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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.
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.
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.
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!
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!
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!
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!
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.
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.
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.