crowder sees the adoption and interests by universities and recent U.S. supercomputer projects as a key sign that cognitive computing will break through to the mainstream. He writes, “In the last few months, I’ve witnessed the beginning of a sea change in the way people at the forefront of computer science think about the future of our field. Faculty members at universities are showing a keen interest in cognitive systems. And I’m not talking just about algorithms and software. They want to discuss the processor and system technologies that will support a new generation of applications and a new era of computing.”

Two new supercomputers are also being developed using hardware built for big data and architecture that has data-centric computing. The project leaders are also adopting software-defined storage to more efficiently manage data-intensive tasks. These new super computers under the “CORAL” project are expected to be delivered in 2017 and will perform five to seven times faster than other supercomputers in the U.S.

 The role of hardware

“I expect advances in processors, system design, accelerators and storage to come in waves over the next few years,” writes Mr. Crowder. For example already IBM’s Power systems which were built to support big data are adapting nicely to the needs of cognitive computing due to their “superior throughput for transferring data back and forth between memory and the processor, and because of their ability to execute more computing processes concurrently.”  Mr. Crowder also points out that accelerators will be a key element in cognitive computing and already are used to support machine learning applications.

Mr. Crowder expects the coming years to be an exciting time in hardware as more companies look to harness the power of cognitive computing.

Industry News Round- up – What is Watson Up To?

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With a new year upon us, today let’s look at what IBM Watson has been up to so far in 2016.

More Work for Watson

Fortune recaps Watson at CES where IBM announced new ways in which Watson is being used. Stacey Higginbotham writes, “Watson now offers coaching through Under Armour’s app for consumers using certain products. Watson can also warn diabetics using certain Medtronic devices if their blood sugar gets too low. Whirlpool is using the cognitive computer to grab data from its connected appliances in hopes of understanding if there are flaws in its manufacturing lines. And finally, Watson is now the brains inside SoftBank’s Pepper robot.”  Ms. Higginbotham points out Watson’s ever expanding capabilities – voice recognition, computer vision and predictive intelligence.

For Whirlpool using Watson is about handing data from connected appliances better and for Medtronic and SoftBank it is more about extracting more value and opportunities from existing data.  All in all, more industries are finding work for Watson.

Watson-powered Wellness App

Natalie Gagliordi of ZDNet reports, “IBM’s Watson-powered wellness app with Pathway Genomics enters alpha release.The app focuses on a wellness report compiled from a bevy of data sources, such as a user’s genetic test, health habits, and health tracker information from the likes of Apple HealthKit.”  This new app crunches diet, exercise and metabolism to build a “personalized approach to preventative medicine.” Ms. Galiordi points out that this is the latest effort by IBM to position Watson at the forefront of medical innovation.

Watson Takes on the World

Bernard Marr takes a step back on Thoughts on Cloud and digs into what makes Watson tick. In his interview with IBM, he describes the three “legs” that form Watson’s cognitive computing. First, Watson is designed to use natural language processing so it can work and react in real language and IBM points out it can even learn nuances, idiosyncrasies and colloquialisms of human language. Second, Watson can process enormous amounts of data to come up with probabilistic answers. And finally Watson’s ability to learn, fueled by big data Mr. Marr points out, means Watson can only get better with every use.