Last week I posted a rant about the failure of CIOs to take a leadership role in their company’s social media strategies. Having played the scold, I also want to recognize the efforts of CIOs who get it.

One of them is Ed Marx (below right), the CIO at Texas Health Resources, a system of 24 hospitals in North Texas that employs more than 21,500 people and serves 6.5 million customers. Over the last three years, Texas Health has grown its use of a behind-the-firewall social network to more than 3,500 employees (the organization can’t reveal the product’s name because of a non-endorsement policy). The employee-focused social network is changing the way the business operates. Its hospitals are spread across hundreds of miles, meaning that employees in one location rarely have a chance to meet their peers. Sharing best practices in an organization of that kind has traditionally been all but impossible, but Texas Health is pulling its resources together with often remarkable results.

Nearly every kind of health care professional at the organization is participating. For example:

• As electronic health records (EHR) are adopted throughout the system, physicians set up a forum to help each other champion the new technology. Acceptance by MDs is crucial to the success of EHR, and Texas Health’s rank among the top 5% of all hospital systems in EHR adoption is due in no small part to the speed with which employees have shared their successes.
• A group has formed to design local TEDx events. These independently organized conferences are intended to carry the spirit of innovation and collaboration that characterizes the popular TED conference to the local level. The first event was a sellout.
• When the H1N1 flu pandemic broke out two years ago, hospital presidents at first didn’t know what to make of it. Many went to the network to ask if their colleagues were seeing a surge in flu-related admissions. Rapid communication helped Texas Health become one of the first hospital networks to identify the outbreak of the virus and move to protect members.
• More than 160 groups have formed around everything from children with disabilities to sports to prayer. People are forming relationships with others in the organization whom they never would have otherwise met, and that’s creating ideas that make the business better.

The social network “has became our primary form of communication,” Marx said. “I stopped all e-mail to my staff.” But the network is more than an e-mail replacement. The ability for employees to find expertise at facilities hundreds of miles from their own has improved institutional knowledge and sped up the pace of business.

Relinquishing Control                                          

One thing that distinguishes Ed Marx from a lot of other CIOs is that he isn’t addicted to control. When he arrived at Texas Health four years ago, most social networks and even Google’s Gmail were locked down. Marx unlocked them. The world of health care was about to begin its rapid march toward EHR, and there was no room for the technophobia that has long characterized the profession. “My attitude is to let people use their breaks to develop a Facebook account or check their Gmail so they can get used to how computers work,” Marx says.

The social network is spread through peer referrals without the benefit of a mandate from the IT organization. People use it because they see value. “When we mount a top-down push for adoption, I think we’re going to see growth shoot through the roof,” Marx says.

Giving up control doesn’t mean sacrificing security. Texas Health maintains all the firewalls, password controls and audit trails that its highly regulated business requires. The difference is that the IT organization manages by exception rather than by rule. “If the environment isn’t safe, we block it,” says Marx. But it turns out that social networks haven’t posed a security problem. Instead, they’ve spurred collaboration and raised morale. “We want engaged employees and you can’t have that when people see themselves as punching a clock,” Marx says.

Texas Health’s example illustrates how overblown many of the conventional concerns are about the perils of social networks. Security hasn’t been a problem in an organization whose industry imposes some of the world’s toughest regulations. Discussion groups have formed around topics that have nothing to do with healthcare, but that’s okay. By playing, people are learning skills that are paying dividends in other ways. Corporate transparency is humanizing executives and breaking down hierarchical barriers. People are learning to treat each other as people instead of titles, and that improves the quality of interaction.

For Ed Marx, openness to new ideas is a guiding principle. “If you want to expand your influence, you need to be open to other people’s ideas,” he says. “One is too small a number for greatness.”

Ed Marx’s Social Network Success Tips

Don’t mandate; elevate. IT organizations are rapidly losing the ability to tell people what devices and applications to use. Encourage people to use the tools they find valuable, find ways to accommodate them into your infrastructure and focus on security and data protection.

Use the tools yourself. One of the reasons the social network has succeeded at Texas Health is because the IT organization is one of its biggest boosters and most active users.

Enlist executive support. Once the people at the top of the organization climb on board, adoption spreads quickly. Find and encourage executive enthusiasts.

Promote success. Make it your business to be aware of people’s success stories and to share them broadly across the organization.

Live blogging Dr. David Ferrucci’s address to the IBM Watson University Symposium at Harvard Business School and MIT Sloan School of Management. Ferrucci was director of the IBM Watson project.

Additional coverage is on the Smarter Planet Blog. .

Ferrucci tells a story about his daughter’s quote: “Interesting things are boring.” Because that was her frame of reference. Interesting things involve a lot of complexity that makes them boring (to people who aren’t explicitly interested in them).

We’re trying to move from the age of moving bits to the age of understanding their meaning. Meaning is ultimately subjective and we are the subjects. We see a calculator with dice on top and we infer it has something to do with calculating odds. We bring a huge wealth of background knowledge to interpreting what an image means.

Don’t expect AI systems to originate meaning, but expect them to infer meaning. We have to get the AI systems to make the right assignments of meaning but we can’t expect them to originate meaning.

You can look at context from a chronology or magnitude or causation perspective. If you want computers to detect human meanings, it takes advanced technology.

Jeopardy was an interesting challenge. The end goal was to get smarter about processing a language that humans use to detect intended meanings. Watson and Jeopardy were milestones in that direction.

You couldn’t play Jeopardy by having thousands of FAQs and linking to their answers. Example: “The first person mentioned by name in  ‘The Man in the Iron Mask’ is this hero of a previous book by the same author.” The answer is D’Artagnan. If you’re going to buzz in on that question, you’d better have an accurate probability that your answer is correct. You have to look at all the evidence supporting that answer and analyze it so that that probability gets better and better.

Example: “This actor, Audrey’s husband from 1954 to 1968, directed her as Rima the bird girl in ‘Green Mansions.’” You need to parse what “direct” means: to guide, to lead, to direct? What is “Green Mansions?” Watson would produce multiple syntactic assignments because there was no guarantee of approaching that problem correctly. You need to parse all of these assignments in parallel to see which are most likely.

In a random sample of 20,000 past Jeopardy questions we found 2,500 distinct types. The most frequent occurred less than 3% of the time. They didn’t help us map to answer, but it did help us identify different ways of understanding how questions are parsed.

Plausible inference varies by context. Example: In the category of Lincoln Blogs: “Treausre secy Chase just submitted this for the third time. Guess what, pal? This time I’m accepting it.” Answer is “resignation.” Sixth-grade class came up with a different answer: “Friend request.”

“Vessels sink.” “Sink” can refer to submerged but you can also sink a cue ball.

We calculated the winning player on Jeopardy gets about 50% of the answers. Those that they got a chance to answer they answered correctly about 85% of the time. Ken Jennings was able to answer 62% of the board, which was phenomenal.

Fundamental bets we made:

• Large hand-crafted models are too limited.
• Intelligence from the capability oif many: We had to come up with many hypotheses and algorithms to figure out how to attack the problem. We had to combine those to balance their application.
• Massive parallelism was a key enabler: We had to pusue many competing independent hypotheses over large amounts of data.

The notion of intelligence from the capability of many enabled us to get a lot of different components to interoperate with each other. We had to build many different ways to combine them.

Watson Architecture

Watson takes many possible alternatives or candidate answers. For each of those hypotheses it gathers a large amount of evidence. So it may take 100 interpretations and gather 100 facts for each, or 10,000 factors. We then run them through filters to calculate a probability of each answer, and if it’s above a certain threshold, like 50%, then we buzz in.

Then we also have to balance in the competition. If Watson is way ahead, it’s less likely to buzz in on a lower probability. If it’s way behind, it gets more aggressive.

The goal of all researchers was can they drive the end-to-end system performance? We changed the cultural incentives. We all go into one room and everyone focused on natural language processing, information retrieval, knowledge representation and more. We produced more than 8,000 documents.

Some early answers:

Decades before Lincoln, Daniel Webster spoke of government “made for”, “made by” & answerable to them.” Watson: “No One.”

“Give a Brit a tinnkle when you get into town and you’ve done this.” Watson: “Urinate”

This system is complex in ways that no one imagined. We did end-to-end integration almost every two weeks, then drove into data to determine which factors were contributing to right and wrong answers.

We got to 87% to 90% precision, which was good enough to compete. We went into the final game with Ken Jennings. Our analytics told us we had about a 74% chance of winning.

It took about two hours to answer one question on a single 2.6Ghz CPU. We used about 15TB of RAM and 2,000 parallel cores.

I fought hard to get an answer panel on TV so people could see Watson computing possibilities. This got people thinking about what was going on behind the scenes.

An answer doesn’t really matter if you can’t back it up with evidence that a human can understand. When we apply this to health care or finance, you need to provide the evidence of why you think this is an important answer, but you have to do it in a way that people can understand. The computer isn’t giving answers but providing evidence.

Applications in health care: You’re finding hypotheses that weren’t clearly evident and gathering information that supports evidence. You can imaging information comine from patient data, symptoms, office visits, and background databases, computing these profiles, providing these profiles to the medical teams and helping them to see how the evidence supports the hypothesis.

What do people intend when they use words: “Geeshe, she was only 10 when she took ohome an Oscar in 1974. She’s 40 now.” Watson’s confidence was low. It knew that Tatum O’Neal won an Oscar at age 10, but it doesn’t understand “take home.” So we asked people to answer that question so that Watson could understand what the term meant.

It’s not enough to assign semantics to a sentence. You want to interact with people who can help you understand meaning.

Question: How long until Watson can program itself?

As Watson is training on prior questions and answers, it’s balancing those inputs and learning how to weight the possibilities. Different problems require different balancing. I can’t give you a short answer because what do you mean by programming yourself? Short answer is I don’t know.

Question: It sounds like you ran out of gas at some point in comparing Watson’s performance to humans. Is there some limit?

It does slow down. At some point, there are things that are incredibly hard for a computer to do. Can we push that line upward? I think we can, but for Jeopardy there was only so far we needed to push it. The game is also evolving. We learned that data prior to 2003 was easier for the computer to understand. After we learned that, we focused only on data post-2003.

Question: What areas are this technology more appropriate for?

We’ve only focused on health care so far. It’s harder for the machine to understand different domains. In health care, you don’t have as much training data and the input is not just a small graph. It’s a huge graph of different factors and relationships. In health care we had to tackle the multi-dimensional input problem. Because of the limited ontology in that domain, it’s somewhat easier to tackle health care than some other areas.