IBM Watson and Artificial Intelligence

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Computer Science

CIS 110

Montgomery County Community College

CIS

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IBM Watson and Artificial Intelligence This is the third discussion of the semester, and I once again invite you to participate. Read the following information and then add your comments to the discussion. You may pick a topic from the information below, do some of your own research, and write your comments. This discussion is about something very interesting: artificial intelligence (abbreviated as AI). That phrase has been around for a long time, and a lot of people have been disbelievers for many years. Those people (and all the rest of us) are finally starting to see products and applications that are built using artificial intelligence technology. The software that drives AI is amazing. Apple’s Siri, Amazon Echo, and Google Home use AI. Even the Google search engine now uses AI to try to predict what you are searching for. I’m sure you’ve seen this. It was a bit eerie at first. There are thousands of other applications of AI, some of which we don’t even know about but are there anyway. AI has arrived! This discussion wouldn’t be complete without mentioning the computer hardware that drives AI systems. The hardware company that tops that list happens to be IBM, a company that was the dominant company (by far) in the computer industry for many years. Times have changed and IBM is not “Number 1” any more. They’re in a very competitive industry and are investing heavily in AI technology. Their system is called Watson, and is probably best known in “pop culture” for winning the Jeopardy TV show a few years ago. Watson is much more than a game show champion, of course, and we’ll find out more it as this discussion takes shape. This discussion looks at the combination of hardware and software that’s behind one of the major segments of the computer industry. Let’s rewind the clock a little bit. So here we are in the late 1880s. The United States Census Bureau is getting ready to conduct its once-in-a-decade count of the number of people in the country. Why is this done every 10 years? Simple: The United States Constitution says we have to. For you history buffs, Article 1, Section 2 says: [An] Enumeration shall be made within three Years after the first Meeting of the Congress of the United States, and within every subsequent Term of ten Years, in such Manner as they shall by Law direct. Why is this done? It’s the only way that our government can determine who will represent us in Congress. The House of Representatives districts are determined by the census. OK, back to the late 1880s. The Census Bureau recognized that it had a potential problem in the making. It had taken them over 7 years to complete the 1880 census. Not surprising, really. Everything was done by hand. No machines (yet). Here’s what the 1880 census form looked like. Here was the looming problem. There was such a huge population explosion in the United States during the 1880s that the Census Bureau started to wonder if they could complete their 1890 counting in less than 10 years. If they couldn’t have, they would have been violating the Constitution. They needed a faster way to count the people. They found their solution in one of their bright employees, Herman Hollerith. He designed and built a mechanical machine that could input data from stiff pieces of paper with holes punched in them, tabulate that data, and show the results. Along the way, he invented the machines that punched the holes and the machine that displayed the answers. A complete solution! And, you know what, it really worked. So well, in fact, that the census counting was completed in a matter of months. Not bad. Hollerith was smart in other ways, too. He quit his job and started a company he named The Tabulating Machine Company, and that’s exactly what they built. Over the next few decades, the company grew and grew, and another person became president. His name was Thomas Watson (remember that name). In 1924, the company was renamed International Business Machines – IBM. They built electro-mechanical machines for a while, and then ventured into electronics. World War II came along, and the U.S. Government invested heavily in large electronic systems that could help with the war effort. Two applications stood out: breaking the German secret codes, and calculating the trajectory of the guns that were aimed at enemy targets. The first electronic computers were the result of those expenditures. IBM wasn’t the inventor of those – that work happened at some large universities, including Harvard and the University of Pennsylvania. Post-war America brought great prosperity, another population growth period (the “baby boom”), and the need for electronic machines that could process the ever-growing amount of data. We had to count people (the census again), money, inventory items, customers, bank accounts, etc. That’s when IBM stepped up again, this time under the leadership of Thomas Watson, Junior. The company started to build electronic “mainframe” computers that couldn’t be beat. IBM dominated the field. So much that they later faced monopoly issues with governments around the world. They won some and lost others, but still maintained a leadership role in the computer industry for several decades. IBM created their own operating systems, OS/360, MVS, z/OS – things you’ve probably never heard of before. Most of the application programming was business-oriented using a programming language that had been invented by the U.S. Department of Defense in the late 1950s – the COBOL language. That software language, designed by Admiral Grace Hopper, worked on many types and brands of computers and was the most efficient language around. Some people argue that it still is. If you wanted a fast program, you chose COBOL. In all the years since, more lines of COBOL code have been written than any other language (such as Java, C++ and a myriad of others). Your cell phone records, bank statements, insurance bills, health records, etc. are very likely to have been created using COBOL. I have a good friend who just retired after spending 35 years writing large applications in COBOL. (An interesting point: COBOL is hardly taught anywhere in the United States these days, but is still a major programming language around the world. It’s just that the programming is now done in other countries. As my friend says, “The COBOL jobs didn’t go away, they just moved elsewhere.”) IBM dominated software development with COBOL applications. IBM built less expensive “mid-range” computers in the 1970s and were very successful in bringing computer technology to smaller businesses. You no longer had to spend millions of dollars on a computer, but less than $100,000. (I know that’s still a lot, but it opened up the availability of electronic data processing to many more organizations. I started working in the computer industry when computers were that size and price. I worked for a computer manufacturer in Hayward and our systems competed against the IBM mid-range systems.) In the early 1980s, IBM recognized that the invention of the microprocessor by Intel Corporation meant that computers cost less to build, so they decided to take a stab at the “personal computer” market. Other companies had already designed small computers, most notably Altair, Apple and Radio Shack. IBM came along and took a different approach. They chose a separate company, Intel, to build the central processing units for their new systems, and chose other companies to build memory, disk drives, monitors, printers, etc. IBM’s job was to put everything together in a package called “The IBM Personal Computer.” IBM needed an operating system to run their personal computers, and they set out to find another independent company to design it (they were so used to making software for large systems that they thought it would be better to let someone else do the design for small computers). They did some searching and eventually found a VERY small company in Seattle named Microsoft, run by a couple of young programmers – Paul Allen and Bill Gates. Their product was named Disk Operating System (DOS), and IBM wanted it. Microsoft didn’t want to sell their product outright, so they decided to “license” it to IBM. This was the smartest business move of all times, because Bill Gates is now one of the richest people in the world. Paul Allen is right up there, too (According to the latest Forbes ranking, he’s the 26th richest person in the U.S.). You’re in this class right now because of the efforts of the entrepreneurs and inventors of that time, including Gates, Allen, Steve Jobs and Steve Wozniak (Apple), Nolan Bushnell (Atari), Robert Noyce and Gordon Moore (Intel), Larry Ellison (Oracle), Jack Kilby (the inventor of the transistor while working at Texas Instruments), Ted Hoff (the inventor of the microprocessor while working at Intel in 1971), and many others. A lot of them were influenced by Bill Hewlett and Dave Packard (“H-P”), both graduates of Stanford University and the inventors of many computer technologies that are still is use today. The “IBM PC” was a smash hit. IBM assembled the computers, even though they didn’t build all the hardware. The main software for these computers was DOS. For a while, IBM called it “IBM DOS,” but in reality it was Microsoft DOS. IBM’s main goal was to put the “IBM brand” on these computers. They made their specifications open to the public. Lots of companies started to build “IBM compatible” products. I worked for one of those companies for many years. Eventually, companies like Dell, Compaq, Hewlett Packard, and other started to build complete PC systems and ended up giving IBM competition in the business they had created in the first place. Even Microsoft recognized its limits, and they made their software specifications open to the public. The company I worked for also made “Microsoft compatible” software. (Apple, by contrast, never did make its hardware and software specifications open to the public. They chose to always build “proprietary” systems. As a result, Apple had less competition, which is probably the main reason why they still charge more for its products than other companies do. Sure, their products are popular, but they’re still proprietary.) The “good times” continued for a long time. Thanks to Xerox, computer networks came into existence (Xerox called their invention “Ethernet,” and you’ve read about that in the book and I can guarantee that you use it all the time), which allowed people to have processing power on their desktop (or laptop) while still being connected to other people and computers. This was the polar opposite of what IBM mainframes were all about – the centralized processing of all data. It was only natural that someone would build a computer to tie the personal computers together to a central place where data was stored (but not processed), and that’s what a server is. IBM was a player, but not the dominant one. Companies like Sun Microsystems and Silicon Graphics took over that segment of the computer marketplace. (By the way: Sun is now owned by Oracle, a major software company headquartered in Belmont.) Microsoft designed Windows, which ended up being the replacement for DOS. At one point, the Windows operating system controlled 95 percent of the personal computer marketplace (which, or course, brought monopoly issues to Microsoft). The whole market started to move away from the large mainframe systems towards networks and servers. The Internet came along, and that’s full of servers. Personal computers got cheaper and cheaper. IBM found that it couldn’t complete in that business, so it sold its personal computer division to a Chinese company named Lenovo. They divested other divisions, too. Their mid-range systems stopped being as popular as they had been. IBM lost its dominance. They had to do something if they were going to continue to stay in business and, it is their hope to regain their place atop the computer world. They chose two areas: virtualization and AI. We will discuss virtualization later on, but today’s main topic is AI. Artificial Intelligence was first discussed as far back as the 1940s. The English mathematician, Allen Turing, postulated that electronic machines that could solve virtually all math problems and would eventually be able to “think.” By the mid-1950s Allen Newall, Herbert Simon, John McCarthy, Marvin Minsky, and Arthur Samuel became the leaders of AI research. Their initial successes led to over-confidence. Simon said, “machines will be capable, within twenty years, of doing any work a man can do." Minsky wrote, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." Unfortunately, it was harder than they thought. The math was harder than they imagined and the computers just weren’t fast enough to compute all the formulas they needed to. For a long time, programmers kept trying to design a program that could play chess and beat a world-renowned chess master. They designed better and better algorithms to compensate for the fact that their computers weren’t fast enough to try all possible alternative combinations to arrive at the answer. The “pop culture” interpretation of this was that AI was all about playing games. Sadly, that misconception was perpetuated by Watson’s appearance on Jeopardy. Of course, the goal wasn’t to win games; it was to figure out if computers could change their own programming as the result to doing something over and over, studying the successes and failures, eventually arriving at a new result. This came to be called “machine learning.” For a while, most people thought that was a joke. But, you know, that turned out to the breakthrough that defined the whole AI movement. Even now, companies like Google are studying and applying this type of technology. Google’s DeepMind project developed a program that plays the game of “Go” – a game again (!), and one that is much more complex than chess. Google put a new spin on it. They have the computer play against itself to come up with ways to automatically improve its own algorithms needed to win the game. Some people hear the word “game” and still think that’s what it’s all about. It’s not – it’s about the very nature of learning. One of the phrases I’ve heard throughout my career is that “computers only do what humans tell them to do.” I’m sure I taught that at one point. Not anymore. I worked for a software company in the 1980s that had a product named “Automatic Program Generator.” It was a great product. You could type a few commands, and the program would write hundreds of lines of code that solved the problem you were trying to solve. It was sort of a “parlor trick” though – our program could only solve a small number of problems. It didn’t have a way to modify itself or to “learn.” It’s a lot different today. Software can and does change itself, based on the different types of data and processing it encounters. The math is still very hard, but problem solving techniques have really advanced a lot in the past 60 years. And, the big breakthrough is that computers are MUCH faster than before. Computers no longer need to use “clever” ways to devise strategies to win chess games (as human players do), they can analyze EVERY possible move and then pick the best one. On top of that, machine learning means that they can also learn better strategies the longer they play. It’s a double win. All of this was not lost on IBM. Several years ago, they created a major initiative to develop and sell AI solutions – both hardware and software. The blanket name for their product is “Watson” (clearly a tribute to the early leaders of the company). IBM’s catchphrase is, “The Power of Knowledge.” There have been two goals of the Watson initiative: to discover ways that machines learn, and to build related hardware and software products for a wide variety of applications. Their business strategy looks like it is really working. One of the first Watson products is “Watson Discovery.” IBM’s web site describes this it this way: “Rapidly build a cognitive search and content analytics engine. Watson Discovery helps developers quickly ingest data to find hidden patterns and answers, enabling better decisions across teams.” I get that, it’s about patterns and decision making. “Watson Conversation” is another major product. Here’s the pitch from the IBM Watson web site: “Quickly build, test and deploy bots or virtual agents across mobile devices, messaging platforms, or even on a physical robot to create natural conversations between your apps and users.” That makes sense, too. Use machine learning to figure out better ways to communicate with information systems. With the “Watson Virtual Agent,” you can “quickly configure virtual agents with company information, using pre-built content and engage customers in a conversational, personalized manner, on any channel.” Ah, I get it, better (and less expensive) technical support systems. Or even airline reservation systems. You can probably think of more applications… This one is also interesting; the “Watson Knowledge Studio” lets you “teach Watson to discover meaningful insights in unstructured text without writing any code.” What, no code? I’m in favor of that! I’ve also noticed that “unstructured text” is becoming a major interest in computer science. Indeed, one of the newer classes at College of San Mateo covers “NoSQL,” a type of database management that deals with data other than the rigid “row and column” approach of “relational database management.” If you are interested in studying a high “growth potential” area in computer science, check this out. IBM has even created a mechanism for programmers around the world to use Watson technology to create their own AI products. IBM calls this the “Watson APIs” (“API” is “application programming interface,” a term that’s been around for many years that describes how a software company can make some of its features available to other programmers, so people don’t always have to reinvent the wheel.) IBM’s web site says, “Use Watson language, conversation, speech, vision and data insight APIs to add cognitive functionality to your application or service.” None of this is theoretical; these are all products you can get now. IBM mainframes have been “re-purposed.” IBM even went back to its roots of solving business computing problems, and has AI products called “Watson Commerce” and “Watson Financial Services.” (During tax season earlier this year, I saw a lot of advertising for H&R Block that touted their Watson technology. I guess that means they are using AI to help minimize the taxes their customers have to pay.) Even my field has a Watson product – “Watson Education.” It’s a powerful tool to discover how people learn. I have read a lot about “Watson Health,” an AI solution for the healthcare field. IBM calls this “Cognitive Healthcare Solutions,” and describes is as follows: Our purpose is to empower leaders, advocates and influencers in health through support that helps them achieve remarkable outcomes, accelerate discovery, make essential connections and gain confidence on their path to solving the world’s biggest health challenges. Whether advancing toward a big-picture vision or delivering meaningful experiences to a single individual, our mission is to improve lives and enable hope. We arm health heroes with the technology and expertise they need to power thriving organizations, support vibrant communities and solve health challenges for people everywhere. The latest Watson product I’ve seen advertised is the “Watson Tone Analyzer,” and it is said to “understand emotions and communication style in text” (quoted from the IBM Watson website). These claims include the following descriptions: Conduct social listening Analyze emotions and tones in what people write online, like tweets or reviews. Predict whether they are happy, sad, confident, and more. Enhance customer service Monitor customer service and support conversations so you can respond to your customers appropriately and at scale. See if customers are satisfied or frustrated, and if agents are polite and sympathetic. Integrate with chatbots Enable your chatbot to detect customer tones so you can build dialog strategies to adjust the conversation accordingly. I tried the online demo, and it really works. I’ve included the URL to the demo page (see below), where you can enter your own text for the “Watson Tone Analyzer” to dissect. Try it for yourself for an interesting and eye-opening experience. This is starting to sound like an IBM commercial, so I’ll stop. Now it’s your turn. What do you think of AI? Do you know about any AI products that you use that haven’t been mentioned here? Are you aware of other platforms besides Watson? Are you aware of other Watson products that I haven’t mentioned here? Where do you think this will go in the future? Could this be a solution to cybersecurity attacks? Could it make you a better investor? Could it improve the way farmers grow food? This may take some research on your part, and I’m really looking forward to reading your contributions to this discussion. Sources of information include the IBM Watson web site, lots of my own notes on the development of artificial intelligence, and information from the U.S. Census Bureau. Here is the link to the Watson Tone Analyzer: https://www.ibm.com/watson/services/tone-analyzer/?cm_mmc=PSocial_Facebook-_Watson%20Core_Watson%20Core%20-%20Platform-_-NA_NA-_21857039_Clicktracker&cm_mmca1=000000OF&cm_mmca2=10000409&cm_mmca4=218570 39&cm_mmca5=46333619&cm_mmca6=aad5bc8d-7d72-4409-b0a5757418344733&cvosrc=social%20network%20paid.facebook.WDC%20API%20Carousel%20F BC%202%20Tone%20Analyzer_SD%20Behav_DesktopMobileTablet_1x1&cvo_campaign=00 0000OF&cvo_pid=21857039
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Running head: ARTIFICIAL INTELLIGENCE (AI)

Artificial Intelligence (AI)
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ARTIFICIAL INTELLIGENCE (AI)

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Artificial Intelligence (AI)
Artificial Intelligence (AI) once appeared as a skeptical idea, but the current
developments and proofs of its significance show that it may be the key to most if not all of the
problems that we face in society from time to time. Unlike in the olden days when you had to
scratch your head trying to think of an example of anything that...


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