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Tonight PBS plans to air Season 38, Episode 6 of NOVA, titled [Smartest Machine On Earth]. Here is an excerpt from the station listing:
"What's so special about human intelligence and will scientists ever build a computer that rivals the flexibility and power of a human brain? In "Artificial Intelligence," NOVA takes viewers inside an IBM lab where a crack team has been working for nearly three years to perfect a machine that can answer any question. The scientists hope their machine will be able to beat expert contestants in one of the USA's most challenging TV quiz shows -- Jeopardy, which has entertained viewers for over four decades. "Artificial Intelligence" presents the exclusive inside story of how the IBM team developed the world's smartest computer from scratch. Now they're racing to finish it for a special Jeopardy airdate in February 2011. They've built an exact replica of the studio at its research lab near New York and invited past champions to compete against the machine, a big black box code -- named Watson after IBM's founder, Thomas J. Watson. But will Watson be able to beat out its human competition?"
Like most supercomputers, Watson runs the Linux operating system. The system runs 2,880 cores (90 IBM Power 750 servers, four sockets each, eight cores per socket) to achieve 80 [TeraFlops]. TeraFlops is the unit of measure for supercomputers, representing a trillion floating point operations. By comparison, Hans Morvec, principal research scientist at the Robotics Institute of Carnegie Mellon University (CMU) estimates that the [human brain is about 100 TeraFlops]. So, in the three seconds that Watson gets to calculate its response, it would have processed 240 trillion operations.
Several readers of my blog have asked for details on the storage aspects of Watson. Basically, it is a modified version of IBM Scale-Out NAS [SONAS] that IBM offers commercially, but running Linux on POWER instead of Linux-x86. System p expansion drawers of SAS 15K RPM 450GB drives, 12 drives each, are dual-connected to two storage nodes, for a total of 21.6TB of raw disk capacity. The storage nodes use IBM's General Parallel File System (GPFS) to provide clustered NFS access to the rest of the system. Each Power 750 has minimal internal storage mostly to hold the Linux operating system and programs.
When Watson is booted up, the 15TB of total RAM are loaded up, and thereafter the DeepQA processing is all done from memory. According to IBM Research, "The actual size of the data (analyzed and indexed text, knowledge bases, etc.) used for candidate answer generation and evidence evaluation is under 1TB." For performance reasons, various subsets of the data are replicated in RAM on different functional groups of cluster nodes. The entire system is self-contained, Watson is NOT going to the internet searching for answers.
With all the excitement of the [IBM Challenge], where the [IBM Watson computer] will compete against humans on [Jeopardy!], I thought it would be good to provide the following homework exercise to help you appreciate how challenging the game is and the strategies required.
Overview of the game of Jeopardy!
If you are familiar with the show, you can safely skip this section.
Known as "America's Favorite Quiz Show", the Jeopardy pits three contestants against each other. The board is divided into six columns and five rows of answers. Each column indicates the category for that column of answers. The rows are ranked from easiest to most difficult, with more difficult answers being worth more money to wager.
The contestants take turns. The returning champion gets to select a spot on the board, by indicating the category (column) and wager (row), such as "I will take Animals for 800 dollars!" Contestants must then press a button to "buzz in", be recognized by the host, and respond correctly. If the contestant responds incorrectly, the other two contestants have the opportunity to respond. The contestant with the correct response gets to chose the next answer.
For each turn, the host, Alex Trebek, shows the answer on the board, and spends three seconds reading it aloud to give everyone a chance to come up with a corresponding question. This is perhaps what Jeopardy is most famous for. In a traditional "Quiz Show", the host asks questions, and the contestants answer that question. On Jeopardy, however, the host poses "answers", and the contestants provide their response in the form of a "questions" that best fit the category and answer clues. For example, if the categories were "Large Corporations" and the answer was "Sam Palmisano", the contestant would answer "Who is the CEO of IBM Corporation?" Both the categories, and the answers are filled with puns, slang and humor to make it more challenging. Often, the answer itself is not sufficient clue, you have to factor in the category as well to have a complete set of information.
The game is played in three rounds:
In the first round, there are six categories, and the rows are worth $200, $400, $600, $800 and $1000 dollars. If you respond correctly on all five answers in a category column, you would win $3000. If you respond to all thirty answers correctly, you would earn $18,000.
In the second round, there are six different categories, and the rows are worth twice as much.
The final round has a single category and a single question. Each player can decide to wager up to the full amount of their score in this game. This wager is done after they see the category, but before they see the answer.
After the host finishes reading the answer aloud, the buzzers are lighted so that the contestants can buzz in. If a contestant gets the question correctly, he earns the corresponding money for the row it was in. If the contestant guesses incorrectly, the money is subtracted from his score. If the first contestant fails, the buzzers are re-lit so the other two contestants can then buzz in with their answers, learning from previous failed attempts.
To provide added challenge, some of the answers are surprise "Daily Double". Instead of the dollar amount for the row, the contestant can wager any amount, up to their total score they have won so far in that game, or the largest dollar amount for that round, whichever is higher, based on his confidence in that category. There is one "Daily Double" surprise in the first round, and two in the second round.
In the final round, each contestant wagers an amount up to their total score, based on their confidence on the final category. A common strategy for the leading contestant with the highest score is to wager a low amount, so that if he fails to guess the response correctly, he will still have a large dollar amount. For example, if the leader has $2000 and the second place is $900, the leader can wager only $100 dollars, and the second place might wager his full $900. If the leader loses the round, he still has $1900, beating the second place regardless of how well he does.
Whomever has the most money at the end of all three rounds wins that amount of cash, and gets to return to the show for another game the next day to continue his winning streak. The other two contestants are given consolation prizes and a nominal appearance fee for being on the show, and are never seen from again.
The show is only 30 minutes long, so the folks at Sony Pictures who produce the show can film a full weeks' worth of television shows in just two days of real-life, Tuesday and Wednesday, allowing the host Alex Trebek and his "Clue Crew" time to research new categories and answers.
So, here is your homework assignment. Record a full episode of Jeopardy on your VCR or Digital Video Recorder (DVR) and have your thumb ready to press the pause button. For each round, listen to each category, pause, and try to guess what all the answers in that column will have in common. For each category, write down a statement like "All the responses in this category are ...".
The answers could be people, places or things. Suppose the category "Chicks Dig Me". In English, "chicks" can be slang for women, or refer to young chickens. The term "dig" can be slang for admires or adores, so this could be "Male Celebrities" that women find attractive, it could be objects of desire that women fancy (diamonds, puppies, etc.), or it could be places that women like to go to. As it turns out, the "dig" referred to archaeology, and the responses were all famous female archaeologists.
Once you have those all your statements written down, press play button again.
Next, as each answer is shown, you have three seconds to hit the pause again, so that you have the question on the screen, but before any contestants have responded. Go on your favorite search engine like Google or Bing and try to determine the correct response based on the category and answer. Consider these [tips for being an Internet Search ninja]. Once you think you have figured out your response, write it down, and the dollar amount you wager, or decide you will not respond for that answer, if you are not sure about your findings.
Even if you think you already know the correct response, you may decide to gain more confidence of your response by finding confirming or supporting evidence on the Internet.
Press play. Either one of the contestants will get it right, or the host will provide the question that was expected as the correct response.
How well did you do? Were you able to find on the the correct response online, or at least confirm that what you knew was correct. If you got it correct, add in your dollar amount to your score. If you got it wrong, subtract the amount.
At the end of each round, look back at your statements for each category. Did you guess correctly the common theme for each category column of answers? Did you misinterpret the slang, pun or humor intended?
At the end of the game, you might have done better than the contestant that won the game. However, check how much added time you took to do those Internet searches. The average winner only questions half of the answers and only gets 80 percent of them correctly.
If you are really brave, take the [Jeopardy Online Test]. If you do this homework assignment, feel free to post your insights in the comments below.
We are only days away from the big IBM Challenge of Watson computer against two human contestants on the show Jeopardy!
I watched two episodes of Jeopardy! on my Tivo, pausing it to follow the [homework assignment] I suggested in my last post. Here are my own results and observations.
Episode  involved a web programmer, a customer service representative, and a bank teller.
Of the first six categories in Round 1, I guessed four of the six themes for each category. For the category "Diamonds are Forever", I wrote down "All answers are some kind of gem or mineral", but the reality was that all the answers were some physical characteristic of diamonds specifically. For the category "...Fame is not", I wrote down "All answers are TV or Movie celebrities". I was close, but actually it was famous celebrities, rock bands and pop culture of the 1980s. (The movie "Fame" came out in 1980).
In the round, there were 27 of the 30 answers given before they ran out of time. Of these, I was able to get 24 of 27 correct by searching the Internet. That is 88 percent correct. Here were the ones that eluded me:
Answer related to a "multi-chambered mollusk". I could not find anything on the Internet definitively on this, so abstained from wager. The correct question was "What is Nautilus?".
Answer was the Irish variant of "Kathryne". I found Kathleen as a variant, but did not investigate if it had Irish origins. The correct question was "What is Caitlin?"
Answer was this Norse name for "ruler" whether you had red hair or not. I found "Roy" and "Rory" so guessed "What is Rory?" The correct question was "What is Eric?"
The second round, I guesed three of the six themese for the categories. For category "Musical Titles Letter Drop" I wrote down "All the answers are titles of musical songs" but it was actually "Musicals" as in the Broadway shows. For category "Place called Carson", I wrote down "All the answers are places" and was way off on that one, with answers that were people, places and names of corporations. And for "State University Alums", I wrote down "All the answers are college graduates", but instead they were all "State Universities" such as the University of Arizona.
In this second round, only 26 answers were posed. I got 80 percent correct with Internet searching. I missed three on the "Musical Titles", one in "Pope-pourri" and one State University (sorry SMU). The "Musical Titles Letter Drop category" was especially difficult, as for each title of a Musical, you had to remove a single letter out of it to form the correct response.
For the answer "Good luck when you ask the singers "What I Did For Love"; they never tell the truth", you would need to take "Chorus Line" the musical, where the song "What I did for Love" appears, and ask "What is Chorus Lie?" Note that "line" changed to "lie" and the letter "n" was dropped out.
For the answer "Embrace the atoms as Simba and company lose and gain electrons en masse in this production", you would need to recognize that Simba was the main character of "The Lion King" and change it to "What is The Ion King".
I think these play-on-words are the questions that would stump the IBM Watson computer.
In the final round, the category was "Ancient Quotes". I thought the answer would be a famous adage or quotation, but it was instead famous people who uttered those phrases. The answer was "He said, to leave this stream uncrossed will breed manifold distress for me; to cross it, for all mankind". I was able to determine the correct response readily from searching the Internet: The river was the Rubicon, the border of the Gaul region governed by an ambitious general. The correct response "Who was Julius Caesar?"
Total time for the entire exercise: 87 minutes.
The following night, episode  brought back Paul Wampler, the returning champion web programmer, against two new contestants: an actor, and high school principal.
Of the first six categories in Round 1, I guessed five of the six themes for each category. For the category "Nonce Words", I wrote all the answers would be nonsense words. I was close, the clues had words invented for a particular occasion, but the correct responses did not.
I was able to get 29 of 30 correct by searching the Internet. That is 96 percent correct. The one I missed was in the category "Nonce Words" and the answer was "In an arithmocracy, this portion of the population rules, not trigonometry teachers.." My response was "What is Math?" but the correct answer was "What are the majority?" It did not occur for me to even look up [Arithmocracy] as a legitimate word, but it is real.
The second round, I guesed five of the six themese for the categories. For category "Hawk" eyes, the "Hawk" was in quotation marks, so I wrote "All answers would start with the word Hawk or end with the word "eyes". I was close, the correct theme was that the word "hawk" would appear in the front, middle or end of the correct response.
In this second round, I got 28 of 30 correct. I got 93 percent correct with Internet searching. Ironically, it was the category "German Foods" that caught me off guard.
For, the answer was "Pichelsteiner Fleisch, a favorite of Otto von Bismarck, is this one-pot concoction, made with beef & pork". I know that "fleisch" is a German word for meat, so I guessed "What is sausage?" but the correct response was "What is stew?" I should have paid more attention to the "one-pot concoction" part of the answer.
For the answer was "Mimi Sheraton says German stuffed hard-boiled eggs are always made with a great deal of this creamy product". I didn't realize that "stuffed eggs" was German for "deviled eggs". Instead, I found Mimi Sheraton's "The German Cookbook" on Google Books, and jumped to the page for "Stuffed Eggs" The ingredients I read included whippedc cream, cognac, and worcestershire sauce. Taking the "creamiest" ingredient of these, I wrote down "What is whipped cream?" However, it turned out I was actually reading the ingredients for "Crabmeat Cocktail" that was coninuing from the previous page. I thought it was gross to put whipped cream with eggs, and should have known better. The correct response was "What is mayonnaise?"
In the final round, the category was "Political Parties". This could either be political organizations like Republicans and Democrats, or festivities like the Whitehouse Correspondents Dinner. The answer was "Only one U.S. president represented this party, and he said, I dread...a division of the republic into two great parties." So, we can figure out the answer refers to political organizations, but both Democrat and Republican are ruled out because each has had multiple presidents. So, looking at a [List of Political Parties of each US President], I found that there were four presidents in the Whig party, four in the Democrat-Republic party, but only one president in the Federalist party (John Adams), and one in the War Union party (Andrew Johnson). Looking at [famous quotes from John Adams] first, I found the quote, it matched, and so I wrote down "What is the Federalist party?". I got it right, as did two of the three contestants. Ironically, the one contestant who got it wrong, the returning champion web programmer, wagered a small amount, so he still had more money after the round and won the game overall.
Total time for the entire exercise: 75 minutes. I was able to do this faster as I skipped searching the internet for the responses I was confident on.
To find out when Jeopardy is playing in your town, consult the [Interactive Map].
"When Watson is booted up, the 15TB of total RAM are loaded up, and thereafter the DeepQA processing is all done from memory. According to IBM Research, the actual size of the data (analyzed and indexed text, knowledge bases, etc.) used for candidate answer generation and evidence evaluation is under 1 Terabyte (TB). For performance reasons, various subsets of the data are replicated in RAM on different functional groups of cluster nodes. The entire system is self-contained, Watson is NOT going to the internet searching for answers."
I had several readers ask me to explain the significance of the "Terabyte". I'll work my way up.
A bit is simply a zero (0) or one (1). This could answer a Yes/No or True/False question.
Most computers have standardized a byte as a collection of 8 bits. There are 256 unique combinations of ones and zeros possible, so a byte could be used to storage a 2-digit integer, or a single upper or lower case character in the English alphabet. In pratical terms, a byte could store your age in years, or your middle initial.
The Kilobyte is a thousand bytes, enough to hold a few paragraphs of text. A typical written page could be held in 4 KB, for example.
The IBM Challenge to play on Jeopardy! is being compared to the historic 1969 moon landing. To land on the moon, Apollo 11 had the "Apollo Guidance Computer" (AGC) which had 74KB of fixed read-only memory, and 2KB of re-writeable memory. Over [3500 IBM employees were involved] to get the astronauts to the moon and safely back to earth again.
The importance of this computer was highlighted in a [lecture by astronaut David Scott] who said: "If you have a basketball and a baseball 14 feet apart, where the baseball represents the moon and the basketball represents the Earth, and you take a piece of paper sideways, the thinness of the paper would be the corridor you have to hit when you come back."
The Megabyte is a thousand KB, or a million bytes. The 3.5-inch floppy diskette, mentioned in my post [A Boxfull of Floppies] could hold 1.44MB, or about 360 pages of text.
In the article [Wikipedia as a printed book], the printing of a select 400 articles resulted in a book 29 inches thick. Those 5,000 pages would consume about 20 MB of space.
One of my favorite resources I use to search is the Internet Movie Data Base [IMDB]. Leaving out the photos and videos, the [text-only portion of the IMDB database is just over 600 MB], representing nearly all of the actors, awards, nominations, television shows and movies. A standard CD-ROM can hold 700MB, so the text portion of the IMDB could easily fit on a single CD.
The Gigabyte is a thousand MB, or a billion bytes. My Thinkpad T410 laptop has 4GB of RAM and 320GB of hard disk space. My laptop comes with a DVD burner, and each DVD can hold up to 4.7GB of information.
The popular Wikipedia now has some 17 million articles, of which 3.5 million are in English language. It would only take [14GB of space to hold the entire English portion] of Wikipedia. That is small enough to fit on twenty CDs, three DVDs, an Apple iPad or my cellphone (a Samsung Galaxy S Vibrant).
Perhaps you are thinking, "Someone should offer Wikipedia pre-installed on a small handheld!" Too late. The [The Humane Reader] is able to offer 5,000 books and Wikipedia in a small device that connects to your television. This would be great for people who do not have access to the internet, or for parents who want their kids to do their homework, but not be online while they are doing it.
In the latest 2009 report of [How Much Information?] from the University of California, San Diego, the average American consumes 34 GB of information. This includes all the information from radio, television, newspapers, magazines, books and the internet that a person might look at or listen to throughout the day. This project is sponsored by IBM and others to help people understand the nature of our information-consuption habits.
Back in 1992, I visited a client in Germany. Their 90 GB of disk storage attached to their mainframe was the size of three refrigerators, and took five full-time storage administrators to manage.
The Terabyte is a thousand GB, or a trillion bytes. It is now possible to buy external USB drive for your laptop or personal computer that holds 1TB or more. However, at 40MB/sec speeds that USB 2.0 is capable of, it would take seven hours to do a bulk transfer in or out of the device.
IBM offers 1TB and 2TB disk drives in many of our disk systems. In 2008, IBM was preparing to announce the first 1TB tape drive. However, Sun Microsystems announced their own 1TB drive the day before our big announcement, so IBM had to rephrase the TS1130 announcement to [The World's Fastest 1TB tape drive!]
A typical academic research library will hold about 2TB of information. For the [US Library of Congress] print collection is considered to be about 10TB, and their web capture team has collected 160TB of digital data. If you are ever in the Washington DC, I strongly recommend a visit to the Library of Congress. It is truly stunning!
Full-length computer animated movies, like [Happy Feet], consume about 100TB of disk storage during production. IBM offers disk systems that can hold this much data. For example, the IBM XIV can hold up to 151 TB of usable disk space in the size of one refrigerator.
A Key Performance Indicator (KPI) for some larger companies is the number of TB that can be managed by a full-time employee, referred to as TB/FTE. Discussions about TB/FTE are available from IT analysts including [Forrester Research] and [The Info Pro].
The website [Ancestry.com] claims to have over 540 million names in its genealogical database, with a storage of 600TB, with the inclusion of [US census data from 1790 to 1930]. The US government took nine years to process the 1880 census, so for the 1890 census, it rented equipment from Herman Hollerith's Tabulating Machine Company. This company would later merge with two others in 1911 to form what is now called IBM.
A Petabyte is thousand TB, or a quadrillion bytes. It is estimated that all printed materials on Earth would represent approximately 200 PB of information.
IBM's largest disk system, the Scale-Out Network Attach Storage (SONAS) comprised of up to 7,200 disk drives, which can hold over 11 PB of information. A smaller 10-frame model, the same size as IBM Watson, with six interface nodes and 19 storage pods, could hold over 7 PB of information.
For those of us in the IT industry, 1TB is small potatoes. I for one, was expecting it to be much bigger. But for everyone else, the equivalent of 200 million pages of text that IBM Watson has loaded inside is an incredibly large repository of information. I suspect IBM Watson probably contains the complete works of Shakespeare as well as other fiction writers, the IMDB database, all 3.5 million articles of Wikipedia, religious texts like the Bible and the Quran, famous documents like the Magna Carta and the US Constitution, and reference books like a Dictionary, a Thesaurus, and "Gray's Anatomy". And, of course, lots and lots of lists.
For those on Twitter, follow [@ibmwatson] these next three days during the challenge.
The Tucson Executive Briefing Center hosted 20 dignitaries from local companies and academia.
This is a historic competition, an exhibition match pitting a computer against the top two celebrated Jeopardy champions:
Brad Rutter, won $3.2 million USD on Jeopardy!, winning 5 days on the show, and then three later tournamets.
Ken Jennings, winning $2.5 million in a 74-day winning streak on Jeopardy!
One of the members of the audience had never seen an episode of Jeopardy! in his life.
(Note: there are NO SPOILERS in this blog post. If you have not yet watched the show, you are safe to continue reading the rest of this post. I will not
disclose the correct responses to any of the clues nor how well each contestant scored.)
Calline Sanchez, IBM Director, Systems Storage Development for Data Protection and Retention, kicked off today's ceremonies.
The IBM Watson computer, named after IBM founder Thomas J. Watson, has been developed over the past 4 years by a team of IBM scientists who set out to accomplish a grand challenge - build a computing system that rivals a human's ability to answer questions posed in natural language with speed, accuracy and confidence. IBM Research labs in the United States, Japan, China and Israel [collaborated with Artificial Intelligence (AI) experts at eight universities], including Massachusetts Institute of Technology (MIT), University of Texas (UT) at Austin, University of Southern California (USC), Rensselaer Polytechnic Institute (RPI), University at Albany (UAlbany), University of Trento (Italy), University of Massachusetts Amherst, and Carnegie Mellon University.
(Disclaimer: I attended the University of Texas at Austin. My father attended Carnegie Mellon University.)
Last week, NOVA on PBS had a special episode on the making of IBM Watson, you can [watch it online] on their website. Delaney Turner, IBM Social Media Communications Manager for Business Analytics Software, has posted [his observations of Nova].
Since IBM Watson is the size of 10 refrigerators and weighs over 14,000 pounds, it was easier to design the Jeopardy! set at the TJ Watson Research lab in Yorktown Heights, NY, than to ship it over to California where the show is normally recorded. Two of the visual designers that worked on this set, as well as on the visual appearance of Watson, live in Tucson and were part of our audience today.
The IBM Challenge consists of a two-game tournament, where the scores of both games will be added to determine winner rankings. The producers of Jeopardy! will give $1 million dollars USD to first place, $300,000 to second place, and $200,000 to third place. Regardless of outcome, [IBM will donate all of its winings to charity]. The two human contestants plan to donate half of their earnings to their favorite charities as well.
Jeopardy! The IBM Challenge
Alex Trebek introduces IBM Watson, explaining that it can neither hear nor see. It will receive all information electronically. Categories and clues will be sent as text files via TCP/IP over Ethernet at the same time the two human contestants see them so that all have the same time to think about the right answer.
Watson has two rows of five racks, back to back. This was done so that cold air could rise up from holes in the tile floors around the unit, and all the hot air would be forced into the center and up to the ceiling return. This technique is known as "hot aisle/cold aisle" design. Alex Trebek opens one of the rack doors to show a series of 4U-high IBM Power 750 servers.
The avatar is a representation of Watson, as the machine itself is too big to fit behind the podium. The avatar is IBM's "Smarter Planet" logo with orbiting streaks and circles. It shows "Green" when it has high confidence, and orange when it gets an answer wrong. When busy thinking, the streaks and circles speed up, the closest we will see to "watching a computer sweat."
During the show, an "Answer panel" shows Watson's top three candidate responses, with confidence level compared to its current "buzz threshold".
Watson knows what it knows, and knows what it doesn't know. Here is an [Interactive Watson Game] on New York Times website to give you an idea of how the answer panel works. I was impressed with how close all three candidate answers were. In a question about Olympic swimmers, all three candidates are Olympic swimmers. In a question about the novel "Les Miserables", all three candidates were characters of that novel.
Well, IBM Watson did well, but missed answered some questions incorrectly. This [parody Slate video] pokes fun at this. Here were some discussions we had after the show ended:
IBM did not do well in categories that required [abductive reasoning]. For example, to identify two or three things that happened in different years, and then postulate that what they all have in common is a specific decade (such as the 1950s) is difficult.
Watson does not hear the wrong answers from the two human contestants. For one question, Ken buzzes in first, guesses wrong, then Watson buzzes in with the same exact response. Alex Trebek rebukes Watson with "No, Ken just said that!" Brad would learn from their mistakes and guess correctly for the score.
Watson is provided the correct answer after a contestant guesses it correctly, or if nobody does, when Alex provides the correct response. This is sent as a text message to Watson immediately, so that it can use this information to adjust its algorithms and machine-learning for future clues in that same category. This was evident in the "Answer panel" on the fourth and fifth attempts on the category of "Decades".
With this demonstration, IBM Research has advanced science by leaps and bounds for the Articial Intelligence community. IBM is a leader in Business Analytics, and this technology will find uses in a variety of industries. The average knowledge worker spends 30 percent of her time looking for information on corporate data repositories. By demonstrating a computer that can provide answers quickly, employees will be more productive, make stronger business decisions, and have greater insight.
Day 1 was only able to cover the first round of Game 1. This allowed more time to talk about the history and technology of IBM Watson. Tomorrow, the contestants will finish Game 1 and head into Game 2.
The IBM Challenge was a big success. One of the contestants, Ken Jennings, [welcomes our new computer overlords]. Congratulations are in order to the IBM Research team who pulled off this Herculean effort!
Some folks have poked fun at some of the odd responses and wager amounts from the IBM Watson computer during the three-day tournament. Others were surprised as I was that the impressive feat was done with less than 1TB of stored data. Here is what John Webster wrote in CNET yesterday, in hist article [What IBM's Watson says to storage systems developers]:
"All well and good. But here's what I find most interesting as a result of what IBM has done in response to the Grand Challenge that motivated Watson's creators. We know, from Tony Pearson's blog, that the foundation of Watson's data storage system is a modified IBM SONAS cluster with a total of 21.6TB of raw capacity. But Pearson also reveals another very significant, and to me, surprising data point: "When Watson is booted up, the 15TB of total RAM are loaded up, and thereafter the DeepQA processing is all done from memory. According to IBM Research, the actual size of the data (analyzed and indexed text, knowledge bases, etc.) used for candidate answer generation and evidence evaluation is under 1 Terabyte."
What Pearson just said is that the data set Watson actually uses to reach his push-the-button decision would fit on a 1TB drive. So much for big data?"
To better appreciate how difficult the challenge was, and how a small amount of data can answer a billion different questions, I thought I would cover Business Intelligence, Data Retrieval and Text Mining concepts.
"In this paper, business is a collection of activities carried
on for whatever purpose, be it science, technology,
commerce, industry, law, government, defense, et cetera.
The communication facility serving the conduct of a business
(in the broad sense) may be referred to as an intelligence
system. The notion of intelligence is also defined
here, in a more general sense, as the ability to apprehend
the interrelationships of presented facts in such a way as
to guide action towards a desired goal."
Ideally, when you need "Business Intelligence" to help you make a better decision, you perform data retrieval from a structured database for the specific information you are looking for. In other cases, you might be looking for insight, patterns or trends. In that case, you go "data mining" against your structured databases.
Here's a simple example. John runs a fruit stand. One day, he kept track of how many apples and oranges were bought by men and women. How many questions can we ask against this small set of data? Let's count them:
How many apples were sold to men?
How many apples were sold to women?
How many oranges were sold to men?
How many oranges were sold to women?
But wait! For each row and column, we can combine them into totals.
How many apples were sold in total?
How many oranges were sold in total?
How many fruit in total were sold to men?
How many fruit in total were sold to women?
How many fruit in total were sold?
But wait, there's more! Each row and column can be evaluated for relative percentages, as well as percentages of each cell compared to the total. You could make five relevant pie-charts from this data. This results in 16 more questions, such as:
Of the fruit purchased by men, what percentage for apples?
Of all the apples purchased, what percentage by women?
And that's not including more ethereal questions, such as:
Are there gender-specific preferences for different types of fruit?
What type of fruit do men prefer?
This is just for a small set, two market segments (by gender) and two products (apples and oranges). However, if you have many market segments (perhaps by age group, zip code, etc.) and many products, the number of queries that can be supported is huge. For small sets of data, you can easily do this with a spreadsheet program like IBM Lotus Symphony or Microsoft Excel.
But why limit yourself to two dimensions? The above example was just for one day's worth of activity, if John captures this data for every day for historical and seasonal trending, it can be represented as a three-dimensional cube. The number of queries becomes astronomical. This is the basis for Online Analytical Processing (OLAP), and three-dimensional tables are often referred to as [OLAP cubes].
Back in 1970, IBM invented the Structured Query Language [SQL], and today, nearly all modern relational databases support this, including IBM DB2, Informix, Microsoft SQL Server, and Oracle DB. SQL poses two challenges. First, you had to structure the data in advance to the way you expect to perform your ad-hoc queries. Deciding the groups and categories in advance can limit the way information is recorded and captured.
Second, you had to be skilled at SQL to phrase your queries correctly to retrieve the data you are after. What ended up happening was that skilled SQL programmers would develop "canned reports" with fixed SQL parameters, so that less-skilled business decision makers could base their decisions from these reports.
IBM has fully integrated stacks to help process structured data, combining servers, storage, and advanced analytics software into a complete appliance. IBM offers the [Smart Analytics System] for robust, customized deployments, and recently acquired [Netezza] for pre-configured, and more rapid deployments.
However, the bigger problem is that more than 80 percent of information is not structured!
Semi-structured data like email provides some searchable fields like From and Subject. The rest of the information is unstructured, such as text files, photographs, video and audio. To look for specific information in unstructured sources can be like looking for a needle in a haystack, and trying to get insight, patterns or trends involves text mining.
This, in effect, is what IBM Watson was able to perform so well this week. Finding the needle in the haystacks of unstructured data from 200 million pages of text stored in its system, combined with the ability to apprehend the interrelationships of meaning and subtle nuance, resulted in an impressive technology demonstration. Certainly, this new technology will be powerful for a variety of use cases across a broad set of industries!
For the longest time, people thought that humans could not run a mile in less than four minutes. Then, in 1954, [Sir Roger Bannister] beat that perception, and shortly thereafter, once he showed it was possible, many other runners were able to achieve this also. The same is being said now about the IBM Watson computer which appeared this week against two human contestants on Jeopardy!
(2014 Update: A lot has happened since I originally wrote this blog post! I intended this as a fun project for college students to work on during their summer break. However, IBM is concerned that some businesses might be led to believe they could simply stand up their own systems based entirely on open source and internally developed code for business use. IBM recommends instead the [IBM InfoSphere BigInsights] which packages much of the software described below. IBM has also launched a new "Watson Group" that has [Watson-as-a-Service] capabilities in the Cloud. To raise awareness to these developments, IBM has asked me to rename this post from IBM Watson - How to build your own "Watson Jr." in your basement to the new title IBM Watson -- How to replicate Watson hardware and systems design for your own use in your basement. I also took this opportunity to improve the formatting layout.)
Often, when a company demonstrates new techology, these are prototypes not yet ready for commercial deployment until several years later. IBM Watson, however, was made mostly from commercially available hardware, software and information resources. As several have noted, the 1TB of data used to search for answers could fit on a single USB drive that you buy at your local computer store.
Take a look at the [IBM Research Team] to determine how the project was organized. Let's decide what we need, and what we don't in our version for personal use:
Do we need it for personal use?
Yes, That's you. Assuming this is a one-person project, you will act as Team Lead.
Yes, I hope you know computer programming!
No, since this version for personal use won't be appearing on Jeopardy, we won't need strategy on wager amounts for the Daily Double, or what clues to pick next. Let's focus merely on a computer that can accept a question in text, and provide an answer back, in text.
Yes, this team focused on how to wire all the hardware together. We need to do that, although this version for personal use will have fewer components.
Optional. For now, let's have this version for personal use just return its answer in plain text. Consider this Extra Credit after you get the rest of the system working. Consider using [eSpeak], [FreeTTS], or the Modular Architecture for Research on speech sYnthesis [MARY] Text-to-Speech synthesizers.
Yes, I will explain what this is, and why you need it.
Yes, we will need to get information for personal use to process
Yes, this team developed a system for parsing the question being asked, and to attach meaning to the different words involved.
No, this team focused on making IBM Watson optimized to answer in 3 seconds or less. We can accept a slower response, so we can skip this.
(Disclaimer: As with any Do-It-Yourself (DIY) project, I am not responsible if you are not happy with your version for personal use I am basing the approach on what I read from publicly available sources, and my work in Linux, supercomputers, XIV, and SONAS. For our purposes, this version for personal use is based entirely on commodity hardware, open source software, and publicly available sources of information. Your implementation will certainly not be as fast or as clever as the IBM Watson you saw on television.)
Step 1: Buy the Hardware
Supercomputers are built as a cluster of identical compute servers lashed together by a network. You will be installing Linux on them, so if you can avoid paying extra for Microsoft Windows, that would save you some money. Here is your shopping list:
Three x86 hosts, with the following:
64-bit quad-core processor, either Intel-VT or AMD-V capable,
8GB of DRAM, or larger
300GB of hard disk, or larger
CD or DVD Read/Write drive
Computer Monitor, mouse and keyboard
Ethernet 1GbE 4-port hub, and appropriate RJ45 cables
Surge protector and Power strip
Local Console Monitor (LCM) 4-port switch (formerly known as a KVM switch) and appropriate cables. This is optional, but will make it easier during the development. Once your implementation is operational, you will only need the monitor and keyboard attached to one machine. The other two machines can remain "headless" servers.
Step 2: Establish Networking
IBM Watson used Juniper switches running at 10Gbps Ethernet (10GbE) speeds, but was not connected to the Internet while playing Jeopardy! Instead, these Ethernet links were for the POWER7 servers to talk to each other, and to access files over the Network File System (NFS) protocol to the internal customized SONAS storage I/O nodes.
The implementation will be able to run "disconnected from the Internet" as well. However, you will need Internet access to download the code and information sources. For our purposes, 1GbE should be sufficient. Connect your Ethernet hub to your DSL or Cable modem. Connect all three hosts to the Ethernet switch. Connect your keyboard, video monitor and mouse to the LCM, and connect the LCM to the three hosts.
Step 3: Install Linux and Middleware
To say I use Linux on a daily basis is an understatement. Linux runs on my Android-based cell phone, my laptop at work, my personal computers at home, most of our IBM storage devices from SAN Volume Controller to XIV to SONAS, and even on my Tivo at home which recorded my televised episodes of Jeopardy!
For this project, you can use any modern Linux distribution that supports KVM. IBM Watson used Novel SUSE Linux Enterprise Server [SLES 11]. Alternatively, I can also recommend either Red Hat Enterprise Linux [RHEL 6] or Canonical [Ubuntu v10]. Each distribution of Linux comes in different orientations. Download the the 64-bit "ISO" files for each version, and burn them to CDs.
Graphical User Interface (GUI) oriented, often referred to as "Desktop" or "HPC-Head"
Command Line Interface (CLI) oriented, often referred to as "Server" or "HPC-Compute"
Guest OS oriented, to run in a Hypervisor such as KVM, Xen, or VMware. Novell calls theirs "Just Enough Operating System" [JeOS].
For this version for personal use, I have chosen a [multitier architecture], sometimes referred to as an "n-tier" or "client/server" architecture.
Host 1 - Presentation Server
For the Human-Computer Interface [HCI], the IBM Watson received categories and clues as text files via TCP/IP, had a [beautiful avatar] representing a planet with 42 circles streaking across in orbit, and text-to-speech synthesizer to respond in a computerized voice. Your implementation will not be this sophisticated. Instead, we will have a simple text-based Query Panel web interface accessible from a browser like Mozilla Firefox.
Host 1 will be your Presentation Server, the connection to your keyboard, video monitor and mouse. Install the "Desktop" or "HPC Head Node" version of Linux. Install [Apache Web Server and Tomcat] to run the Query Panel. Host 1 will also be your "programming" host. Install the [Java SDK] and the [Eclipse IDE for Java Developers]. If you always wanted to learn Java, now is your chance. There are plenty of books on Java if that is not the language you normally write code.
While three little systems doesn't constitute an "Extreme Cloud" environment, you might like to try out the "Extreme Cloud Administration Tool", called [xCat], which was used to manage the many servers in IBM Watson.
Host 2 - Business Logic Server
Host 2 will be driving most of the "thinking". Install the "Server" or "HPC Compute Node" version of Linux. This will be running a server virtualization Hypervisor. I recommend KVM, but you can probably run Xen or VMware instead if you like.
Host 3 - File and Database Server
Host 3 will hold your information sources, indices, and databases. Install the "Server" or "HPC Compute Node" version of Linux. This will be your NFS server, which might come up as a question during the installation process.
Technically, you could run different Linux distributions on different machines. For example, you could run "Ubuntu Desktop" for host 1, "RHEL 6 Server" for host 2, and "SLES 11" for host 3. In general, Red Hat tries to be the best "Server" platform, and Novell tries to make SLES be the best "Guest OS".
My advice is to pick a single distribution and use it for everything, Desktop, Server, and Guest OS. If you are new to Linux, choose Ubuntu. There are plenty of books on Linux in general, and Ubuntu in particular, and Ubuntu has a helpful community of volunteers to answer your questions.
Step 4: Download Information Sources
You will need some documents for your implementation to process.
IBM Watson used a modified SONAS to provide a highly-available clustered NFS server. For this version, we won't need that level of sophistication. Configure Host 3 as the NFS server, and Hosts 1 and 2 as NFS clients. See the [Linux-NFS-HOWTO] for details. To optimize performance, host 3 will be the "official master copy", but we will use a Linux utility called rsync to copy the information sources over to the hosts 1 and 2. This allows the task engines on those hosts to access local disk resources during question-answer processing.
We will also need a relational database. You won't need a high-powered IBM DB2. Your implementation can do fine with something like [Apache Derby] which is the open source version of IBM CloudScape from its Informix acquisition. Set up Host 3 as the Derby Network Server, and Hosts 1 and 2 as Derby Network Clients. For more about structured content in relational databases, see my post [IBM Watson - Business Intelligence, Data Retrieval and Text Mining].
Linux includes a utility called wget which allows you to download content from the Internet to your system. What documents you decide to download is up to you, based on what types of questions you want answered. For example, if you like Literature, check out the vast resources at [FullBooks.com]. You can automate the download by writing a shell script or program to invoke wget to all the places you want to fetch data from. Rename the downloaded files to something unique, as often they are just "index.html". For more on wget utility, see [IBM Developerworks].
Step 5: The Query Panel - Parsing the Question
Next, we need to parse the question and have some sense of what is being asked for. For this we will use [OpenNLP] for Natural Language Processing, and [OpenCyc] for the conceptual logic reasoning. See Doug Lenat presenting this 75-minute video [Computers versus Common Sense]. To learn more, see the [CYC 101 Tutorial].
Unlike Jeopardy! where Alex Trebek provides the answer and contestants must respond with the correct question, we will do normal Question-and-Answer processing. To keep things simple, we will limit questions to the following formats:
Who is ...?
Where is ...?
When did ... happen?
What is ...?
Host 1 will have a simple Query Panel web interface. At the top, a place to enter your question, and a "submit" button, and a place at the bottom for the answer to be shown. When "submit" is pressed, this will pass the question to "main.jsp", the Java servlet program that will start the Question-answering analysis. Limiting the types of questions that can be posed will simplify hypothesis generation, reduce the candidate set and evidence evaluation, allowing the analytics processing to continue in reasonable time.
Step 6: Unstructured Information Management Architecture
The "heart and soul" of IBM Watson is Unstructured Information Management Architecture [UIMA]. IBM developed this, then made it available to the world as open source. It is maintained by the [Apache Software Foundation], and overseen by the Organization for the Advancement of Structured Information Standards [OASIS].
Basically, UIMA lets you scan unstructured documents, gleam the important points, and put that into a database for later retrieval. In the graph above, DBs means 'databases' and KBs means 'knowledge bases'. See the 4-minute YouTube video of [IBM Content Analytics], the commercial version of UIMA.
Starting from the left, the Collection Reader selects each document to process, and creates an empty Common Analysis Structure (CAS) which serves as a standardized container for information. This CAS is passed to Analysis Engines , composed of one or more Annotators which analyze the text and fill the CAS with the information found. The CAS are passed to CAS Consumers which do something with the information found, such as enter an entry into a database, update an index, or update a vote count.
(Note: This point requires, what we in the industry call a small matter of programming, or [SMOP]. If you've always wanted to learn Java programming, XML, and JDBC, you will get to do plenty here. )
If you are not familiar with UIMA, consider this [UIMA Tutorial].
Step 7: Parallel Processing
People have asked me why IBM Watson is so big. Did we really need 2,880 cores of processing power? As a supercomputer, the 80 TeraFLOPs of IBM Watson would place it only in 94th place on the [Top 500 Supercomputers]. While IBM Watson may be the [Smartest Machine on Earth], the most powerful supercomputer at this time is the Tianhe-1A with more than 186,000 cores, capable of 2,566 TeraFLOPs.
To determine how big IBM Watson needed to be, the IBM Research team ran the DeepQA algorithm on a single core. It took 2 hours to answer a single Jeopardy question! Let's look at the performance data:
Number of cores
Time to answer one Jeopardy question
Single IBM Power750 server
< 4 minutes
Single rack (10 servers)
< 30 seconds
IBM Watson (90 servers)
< 3 seconds
The old adage applies, [many hands make for light work]. The idea is to divide-and-conquer. For example, if you wanted to find a particular street address in the Manhattan phone book, you could dispatch fifty pages to each friend and they could all scan those pages at the same time. This is known as "Parallel Processing" and is how supercomputers are able to work so well. However, not all algorithms lend well to parallel processing, and the phrase [nine women can't have a baby in one month] is often used to remind us of this.
Fortuantely, UIMA is designed for parallel processing. You need to install UIMA-AS for Asynchronous Scale-out processing, an add-on to the base UIMA Java framework, supporting a very flexible scale-out capability based on JMS (Java Messaging Services) and ActiveMQ. We will also need Apache Hadoop, an open source implementation used by Yahoo Search engine. Hadoop has a "MapReduce" engine that allows you to divide the work, dispatch pieces to different "task engines", and the combine the results afterwards.
Host 2 will run Hadoop and drive the MapReduce process. Plan to have three KVM guests on Host 1, four on Host 2, and three on Host 3. That means you have 10 task engines to work with. These task engines can be deployed for Content Readers, Analysis Engines, and CAS Consumers. When all processing is done, the resulting votes will be tabulated and the top answer displayed on the Query Panel on Host 1.
Step 8: Testing
To simplify testing, use a batch processing approach. Rather than entering questions by hand in the Query Panel, generate a long list of questions in a file, and submit for processing. This will allow you to fine-tune the environment, optimize for performance, and validate the answers returned.
There you have it. By the time you get your implementation fully operational, you will have learned a lot of useful skills, including Linux administration, Ethernet networking, NFS file system configuration, Java programming, UIMA text mining analysis, and MapReduce parallel processing. Hopefully, you will also gain an appreciation for how difficult it was for the IBM Research team to accomplish what they had for the Grand Challenge on Jeopardy! Not surprisingly, IBM Watson is making IBM [as sexy to work for as Apple, Google or Facebook], all of which started their business in a garage or a basement with a system as small as this version for personal use.
My series last week on IBM Watson (which you can read [here], [here], [here], and [here]) brought attention to IBM's Scale-Out Network Attached Storage [SONAS]. IBM Watson used a customized version of SONAS technology for its internal storage, and like most of the components of IBM Watson, IBM SONAS is commercially available as a stand-alone product.
Like many IBM products, SONAS has gone through various name changes. First introduced by Linda Sanford at an IBM SHARE conference in 2000 under the IBM Research codename Storage Tank, it was then delivered as a software-only offering SAN File System, then as a services offering Scale-out File Services (SoFS), and now as an integrated system appliance, SONAS, in IBM's Cloud Services and Systems portfolio.
If you are not familiar with SONAS, here are a few of my previous posts that go into more detail:
This week, IBM announces that SONAS has set a world record benchmark for performance, [a whopping 403,326 IOPS for a single file system]. The results are based on comparisons of publicly available information from Standard Performance Evaluation Corporation [SPEC], a prominent performance standardization organization with more than 60 member companies. SPEC publishes hundreds of different performance results each quarter covering a wide range of system performance disciplines (CPU, memory, power, and many more). SPECsfs2008_nfs.v3 is the industry-standard benchmark for NAS systems using the NFS protocol.
(Disclaimer: Your mileage may vary. As with any performance benchmark, the SPECsfs benchmark does not replicate any single workload or particular application. Rather, it encapsulates scores of typical activities on a NAS storage system. SPECsfs is based on a compilation of workload data submitted to the SPEC organization, aggregated from tens of thousands of fileservers, using a wide variety of environments and applications. As a result, it is comprised of typical workloads and with typical proportions of data and metadata use as seen in real production environments.)
The configuration tested involves SONAS Release 1.2 on 10 Interface Nodes and 8 Storage Pods, resulting a single file system over 900TB usable capacity.
10 Interface Nodes; each with:
Maximum 144 GB of memory
One active 10GbE port
8 Storage Pods; each with:
2 Storage nodes and 240 drives
Drive type: 15K RPM SAS hard drives
Data Protection using RAID-5 (8+P) ranks
Six spare drives per Storage Pod
IBM wanted a realistic "no compromises" configuration to be tested, by choosing:
Regular 15K RPM SAS drives, rather than a silly configuration full of super-expensive Solid State Drives (SSD) to plump up the results.
Moderate size, typical of what clients are asking for today. The Goldilocks rule applies. This SONAS is not a small configuration under 100TB, and nowhere close to the maximum supported configuration of 7,200 disks across 30 Interface Nodes and 30 Storage Pods.
Single file system, often referred to as a global name space, rather than using an aggregate of smaller file systems added together that would be more complicated to manage. Having multiple file systems often requires changes to applications to take advantage of the aggregate peformance. It is also more difficult to load-balance your performance and capacity across multiple file systems. Of course, SONAS can support up to 256 separate file systems if you have a business need for this complexity.
The results are stunning. IBM SONAS handled three times more workload for a single file system than the next leading contender. All of the major players are there as well, including NetApp, EMC and HP.
This week was the IBM Pulse 2011 converence in Las Vegas, Nevada, with over 7,000 attendees. I wasn't there, and my on-the-scene correspondent was too busy running the hands-on lab to get out and attend sessions. Fortunately, I was able to watch some of the [IBM Software live stream], and here are my thoughts and observations.
Fellow inventor [Dean Kamen] was the keynote speaker. His inventions help people, making the world a better place. Here are three examples I found interesting during his talk:
Helping third world countries
Dean started out with his favorite quote:
"A problem well defined is a problem half-solved." - John Dewey
Dean mentioned that we are fortunate, having both potable drinking water and a reliable supply of electricity, but 2 to 4 billion people on the planet do not. Sponsored by Coca-Cola, Dean and his team of innovators were able to come up with small units that can be placed in a village or town. One unit takes in wet liquid and produces potable drinking water. The other unit takes combustible materials, like cow dung, and products electricity. Each unit is roughly the size of half a standard server rack. What does Coca-Cola get out of this? New "vending machines"! By combining drinking water with flavored syrups, they can create soft drinks on demand.
Dean's opinion was that if you want something done, you need to work with large corporations, as governments are mired in beauracracy and rules. I agree. When I first joined IBM, I was introduced to [TRIZ] which was a systematic method for solving problems. IBM's best and brightest are working to solve some of the toughest computer science challenges. For more on TRIZ, see this blog post about [TRIZ in BusinessWeek].
Helping injured veterans
Dean Kamen is well known for inventing the two-wheeled [Segway Personal Transporter], but his company, [DEKA], makes all kinds of things, mostly medical equipment. To help wounded soldiers returning from Iraq or Afghanistan without one or both arms, Dean and his team developed a robotic arm that has enough motor dexterity to pick up a raisin or grape off the table without dropping or squashing it. Dean has appeared several times on the Colbert Report, and here is a video of the robotic arm:
I have myself enjoyed riding a Segway. A local place in Tucson uses them to lead tourists through downtown Tucson and the University of Arizona campus.
Helping young students to learn science and technology
Dean wrapped up his talking by talking about his passion about "For Inspiration and Recognition of Science and Technology" or [FIRST]. Modeled after sports competitions, FIRST encourages teams of kids to build robots that perform specific tasks. Every year, companies and universities sponsor teams by purchasing robot kits from FIRST. Teams compete in regional competitions, and then the best of those go on to compete in a stadium in Atlanta, Georgia, hosting 76,000 people cheering for their teams.
Unlike other school sports (Football, Basketball, Baseball, etc.) where a student is more likely to win the lottery than get a successful career as a professional athlete, every student involved in FIRST competitions can "go pro". A study of FIRST success tracked students who participated in competitions, and found a substantial improvement in percentage of those students attending college and working as science and engineering professionals.
I am a big fan of encouraging kids of all ages to learn more about science, technology, engineering and math [STEM]. Back in 2009, I blogged about my involvement with [One Laptop Per Child] and [Junior FIRST Lego League]. I've gotten a great reaction to my latest challenge, to build a Watson Jr. in your own basement, based on my [step-by-step] instructions.
If you attended IBM Pulse this week, please comment on your thoughts and observations!
Wrapping up my week's coverage of the IBM Pulse 2011 conference, I have had several people ask me to explain IBM's latest initiative, Smarter Computing, which IBM launched this week at this conference. Having led the IT industry through the Centralized Computing era and the Distributed Computing era, IBM is now well-positioned to help companies, governments and non-profit organizations to enter the new Smarter Computing era, focused on insight and discovery.
Thousands of IT professionals
Effiicent, but only the largest companies and governments had them
Millions of office workers
Personal computers (PC)
Innovative, extending the reach to small and medium-sized businesses, but resulted in server sprawl and increased TCO
Billions of people
Smart phones and other handheld devices
Efficient and Innovative, combining the best of centralized and distributed computing
1952 to 1980
1981 to 2010
2011 and beyond
To help clients with this transition, IBM's Smarter Computing initiative has three main components. This is a corporate-wide strategy, with systems, software and services all working together to realize results.
The first component is Big Data. This combines three different sources of data:
Traditional structured data in OLTP databases and OLAP data warehouses, using data management solutions like DB2 and IBM Netezza.
Unstructured data, including text documents, images, audio, and video, processed with massive parallelism using IBM BigInsights and Apache Hadoop.
Real-Time Analytics Processing (RTAP) of incoming data, including video surveillance, social media, RFID chips, smart meters, and traffic control systems, processed with IBM InfoSphere Streams
Of course, Big Data will bring new opportunities on the storage front, which I will save for a future post!
Rather than general purpose IT equipment, we have now the scale and scope to specialize with systems optimized for particular workloads, the second component of the Smarter Computing initiative. Of course, IBM has been delivering integrated stacks of systems, software and services for decades now, but it is important to remind people of this, as IBM now has a spate of competitors all trying to follow IBM's lead in this arena.
As with Big Data, the focus on Optimized Systems has impacted IBM's strategy on storage as well. I'll save that discussion for a future post as well!
I am glad that nearly all of the storage vendors have standardized to a common definition for Cloud, the third component of Smarter Computing, which shows that this concept has matured:
Cloud computing is a pay-per-use model for enabling network access to a pool of computing resources that can be provisioned and released rapidly with minimal management effort or service provider interaction. -- U.S. National Institute of Standards and Technology [nist.gov]
Of course, Cloud is just an evolution of IBM's Service Bureau business of the 1960s and 1970s, renting out time-sharing on mainframe systems, Grid Computing of the 1980s, and Application Service Providers that popped up in the 1990s. While the [butchers, bakers and candlestick makers] that IBM competes against might focus their efforts on just private cloud or just public cloud, IBM recognizes the reality is that different clients will need different solutions. Rather than rip-and-replace, IBM will help clients transition to cloud via inclusive solutions that adopt a hybrid approach:
Traditional enterprise with private cloud deployments, using solutions like IBM CloudBurst, SONAS and Information Archive
Traditional enterprise with public cloud services to handle seasonable peaks, providing offsite resiliency, and solutions for a mobile workforce
Hybrid clouds that blend private and public cloud services, to handle seasonal peak workloads, remote and branch offices
IBM's emphasis on IT Infrastructure Library (ITIL), Tivoli and Maximo products will play well in this space to provide integrated service management across traditional and cloud deployments. This is why IBM decided to launch Smarter Computing initiative at Pulse 2011 conference, the industry's premiere conference on intergrated service management.
The IBM Watson that competed on Jeopardy! is an excellent example of all three components of Smarter Computing at work.
IBM Watson was able to respond to Jeopardy! clues within three seconds, processing a combination of database searches with DB2 and text-mining analytics of unstructured data with IBM BigInsights.
IBM Watson combined servers, software and storage into an integrated supercomputer that was optimized for one particular workload: playing Jeopardy!
IBM Watson used many technologies prevalent in private and public cloud computing systems, storing its data on a modified version of SONAS for storage, using xCat administration tools, networking across 10GbE Ethernet, and massive parallel processing through lots of PowerVM guest images.
IBM and the Austin Chamber of Commerce is inviting registered SXSW Interactive attendees to the networking reception being hosted by the IBM Innovation Center and the IBM Venture Capital Group. Power Systems and Watson will have a significant feature at this SXSW event to be held on March 14, 2011.
While I won't be there personally at the SXSW conference, I strongly recommend you to attend this event.
Innovators and Entrepreneurs Networking Reception
Four Seasons Hotel
March 14, 2011
Hosted by IBM Venture Capital Group, Austin Chamber of Commerce, and the IBM Innovation Center.
This reception will provide a rare opportunity to network and collaborate with your professional community of industry leaders, entrepreneurs, developers, academics, venture capitalists, members of the Austin Chamber of Commerce.
Last night, I presented an E-Talk to the Engineering Student Council (ESC) of the University of Arizona (UofA).
The ESC is the student governing body of The University of Arizona’s College of Engineering. The organization works with scholastic honorary societies, professional organizations, and project clubs to aid and encourage the professional and social development of students. This year, ESC launched a new program, Engineering Talks (E-Talks), consisting of workshops and lectures, which will focus on teaching students what it takes to work within a company, before they enter the workforce. To make this program successful, career advice from professionals working at established companies is essential.
The audience was a mix of undergraduate and graduate engineering students from a variety of disciplines, such as Petroleum, Hydrology, Mining, Biomedical, Electrical and Computer Engineering. Only a few were graduating this May. There were roughly an equal number of boys and girls, which was encouraging. When I was an engineering student at the UofA, women engineers were very rare.
A little about myself, my academic and professional career over the past 30 years, and some background of IBM as a company, how it is organized, and its 100 year Centennial celebration.
An overview of IBM's corporate strategy for Smarter Computing, explaining how IBM is solving the world's toughest challenges for analyzing Big Data, developing Optimized Systems for particular workloads, and new delivery and deployment models for Cloud Computing.
Some career advice, based on my decades of work experience at IBM and elsewhere
After the Q&A, several students stayed around afterwards to ask questions. This seems to happen every time I give a presentation to a mixed audience. I handed out plenty of business cards, and offered to make the charts available to all the students via the IBM Expert Network on Slideshare.net website.
This last one on how to build your own Watson, Jr. has gotten over 69,000 hits! While several people told me they plan to build their own, I have not heard back from anyone yet, so perhaps it is taking longer than expected.
IBM and Wellpoint announced this week that it will be [putting Watson to work] in healthcare. [Wellpoint] is one of the largest health benefits company in the United States, with over 70 million people served through its affiliate plans and its various subsidiaries. I am one of the development lab advocates for Wellpoint, and have been proud to work with the account team to help Wellpoint achieve their goals.
This marks the first commercial deployment of IBM Watson. This is a joint effort. IBM will develop the base IBM Watson for healthcare platform, and Wellpoint will then develop healthcare-specific solutions to run on this platform. Watson's ability to analyze the meaning and context of human language, and quickly process vast amounts of information to suggest options targeted to a patient's circumstances, can assist decision makers, such as physicians and nurses, in identifying the most likely diagnosis and treatment options for their patients.
Is this going to put doctors out of business? No. Physicians find it challenging to read and understand hundreds or thousands of pages of text, and put this into their practice. IBM Watson, on the other hand, can scan through hundred of millions of pages in just a few seconds to help answer a question or provide recommendations. Together, doctors armed with access to IBM Watson will be able to improve the quality and effectiveness of medical care.
From an insurance point of view, improving the quality of care will help reduce medical mistakes and malpractice lawsuits. This is a win-win for everyone except ambulance-chasing lawyers!
This week, I will be in Las Vegas for the 30th annual [Data Center Conference]. For those on Twitter, follow the conference on hashtag #GartnerDC, and follow me at [@az990tony].
Once again, I will be working the IBM Exhibition Booth of the Solution Showcase, attending keynote and break-out sessions, and meeting with clients and analysts. Today is mostly setting up the booth, getting my registration badge and materials, an orientation meeting for first-timers, and finish off the evening with a networking event to get the party started!
Traffic to and from the hotel was a mess today because of the [Las Vegas Strip at Night Rock-n-Roll Marathon]. The entire Las Vegas Boulevard was blocked off from 2pm to 11pm, causing taxis some headaches getting to and from each hotel. This marathon included a "Stiletto Dash" where women had to run in shoes that had at least three inch heels! (Only in Las Vegas!)
The conference is organized into 8 tracks:
Navigating the Journey to Cloud-Delivered Services
Achieving and Maintaining IT Operational Excellence
Modernizing Your Storage Strategy to Keep Pace with Burgeoning Demand
Ensuring Your Business Continuity Management Plan Reflects Today's Realities and Tomorrow's Challenges
Virtualization: Moving at Light Speed While Leveraging Your Existing Investments
The Future of Servers and Operating Systems
Data Center Modernization: Staying Agile in Chaotic Times
Pervasive Mobility: What Infrastructure and Operations Needs to Know Now
I am glad to see that storage got its own track this year! If you are attending the conference, here are the sessions that IBM is featuring for Monday:
IBM: Watson and Your Data Center
This is a lunch-time talk. Steve Sams, IBM VP of Sites and Facilities, will explain how to leverage Watson-like analytic approaches to provide flexible, cost-effective data center solutions. Analytics can be used to better align IT to the business needs, optimize server, storage and network utilization and improve data center design.
IBM: University of Rochester Medical Center cracks the code on data growth
Rick Haverty, Director of Infrastructure for University of Rochester Medical Center (URMC), will discuss how his team built a storage strategy that transformed their environment to bring savings right to their bottom line without sacrificing the speed, criticality and performance requirements of their imaging and EMR systems. I will be there to introduce Rick at the beginning, and then moderate the Q&A after the talk.
Solution Showcase Reception
The Solution Showcase opens up Monday night with a reception, serving food and drinks. Look for the IBM Portable Mobile Data Center (PMDC), the big trailer on the show floor. We also have an exhibit booth, across from the PMDC, to ask questions and talk with various IBM experts. You can look for me and the other experts wearing white lab coats!
Continuing my coverage of the 30th annual [Data Center Conference]. Here is a recap of the Monday afternoon sessions:
IBM Watson and your Data Center
Steve Sams, IBM VP of Site and Facilities Services, cleverly used IBM Watson as a way to explain how analytics can be used to help manage your data center. Sadly, most of the people at my table missed the connection between IBM Watson and Analytics. How does answering a single trivia question in under three seconds relate to the ongoing operations of a data center? If you were similarly confused, take a peak at my series of IBM Watson blog posts:
The analyst who presented this topic was probably the fastest-speaking Texan I have met. He covered various aspects of Cloud Computing that people need to consider. Why hasn't Cloud taken off sooner? The analyst feels that Cloud Computing wasn't ready for us, and we weren't ready for Cloud Computing. The fundamentals of Cloud Computing have not changed, but we as a society have. Now that many end users are comfortable consuming public cloud resources, from Facebook to Twitter to Gmail, they are beginning to ask for similar from their corporate IT.
Legal issues - see this hour-long video, [Cloud Law & Order], which discusses legal issues related to Cloud Computing.
Employee staffing - need to re-tool and re-train IT employees to start thinking of their IT as a service provider internally.
Hybrid Cloud - rather than struggle choosing between private and public cloud methodologies, consider a combination of both.
University of Rochester Medical Center (URMC) Cracks Code on Data Growth
Often times, the hour is split, 30 minutes of the sponsor talking about various products, followed by 30 minutes of the client giving a user experience. Instead, I decided to let the client speak for 45 minutes, and then I moderated the Q&A for the remaining 15 minutes. This revised format seemed to be well-received!
University of Rochester is in New York, about 60 miles east of Buffalo, and 90 miles from Toronto across Lake Ontario. Six years ago, Rick Haverty joined URMC as the Director of Infrastructure services, managing 130 of the 300 IT personnel at the Medical Center. I met Rick back in May, when he presented at the IBM [Storage Innovation Executive Summit] in New York City.
URMC has DS8000, DS5000, XIV, SONAS, Storwize V7000 and is in the process of deploying Storwize V7000 Unified. He presented how he has used these for continuous operations and high availability, while controlling storage growth and costs.
The Q&A was lively, focusing on how his team manages 1PB of disk storage with just four storage administrators, his choice of a "Vendor Neutral Archive" (VNA), and his experiences with integration.
This was a great afternoon, and I was glad to get all my speaking gigs done early in the week. I would like to thank Rick Haverty of URMC for doing a great job presenting this afternoon!
I presented IBM's Smart Archive strategy and the storage products IBM offers to archive data and meet compliance regulations:
The differences between backup and archive, including a few of my own personal horror stories helping companies who had foolishly thought that keeping backup copies for years would adequately serve as their archive strategy
The differences between Write-Once Read-Many (WORM) media, and Non-Erasable, Non-Rewriteable (NENR) storage options.
How disk-only archive solutions become "space heaters" for your data center.
An overview of the various storage hardware options from IBM.
An explanation of the different IBM software offerings to help complement the storage hardware choices.
IBM TotalStorage Productivity Center (TPC): New Features and Functions
Mike Griese, IBM program manager for TPC, presented the latest in TPC 5.1 version announced this week. His session was organized into four key sections:
Insights - TPC 5.1 integrates COGNOS reporting, which allows custonmization of reports and ad-hoc exploration and analysis. Since the reports are not binary-compiled into the product, IBM can ship new COGNOS reports as templates outside the normal TPC release schedule. Also, TPC 5.1 got smarter on reporting on server virtualization hypervisor environments to avoid double-counting.
Recommendations - TPC 5.1 can analyze your usage patterns across the entire data center and make recommendations to move data from one storage tier to another. You can then act on these recommendations by moving data from one tier to another, either "up-tier" to faster storage, or "down-tier" to less expensive storage, using a storage hypervisor like IBM SAN Volume Controller. This is complementary to features like Easy Tier which optimize within a single disk system.
Performance - TPC 5.1 uses a new web-based GUI, based on AJAX, HTML5 and Dojo widgets, inspired by the IBM XIV GUI, and similar to the web-based GUI of SAN Volume Controller, Storwize V7000 and SONAS.
Mike also explained the new TPC 5.1 packaging. Instead of having a variety of components like "TPC for Disk", "TPC for Data", and "TPC for Replication", the new packaging simplifies this down to two levels of functionality. The basic level supports block-level devices, including disk performance, replication and SAN fabric management. The advanced level adds support for files and databases, including support for Cloud management such as SONAS environments.
Dan Zehnpfennig, Solution Architect, talked about his experiences installing TPC 5.1 and how this was much improved over previous TPC versions.
IBM Watson: How it Works and What it Means for Society Beyond Winning Jeopardy!
Sadly, only 70 percent of doctors in the United States use Electronic Medical Record [EMR] systems. My own Primary Care Physician has made the switch, and told me he how much he loves having ready access to the information he needs. EMR systems reduce costs, help manage risk, and improve healthcare outcomes. It is no surprise that the U.S. government has taken a [stick-and-carrot approach] to encourage doctors to use them.
A frequent topic at the Tucson Executive Briefing Center where I work is how to make the most use of IT for healthcare and life sciences. For much of 2011 and 2012, I was also one of the technical advocates assigned to Wellpoint Insurance, in support of their adoption of IBM Watson technology for healthcare.