TOW Documentary Analysis Part 2: Smartest Machine on Earth

If you’re going to teach a computer how to play Jeopardy!, how would you do it?  Would you teach it all the rules of language that you can think of or find, all the common sense nuances that are inherent in language and idioms and slang terms that come with it?  Or would you feed the computer millions of examples and let the computer figure out its own rules?  In Smartest Machine on Earth, the programmers of Watson chose the latter.  Why? Well, another, similar program utilising the “recipe” technique took six years to program and new rules are still being added to it.  Also, when initially using that technique with Watson, when a test was run Watson was only able to answer with a 10% accuracy rating.  Machine learning had to be employed.  An anecdote to prove the point existed in the form of the program used by the U.S. Postal service to read addresses on letters in order to send them.  Each person writes a letter in the alphabet differently.  The example used in the documentary being ‘A’.  It can be anywhere from a lowercase ‘a’ to an uppercase ‘a’ to an upside-down ‘V’ or a simple triangle.  No human can write a rule to tell the computer that a letter is ‘A’, but a human can give the computer millions of examples of the letter ‘A’, and let it create its own rules and find patterns that a human cannot to use to identify the letter ‘A’.  Machine learning is an innovative technique, utilised in voice recognition, and even language translation.  It’s flexibility is what makes it so valuable, and so usable, as well as being the program that Watson needs.  

Alex Wabel takes Machine Learning to a whole new level, creating a program that translate spoken text from one language to another.  It’s an innovative new application that can be used with multiple languages, including Chinese and Japanese, notoriously difficult languages to translate due to language nuance created by tonal changes.  As Alex takes his application abroad, people are amazed at its abilities, from restaurant workers, to the shopper, and managers.  Based in Machine Learning, this new application is revolutionary, and full of potential, able to help facilitate communication between people who speak different languages who otherwise would not be able to understand one another, while broadening the social and business web worldwide, and removing language barriers.  

Watson may seem complex, but his program is built upon a foundation of a very basic language: on and off, true and false.  This is the foundation of binary and binary code, which is then the basis of the programming languages used to create Watson.  Programming language is a reflection of the age-old concept of true and false.  If I have three apples and he has four apples, then we have in total seven apples, which is true, we don’t have eight apples, which is false.  It’s how we know that nine is greater than two, but negative nine is less than negative two.  True and false forms the basis of our perception, which we then transform into hypotheses, then into laws and rules.  These laws and rules then become the basis of language, and in the case of Watson it the the programming language that allows him to run.  

Many computer and robotics experts are fascinated with the concept of AI.  Sci-Fi films depict robots that are able to think for themselves and understand their surroundings, that are capable of feeling emotion.  Many films depict robots as threats to the human race, as powerful beings that surpass human power, like HAL of 2001 Space Odyssey.  The new advances in AI, especially the program used with Watson, can seem threatening to humans.  However, as shown with the anecdote of Deep Blue, a computer whose purpose is solely to play chess, Watson may seem intelligent, but he can only play Jeopardy!.  Watching make silly mistakes in Smartest Machine on Earth simply because he doesn’t understand gender perceptions nor care about them, it’s easy to see that Watson isn’t a threat.  Computers do not have the human ability of language and object recognition, they have no associative memory.  You can give a computer the entire compilation of the holy books of the many religions of Man to read, but the computer can pull no meaning from the words.  Computers and Robots aren’t a threat to humanity, they are tools with great potential to aid humanity.  

http://www.pbs.org/wgbh/nova/tech/smartest-machine-on-earth.html

TOW Documentary Rhetorical Analysis: Smartest Machine on Earth

Gondek, Dave, David Ferrucci, Luis Von Ahn, Todd Crain, Ken Jennings, Harry Friedman, Eric Brown, Terry Winograd, Rodney Brooks, Doug Lenat, Charles Lickel, Marvin Minsky, Sajit Rao, Sebastian Thrun, Alex Trebek, Alex Waibel, Chris Welty, and Patrick Winston. Interview. NOVA. PBS. PBS, 2 May 2012. Television. Transcript.

Baker, Stephen. 2011. Final Jeopardy: Man vs. Machine and the Quest to Know Everything. Houghton Mifflin Harcourt.

What is AI? It stands for artificial intelligence, and has been both a dream and a nightmare for humans since its conception by Isaac Asimov.  In this Documentary, Smartest Machine on Earth, IBM, MIT, Stanford, and Carnegie Mellon researchers explain the concept of AI and the different ways of achieving AI with language recognition.  There are two concepts utilised when forming AI, the “writing down the recipe” concept is where the programmer tells the computer a set of rules that govern language such as common sense.  Unfortunately, with such intuitive things as word puns and the tediousness of finding and inputting over 6,000 common-sense rules into the computer, such a technique isn’t viable for the language recognition challenge, and so the documentary explains machine learning, where the programmer feeds the computer millions of examples and the computer creates its own rules from them.  It’s basic logic, since figuring out and programming each rule into a system is much more time-consuming than simply letting the machine figure out rules for itself.  It explains how we underestimate the complexities of the human brain, and the difficulties of understanding language and object recognition.  

This first lady was born Thelma Catherine Ryan, on March 16, 1912, in Nevada. Watson?

Who was Richard Nixon?  With so many robotic apocalyptic sci-fi films, many humans are worried of a possible robot takeover should we ever develop a ‘true’ AI with human emotions and thinking capabilities.  So it’s a little worrying to some viewers who see Watson, so used to old video clips of evil AI’s that try to take over the world, playing Jeopardy! as an AI and dominating the factual questions in Jeopardy!.  Watson doesn’t seem quite as threatening when he can’t even tell the difference between male and female, though, and we all get a good laugh at that.  The team of researchers creating Watson created a ‘cloud’ graph of accuracies and scores to compare the AI’s capabilities to those of human contestants.  Through viewing the graph, with a blue cloud of human players and red line of the AI standing, it’s easy to see that initially there was much work to do in order to get Watson at a level capable of competing with the best.  

i shot an elephant in pajamas.  Well, was the elephant in pajamas, was ‘I’ in pajamas? Did you actually shoot the elephant with a gun? Or, did you take a picture, ‘shoot a picture’, of an elephant, while in pajamas?  This interesting cartoon is used in the documentary to show just how complex the human language is, having so many nuances and interpretations within each phrase.  It’s also a great laugh and keeps us interested in the topic, how the researchers will overcome this common sense language barrier that is so difficult for computers to ‘understand’.  Wonderful little anecdotes like Deep Blue, the computer programmed solely to play chess, helps to alleviate some of the worry of a robotic takeover, since it is explained that Deep Blue doesn’t actually think, it just plots out every possible outcome in a game of chess and then proceeds to go with the process that will result in victory.  It can only play chess, as Watson can only play Jeopardy!.  The researchers themselves hope that one day this technology may be utilised for things such as medical diagnosis, a program able to sift through thousands of medical journals and keep track of thousands of symptoms and examples, creating rules of diagnosis that will be much more accurate than a human doctor and may help save lives.  

http://www.pbs.org/wgbh/nova/tech/smartest-machine-on-earth.html

TOW Reflection

Ever since starting these TOW’s, the way I’ve approached them has changed.  In the beginning with my very first TOW, A First: Organs Tailor-Made With Body’s Own Cells, I was very focused on following the parameters of the assignment, simply because I had no idea what leeway was allowed.  It was safe to just hit everything on the list and be done.  So we get very formulaic writing.  Author, author background information, tiny summary, rhetorical devices and analysis, then add a little bit about his purpose and done! Double-check to make sure that I didn’t use too many words for the TOW, check the article again for word count, its all good, lets post it on the pretty blog with pink flowers and hummingbirds.  Why choose that for a background?  Well, as much as I wished to make it orange, most people somehow find bright orange and blue an eyesore, and the color scheme worked, so might as well, and the autumn leaves were kind of garish, so better to just stick with the pink flowers. at least there’s SOME orange... But of course, this type of writing only lasted to about the second marking period, where the class received a notice to actually do GOOD writing.  

There were no limits on the number of words, hallelujah, because sometimes I went over and had to cut, but then there was more thought added in because the ideas should flow, instead of just acting as an information dump.  Cut and paste, choppy, little flow between ideas, that was how the oldest TOW’s were like, but my rewrite of the TOW on the advertisement for the The Phantom of the Opera at Royal Albert Hall: In Celebration of 25 years was more story-like.  It was less a fact dump and more a story, albeit one based upon a picture, but are not those fun stories?  You can say whatever you want as long as you have evidence to back up your point of view, as sometimes there really is no right or wrong interpretation.  With the word count limitation removed, there was more room to think and express.  Yes, there was less, some things get cut out or pushed back as unimportant to the audience, but that’s the point, right? To learn how to express things concisely in a way that is interesting and use one’s own judgement to utilise rhetorical tools in one’s own writing, even if subconsciously, while studying how others use their tools so that we may emulate them in our own writing should we have need to do so.  

The blogs, as they went on, became less of an impersonal chore, but a way to share interpretations.  In my Student Debts Cartoon post, I found myself connecting with my choice, as a soon-to-be college student, and my writing reflected my own personal opinions that I found were reflected in the cartoon.  The more formal tone adopted in my first few posts had mostly fled, and it was as if I were talking to someone else while writing.  Of course, any responses to my words were only in my head, but it was still a nice way to go about it...

To say I’ve mastered something about blog writing is probably going too far.  I can say that I’ve improved in identifying rhetorical elements and analyzing them in writing, but I’m sure there will always be plenty of room to improve.  Identifying the purpose goes a long way in helping to write posts, and while I’ve gotten the hang of identifying the purpose of visual texts, I still have a long way to go with journal articles.  Not many of them are obvious in purpose, and while one can always argue one purpose or another based upon evidence in the article, it will never be the same as taking a peek at the author’s brain and actually knowing and understanding the purpose of an article.  I do feel as though the flow has gotten better, since it isn’t quite as much an information dump.  I’ve still got much to learn, and it’d be nice if I actually got some feedback on my work, but I suppose it’s not that good if I don’t get any sort of commentary.  Still, I’ve definitely improved since the beginning of the year, so I’m happy.  

FDA Studies Caffeine's Effects on Children, Teens

How many cups of coffee do you drink a day?  Do you worry about your caffeine intake?  Well, as Wes Venteicher of the Los Angeles Times reports, you won’t have to just worry about how much coffee you drink anymore.  Now the FDA is researching the effect of caffeine on children, as companies are now putting caffeine in gum, jelly beans, even waffles and maple syrup.  Considering the FDA has no rules on caffeine in food as of now, nor any data on the limits that children can safely take, it is imperative that the FDA work with food companies to find and set limits on marketing and caffeine amounts.  As the author points out while citing a study, “Avoidance of caffeine in young people poses a great societal challenge because of the widespread availability of caffeine-containing substances and a lack of awareness about potential risks." Those risks include negative effects on the development of a child’s neurological and cardiovascular system, which can be a huge issue for children in the long run.  

Venteicher juxtaposes the concept of caffeine in liquid with caffeine in food to emphasize the lack of laws in place for caffeine in food, as well as the necessity of the implementation of restrictions and research on the effects of caffeine.  Is it really such a great idea to make caffeine so readily available?  It is already posing problems in a liquid form, is it really worth the profit to possibly endanger the health of the youth to add caffeine to food?  Either way, there must be some sort of law and/or regulation in place.  

http://www.latimes.com/business/la-fi-fda-caffeine-20130504,0,2828283.story?track=rss

Same How, Different Whys

What’s wrong with this picture?  Nothing really, just that the democratic donkey and the republican elephant are agreeing on conduct.  So what is wrong with this picture?  If they are agreeing, then why are they glaring at each other?  Well, the cartoonist makes it very clear.  Democrats and Republicans agree on hows.  But the Whys, well, there’s a reason why Gun Control is in blue and Immigration Reform is in red.  Democrats and Republicans may both want strict background checks, limited numbers, and beefed up enforcement, but while the Democrats want them to be implemented for Gun reform, the Republicans want them for Immigration Reform.  Same hows, different whys.  The two parties are saying the same thing, just with different reasons.  So why are they glaring at each other?  

Well, I believe that’s the whole point of this little cartoon.  The two parties are too stubborn to compromise on the one point they actually agree on, because of categorical discrepancies, the very sad thing about party politics. The question left is, will the two parties compromise on this point, or will their separate 'whys' prevent them from moving forward?

Most Earthlike Planets Found Yet: A "Breakthrough"

Aliens, or at least alien organisms, may exist in space, even within the Milky Way.  As Marc Kaufman, writer for National Geographic, reports, the NASA Keplar Mission has found two Earth-sized planets at the right distance from their suns to support life.  This ‘right distance’ as Kaufman defines, is just far enough where water remains liquid, which is essential to life.  According to William Borucki of NASA's Ames Research Center, this is, ‘a breakthrough discovery’.  Scientists call planets like these two ‘exoplanets’, and they are, as Kaufman explains in layman’s terms, planets that are Earth-sized that are rocky or watery or exist in habitable regions in relation to their suns.  But what makes these two new planets so remarkable is that they match all three criteria.  Is this really such a great thing?  Well, according to the quotes Kaufman includes, yes, yes it is, because it is “a very moving moment in humanity's efforts to understand our home planet and the possibility of other habitable planets in the universe”.  

Quotes are great and all, but the fact is that this is all scientific conjecture.  There is a possibility that there is life, and what is important is that that possibility exists.  Through these stars scientists can learn more about our own planet.  Kaufman is informing us of this discovery, and he makes this information clear and to-the point, while also pointing out the opportunities presented by this new data.  Though, he does not mention any sort of downsides or possible negative ramifications of this data, he succeeds in portraying this new data as a breathtaking new discovery for Keplar scientists.  

The White City by Erik Larson

Two great men, existing at the same time, brilliant at two very different things, both handsome and blue-eyed, lived in Chicago in 1893.  One, Daniel Burnham, became the great architect behind the World’s Fair, known as the White City, while the other, Dr. H. H. Holmes, became a serial murderer.  Erik Larson, four times New York Times bestseller, masterfully pieces together the events of that time, with old diary entries, newspaper articles, letters, postcards, and pictures, to tell their story.  

The arrangement of the story in chronological order, switching between Burnham and his associates and Holmes and his psychopathic charms builds the suspense.  When reading of Holmes and his seduction of his latest female victim, a reader cannot wait to see how Burnham will handle his latest challenge in building the fair.  When reading about Burnham one cannot help but wonder as to the fate of Holmes’ latest woman, and whether or not she will live to tell her tale.  Varied sentence structures and abrupt phrases that begin or end a chapter help convey urgency and heighten tensions within the reader.  Larson successfully conveys the history of the two men, their brilliance, and their challenges.