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Finding The Humanity In Data: IBM Watson’s CTO Rob High Defines The Emerging Era Of Cognitive Computing [PODCAST]

Hessie Jones

It’s increasingly clear that we, as humans, continuously upload our identities every day. Those needs and propensities become quantified and contextualized. For companies like IBM Watson, understanding the human condition is important so technology can increasingly define patterns, learn, and potentially predict outcomes that benefit both business and industry.

We were pleased to host Rob High, IBM Fellow, vice president and chief technology officer, Watson Solutions, IBM Software Group. In this episode, Rob talks about:

  • The definition of cognitive computing
  • How Watson is aiding the advancement of health care
  • Chef Watson and recommendation on recipes
  • Advanced cognitive systems and how they’re applied across different mediums
  • The future of AI – Should humans be fearful?

You can listen to the podcast here or catch the episode here on Libsyn.

What is cognitive computing?

  • Ultimately, cognitive computing has the greatest benefit for people. By definition, it is the interpretation of the human condition that includes all those things that we take in every day: the information, our communication. It deciphers the intent we derive from them that is meaningful and used in the way we make decisions in our everyday lives.
  • Cognitive computing augments our own human cognition and gives us the insight and inspiration to those specific things we need to know to do our job better.

Classical computing methods have been unable to understand the underlying intent in how we, as humans, have communicated with each other, through voice and text, audible or written.

Cognitive computing does not replace human thinking. It does the research for you so you can do your thinking better.

How is Watson making strides in healthcare?

Watson can operate only in digital form, aggregating the information and looking at discreet elements to explicitly understand the various treatment options to better inform decision making.  Massive amounts of data will uncover trends across the population and yield certain correlations that may help interpret and predict patient response to various treatments.

Through Watson’s work with MD Anderson Cancer Centre, the Oncology Expert Advisor (OEA) was launched.

By pulling together and analyzing vast amounts of information from patient and research databases, the OEA is expected to help our care teams identify and fine-tune the best possible cancer treatments for our patients, while also alerting them to problems that arise during a patient’s care.

In accessing millions of patient records, Watson can aid in identifying a micro-segmentation of the population that have common traits; i.e., exposure to environmental impacts, genetics, heritage, and symptoms. These will aid in surfacing the opportunities to apply the knowledge and understanding to determine how well someone with the same exposure will respond to certain treatments.

While health information across the world has been fragmented, Watson can aid in processing massive quantities of information (not humanly possible) to create implications in a meaningful way, and in a short period of time. Now doctors and patient caregivers who have documented success can share that information with other medical practitioners across the globe to accelerate diagnosis and treatment.

Chef Watson: “Ready to do some cognitive cooking?”

This was for me the most fascinating part of the segment: Chef Watson enables people to make decisions about menus, identifying and helping us discover new recipes based on our unique preferences.

At IBMchefwatson.com,Watson partnered with Bon Appetit, which provided 9000 recipes for Watson to ingest and learn about the different types and styles of recipes. For a computer which innately has no sense of palette or smell, Watson learned about the taste makeup and flavors and the feeling that results when you consume a particular dish. It also learns about the science of taste chemistry and the chemical compounds that give the recipes their specific tastes. From this perspective, it has the ability to begin to imitate the human senses. As per Rob:

Watson starts from scratch, dealing with many – potentially up to a quintillion– combinations of ingredients when it comes up with its unique recommendation every time.

It’s getting at the root of what makes people who they are – the things we experience are interpretable.

As an example, if you wanted a Belgian flavor for a given recipe, Watson will evaluate the different combinations of ingredients that pair well and produce a Belgian flavor, and may come up with different variations.

Starting out as a fun and interesting project, this has occurred as a result of the cognitive ability and has allowed Watson to venture into the art of the possible.

Patterns and the evolution of interpretations

Similar to the learnings with MD Anderson, there are trends or patterns within the data where we can derive the greatest understanding or intention. Overlay contextual history which informs more of the human understanding. Collectively these allow us to extract meaning. Cognitive systems draw meaning that can bring the right set of information to humans and attention to just the right thing(s) to shape the decision-making process.

Pervasive technology has been able to to process 20% of the world’s information until now. The other 80% of that data is the human condition: the spoken word, written word, music, visual representations – all interpretations of our interests and needs. This is the heart of understanding. As Rob points out:

Multi-modal is how we communicate with each other: Not only what you’re hearing, but the intonation in the voice reflects the substance of that expression that’s being conveyed. Add the cadence that punctuates these points and now we know how humans understand each other. The computer needs to understand that as well.

Cognitive systems are not based on the same mathematical models as traditional computers. Attempting to interpret the human condition is doing so in the presence of idiosyncrasies and nuances carried through conversations and other communications.

Our words, our expressions are ambiguous…

Are these models reliable?

There is “no absolute level of correctness necessary;” these results are being applied in the eyes of the beholder. The computer will need to be exposed to enough examples that it will begin to surface patterns of meaning that will allow it to work well in that context. Be prepared for the outcomes to vary by environment or time period or when new variables are introduced.

What is the future of AI? Should we, as humans, be fearful?

The potential of cognitive is vast and in the near future, the amazing strides that are introduced are evidence of the inherent benefit to our human strength and potential.

Technology will continue to progress and there will always be a risk that people and organizations will use it in nefarious ways.

Technology should not be feared. With increased understanding comes progress. It also means humans should be responsible and use it for the purposes for which it was intended.

As this information becomes for common, technology companies need to ensure safeguards are put in place to mitigate abuse to our privacy.

Rob High is an IBM Fellow, vice president and chief technology officer, Watson Solutions, IBM Software Group. He has overall responsibility to drive Watson Solutions technical strategy and thought leadership. As a key member of the Watson Solutions Leadership team, Rob works collaboratively with the Watson engineering, research, and development teams across IBM.

Want more on future tech and its effect on business? See Bring Your Robot To Work.

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Real-Time Data Transforms Political Journalism, But Context Remains Vital

John Graham

The runup to the 2016 U.S. election is being covered in interesting new ways by the political media, with analysis of Big Data and real-time opinion polling offering journalists much deeper insight than ever before. The trend of “data journalism” is peaking as the media embraces advanced technologies that allow them to deliver a new breed of numbers-driven, fact-based journalism.

The tools being used for data journalism open up possibilities for fresh perspectives, more in-depth reporting, and new stories behind the numbers that have never been seen before. Traditional journalists are beginning to see how data journalism can complement their reporting, and the U.S. election is serving as an ideal testing ground. Political reporters are lapping up the improved data literacy and access to objective analysis, which is helping to make their reports more thorough and informative.

Consequently, American voters are becoming digital voters. They have access to real-time, data-driven information and public sentiment, which is empowering them with broader insight. They’re relying on this to help them make up their minds before they cast their vote, and it’s given many voters a renewed interest in becoming informed citizens able to make an educated choice.

However, the rise of data-driven journalism brings with it a potential pitfall for media organizations and readers alike. Digital information overload will bring about a fatigue around numbers if reporting quantity becomes more highly valued than quality. Having access to mountains of data is a huge benefit, but a reporter still has to be a journalist first to ensure they’re not getting buried under the numbers and missing the stories.

In other words, a political journalist still needs to be a politico, not just a statistician. They could fall into the trap of placing too much importance on meaningless correlations as indicators of voter sentiment, losing their grasp on what made them a great political reporter in the first place. As data gets bigger, this will become harder to resist. So they need to become experts in making Big Data small—rather than obsessing over the numbers, obsessing over figuring out what they really mean. In doing that, they have an unprecedented opportunity to make people more informed rather than simply overwhelming with them a series of conflicting data sets.

Some media organizations are already tackling the challenge of remaining relevant in a world of information overload. Using big data and visualizations, they are making great strides in making data journalism more accessible to reporters, politicos, and voters, which is proving its worth in giving political reporting a new lease of life.

Reuters’ Polling Explorer tool is an example of how this is being done, offering up customizable data visualizations focusing on the biggest talking points in the U.S. leading up to the election. It’s an entirely new scale of public opinion measurement, presented in a way anyone can understand and use, while enabling Reuters to usher in its own improved brand of accurate, fact-based, and timely journalism.

We can see the true potential of using real-time data analysis to measure up-to-the-minute public opinion in one poll on the most important problem facing the US today. Immediately after the Paris attacks in November, terrorism skyrocketed way above the economy as the number-one issue, rising sharply again straight after the December San Bernardino attack. For Reuters, this is just one of many examples of their greatly increased ability to find outliers in the data.

Reuters Polling Explorer runs on SAP HANA, an in-memory data platform that allows Reuters to access and analyze 100 million survey responses for quicker and more efficient reporting of public opinion.

For more on data analytics in today’s media environment, see How Big Data Is Changing The News Industry.

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John Graham

About John Graham

John Graham is president of SAP Canada. Driving growth across SAP’s industry-leading cloud, mobile, and database solutions, he is helping more than 9,500 Canadian customers in 25 industries become best-run businesses.

Smart Machines Create Markets For Cyber-Physical Advances

Marion Heindenreich

Today, industrial machines are more intelligent than ever before. These intelligent machines are changing companies in many ways.

Why smart machines?

Mobile networked computers were a key breakthrough for making smart machines. Big Data allows machines and computers to store information and analyze complex patterns. Cloud computing offers broad access to information and more storage.

These computerized machines are both physical and virtual. Some call them “cyber-physical” machines. Technology lets them be self-aware and connected to each other and larger systems.

Businesses change their approaches

Intelligent machines allow companies to innovate in many areas. For one, the value proposition for customers is evolving. Businesses now model and plan in different ways in many industries.

Makers of industrial machines and parts work in new ways within the organization. Engineering now partners with mechanical, electronic, and software staff to develop new products. Manufacturing now seamlessly ties what happens on the shop floor to the customer.

Service models are changing too. Scheduled and reactionary servicing of machines is fading. Now intelligent machines track themselves. Machines detect problems and report them automatically. Major problems or failures are predicted and reported.

A data mining example

One good industrial example is mining, which can be dangerous and difficult. As ores become scarce, the costs of mining have increased.

“Smart machines” started in mining in the late 1990s. Software and hardware let remote users change settings. Operators moved hydraulic levers from a safe distance. Sensors observed performance and diagnosed issues.

Data cables connected machines to computers on the surface. Continuous and remote monitoring of the machines grew. Over time, embedded sensors helped improve monitoring, diagnostics, and data storage.

The technology means workers only go underground to fix specific issues. As a result, accident and injury risk is lower.

New wireless technology now lets mining companies connect data from many mine sites. Service centers access large amounts of data and can improve performance. Maintenance is prioritized and equipment downtime is reduced.

Opportunity abounds

For companies the time is now. Today, mobile “connected things” generate 17% of the digital universe. By 2020 that share grows to 27%.

You might not be investing in this so-called “Internet of Things” (devices that connect to each other). But it’s a good bet your competitors are. A December 2015 study reported 33% of industrial companies are investing in the Internet of Things. Another 25% are considering it.

There are risks

This new dawning era of manufacturing is exciting. But there are concerns. Cyber attacks on the Internet of Things are not new. But as the use of intelligent machines grows, the threat of cyber attacks in industry grows.

Data confidentiality and privacy are concerns. So too are software and hardware vulnerabilities. Exposure to attack lies not just in the virtual space but the physical too. Tampering with unattended machines and theft pose serious risk.

To address these threats, industries must invest in cybersecurity along with smart machines.

Conclusion

The potential advantages of smart machines are staggering. They can reshape industries and change how companies produce new products and create new markets.

For more information, please download the white paper Digital Manufacturing: Powering the Fourth Industrial Revolution.

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Marion Heindenreich

About Marion Heindenreich

Marion Heidenreich is a solution manager for the SAP Industrial Machinery and Components Business Unit who focuses on solution innovations like Product Costing on SAP HANA and cloud solutions, as well as providing financial and business analysis for industry business strategy definition and business planning.

Robots: Job Destroyers or Human Partners? [INFOGRAPHIC]

Christopher Koch

Robots: Job Destroyers or Human Partners? [INFOGRAPHIC]

To learn more about how humans and robots will co-evolve, read the in-depth report Bring Your Robot to Work.

Download the PDF (91KB)

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About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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What Is The Key To Rapid Innovation In Healthcare?

Paul Clark

Healthcare technology has already made incredible advancements, but digital transformation of the healthcare industry is still considered in its infancy. According to the SAP eBook, Connected Care: The Digital Pulse of Global Healthcare, the possibilities and opportunities that lie ahead for the Internet of Healthcare Things (IoHT) are astounding.

Many health organizations recognize the importance of going digital and have already deployed programs involving IoT, cloud, Big Data, analytics, and mobile technologies. However, over the last decade, investments in many e-health programs have delivered only modest returns, so the progress of healthcare technology has been slow out of the gate.

What’s slowing the pace of healthcare innovation?

In the past, attempts at rapid innovation in healthcare have been bogged down by a slew of stakeholders, legacy systems, and regulations that are inherent to the industry. This presents some Big Data challenges with connected healthcare, such as gathering data from disparate silos of medical information. Secrecy is also an ongoing challenge, as healthcare providers, researchers, pharmaceutical companies, and academic institutions tend to protect personal and proprietary data. These issues have caused enormous complexity and have delayed or deterred attempts to build fully integrated digital healthcare systems.

So what is the key to rapid innovation?

According to the Connected Care eBook, healthcare organizations can overcome these challenges by using new technologies and collaborating with other players in the healthcare industry, as well as partners outside of the industry, to get the most benefit out of digital technology.

To move forward with digital transformation in healthcare, there is a need for digital architectures and platforms where a number of different technologies can work together from both a technical and a business perspective.

The secret to healthcare innovation: connected health platforms

New platforms are emerging that foster collaboration between different technologies and healthcare organizations to solve complex medical system challenges. These platforms can support a broad ecosystem of partners, including developers, researchers, and healthcare organizations. Healthcare networks that are connected through this type of technology will be able to accelerate the development and delivery of innovative, patient-centered solutions.

Platforms and other digital advancements present exciting new business opportunities for numerous healthcare stakeholders striving to meet the increasing expectations of tech-savvy patients.

The digital evolution of the healthcare industry may still be in its infancy, but it is growing up fast as new advancements in technology quickly develop. Are you ready for the next phase of digital transformation in the global healthcare industry?

For an in-depth look at how technology is changing the face of healthcare, download the SAP eBook Connected Care: The Digital Pulse of Global Healthcare.

See how the digital era is affecting the business environment in the SAP eBook The Digital Economy: Reinventing the Business World.

Discover the driving forces behind digital transformation in the SAP eBook Digital Disruption: How Digital Technology is Transforming Our World.

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About Paul Clark

Paul Clark is the Senior Director of Technology Partner Marketing at SAP. He is responsible for developing and executing partner marketing strategies, activities, and programs in joint go-to-market plans with global technology partners. The goal is to increase opportunities, pipeline, and revenue through demand generation via SAP's global and local partner ecosystems.