A 124 Year Odyssey Involving Cases and Records Finally Ends

I first became aware of this matter about 10 years ago when I read a story about a woman named Josephine Wild Gun (yes, that is her name) who then lived in a small run-down house on the Blackfeet reservation in Montana. Like most of her Native American neighbors, she owned several parcels of reservation land that were being held in trust by the U.S. Government (Indian Trust Fund).  The Indian Trust Fund was created in 1887, as part of the Dawes Act, to oversee payments to Native Americans.  This fund managed nearly 10,000 acres on Josephine’s behalf, leasing the property to private interests for grazing and oil drilling fees.  In return, she was supposed to receive royalties from the trust fund.

Despite the lucrative leases, Josephine had allegedly never received more than $1,500 a year from the trust fund.  According to the story, the payments trickled off and one check totaled only 87 cents.  When her husband died, she even had to borrow money to pay for the funeral.  Josephine’s story is compelling … and it stuck with me.   This story, along with some research I was doing on the Cobell v. Salazar lawsuit (involving the same Indian Trust Fund) and the government’s inability to produce records documenting the income accounting of the payments to Josephine and about 300,000 other Native Americans, caused me to wonder how and why something like this could happen.

The 15-year old class action (Cobell v. Salazar) lawsuit was recently settled for $3.4 billion.  I am writing about this today because hundreds of thousands of notices went out this week to American Indians who are affected by the $3.4 billion settlement bringing an end to a 124 year odyssey involving The Department of the Interior, The Bureau of Indian Affairs and many Native Americans and their descendants.  In this suit, Elouise Cobell (a Native American and member of the Blackfeet tribe) sued the federal government over the mismanagement of the trust fund.  In her suit, Cobell claimed that the U.S. Government failed to provide a historical accounting of the money the government held in trust for Native American landowners in exchange for the leasing of tribal lands.  Ultimately, the case hinged on the government’s ability to produce these accounting records showing how the money was managed on behalf of the original landowners.  I find myself wondering if the whole entire thing could have been avoided with better case management and recordkeeping practices.  This 15-year court battle is the culmination of events going all the way back to the 19th Century!  The landowners had a right to expect proper case management, proper records management and proper distribution of funds.  Apparently, none of those things happened.

As a history buff, I find the whole back story fascinating … so here we go …

It all starts with Henry Dawes (1816 – 1903) who was a Yale graduate from Massachusetts.  He was an educator, a newspaper editor, a lawyer and perhaps, somewhat infamously, a Congressman who was both a member of the U.S. House of Representatives (1857 to 1875) and the U.S. Senate (1875 to 1893).

During his time in public service, he had his ups and his downs.  In 1868, he received a large number of shares of stock from a railroad construction company as part of the Union Pacific railway’s influence-buying efforts.  On the positive side, Dawes was both a supporter and involved with the creation of Yellowstone National Park.  He also had a role in promoting anti-slavery and reconstruction measures during and after the Civil War.  In the Senate, he was chairman of The Committee on Indian affairs, where he concentrated on the enactment of laws that he believed were for the benefit of American Indians.

Dawes’s most noteworthy achievement was the passage of The General Allotment Act of 1887 (known as The Dawes Act referenced earlier).  The Dawes Act authorized the government to survey and inventory Indian tribal land and to divide the area into allotments for individual Indians.  Although later amended twice, it was this piece of legislation that set the stage for 124 years of alleged mismanagement and eventually the Cobell v. Salazar lawsuit.

I see this as a cautionary tale … reminding us of the need for enterprise content and case management as well as records management (but more on that later).  I wasn’t around but I would imagine PC’s ran pretty slowly back in 1887 (chuckle) … but I digress, as manual paper based practices did exist.

Back to the story … The Dawes Commission, was established under the Office of Indian Affairs to persuade American Indians to agree to the allotment plan.   Dawes himself, later oversaw the commission for a period of time after his time as a Senator.  It was this same commission that registered and documented the members of the Five Civilized Tribes.  Eventually, The Curtis Act of 1898 abolished tribal jurisdiction over the tribes’ land and the landowners became dependent on the government.  Native Americans lost about 90 million acres of treaty land, or about two-thirds of the 1887 land base over the lifespan of the Dawes Act.  Roughly 90,000 Indians were made landless and the Act forced Native people onto small tracts of land … in many cases, it separated families.  The allotment policy depleted the land base and also ended hunting as a means of subsistence.  In 1928, a Calvin Coolidge Administration study had determined that The Dawes Act had been used to illegally deprive Native Americans of their land rights.  Today, The United States Department of the Interior is responsible for the remnants of The Dawes Act and the Office of Indian Affairs is now known as the Bureau of Indian Affairs.

There is a pretty big taxpayer bill about to finally be paid out ($3.4 billion) to the surviving Native American descendants and for other purposes.  Throughout the lifecycle of this case, there were multiple contempt charges, fines and embarrassing mandates resulting in the government’s reputation taking a significant hit.  Interior Secretary Bruce Babbitt and Treasury Secretary Robert Rubin were found in contempt of court for failing to produce documents and slapped with a $625,000 fine.  And while time went by and Administrations changed, not much else did when Interior Secretary Gale Norton and Assistant Interior Secretary of Indian Affairs Neal McCaleb were also held in contempt.  At one point, the judge also ordered the Interior Department to shut down most of its Internet operations after an investigator discovered that the department’s computer system allowed unauthorized access to Indian trust accounts.  During this time, many federal employees could not receive or respond to emails, and thousands of visitors to national parks were unable to make online reservations for campsites.  The shutdown also prevented the trust fund from making payments to more than 43,000 Indians, many of whom depended on the quarterly checks to make ends meet. In Montana and Wyoming, some beneficiaries were forced to apply for tribal loans to help them through the holidays.

There was plenty of mudslinging as well:

“Federal officials have spent more than 100 years mismanaging, diverting, and losing money that belongs to Indians,” says John Echohawk of the Native American Rights Fund, which directed the lawsuit.  “They have no idea how much has been collected from the companies that use our land and are unable to provide even a basic, regular statement to most Indian account holders.”

Again I ask … where was the accountability for these landowner cases and the associated records?  Could all of this have been prevented with better policies and processes?

The damage was already done but we know that the government invested in an array of systems such as Integrated Records Management System (IRMS), Trust Funds Accounting System (TFAS), Land Records Information System (LRIS) and Trust Asset and Accounting Management System (TAAMS).  These systems were to collect, manage and distribute trust funds in support of the 1994 Indian Trust Fund Management Reform Act.  They were used for historical accounting purposes and contained land ownership records and financial records for the associated cases.  A major premise of the government’s accounting effort was that the transition from paper to electronic records took the accuracy, completeness and reliability of the trust data to a level that far surpassed the “paper ledger era” … seems like it was too little too late.

I guess we’ll never know for sure, but I firmly believe that much, if not most, of this could have been avoided.  It was alleged during the case that as much 90 percent of the Indian Trust Fund’s records were missing, and the few that were available were in comically bad condition. An Interior Department report provided to the court refers to storage facilities plagued by problems ranging from “poisonous spiders in the vicinity of stored records” to “mixed records strewn throughout the room with heavy rodent activity.”

It’s a tragic story and I am glad it’s finally ending.  It’s disheartening that Josephine Wild Gun and many others had to suffer the way they did for the past 124 years.  It’s amazing the number of people that this impacted starting with Henry Dawes and ending with ~300,000 Native Americans (and everyone in between).  It’s encouraging to know that technologies like Enterprise Content Management, Advanced Case Management and Records Management can all be used with great impact in the future to improve processes and outcomes like this.

As always, leave me your thoughts and opinions here.

IBM … 100 Years Later

Nearly all the companies our grandparents admired have disappeared.  Of the top 25 industrial corporations in the United States in 1900, only two remained on that list at the start of the 1960s.  And of the top 25 companies on the Fortune 500 in 1961, only six remain there today.  Some of the leaders of those companies that vanished were dealt a hand of bad luck.  Others made poor choices. But the demise of most came about because they were unable simultaneously to manage their business of the day and to build their business of tomorrow.

IBM was founded in 1911 as the Computing Tabulating Recording Corporation through a merger of four companies: the Tabulating Machine Company, the International Time Recording Company, the Computing Scale Corporation, and the Bundy Manufacturing Company.  CTR adopted the name International Business Machines in 1924.  The distinctive culture and product branding has given IBM the nickname Big Blue.

As you read this, IBM begins its 101st year.  As I look back at the last century, there is a path that led us to this remarkable anniversary which has been both rich and diverse.  The innovations IBM has contributed includes products ranging from cheese slicers to calculators to punch cards – all the way up to game-changing systems like Watson.

But what stands out to me is what has remained unchanged.  IBM has always been a company of brilliant problem-solvers.  IBMers use technology to solve business problems.  We invent it, we apply it to complex challenges, and we redefine industries along the way.

This has led to some truly game-changing innovation.  Just look at industries like retail, air travel, and government.  Where would we be without UPC codes, credit cards and ATM machines, SABRE, or Social Security?  Visit the IBM Centennial site to see profiles on 100 years of innovation.

We haven’t always been right though … remember OS/2, the PCjr and Prodigy?

100 years later, we’re still tackling the world’s most pressing problems.  It’s incredibly exciting to think about the ways we can apply today’s innovation – new information based systems leveraging analytics to create new solutions, like Watson – to fulfill the promise of a Smarter Planet through smarter traffic, water, energy, and healthcare.  This promise of the future … is incredibly exciting and I look forward to helping IBM pave the way for continued innovation.

Watch the IBM Centennial film “Wild Ducks” or read the book.  IBM officially released a book last week celebrating the Centennial, “Making the World Work Better: The Ideas that Shaped a Century and a Company”.  The book consists of three original essays by leading journalists. They explore how IBM” has pioneered the science of information, helped reinvent the modern corporation and changed the way the world actually works.

As for me … I’ve been with IBM since the 2006 acquisition of FileNet and am proud to be associated with such an innovative and remarkable company.

IBM at 100: SAGE, The First National Air Defense Network

This week was a reminder of how technology can aid in our nation’s defense as we struck a major blow against terrorism.  Most people don’t realize IBM contributed to our nation’s defense in the many ways it has.  Here is just one example from 1949.

When the Soviet Union detonated their first atomic bomb on August 29, 1949, the United States government concluded that it needed a real-time, state-of-the-art air defense system.  It turned to Massachusetts Institute of Technology (MIT), which in turn recruited companies and other organizations to design what would be an online system covering all of North America using many technologies, a number of which did not exist yet.  Could it be done?  It had to be done.  Such a system had to observe, evaluate and communicate incoming threats much the way a modern air traffic control system monitors flights of aircraft.

This marked the beginning of SAGE (Semi-Automatic Ground Environment), the national air defense system implemented by the United States to warn of and intercept airborne attacks during the Cold War.  The heart of this digital system—the AN/FSQ-7 computer—was developed, built and maintained by IBM.  SAGE was the largest computer project in the world during the 1950s and took IBM squarely into the new world of computing.  Between 1952 and 1955, it generated 80 percent of IBM’s revenues from computers, and by 1958, more than 7000 IBMers were involved in the project.  SAGE spun off a large number of technological innovations that IBM incorporated into other computer products.

IBM’s John McPherson led the early conversations with MIT, and senior management quickly realized that this could be one of the largest data processing opportunities since winning the Social Security bid in the mid-1930s.  Thomas Watson, Jr., then lobbying his father and other senior executives to move into the computer market quickly, recalled in his memoirs that he wanted to “pull out all the stops” to be a central player in the project.  “I worked harder to win that contract than I worked for any other sale in my life.”  So did a lot of other IBMers: engineers designing components, then the computer; sales staff pricing the equipment and negotiating contracts; senior management persuading MIT that IBM was the company to work with; other employees collaborating with scores of companies, academics and military personnel to get the project up and running; and yet others who installed, ran and maintained the IBM systems for SAGE for a quarter century.

The online features of the system demonstrated that a new world of computing was possible—and that, in the 1950s, IBM knew the most about this kind of data processing.  As the ability to develop reliable online systems became a reality, other government agencies and private companies began talking to IBM about possible online systems for them.  Some of those projects transpired in parallel, such as the development of the Semi-Automated Business Research Environment (Sabre), American Airlines’ online reservation system, also built using IBM staff located inPoughkeepsie,New York.

In 1952, MIT selected IBM to build the computer to be the heart of SAGE. MIT’s project leader, Jay W. Forrester, reported later that the company was chosen because “in the IBM organization we observed a much higher degree of purposefulness, integration and “esprit de corps” than in other firms, and “evidence of much closer ties between research, factory and field maintenance at IBM.”  The technical skills to do the job were also there, thanks to prior experience building advanced electronics for the military.

IBM quickly ramped up, assigning about 300 full-time IBMers to the project by the end of 1953. Work was centered in IBM’s Poughkeepsie and Kingston, NY facilities and in Cambridge, Massachusetts, home of MIT.  New memory systems were needed; MITRE and the Systems Development Corporation (part of RAND Corporation) wrote software, and other vendors supplied components.  In June 1956, IBM delivered the prototype of the computer to be used in SAGE.  The press release called it an “electronic brain.”  It could automatically calculate the most effective use of missiles and aircraft to fend off attack, while providing the military commander with a view of an air battle. Although this seems routine in today’s world, it was an enormous leap forward in computing.  When fully deployed in 1963, SAGE included 23 centers, each with its own AN/FSQ-7 system, which really consisted of two machines (one for backup), both operating in coordination.  Ultimately, 54 systems were installed, all collaborating with each other. The SAGE system remained in service until January 1984, when it was replaced with a next-generation air defense network.

Its innovative technological contributions to IBM and the IT industry as a whole were significant.  These included magnetic-core memories, which worked faster and held more data than earlier technologies; a real-time operating system (a first); highly disciplined programming methods; overlapping computing and I/O operations; real-time transmission of data over telephone lines; use of CRT terminals and light pens (a first); redundancy and backup methods and components; and the highest reliability of computer systems (uptime) of the day.  It was the first geographically distributed, online, real-time application of digital computers in the world.  Because many of the technological innovations spun off from this project were ported over to new IBM computers in the second half of the 1950s by the same engineers who had worked on SAGE, the company was quickly able to build on lessons learned in how to design, manufacture and maintain complex systems.

Fascinating to be sure … the full article can be accessed at http://www.ibm.com/ibm100/us/en/icons/sage/

IBM at 100: The 1401 Mainframe

In my continuing series of IBM at 100, I turn to our data processing heritage with the IBM 1401 Data Processing System (which was long before my time).

While the IBM 1401 Data Processing System wasn’t a great leap in power or speed, that was never the point. “It was a utilitarian device, but one that users had an irrational affection for,” wrote Paul E. Ceruzzi in his book, A History of Modern Computing.

There were several keys to the popularity of the 1401 system. It was one of the first computers to run completely on transistors—not vacuum tubes—and that made it smaller and more durable. It rented for US$2500 per month, and was touted as the first affordable general-purpose computer. It was also the easiest machine to program at the time. The system’s software, wrote Dag Spicer, senior curator at the Computer History Museum, “was a big improvement in usability.”

This more accessible computer unleashed pent-up demand for data processing. IBM was shocked to receive 5200 orders for the 1401 computer in just the first five weeks after introducing it—more than was predicted for the entire life of the machine. Soon, business functions at companies that had been immune to automation were taken over by computers. By the mid-1960s, more than 10,000 1401 systems were installed, making it by far the best-selling computer to date.

More importantly, it marked a new generation of computing architecture, causing business executives and government officials to think differently about computing. A computer didn’t have to be a monolithic machine for the elite. It could fit comfortably in a medium-size company or lab. In the world’s top corporations, different departments could have their own computers.

A computer could even wind up operating on an army truck in the middle of a forest. “There was not a very good grasp or visualization of the potential impact of computers—certainly as we know them today—until the 1401 came along,” said Chuck Branscomb, who led the 1401 design team. The 1401 system made enterprises of all sizes believe a computer was useful, and even essential.

By the late 1950s, computers had experienced tremendous changes. Clients drove a desire for speed. Vacuum-tube electronics replaced the electro-mechanical mechanisms of the tabulating machines that dominated information processing in the first half of the century. First came the experimental ENIAC, then Remington Rand’s Univac and the IBM 701, all built on electronics. Magnetic tape and then the first disk drives changed ideas about the accessibility of information. Grace Hopper’s compiler and John Backus’s FORTRAN programming language gave computer experts new ways to instruct machines to do ever more clever and complex tasks. Systems that arose out of those coalescing developments were a monumental leap in computing capabilities.

Still, the machines touched few lives directly. Installed and working computers numbered barely more than 1000. The world, in fact, was ready for a more accessible computer.

The first glimpse of that next generation of computing turned up in an unexpected place:France. “In the mid-1950s, IBM got a wake-up call,” said Branscomb, who ran one of IBM’s lines of accounting machines at the time. French computer upstart Machines Bull came out with its Gamma computers, small and fast compared to goliaths like the IBM 700 series. “It was a competitive threat,” Branscomb recalled.

Bull made IBM and others realize that entities with smaller budgets wanted computers. IBM scrambled together resources to try to make a competing machine. “It was 1957 and IBM had no new machine in development,” Branscomb said. “It was a real problem.”

During June and July 1957, IBM engineers and planners gathered inGermanyto propose several accounting machine designs. The anticipated product of this seven-week conference was known thereafter as the Worldwide Accounting Machine (WWAM), although no particular design was decided upon.

In September 1957, Branscomb was assigned to run the WWAM project. In March 1958, after Thomas Watson, Jr. expressed dissatisfaction with the WWAM project inEurope, the Endicott proposal for a stored-program WWAM was given formal approval as the company’s approach to meeting the need for an electronic accounting machine. The newly assigned project culminated in the announcement of the 1401 Data Processing System (although, for a time it carried the acronym SPACE).

The IBM 1401 Data Processing System—comprising a variety of card and tape models with a range of core memory sizes, and configured for stand-alone use and peripheral service for larger computers—was announced in October 1959.

Branscomb’s group set a target rental cost of US$2500 per month, well below a 700 series machine, and hit it. They also decided the computer had to be simple to operate. “We knew it was time for a dramatic change, a discontinuity,” Branscomb added. And indeed it was. The 1401 system extended computing to a new level of organization and user, driving information technology deeper into everyday life.

The full article can be accessed at http://www.ibm.com/ibm100/us/en/icons/mainframe/

Watson and The Future of ECM

In the past, I have whipped out my ECM powered crystal ball to pontificate about the future of Enterprise Content Management.  These are always fun to write and share (see Top 10 ECM Pet Peeve Predictions for 2011  and Crystal Ball Gazing … Enterprise Content Management 2020).  This one is a little different though …  on the eve of the AIIM International Conference and Expo at info360, I find myself wondering … what are we going to do with all this new social content … all of these content based conversations in all of their various forms?

We’ve seen the rise of the Systems of Engagement concept and number of new systems that enable social business.  We’re adopting new ways to work together leveraging technologies like collaborative content, wikis, communities, RSS and much more.  All of this new content being generated is text based and expressed in natural language.  I suggest you read AIIM’s report Systems of Engagement and the Future of Enterprise IT: A Sea Change in Enterprise for a perspective on the management aspects of the future of ECM.  It lays out how organizations must think about information management, control, and governance in order to deal with social technologies.

Social business is not just inside the firewall though.  Blogs, wikis and social network conversations are giving consumers and businesses a voice and power they’ve never have before … again based in text and expressed in natural language.  This is a big deal.  770 million people worldwide visited a social networking site last year (according to a comScore report titled Social Networking Phenomenon) … and amazingly, over 500 billion impressions annually are being made about products and services (according to a new book Empowered written by Josh Bernoff and Ted Schadler).

But what is buried in these text based natural language conversations?  There is an amazing amout of information trapped inside.  With all these conversations happening between colleagues, customers and partners … what can we learn from our customers about product quality, customer experience, price, value, service and more?  What can we learn from our internal conversations as well?  What is locked in these threads and related documents about strategy, projects, issues, risks and business outcomes.

We have to find out!  We have to put this information to work for us.

But guess what?  The old tools don’t work.  Data analysis is a powerful thing but don’t expect today’s business intelligence tools to understand language and threaded conversations.  When you analyze data … a 5 is always a 5.  You don’t have to understand what a 5 is or figure out what it means.  You just have to calculate it against other numeric indicators and metrics.

Content … and all of the related conversations aren’t numeric.  You must start by understanding what it all means, which is why understanding natural language is key.  Historically, computers have failed at this.  New tools and techniques are needed because content is a whole different challenge.  A very big challenge.  Think about it … a “5” represents a value, the same value, every single time.  There is no ambiguity.  In natural language, the word “premiere” could be a noun, verb or adjective.  It could be a title of a person, an action or the first night of a theatre play.  Natural language is full of ambiguity … it is nuanced and filled with contextual references.  Subtle meaning, irony, riddles, acronyms, idioms, abbreviations and other language complexities all present unique computing challenges not found with structured data.  This is precisely why IBM chose Jeopardy! as a way to showcase the Watson breakthrough.

IBM Watson (DeepQA) is the world’s most advanced question answering machine that uncovers answers by understanding the meaning buried in the context of a natural language question.  By combining advanced Natural Language Processing (NLP) and DeepQA automatic question answering technology, Watson represents the future of content and data management, analytics, and systems design.  IBM Watson leverages core content analysis, along with a number of other advanced technologies, to arrive at a single, precise answer within a very short period of time.  The business applications for this technology are limitless starting with clinical healthcare, customer care, government intelligence and beyond.

You can read some of my other blog postings on Watson (see “What is Content Analytics?, Alex”, 10 Things You Need to Know About the Technology Behind Watson and Goodbye Search … It’s About Finding Answers … Enter Watson vs. Jeopardy! … or better yet … if you want to know how Watson actually works, hear it live at my AIIM / info360 main stage session IBM Watson and the Impact on ECM this coming Wednesday 3/23 at 9:30 am.

BLOG UPDATE:  Here is a link to the slides used at the AIIM / info360 keynote.

Back to my crystal ball … my prediction is that natural language based computing and related analysis is the next big wave of computing and will shape the future of ECM.  Watson is an enabling breakthrough and is the start of something big.  With all this new information, we’ll want to use to understand what is being said, and why, in all of these conversations.  Most of all, we’ll want to leverage this new found insight for business advantage.  One compelling and obvious example is to be to answer age old customer questions like “Are our customers happy with us?” “How happy” “Are they so happy, we should try to sell something else?” … or … “Are our customers unhappy?” “Are they so unhappy, we should offer them something to prevent churn?” Undestanding the customer trends and emerging opportunities across a large set of text based conversations (letters, calls, emails, web postings and more) is now possible.

Who wouldn’t want to undertstand their customers, partners, constituents and employees better?  Beyond this, Watson will be applied to industries like healthcare to help doctors more effectively diagnose diseases and this is just the beginning.  Organizations everywhere will want to unlock the insights trapped in their enterprise content and leverage all of these conversations … in ways we haven’t even thought of yet … but I’ll save that for the next time I use my ECM crystal ball.

As always … leave me your thoughts and ideas here and hope to see you Wednesday at The AIIM International Conference and Expo at info360 http://www.aiimexpo.com/.

IBM at 100: UPC … The Transformation of Retail

In my continuing series of IBM at 100 achievements … this is one of my favorites of all the ones I plan to republish here. The humble Universal Product Code (UPC), also known as the bar code, along with the related deployment of scanners, fundamentally changed many of the practices of retailers and all organizations that buy and move things, from large industrial equipment to pencils purchased in stationery stores. These two technologies led to the use of in-store information processing systems in almost every industry around the world, applied to millions of types of goods and items. UPC is planet Earth’s most pervasive inventory tracking tool.

N. Joseph Woodland, later an IBMer but then working at Drexel Institute of Technology, applied for the first patent on bar code technology on October 20, 1949, and along with Bernard Silver, received the patent on October 7, 1952. And there it sat for more than two decades. In those days there was no way to read the codes, until the laser became a practical tool. About 1970 at IBM Research Triangle Park, George Laurer went to work on how to scan labels and to develop a digitally readable code. Soon a team formed to address the issue, including Woodland. Their first try was a bull’s-eye bar code; nobody was happy with it because it took up too much space on a carton.

Meanwhile, the grocery industry in post-war America was adapting to the boom in suburban supermarkets–seeking to automate checkout at stores to increase speed, drive down the cost of hiring so many checkout clerks and systematize in-store inventory management. Beginning in the 1960s, various industry task forces went to work defining requirements and technical specifications. In time the industry issued a request to computer companies to submit proposals.

IBM’s team had also reworked its design going to the now familiar rows of bars each containing multiple copies of data. Woodland, who had helped create the original bull’s-eye design, then later worked on the bar code, writing IBM’s response to the industry’s proposal. Another group of IBMers at the Rochester, Minnesota Laboratory built a prototype scanner using optics and lasers. In 1973, the grocery industry’s task force settled on a standard that very closely paralleled IBM’s approach. The industry wanted a standard that all grocers and their suppliers could use.

IBM was well positioned and became one of the earliest suppliers of scanning equipment to the supermarket world. On October 11, 1973, IBM became one of the earliest vendors to market with a system, called the IBM 3660. In time it became a workhorse in the industry. It included a point-of-sale terminal (digital cash register) and checkout scanner that could read the UPC symbol. The grocery industry compelled its suppliers of products in boxes and cans to start using the code, and IBM helped suppliers acquire the technology to work with the UPC.

On June 26, 1974, the first swipe was done at a Marsh’s supermarket in Troy, Ohio, which the industry had designated as a test facility. The first product swiped was a pack of Wrigley’s Juicy Fruit chewing gum, now on display at the Smithsonian’s National Museum of American History in Washington, D.C. Soon, grocery stores began adopting the new scanners, while customers were slowly educated on their accuracy in quoting prices.

If there had been any doubts about the new system’s prospects, they were gone by the end of the 1970s. The costs of checking out customers went down; the accuracy of transactions went up; checkouts sped up by some 40 percent; and in-store inventory systems dramatically improved management of goods on hand, on order or in need of replenishment. And that was just the beginning. An immediate byproduct was the ability of stores to start tracking the buying habits of customers in general and, later, down to the individual, scanning bar coded coupons and frequent shopper cards. In the four years between 1976 and 1980, the number of grocery stores using this technology jumped from 104 to 2,207, and they were spreading to other countries.

In the 1980s, IBM and its competitors introduced the new technology to other industries (including variations of the American standard bar codes that were adopted in Western Europe). And IBM Raleigh kept improving the technology. In December 1980, IBM introduced the 3687 scanner that used holographic technologies—one of the first commercial applications of this technology. In October 1987, the IBM 7636 Bar Code Scanner was introduced–and as a result, throughout the 1980s factories adopted the IBM bar code to track in-process inventory. Libraries used it to do the same with books. In the 1990s, hand-held scanners made it easier to apply bar codes to things beyond cartons and cans and to scan them, eventually using wireless technology. Meanwhile innovation expanded in the ability of a bar code to hold more information.

These technologies make it possible for all kinds of organizations, schools, universities and companies in all industries to leverage the power of computers to manage their inventories. In many countries, almost every item now purchased in a retail store has a UPC printed on it, and is scanned. UPC led to the retirement of the manual and electro-mechanical cash registers which, as a technology, had been around since the 1880s. By the early 2000s, bar code technologies had become a $17 billion business, scanned billions of times each day.

The full text of this article can be found on IBM at 100: http://www.ibm.com/ibm100/us/en/icons/upc/

Humans vs. Watson (Programmed by Humans): Who Has The Advantage?

DAY 3 UPDATE:  If you are a technology person, you had to be impressed.  We all know who won by now so I won’t belabor it.  Ken Jennings played better and made a game of it … at least for a while.  He seemed to anticipate the buzz a little bit better and got on a roll.

You may have noticed that Watson struggled in certain categories last night.  “Actors Who Direct” gave very short clues (or questions) like “The Great Debaters” for which the correct answer was “Who is Denzel Washington”.  For Watson, the longer the question, the better.  If it takes a longer time for Alex to read the question, Watson has more time to consider candidate answers, evidence scores and confidence rankings.  This is another reason why Watson does better in certain categories.  In an attempt to remain competitive in this situation, Watson has multiple ways to process clues or questions.  There is what is called the “short path” (to an answer).  This is used for shorter questions when Watson has less time to decide whether to buzz in or not.  Watson is more inconsistent when it has to answer faster.  As seen last night, he either chose not to answer or Ken and Brad beat him to it.

In the end, the margin of victory was decisive for Watson.  In total, $1.25 million was donated to charity and Ken and Brad took home a parting gifts of $150,000 and $100,000 respectively … pretty good for all involved.  The real winners are science and technology.   This is a major advance in computing that could revolutionize the way we interact with computers … especially with questions and answers.  The commercial applications seem endless.

DAY 2 UPDATE:  Last night was compelling to watch.  I was at the Washington, DC viewing event with several hundred customers, partners and IBMers.  The atmosphere in the briefing center was electric.  When the game started with Watson taking command, the room erupted in cheers.  After Watson got on a roll, and steamrolled Brad and Ken for most of Double Jeopardy, the room began to grow silent in awe of what was happening. 

Erik Mueller (IBM Research) was our featured speaker.  He was bombarded … before, during and after the match with questions like “How does he know what to bet?”  “How does Watson process text?”  How would this be used in medical research?”  “What books were in Watson’s knowledge base?”  “Can Watson hear?” “Does he have to press a button like the human contestants?” and many more.

I was there as a subject matter expert and even though the spotlight was rightfully on Eric, I did get to answer a question on how some of Watson’s technology was being used today.  I explained how our IBM Content Analytics is used and how it is helping to power Watson’s natural language prowess.

When Watson incorrectly answered “What is Toronto????” in Final Jeopardy, the room audibly gasped (myself included).  As everyone seemed to hold their breath, I looked at Erik and he was smiling like a Cheshire cat … brimming with confidence.  The room cheered and applauded when the Watson’s small bet was revealed … a seeming acknowledgement to the technological brilliance.  Applause for a wrong answer!

Afterwards, there were many ideas on how Watson could be applied.  My favorite was from a legal industry colleague who had a number of suggestions for how Watson could optimize document review and analysis that is currently a problem for judges and litigators.

Yesterday (below) I said the human’s have a slight advantage.  And while Watson has built an impressive lead, I still feel that way.  Many of yesterday’s categories played to Watson’s fact based strengths.  It could go the other way tonight and Brad and Ken could get right back into the match.  The second game will air tonight in its entirety and the scores from both games will be combined to determine the $1 million prize winner.  Watson is entering tonight with a more than $25,000 lead.  IBM is donating all prize winnings to charity and Ken Jennings and Brad Rutter are donating 50% to charity.

DAY 1 POST:  After Day 1, Watson is tied with Brad Rutter at $5,000 going into Double Jeopardy – which is pretty impressive.  Ken Jennings has yet to catch his stride.  Brad and Ken seemed a little shell shocked at first, but Brad rebounded right when Watson was faltering towards the end of the first round.  This got me to thinking I should go into a little more detail about who really has the advantage … Watson or the humans? 

If you watched it last night, you may have observed that Watson does very well with factual questions.  He did very well in the Beatles song category – they were mostly facts with contextual references to lyrics.  Answers that involve multiple facts, all of which are required to answer the correct response but are unlikely to be found the same place, are much harder for Watson.  This is why Watson missed the Harry Potter question involving Lord Voldemort.  Watson also switched categories frequently which is part of his game strategy.  You may have also noticed that Watson can’t see or hear.  He answered a question wrong even though Ken gave the same wrong answer seconds before.  More on this later in the post.

Here goes … my take on who has the advantage …

Question Understanding :  Advantage Humans

Humans:  Seemingly Effortless.  Almost instantly knows what is being asked, what is important and how it applies – very naturally gets focus, references, hints, puns, implications, etc.

Watson:  Hugely Challenging.  Has to be programmed to analyze enormous numbers of possibilities to get just a hint of the relevant meaning.  Very difficult due to variability, implicit context, ambiguityof structure and meaning in language.

Language Understanding:  Advantage Humans

Humans:  Seemingly Effortless.  Powerful, general, deep and fast in understanding language – reading, experiencing, summarizing, storing knowledge in natural language.  This information is written for human consumption so reading and understanding what it says is natural for humans.

Watson:  Hugely Challenging.  Answers need to be determined and justified in natural language sources like news articles, reference texts, plays, novels, etc.  Watson must be carefully programmed and automatically trained to deeply analyze even just tiny subsets of language effectively.  Very different from web search, must find a precise answer and understand enough of what it read to know if and why a possible answer may be correct.

Self‐Knowledge (Confidence):  Advantage Humans

Humans:  Seemingly Effortless.  Most often, and almost instantly, humans know if they know the answer.

Watson:  Hugely Challenging.  1000’s of algorithms run in parallel to find and analyze 1000’s of written texts for many different types of evidence.  The results are combined, scored and weighed for their relative importance – how much they justify a candidate answer.  This has to happen in 3 seconds to compute a confidence and decide whether or not to ring-in before it is too late.

Breadth of Knowledge:  Advantage Humans

Humans:  Limited by self-contained memory.  Estimates of >1000’s of terabytes are all much higher than Watson’s memory capacity.  Ability to flexibly understand and summarize human relevance means that humans’ raw input capacity is even higher.

Watson:  Limited by self‐contained memory.  Roughly 1 Million books worth of content stored and processed in 15 Terabytes of working memory.  Weaker ability to meaningfully understand, relate and summarize human‐relevant content.  Must look at lots of data to compute statistical relevance.

Processing Speed:  Advantage Humans

Humans:  Fast Accurate Language Processing.  Native, strong, fast, language abilities.  Highly associative, highly flexible memory and speedy recall.  Very fast to speed read clue, accurately grasp question, determine confidence and answer – in just seconds. 

Watson:  Hugely Challenging.  On 1 CPU Watson can take over 2 hours to answer to a typical Jeopardy! question.  Watson must be parallelized, perhaps in ways similar to the brain, to simultaneously use 1000’s of compute cores to compete against humans in the 3-5 second range.

Reaction Speed:  Toss-up

Humans:  Times the Buzz.  Slower raw reaction speed but potentially faster to the buzz.  Listens to clue and anticipates when to buzz in.  “Timing the buzz” like this providing humans with the fastest absolute possible response time.

Watson:  Fast Hand.  More consistently deliver’s a fast reaction time but ONLY IF and WHEN can determine high enough confidence in time to buzz‐in.  Not able to anticipate when to buzz‐in based on listening to clue, which gives fastest possible response time to humans.  Also has to press same mechanical button as humans do.

Compute Power:  Won’t Impact Outcome

Humans:  Requires 1 brain that fits in a shoebox, can run on a tuna‐fish sandwich and be cooled with a hand‐held paper fan.

Watson:  Hugely Challenging.  Needs 2,880 compute cores (10 refrigerators worth in size and space) requiring about 80Kw of power and 20 tons of cooling.

Betting and Strategy:  Advantage Watson

Humans:  Slower, typically less precise.  Uses strategy and adjusts based on situation and game position.

Watson: Faster, more accurate calculations.  Uses strategy and adjusts based on situation and game position.

Emotions:  Advantage Watson

Humans:  Yes. Can slow down and /or confuse processing.

Watson:  No. Does NOT get nervous, tired, upset or psyched out (but the Watson programming team does!).

In-Game Learning:  Advantage Humans

Humans:  Learn very quickly from context, voice expression and (mostly importantly) right and wrong answers.

Watson:  Watson does not have the ability to hear (speech to text).  It is my understanding that Watson is “fed” the correct answer (in text) after each question so he can learn about the category even if he gets it wrong or does not answer.  However, I don’t believe he is “fed” the wrong answers though.  This is a disadvantage for Watson.  As seen last night, it is not uncommon for him to answer with the same wrong answer as another contestant.  This also happened in the sparring rounds leading up to the taping of last nights show.

As you can see things are closely matched but a slight advantage has to go to Ken and Brad.

And what about Watson’s face?

Another observation I made was how cool Watson’s avatar was.  It actually expresses what he is thinking (or processing).  The Watson avatar shares the graphic structure and tonality of the IBM Smarter Planet marketing campaign; a global map projection with a halo of “thought rays.”  The avatar features dozens of differentiated animation states that mirror the many stages of Jeopardy! gameplay – from choosing categories and answering clues, to winning and losing, to making Daily Double wagers and playing Final Jeopardy!.  Even Watson’s level of confidence – the numeric threshold that determines whether or not Watson will buzz in to answer – is made visible.  Watson’s stage presence is designed to depict the interior processes of the advanced computing system that powers it.  A significant portion of the avatar consists of colored threads orbiting around a central core.  The threads and thought rays that make up Watson’s avatar change color and speed depending on what happens during the game.  For example, when Watson feels confident in an answer the rays on the avatar turn green; they turn orange when Watson gets the answer wrong.  You will see the avatar speed up and activate when Watson’s algorithms are working hard to answer a clue.

I’ll be glued to the TV tonight and tomorrow.  Regardless of the outcome, this whole experience has been fascinating to me … so much so that I just published a new podcast on ECM, Content Analytics and Watson.

You can also visit my previous blog postings on Watson at: IBM at 100:  A Computer Called Watson“What is Content Analytics?, Alex”, 10 Things You Need to Know About the Technology Behind Watson and Goodbye Search … It’s About Finding Answers … Enter Watson vs. Jeopardy!