Thoughts and experiences on various topics: puzzles, games, AI, collaboration, music, politics, and whatever else is on my mind

There are many books that have influenced and shaped both who I am and the directions my life has taken.  I can’t mention them all, but I’d like to note a few highlights.

Robots and Artificial Intelligence

The first robot book I remember was Isaac Asimov’s  I Robot,  a collection of science fiction short stories about robots, especially the “Robbie” story.  These stories introduced me to the idea of “intelligent robots”, and had enormous impact on my career choice to pursue studies and work in Artificial Intelligence and Machine Learning.   It inspired me to commit much of my life to what I consider one of the most challenging and fascinating intellectual questions:  What is intelligence, and How does it work.  Studying human intelligence is just part of that quest – a deeper understanding, I believe, is to be gained by discovering the fundamental principles of intelligence (in all its manifestations), and applying that understanding in creating new forms of intelligence, significantly in machines such as computers and robots.

Another brilliant book is Mindstorms by Seymour Papert, my advisor in grad school at MIT.  I highly recommend it to anyone with interests in learning and education, AI, and understanding thinking.   There are many important ideas in this book, but the single one that most deeply influenced me was Papert’s analogy between “artificial intelligence” and “artificial flight”.  He pointed out that studying neurons in the brain in order to understand intelligence, was rather like studying feathers in order to understand flight.  Early attempts at engineering flight tried to mimic bird flight, with mechanical flapping wings.  In hindsight we realized that the fundamental principles of flight involve aerodynamics and not flapping motion or feathers!   The conclusion suggested is that machine intelligence will grow out of a deeper understanding of higher-level principles governing thinking, and not the low-level substrates such as neurons (or electronic gates, for that matter).   This argument convinced me to abandon studying neurophysiology to gain insights into how the the brain (and intelligence) worked.

Douglas Hofstadter’s book Godel, Escher, Bach was another tour-de-force.  He brilliantly weaves together  themes from mathematics, logic, and computation, with Escher’s graphic art, and Bach’s music.  The book is an exploration of self-reference and formal systems.  Truly fascinating!   It had enormous appeal to me, given my diverse interests in math and computation, my passion for music, and  my enjoyment of Escher’s etchings, especially their exploration of visual-spatial ambiguity and self-reference.

Puzzle Books

I have already mentioned how deeply I was influenced by the writings of Martin Gardner, including his many collections of recreational math puzzles and essays.  Another of my favorite “logic puzzle” books is Raymond Smullyan’s brilliant Chess Mysteries of Sherlock Holmes.   It is a book of chess puzzles, but not traditional “mate in 2 or 3 move” puzzles, but rather retrograde analysis puzzles,  where one has to reason backwards from a given position.  Typical questions posed are “What was the last move?” or “Is it legal to castle in this position?”.  The reasoning and logic of these puzzles is incredibly rich and deep, and naturally appealed to me both as a puzzle-lover and a chess-player.  Smullyan brilliantly presents these puzzles in the context of stories about Sherlock Holmes and Dr. Watson, with occasional references to Moriarty thrown in!  A truly whimsical and thoroughly enjoyable presentation of a delightful collection of retrograde chess logic puzzles!

I also loved the Winning Ways volumes by Berlekamp, Conway, and Guy.  I’m totally in awe of the amazing recreational mathematics collected in these volumes!  Deep analysis of many fascinating games and puzzles.  I thought I had become fairly expert at “peg-jump solitaire” puzzles, with my discovery of “macro-operators”.  but I was humbled to discover the depth and breadth of the analysis covered in Winning Ways — yes, they had “macros”, which they called “packages”, but they went so much further in developing mathematical tools for analyzing questions that I hadn’t even thought to pose, as well as solvability of classes of problems.  I don’t know if I’ll ever finish reading all of the collected puzzle and mathematical wisdom in Winning Ways, but it is an amazing menu of “food for thought”!

Spiritual Books

My spiritual journey has been long and winding.  I plan to devote one or more future blog posts to recounting this journey in more detail.  There are a number of books that have greatly influenced my spiritual seeking and thinking.  Will Durant’s The Story of Philosophy  started me thinking about many fascinating questions, including “Is there a God?”.  After reading about the thinking of many philosophers on this issue, I came to the conclusion that there was no way to “prove the existence of a God”.   I particularly esteemed the philosophical approach called Empiricism, which reflected my scientific world-view.  I couldn’t see how any experiments could bear on the question of God or the supernatural, so I concluded that the simplest explanation was that religions and the notion of a “God” were based on myths and fables, with no bearing on “reality”.

In high school and college, I came across Edgar Cayce, famously known as “The Sleeping Prophet”.  The book There is a River by Thomas Sugrue is a fascinating biography of Edgar Cayce.  This book, in particular (which I highly recommend to anyone), opened my mind to the possibility of the reality of psychic phenomena.   I followed up with additional reading in the area of psychic phenomena, and discovered the scientific field of Parapsychology, and the rigorous work of researchers like J.B. Rhine at Duke University.   My thinking shifted dramatically — if psychic phenomena were genuine, and could be scientifically studied, then that pointed (at the very least) to the existence of dimensions of reality, or the operation of physical phenomena beyond our ordinary scientific and every-day experience.  Perhaps these studies would lead to a “science of the soul” .  If psychically gifted individuals can know things at a distance (without conventional sensory observation) and predict the future with results that are statistically significant), then our understanding of sensory experience, along with time and causality, needs to be revised and expanded!

This line of thinking led me to revisit my conclusions about religions and the existence of a deity.  I began reading and learning about the various religions of the world.  I had been raised in Protestant (Lutheran) Christianity, but rejected it early on when I learned about missionaries, and couldn’t see any rational basis for believing that any one religion was “more correct” than any other.  Now I became curious to learn more about religions and spirituality, with the thought that there might be some common core of truth, even if no single viewpoint was “correct in itself”.  Recall the parable of the “Blind Men and the Elephant” that I mentioned in last week’s blog post.  It seemed that a more complete understanding of an elephant could be arrived at by collecting and comparing the various blind men’s reports of “elephant experience”.  Perhaps similarly, a deeper understanding of the spiritual and supernatural could be gleaned by studying and comparing the various teachings, thinking, and experiences of various religious traditions.   I greatly enjoyed the book The World’s Religions by Huston Smith which was an excellent survey and point of departure in my explorations.

The 1960’s and 1970’s were a time of great interest, especially among the youth culture, in Eastern philosophies, traditions, meditation, etc.  The Beatles, especially George Harrison, made pilgrimages to India, and studied and practiced meditation.   I found myself similarly fascinating with “Eastern thinking”.  I especially was drawn to writings about Yoga, which seemed to offer a scientific (empirical) approach to religion.  One was encouraged to “know God” through personal experience — spiritual practice and meditation.  This was very appealing to me — I had always distrusted dogmatic pronouncements (“This is true because we said so”, or “The Bible says it, so it’s true”).   The book Autobiography of a Yogi  by Paramahansa Yogananda (published by Self-Realization Fellowship) deeply influenced me.  It is a fascinating account of Yogananda’s spiritual journey, from his early experiences to his passionate quest to find teachers (guru’s) and to grow spiritually to know God.  It led me to affiliate with SRF, and practice mediation for many years.

Another book that had enormous influence on me was Many Lives, Many Masters by Brian Weiss, M.D.  This is a book by a medical doctor (psychiatrist), steeped in the scientific world-view, who used hypnosis in his therapy practice, and stumbled upon the phenomenon of past-life regression.  His conventional scientific paradigm could not account for the phenomena he was observing, that patients could regress to past-lives through hypnotic suggestion, and that the recollection of past-life traumas could lead to nearly instantaneous remission of present-life symptoms.  Among other things, this book reinforced my growing belief that reincarnation was genuine, and that we have a soul-consciousness that survives physical death – a notion I was first exposed to in reading about Edgar Cayce.  I believe that a truly open-minded scientist must take note when other scientists report observations that run counter to the accepted scientific paradigms.  That is, in fact, the history of scientific progress – initial rejection of data that contradicts the existing paradigm, and later acceptance of these “contradictory facts”, leading to revised or novel theories that account for the “new phenomena”.   Thomas Kuhn’s book The Structure of Scientific Revolutions is a wonderful exploration of this dynamic in the history of science – another book that I recommend highly!

Books on Philosophy

I already mentioned reading Will Durant’s Story of Philosophy, which provided me an excellent overview of the history of philosophy and various flavors of philosphical thinking.  In college, as an undergraduate, I stumbled upon E.R. Emmet’s excellent book Learning to Philosophize.  This book was about how to do philosophy,  and greatly influenced how I view the enterprise of philosophical inquiry.   Memorably, it described the practice of philosophy as one of posing deep questions,  carefully defining terms, and examining alternative proposed answers to questions.   I recall that this book got me to thinking deeply about the meaning of “meaning”, which I find a very fascinating subject (and of course involves my love of self-referential ideas).

Political Books

As with many of my generation, I was profoundly shaken by the assassination of president John F. Kennedy.   I remember marveling at how quickly (within only a few hours) the media were reporting information about the “suspect”, later identified as Lee Harvey Oswald.   It set off a slight sense of doubt as to whether we were receiving the truth through the media, but I ultimately accepted the Warren Commission conclusions at the time the report came out.  It wasn’t until later, in my college years, that I began to seriously explore alternative theories of the assassination.   There turned out to be a remarkable amount of evidence that contradicted the Warren commission, and suggested that the assassination was the result of a conspiracy.   I read many books and articles on this subject (too many to list here), and have concluded that we have been lied to by our government, and that the JFK assassination was an instance of a coup in our home country!   I believe this has profound implications for understanding our country’s subsequent history, including our involvement in Viet Nam, and our more recent “wars of empire” in Iraq and Afghanistan.   For me, the deep questions here include: “Who really controls our government?”,  “Are we being systematically manipulated by our media?”.   I plan to write much more about this in future blogs.

Recently I read two very informative and insightful books about our current political-economic situation:   Corporations Are Not People  by Jeffrey Clements, and Who Stole the American Dream? by Hedrick Smith.    These books opened my eyes to the fact that the decline of the middle class and the rise of corporations is not an accident — it is the result of a sustained and systematic campaign by Corporations to increase their political and legal power and influence.  I learned that the Citizens United decision by the Supreme Court was not an isolated thing, but was instigated by Justice Lewis Powell, who, through the U.S. Chamber of Commerce, advocated for corporations to work together to increase their influence over legislation to obtain corporate-friendly laws.  This all began back in the 1970’s when Powell worked for Nixon, and before Nixon appointed Powell to the Supreme Court.   The doctrine of “corporate personhood”,  as foolish as it sounds, and as famously and ineptly referred to by Mitt Romney in his sound bite “Corporation are people, my friend”,  only developed over time.  The Citizens United decision, further opening the floodgates to unfettered corporate influence on our elections and legislative processes, was just another small step in corporations march to amass vast political power and influence.   If Fox were reporting on this they would call it the War on the Middle Class,  but they won’t, because they are another tool of  corporations, working to manipulate the uncritical populace into further supporting the “job creators” (corporations) — this would be a funny and ironic joke, if it were not so devastatingly real and harmful to those of the “99%” (middle and so-called lower classes), not to mention harming the health of our country, our economy, and our political processes.  We are facing a crisis – and I promise to devote some posts in the future to more deeply exploring these important and critical issues.

Personal Growth

I have tried to commit my life to personal growth – to becoming the best person that I can be.  My commitment is to life-long learning and self-improvement.  I have ready many books in this area, but the single most inspiring and influential book was a work of fiction:   Musashi  by Eiji Hoshikawa.    The story (saga) of Musashi Miyamoto is a Japanese epic set in Feudal Japan, and recounts the life and development of Musashi from an angy, undisciplined, rebellious, and ambitious farmer into the finest and most expert Samurai swordsman in all of Japan.   Along the way he learns that becoming the best he can be is a lot more than developing skill and proficiency in swordmanship.  It has as much to do with humility, respect and love for others, and learning in all aspects of ones life.   I had first encountered the Musashi story through the movies – a series of three movies called The Samurai Trilogy.   I loved the epic story on many levels and saw the trilogy multiple times (I now own the set of dvds), and it was gripping and inspiring to read the book as well!

to be continued …

Notes from the Library

I Love Libraries and Books

Because I love books, it is simply natural that I love libraries.  It is easy to take them for granted, yet they are amazing and wonderful institutions.  In facts, books themselves are wonderful inventions.  Libraries gather together not only books, but other media (magazines, newspapers, reference works, and more recently music and videos).  They represent a vast collection of knowledge, information, and entertainment.  The Name of the Rose by Umberto Eco is a truly wonderful book that completely changed my thinking about and appreciation of libraries!  It is set in the middle ages, when “books” were hand-copied manuscripts, and were not only rare, but valued and treasured.  The book is a murder mystery detective story set in an old monastery.   One of the greatest tragedies was for a library to burn (with incalculable loss of irreplaceable recorded thinking and knowledge!).  I wonder what wisdom and knowledge was lost when the great Library of Alexandria burned?  Fortunately today, with modern printing, not to mention electronic recording and distribution, collected knowledge is not so vulnerable.

Home “Library”

The very first “library” I remember is the collection of children’s books available in our home.  My parents purchased the Childcraft Collection,  which was a marvelous series of volumes including many fairy tales, Aesop’s fables, poems, and short stories.  I loved having this literature read to me, and later reading it for myself!  My favorite poem was “The Highwayman” by Alfred Noyes.  I loved both the tragic story and the incredible rhythm of the words.  One of my favorite stories was the “parable” of “The Blind Men and the Elephant”, which I understood much later to convey a deep truth about perception and knowledge, and the relativity of understanding.  If you are not familiar with it, essentially there are several blind men engaged in heated dispute about the nature of “the elephant” – each argues that he is the only one who truly appreciates and understands the nature of the elephant (one likens it to a wall since he felt the elephant’s side, another to a rope after touching the tail, still another to a snake because he felt the trunk, a tree trunk from feeling the leg, and so on…).  Much later, I came to view this as a parable about various religions, and the silly, pointless arguments about which is the “one true religion” and who “really” understands the nature of God.

Our “home library” also included many Dr. Seuss books.  These were read to me often enough that I soon had them virtually memorized, especially my favorite The Cat in the Hat.  This was perhaps the first book I “read to myself” as I was learning to read.  I loved the rhythm and rhyme, as well as the incredible playfulness of all the Seuss books.   In third grade, we students had on opportunity to order and purchase books through Scholastic Book Club, and I remember acquiring and reading some wonderful books like:  Snow Treasure,  Stranger than Fiction,  and many, many more!

Fifth Grade Classroom “Library”

My fifth grade teacher had an informal library of books and puzzles.  I remember reading a few Tom Swift books she had, and then starting to buy them for myself (my first personal book collection).  Tom Swift stories combined science fiction with Tom’s wonderful inventions and exciting adventures.   Her puzzle collection included Kohner Brothers’  “Hi-Q” peg jump solitaire puzzle.  I loved playing with that, and remember how difficult it was.   Later, in college, when I discovered “macro-operators”, it became easy to solve puzzles like this by thinking about them in higher-level chunks.

Hershey Public Library

I have frequented many public and school libraries over my lifetime.  The first I remember is the Hershey Public Library, which was located in the center of town in the Community Center.  It was not a large library, but I found lots of fascinating material to read both for enjoyment and learning.  My most vivid memory is discovering the “mathematics” shelves, there.  In the Summer after 7th grade, I spent a lot of time reading as many of the math books as I could.  I particularly loved the MAA series, which seemed reasonably accessible, if still challenging.  Here is where I first learned about the various classes of numbers (integers, rationals, reals, and imaginaries).  I remember struggling to accept imaginary numbers because the conventional names suggested “they weren’t real“.   Great food for my growing mathematical appetite!   I also continued to explore science fiction – I remember reading a lot of Heinlein in those days, and I think this is where I discovered Isaac Asimov’s I Robot (and other robot stories).

Hershey Junior High School Library

   I continued to explore SciFi, reading more Asimov (especially loved Foundation Trilogy), Andre Norton, and more Heinlein.  SciFi reinforced my interest and passion for technology and science, and stretched the boundaries of my imagination.  I also developed an interest in biography, reading about many famous people.  I was especially interested in inventors, and the most memorable biography was about Nicola Tesla, the inventor of generators, motors, and alternating current.   Thomas Edison tried to suppress his ideas about A.C. but they have won out in the end. Alternating current is what supplies power to our homes and appliances, though it now needs to be converted back to DC to power computers and charge phones and other devices.

Hershey High School Library

Here I continued to learn all I could from books on math and science.  My favorite magazine was Scientific American, and of course my favorite feature was Martin Gardner’s monthly Mathematical Games column.  There were also a number of collections of Martin Gardner’s essays (Mathematical Diversions), which I also enjoyed tremendously.  Martin Gardner, as I’ve noted in previous posts, had an immeasurable impact on the growth of my passions for math and puzzles.    I also recall reading the various chess books in the H.S. collection – I not only loved puzzles, but board games as well, and developed a strong interest in chess.

MIT Libraries

MIT has a large and distributed library system, spread over many different campus buildings.  The “main library” is the Hayden Humanities and Sciences library.   I spent a lot of time here, since this is where the math books were!  I could also be found at the Barker Engineering library, where more technical books, journals, and proceedings in computer science and artificial intelligence were available.  I also enjoyed the Music library, which has a great collection of classical sheet music which I took advantage of to feed my music (piano and keyboards) passion.  I was proud when my first technical publication appeared in the MIT math journal collection:  Greene,C., and Iba,G., “Cayley’s Formula for Multidimensional Trees,” Discrete Mathematics, vol. 13, no. 1 (1975), pp. 1-11.  A tiny drop added to the “sea of knowledge”.

Lexington’s Cary Memorial Public Library

We residents of Lexington, MA, are fortunate indeed to have a truly wonderful Public Library.  I am particularly fortunate in that I live (since the divorce and move in 2002) just 1 block away from the library, so I can walk there very easily.  Cary Library has an incredible collection of resources:  books, magazines, newspapers, electronic data bases, eBooks, videos, and CD’s.   Even better is the fact that Cary Library belongs to the Minuteman Regional Library Network, and offers the ability to do a single on-line search of all the libraries in the system, and then request materials, which get delivered to the local library!  A tremendous resource and service!   I found that there are numerous advantages to borrowing books instead of buying them.   Buying books has the advantage of (sometimes) getting them more quickly, and providing as much time as you care to take to read them (but this is also a disadvantage, as I will describe).  The downside is that purchased books accumulate and take up space (I’m a pack rat, and have difficulty casting off possessions, especially books).  It also turns out that many of my purchased books sit around unread for the longest time (I estimate that I’ve only read perhaps half of the books I’ve collected over the years).  Borrowing from the library has a built in “deadline” for reading it — the “final” due date (when there are no more renewals possible).  Sometimes I don’t finish a book before I have to return it, but I then simply request it again.   For popular books (especially new releases) there are no renewals because there is a waiting list, and I often have to wait weeks or months before a request finally arrives – but there are always other things to read (and lots of other things to do!) in the meantime.

Being a music lover (especially of the blues), I have purchased a rather extensive CD collection, but I simply cannot afford to buy all the music I’d like to.  The library, especially through the Minuteman Network of libraries, gives me access to a broad and rich variety of music on CD.  It lets me explore new music (new groups, new albums and songs) to see what I like and then only purchase that which I truly love.  This all gives an entirely alternate and new meaning to the title of this post:  “Notes from the Library”. 

The staff of Cary Library are dedicated, professional, and friendly.  I am profoundly grateful to have Cary Library as a convenient resource, and personally wish to thank all the staff for their hard work and service!

[… hey, looks like I finished this week’s post a little earlier for a change, and managed to keep it a little shorter!  Stay tuned for next week…]

Divorce 2002

If my first years at MIT were peak years in my life, then 2002 was an absolute low.  In June of that year, my (now-ex) wife divorced me.  It came as a shock, though in hindsight perhaps I should have seen it coming.  The first clue was in 1998 when my wife received her inheritance following her father’s death.  One of her first reactions was to tell me “Now I don’t need you any more”.   More evidence that economic issues were the basis of our relationship in my wife’s mind.   I couldn’t believe she would think like that – even if she didn’t need me economically, there was a very strong reason to persevere in the marriage (difficult as it was) – specifically our co-parenting our 3 children.   I was always committed to trying to work things out in our marriage, and we tried lots of counseling  (individual, couples, and even couples groups).   In fact on the morning of June 6, 2002  (a day that will live in infamy in my memory, and ironically D-Day, where D could stand for “divorce”), my wife and I were (so she told me) scheduled to meet a new counselor to try working again on our marriage difficulties.  It turns out there was actually no therapy appointment, rather I was set up to have “divorce papers” served on me at that time.  I was totally shocked, and terribly upset at being lied to.  The old cliche is “it takes two to make a marriage work”, and apparently my wife wasn’t committed to that.  I remember telling my boss at Gensym (back in 1998) that I might be facing a divorce, given my wife’s attitude.  Fortunately, it didn’t happen at that time.  Later I learned that she in fact had consulted lawyers, who advised her that divorcing me immediately after receiving her inheritance might lead to her having to split it with me.  So she waited, but (whether consciously or not)  made my life miserable – perhaps trying to push me into divorcing her.  That wasn’t going to happen, because my children were so important to me that I’d put up with almost anything to maintain my close relationship with them. During those intervening years she used her inheritance money to hire an architect and have extensive renovations done on our house – which was enormously stressful, and included our having to move out to an apartment for 3 months.

I became extremely depressed that day (June 6, 2002) and the weeks following.  I was walking around in a daze.  I had to scramble to line up a lawyer to represent me, and I was in fear and terror of what would happen to me.   I didn’t know where I’d be living, how I’d manage economically (I was still working part-time at MIT, but that was more for tuition savings than for income), or most importantly what would happen to my relationship with my children (2 of which were still living at home).  I quickly sought out a psychiatrist and got on medications to help me cope.   Throughout this time, my son Aaron, who was then at MIT, tried to reassure me that this would all turn out to be a blessing in disguise, and that I’d end up much happier being out of the unhappy marriage.  He was absolutely right!  In just 2 or 3 months I was changing my attitude and looking at the bright side of things — something I learned particularly from my mother!   Fortunately I didn’t have to move out immediately (my wife intended to stay in the house), and most importantly I found a terrific lawyer that I really enjoyed working with.  Unfortunately, my 50th birthday (July 4, 2002) came at a time when I was not in any mood to celebrate, so I felt I missed out on marking that half-century milestone.

By October, I had found a great (if expensive) apartment in Lexington, very near the high school  — great for my son, David, who was attending LHS at the time.  Once I arranged for the apartment, David committed to moving with me — the choice was entirely left up to him.  I was very grateful to have the opportunity to live together with him during these years.   Sadly, if understandably, my daughter Rachel stayed with her mother, and I felt a sense of loss for the reduced level of our interaction and relationship during her middle school and high school years.  Of course I saw her as much as I could, but I missed seeing her on a daily basis, reading with her, working on homework together, and simply kissing her goodnight when she went to bed (I used to sing her lullabies when she was younger).  She occasionally stayed over, but that was inconvenient for her – since all her “stuff” was at her mother’s house.

The divorce turned out much better than I had feared.  We finalized an agreement in November, 2002.  I got enough of a settlement (mostly for my half of the equity we built up in our house) that I was ok financially — turned out that by living frugally, I could live off the income from investing my assets.  Most importantly, I didn’t have to pay alimony, and my ex-wife assumed responsibility for the kid’s college expenses (something that had always been promised by her father while he was still alive, so we had never “saved for college for the kids”).

Freedom

I discovered the joys of freedom!  I was free of the constant stresses of a difficult marriage.  Economically free to pursue any career directions I wished.  And free to seek out new relationships.

I started dating even in the Fall of 2002, as I was getting re-settled and finalizing the divorce.  At first it was difficult to re-accustom to the dating world, but I ended up trying on-line dating (JDate), and met quite a number of very interesting women, some of whom are still friends.  No, I was not a JDate “success story”, but I ended up in a wonderful relationship with someone I had known even before the divorce.  But that was a bit later (2006).

In Summer 2003,  I decided to have a “make-up” celebration for my 51st birthday (half-century + 1).   July 4, 2003 was a very special Independence Day!  I was much happier by this point, and invited a number of my friends and former classmates and colleagues to celebrate with me.   We had lots of food, and had an open musical “jam session” in our back yard.   I had written a number of songs during this period (song-writing and music helped me in dealing with the emotional roller-coaster of 2002).  One of the songs I wrote and shared at the birthday bash was “Losin’ the Blues”.  I was always a blues fan, but I was feeling so happy those days that I felt it was more difficult to play and write blues songs.  I plan to share that song and others on my web site, but I’m not quite there yet, so look for it in a later post on “Musical Notes”.

Self-Employment

My part-time teaching at ESG/MIT ended in 2004.  After that, aside from the occasional consulting gigs, I devoted myself to working on my game and puzzle interests, as well as trying to get back into basic AI research.   While there are many advantages to working at home, I missed having colleagues and co-workers to interact with (and learn from!).  I started seeking out collaborators, but it was hard to find them — academics were wary of “working with someone who technically was un-employed”.

In 2008, I went to the G4G8 conference, the 8th Gathering for (Martin) Gardner.  I neglected to mention in my earlier posts how deeply I was influenced in high school by Martin Gardner’s famous Mathematical Games column published in Scientific American.  I was so glad our high school library subscribed to  Scientific American, and I, like many other budding mathematicians and puzzle lovers, voraciously read Martin Gardner’s column every month!   The G4G8 was the first “Gathering for Gardner” that I attended. It was an expensive trip for me to go to Atlanta for nearly a week, but I’m so glad I did.   It was great to see old math and puzzle friends, and to also find new ones!  These gatherings bring together people interested in the areas of Recreational Mathematics, Puzzles, and Magic (all interests of Martin Gardner’s).   Thank you, Martin, for fanning the flames of my puzzle passion, and introducing me to so many fascinating mathematical topics!  I’m sorry I haven’t been able to meet you in person and thank you directly for all your inspiration.  At G4G8  I presented a short talk on my Target Tiling video game inspired by Tetris.  Another major highlight of G4G8, for me, was meeting an editor from Sterling Publishing, who encouraged me to submit a proposal for a puzzle book based on my Round Trip puzzles.

Becoming a puzzle book author

I submitted a proposal, which was eventually accepted, so I set to work producing several hundred Round Trip puzzles, for the book.  Although I had a computer program that generated these puzzles, I still needed to hand-solve every one to rate it for difficulty.  There were also some nitty-gritty technical issues surrounding creating images for the puzzles — turned out that screen-grabs were not of sufficiently high-quality resolution (I had modified my program to automatically display and do an image capture of puzzles).  So I turned to postscript, and learned how to do rudimentary postscript programming – I was totally surprised to discover that (.ps) postscript files were simply text files with postscript commands in them and that postscript was simply another programming language.  I learned enough postscript to format .ps files of my Round Trip puzzles, and then wrote a program to automatically generate all the .ps images in a batch!   I was fortunate that my editor (or one of the publisher’s departments) was willing to do the layout of the images – that saved me a lot of work.   I did write a 20-page introduction which you can find on my web-site just under the Round Trip Puzzles book icon.   Turned out the publisher only wanted a 4-page intro, so I put the full (what I call “expanded”) intro on my web page.   After nearly 3 years, my book Round Trip Puzzles finally appeared in January 2011.   I personally think these Round Trip puzzles have the potential to be the next Sudoku and KenKen puzzle success phenomenon — and they fill a niche of logical-spatial-geometric puzzles, which I personally (being a spatial-visual thinker) relate to.  I have tried to publish a column (for free!) in various newspapers, but with very limited success thus far.  With the right promotion, I truly believe these puzzles could take off!  I should mention that Scott Kim’s “Brainteasers and Mind Benders” Page-a-Day calendar  typically includes 12 or 13 of my Round Trip puzzles, and in some years the variant One-Way-Trip puzzles.

Becoming an iPhone game developer

When my book appeared, I mentioned to my son, Aaron, that it might be cool and fun (as well as good business promotion for the book and the puzzles) to make an iphone app based on my Round Trip Puzzles.  Aaron liked that idea, and said he’d be interested in learning how to program for iphone.  I originally was hoping to learn from an iPhone app development mini-course through MIT’s IAP (January intersession), since such a course had been offered through IAP in 2010.   Sadly, it wasn’t offered in 2011, but Aaron said we could learn on our own — he is an amazing programmer (and I consider myself to be a rather accomplished programmer), and in addition he has unbounded confidence (which I lack).  So he relocated to Boston (from San Francisco) for the month of January and we started our intensive self-study in Objective C and iPhone programming.  It took us a good month just to learn how to do basic graphics display – it really shouldn’t be that hard, but it was!  We both signed up as Apple developers to get the iOS programming and development tools.  By April, we had a working rough draft of our app, and published it on the iTunes store!   The graphics and interface were all pretty rough, but the game was pretty solid and playable (and, I think, fun!), not to mention free!  We initially got a rather negative review based on the low production quality, despite our having announced it very clearly as a rough draft with the intention of getting user feedback.   There was a 2nd review that was much more encouraging – it pointed out that the app was only a “rough draft” and that the puzzles and playability were quite good!

We decided to name our app Monorail.  By April we were ready to launch our 1.0  official first release, including 50 free puzzles, and 350 more puzzles available through In-App-Purchase.  With the help of some TapJoy promotion, we had 500,000+ downloads during July, and reached the top 10 in educational puzzles (briefly #1 in a few foreign stores) in many international iTunes stores.   Actual paid sales were a small fraction (maybe 1%) of the free downloads.   Downloads (and sales) slowly tapered off as the promotion ended, but by January 2012, we passed the 1,000,000 (free) downloads milestone.  Reviews have been overwhelmingly positive, with many reporting that “Monorail is addictive”.

I truly enjoyed collaborating with Aaron.  I know myself well enough to realize that I would never (and I never say “never” lol) have been able to create Monorail on my own.  It was another important lesson in the power (and enjoyment) of collaboration!  I really loved working with Aaron on this project, and wished our collaboration could have continued, but he decided to return to his ambitions of creating new startups (he had already created one startup, called AppJet, and sold it to Google several years earlier).

I was fortunate to find some new collaborators who helped me update Monorail .  In November 2011, we released a version that included ads in the free version, and 2 additional puzzle packs for In-App-Purchase (total of 880 puzzles in all).  Also made some improvements to  the graphics and interface.  Unfortunately, the updated Monorail did not generate enough revenue to keep them involved, so there has not been an update in well over a year.

Experiencing a “Glennaissance”?

Over the last few years, I feel I’ve been entering a period of my life where I’m (once again) becoming more productive, happy, and energized with my various creative and “work” endeavors.  I quote “work” since so much of it is truly great fun.  I’m trying to follow Steve Job’s mantra of “Do what you love!”  and have been fortunate of late to be able to do that.  The things I love are:  puzzles, programming, math, and music.  I’ve been doing a good bit of each, lately.  I even started performing (keyboards and singing) again at local Open Mics this last year.  I had written a lot of songs over the years, many of them in the period following my divorce — which was a period of intense emotions (both negative and positive), and it’s been fun to share those in small supportive venues!

My search for collaborators, though on-going, has led to a number of fruitful joint-endeavors.   I loved the iphone puzzle app Relix, which I think is wonderfully challenging, so I was very excited to be able to work with the developer in producing a sequel Relix 2 (which I like even better).   I contributed a pack of really hard puzzles for Relix 2, called the Iba Insanity Pack.  I would love to hear from anyone who has solved , or even attempted(!), any of those puzzles.  They are not for beginners (fair warning!).

More recently, I’ve been enjoying working (as a level-designer) on a new iPhone puzzle app that should appear sometime this Fall.  I’m also working with one of my brother’s former students on an Android version of my Target Tiling (Tetris-inspired) game.   It would be great if some of these ventures turned out to be financially rewarding, but even if not, I’ll be very happy to share some of my puzzle ideas with a broader audience.

I still hope to get back to AI research, but to make significant progress, I know that I need collaborators to work with.  I’ll be blogging about my AI ideas in future posts, so stay tuned, and if any of the ideas interest you, please get in touch.

Whew! 

Made it through a first-pass overview of my life.  Seems it mostly focused on career, with other elements tossed in.  There are lots of life arcs I’d like to review and share in more detail, including my spiritual journey, more on parenting, more on my childhood / family environment, and the books that I’ve found particularly influential over my lifetime.  I also plan to elaborate on my philosophical explorations and thinking, my ideas on AI research, programming environments (programming should be much easier!),  puzzle explorations, my musical creations, and may even venture into my personal political thinking and viewpoints.  Lots to write about — I should be kept busy for a long time!

Software Developer (Gensym 1997-1998)

In January, 1997 I found a job as a software developer (programmer) at a small company called Gensym (not to be confused with the biotech company Genzyme!).  Gensym developed an expert system product called G2, which it sold to large corporations.   Gensym did its internal development in CommonLISP, which was great for me since that was (and still is!) my primary language.  Deployment was done in the C language, with cross-compilation for multiple platforms.  To accomplish this, Gensym used a Lisp-to-C translator (created by a different company).  I was hired to work with my boss (who was an amazing and accomplished programmer, as well as an all-around great guy!) on building Gensym’s own in-house Lisp-to-C translator.  Of course this translator was being written in Lisp, and I learned a lot of C in the process of working on the project.  This was not research by any stretch of the imagination, but it was technically challenging and interesting work, and I really enjoyed collaborating with my boss, who had designed the translator, and actively worked with me on the implementation.  I learned a lot, and enjoyed the first year immensely!

Unfortunately Gensym started to hit trying times, and after a year my boss became disillusioned with management and chose to leave.  This left me in a quite difficult position – I was not fully up to taking over the translator project on my own, so I ended up transitioning to a more “standard” developer position – doing bug fixing and feature coding directly on the G2 product.  I was not particularly good at this, and my heart was not in it, so my second year at Gensym was a downward spiral, ending in my own decision to leave later that year.

Free-lance puzzle consultant / designer (1998-2001)

I had tried teaching (Hampshire), research (GTE Labs), programming (Gensym), but now I had thoughts of pursuing my puzzle passion as a free-lance consultant and puzzle designer.

I had tried to sell a few puzzles in the past, but only in passing, and not very successfully.  My first non-trivial sale was modifying and licensing a “sliding block puzzle” search engine that I had developed for fun over the preceding years.  ThinkFun (maybe it was still named Binary Arts at that point), used my search engine to analyze and design puzzle levels for their Railroad Rush Hour puzzle (a sequel to the enormously popular Rush Hour puzzle by Nob Yoshigahara).  I ended up designing a few levels myself, and received a credit on the box of the game when it was released (though you might need a magnifying glass to find it).

In the early 1990’s I also published a few puzzles in my favorite puzzle magazine: Dell Champion Variety Puzzles  (sadly now out of print).  I started with RoundTrip path puzzles, invented and first published by a puzzle author using the name Stitch (that’s his “nom” in the National Puzzler’s League, which I later also joined – my “nom” is now Macro).  I started out simply writing (for fun!) a program to solve RoundTrip puzzles.  It then occurred to me that I could use the solver as part of a puzzle generator.  As often is the case with fun “hacking” projects, I kept adding new features and variations.  I had the idea of changing the geometry from square/rectangular to triangular/hexagonal.  You can see samples of my puzzles (under the name Grand Tour) on my web page:

http://glenniba.com/grandtourhexbranch/GrandTour.html

I wasn’t sure if I could publish Round Trip puzzles in Dell Champion Variety Puzzles (or any other magazine), since I did not originate the puzzle idea itself, not to mention possible issues with using the name “Round Trip”.   When I contacted Dell about this, they aid it was fine, and encouraged me to to send in puzzle submissions.  So I sent them a batch of both square and hex RoundTrip puzzles.   Later I sent in some “Dominoes Logic” puzzles (both square and hex variants) that I wrote a different program to generate.  These puzzles began to appear in the magazines.  The most amazing result happened once while I was on a plane trip:  I was reading the In-Flight magazine, and I saw a Round Trip puzzle!  Wow, I thought, someone else published a round trip puzzle!  Then I looked, and the name was mine!   Turns out Dell had “re-printed” my puzzle in the flight magazine.   I didn’t know they could do that, but I didn’t complain — I was pleased to have my puzzles (and name) getting “out there”.

Later I implemented a suggestion by Scott Kim to generate One-Way-Trip puzzles which feature a start and end vertex for the path.  In both types of puzzles the object is to create a path that connects all the dots (vertices) visiting each dot once and only once.  For RoundTrip puzzles the path is closed, i.e. it returns to its “starting point” (though you can start anywhere – it makes 1 big loop, or a “Grand tour”).  For OneWayTrip, the solution path starts and ends at the specified dots.  My program that generates these puzzles ensures that there is always only 1 solution.  The link to the OneWayTrip Java applet on my web site is:

http://glenniba.com/OneWayTripHexBranch/OneWayTrip.html

The JavaApplets provide puzzles of different sizes and both square and hex geometries.  They range from simple to very challenging!

Jumping ahead a bit in the story, I later wrote a book titled  Round Trip Puzzles  (published through Sterling in January 2011).  Following that, my son, Aaron, and I wrote an iphone logic puzzle app called  Monorail, which has hundreds of challenging puzzles of this type (it’s also out for Android, now, too – but be sure to look for the Monorail by IBA Puzzles  (there is another app that uses the same name, which only creates confusion).  Here is link to my Monorail puzzle page (where you can find info about both the RoundTrip puzzles book and the Monorail apps for smartphones):

http:// glenniba.com/monorail/

Making a puzzle variation of Tetris  (1989-present)

Another puzzle project I worked on, which I consider potentially my “magnum puzzle opus”, is an extremely challenging version of the game, Tetris.  When Tetris appeared in the late 1980’s (I’m thinking I first started playing it on MacSE’s around 1989),  I was instantly addicted.  I loved the music (Russian folk songs) and the video game challenge (play as fast and survive as long as you can).  At GTE Labs we actually formed a Tetris Team and I remember we had a “match” against the team from MITRE Corp.  Great fun.

I wasn’t satisfied to simply play the existing version(s) of Tetris.  I wanted to try out my own variations.  So, naturally, I started programming my own Tetris simulator, first running on Lisp Machines, and later on Macintoshes.  With my own simulator, I could vary all sorts of game parameters such as:  board width, and height, piece set (no reason to limit pieces to size 4 — I supported both smaller and larger pieces, too), speed of the game.

While playing around with this simulator, I came up with a novel challenge:  to wipe out the Tetris board, i.e. eliminate all the cells (in Tetris, if you don’t already know, filled rows are cleared and the cells eliminated – then the rows above any cleared rows will move down to take up the cleared space).  The typical objective in playing “normal” Tetris is to survive as long as possible, and score as many points as you can.   This wipeout idea provided novel puzzle challenge, and it appealed to me because it was something you could succeed at (rather than postponing inevitable failure when the board fills up).  I implemented this in my simulator, and discovered that it was great fun (for me at least) and required developing a variety of skills to become “expert” at it.   Accomplishing a wipeout is extremely difficult in original Tetris, or any variation where the next piece is chosen randomly.  This makes it nearly impossible to plan ahead more than just 1 or 2 moves.  I decided to make the moves deterministic (predictable) and this worked quite nicely.   I found that even the simplest deterministic variant (where there was only a single piece type, and you’d always get that piece, was both interesting and challenging.  My experiments led me to discover that board widths 5, 6, and 7 led to interesting and enjoyable puzzle challenge, especially combined with a choice of piece such as “T” or “L” (2 of the standard Tetris pieces).  I also introduced the idea of piece reflection, which makes solving easier or harder depending on whether or not reflection is permitted.

I came to think of this Tetris-inspired puzzle challenge as a kind of Dynamic Tiling Problem  which generalizes the static tiling problem of filling a specified area with a particular set of piece shapes (eg. fill a 4×4 square grid with 4-cell T pieces, or fill an 8×8 grid with 4-cell  L pieces).  In dynamic tiling the Tetris row-clear operation dynamically modifies the fixed cell pattern.

Later, when the Nintendo Entertainment System (NES) came out, it included Tetris, and I spent numerous hours (really should be measured in months and years) playing the game.  One day, in order to challenge myself, I posed the question (remember: – Think deeply about simple things!):  Might it be possible to fill cells on the Tetris board that are not supported by (or connected to) other cells or the base of the board area?  This amounted to what I call “floating a piece (or set of cells) in mid-air“.   It is natural to think that there is a kind of gravity operating in Tetris, especially since pieces fall (slowly at first), and you can drop pieces (in which case they keep falling until some part of the piece lands on some kind of support (another fixed cell, or the base of the board).  But deeper examination revealed that row-clearing obeys a different rule of gravity.  The row-clear rule is that all the cells above a cleared row (or rows) move down exactly a number of cells equal to the number of rows cleared below them.   So, in particular,  cells above a cleared row do not keep falling until they touch a support!   So naturally, I set about trying to prove that this was possible, and before long I had learned how to create a platform (rows that could be cleared by placing a piece in a hole in the platform), and could use the platform to support a piece – and then, after clearing the platform, have the piece placed on the platform end up “suspended in mid-air”.

I used this discovery to create a Target variation of my Tetris-inspired puzzle, and I named it Target Tiling.   Instead of an empty pattern (wipeout)  being the target, why not have certain cells  (Target cells) be marked (specified) and have a more general objective of contstructing that exact target pattern  (each of the marked target cells must be filled, and no other cells can be filled).   This provided for lots of additional challenge, including the goal (in some target patterns) of suspending cells in “mid-air”.   I considered names such as Tetris Architect  (because you are building up a target pattern according to a “blueprint”), and also Tetris Target,   I think has a nice ring to it, but finally settled on Target Tiling because the name Tetris is trade-marked.

There are many levels of skill one can acquire in mastering Target Tiling.  At the low level there is learning to use the controls to manipulate and place pieces.  Then there is the standard Tetris skill of filling and clearing rows, and uncovering covered “holes” so they can be filled.  Then there is the “working down” skill — reducing a board pattern of many rows to a simpler one with just 1 or 2 rows left.  Next comes the skill of wiping out the board,  which requires much more precise play, and often involves learning lengthy sequences of “moves” (macro-moves)   that accomplish desired transformations, simplifications.  Finally there is the target constructing skill which requires building up the target pattern row by row, and finally wiping out any excess above the top target row (carefully!  since you don’t want to mess up any of the completed target rows below!).   I think this game would be ideal for teaching meta-skills for learning and problem-solving.   I also believe it would make a great testbed for AI skill learning research, and I hope to work on this sometime soon!  If  this sound interesting to you, please contact me (giba@alum.mit.edu) — I’m always looking for collaborators!

You can actually try out my game!  I have posted a free version of Target Tiling on my website, and you are welcome (actually encouraged!) to play it and let me know what you think.  The game has evolved, and now includes a 3D version with 3D pieces.    The latest version has piece sequences (some pre-set for you to choose among), but also lets you pick your own piece sequences.  You can use randomized Start and Target board configurations, or you can edit either or both of these patterns to try out your own specific patterns.  You can find information about the game, the CommonLisp source code, instructions on how to run it, and lots of puzzle challenges,  all at my web page:

http://glenniba.com/target-tiling.html

I made a serious effort to sell this game idea to The Tetris Company, back in the mid-to-late 90’s, but ultimately they turned me down.  Later I tried again with Microsoft, after I learned that Alexey Pajitnov (the original creator of Tetris!) worked there, but sadly, he left Microsoft before we could get a deal in place (even before I got to show him the game, which I would still love to do – does anyone know how to contact him?)

Full Circle – back to ESG at MIT, now as an instructor! (2001- 2004)

In 2001, my oldest son, Aaron, enrolled at MIT as a freshman.  I wasn’t making enough income from my puzzle work to be self-sustaining, so I contacted ESG and was able to get part-time employment there as a Lecturer (staff instructor).  This did not pay particularly well (think adjunct salary level), but the major benefit was that we got a pro-rated tuition break on my son’s MIT tuition, which was a huge financial plus!   I mostly was teaching basic math courses (calculus), but got to teach the occasional seminar on puzzles, or AI/Machine Learning research.  I even got to work with some undergrad students on UROP (Undergrad Research Opportunities Program) projects.  This was great fun, and I enjoyed being closely connected with both ESG and MIT once again.

to be continued …

Next up:  Divorce (2002) and freedom!

GTE Laboratories (1985-1996)

In July of 1985, I started work at GTE Laboratories in Waltham, MA.  I was a Senior Member of Technical Staff in the Self-Improving Systems (Machine Learning) department of the Fundamental Research Lab.  Our mission was to do long-term basic research in machine learning.  This was exactly the environment I wanted to be in – I had stimulating colleagues who shared my interest in machine learning (many of them were PhD’s), and the opportunity to “think deeply about simple things”, i.e. perform basic research.

This was the ideal environment in which to continue my research on “discovery of macro-operators in problem-solving”.  I had begun thinking about this topic back at CMU, when reflecting on how I learned to solve the Rubik’s Cube.  Rich Korf did his Ph.D. thesis on an algorithm for filling in a table of macro-operators to solve certain types of puzzles, such as Rubik’s Cube and the 15-puzzle.  I was thinking about a more general and more heuristic approach where macros would be proposed and then evaluated on the basis of their contribution to improved problem-solving performance.  I continued thinking about this, and began writing some programs to encode my ideas, while teaching at Hampshire.  I wrote and published a paper in IJCAI-85 describing my initial ideas and results.  Because this paper was submitted near the end of my time at Hampshire, and because I anticipated starting work at MITRE, I listed MITRE as my affiliation in the title of the paper.  This turned out to be ironic, when I ended up working at GTE Labs instead.   I was happy that GTE supported my attending IJCAI in Los Angeles that year.  It was my first conference paper and presentation.

The basic idea of my work was based on the informal observation from Rubik’s Cube solving, that it was necessary to (temporarily) undo progress, i.e. subgoals achieved, in order to achieve new subgoals, and ultimately solve the puzzle.  The idea of a macro-operator is a sequence of moves that can transform the puzzle state in a predictable way.  One simple example is to permute the positions of 3 cubies (1x1x1 puzzle elements) but leave everything else unchanged.  Lots of other things get changed (messed up) during the macro, but when the sequence is complete and the dust (mess) has cleared, everything is back where it was except for the 3 cubies that played “musical chairs” (changed their positions).  The learning problem that interested me was how these macros could be discovered  by a program.  My basic approach was to use a heuristic evaluation function  to count the number of subgoals achieved.   My program examined sequences of moves along paths down the search tree, and looked at the values of this heuristic function for each puzzle state encountered along the paths.  Typically, in puzzles like Rubik’s cube, there would be peaks and valleys along the path.  Peaks were where more subgoals were satisfied than in the states before and after.  The valleys corresponded to undoing subgoals, and it seemed natural that the sequence of moves spanning a valley (extending from one peak to the next peak) would be a good candidate for a macro, especially if the 2nd peak was “higher” than the first (meaning that overall progress had been made).

I also made a representational commitment to having macros be described in the same format as primitive problem-solving operators (e.g. the basic twists of Rubik’s cube).   I represented all operators (both primitives and macros) in terms of the change of state they entailed – specifically, in terms of a before and after state.  These were actually partial states, since they could have some parts of the state specified as not relevant (I called them “don’t cares”).  The principal advantage of this representational commitment is that the problem solver does not require modification in order to use additional macros!  The problem-solver (performance system) simply used a set of available operators, and if new macros were found, they could be added to the operator-set and could then get used like any other operator.  The main performance gain  resulting from learning macros is that the search can take larger steps in the problem space, since each macro actually involves multiple primitive steps.

I like to think of this as a chunking model of skill acquisition, with macros being larger chunks defined in terms of simpler chunks.  Chunking is a well-known and well-studied psychological phenomenon, and is the source (imho) of human ability to deal with complexity.   In order to represent my macros in the same format as primitive operators, I needed to compile a sequence  of steps into a single step, which involved analyzing the before and after states spanned by the sequence.  In addition, this compilation process produced a macro-definition or expansion, which could allow any solution found in terms of macros to be expanded into a solution using only primitive operators.  Primitive operators were distinguished and recognized by the fact that their definition (expansion) was empty (NIL).   In fact, macros could be learned in terms of other macros leading to a definitional hierarchy.  One final advantage in my approach was that learning could take place during problem-solving, even before a solution was found.  My first program only learned macros from analyzing a completed solution path, but I later generalized this so that macros could be learned from any path in the search tree, even before a solution was discovered.

I continued to work on this heuristic macro-learning project while employed at GTE Labs.   My work led to the publication, in the prestigious Machine Learning Journal in 1989, of my paper “A heuristic approach to the discovery of macro-operators”.  I am indebted to my friend and editor, Pat Langley, as well as my GTE Labs colleague, Oliver Selfridge, for invaluable help in finishing and publishing this paper.

Sadly, the ideal research environment I experienced at GTE Labs was time-limited.  After my first year there, GTE decided to eliminate the Fundamental Research Lab, and focus on more “applied research”.  This presented a challenge to all of us in the Machine Learning department. Fortunately, we kept our jobs, but the department was moved into a more applied  “Computer and Intelligent Systems Lab”.  We continued to work on machine learning, but there was much greater pressure to apply it to GTE telephone or other operations.

There were (at least) 3 different machine-learning projects within our group, and they had been pursued fairly independently by 3 different researchers.  We were directed to work on ways to integrate these disparate learning approaches (Decision-tree Induction,  Rule-based Learning, and Macro Learning) into a unified project.  We struggled with how to do this, but in the end we succeeded in creating the ILS (Integrated Learning System), in which each learning method both proposed actions to take (performance system) and tried to improve it’s own behavior (using it’s individual learning system).  The integration involved a TLC  (guess who proposed that acronym?) which I called The Learning Coordinator.  The TLC would collect action proposals from each sub-system, and distribute all the proposals to each sub-system.  Then each sub-system would give a rating to each proposal, according to how well it thought the proposed action would work out if actually performed.  These ratings (numerically weighted votes) were collected and averaged, and the highest rated action would be performed.   The results of the action (the next state of the world or environment) would be made available to each sub-system for use in its own learning.  This seemed to me like a fairly simple idea, and it was the only one we implemented – it was actually a fallback from discussions and proposals we had for much more complex systems, but we never got agreement or traction on implementing the more ambitious proposals.

I thought this ILS framework was an interesting idea that merited further develop, and quantitative analysis.  I proposed experiments I thought we should do to see how much benefit arose from the collaboration of the different systems.  I was already a fan of collaboration from my ESG experiences at MIT.   There are careful and (to me) obvious experiments that could be done to measure and compare the learning of each sub-system (in isolation) with its learning in the context of joint collaboration.  It’s not clear whether the ILS would outperform the individual systems working alone, but there are at least 2 reasons for hope:

1.   With 3 alternate actions to choose from, one hopes that the action chosen would often be better than that proposed by any single sub-system.  This is simply a performance issue, and does not rely on learning.

2.   Each sub-system (agent) would likely see actions taken that were different from its own proposal, and this should expand its learning opportunities.

My proposed experimental framework was fairly simple:

a.  Define a performance metric to  evaluate performance (on a simulated, thus repeatable, world)

b.  Use the performance metric to evaluate Agents, and the ILS as a whole, with all learning turned off.  These provide the baselines

c..  Have each Agent perform and learn in isolation, and also have the ILS system as a whole perform and learn.

d.  Finally evaluate the performance of each individual Agent, and the whole ILS, again with learning turned off.

The interesting questions to me are:

1. How much learning occurred within individual agents?

2.  Did the ILS ensemble learn more than the individual agents on their own?

Learning would be “measured” as the difference in performance scores.

It saddened me that my colleagues resisted doing these experiments.  I understood this to be for “political reasons” – the expressed concern was that failure (if the system didn’t learn well) was viewed as much more dangerous than any success.   I hated this attitude – which strikes me as unscientific (“I’d rather not find out at all, than find out I was wrong”?).   In case you haven’t figured this out about me,  I deplore politics, especially when it impedes progress.   I still think these experiments would be interesting to perform (perhaps using different agents), and maybe I’ll get back to them someday …

Oh yeah – one other ironic note:   Our team was nominated for and received a prestigious corporate award for our work!  It makes a great resume entry under Awards and Honors:

      GTE’s Leslie H. Warner Technical Achievement Award, May, 1992, for work on the Integrated Learning System. The Warner Award is GTE’s highest technical achievement award.

We got a chunk of money to divide up, and an all-expenses paid trip to New York City for the award ceremony (presented by the CEO of GTE).  We also got several publications out of the work.  But sadly, I don’t think it has had much impact on the world – unless someone read our papers and found a way to use the work.  My colleagues, being risk-averse, would not consider trying to deploy the system anywhere within GTE – the dangers of failure outweighing any possible benefits.

This all strikes me as Dilbert-esque.  Maybe there’s a good reason – Scott Adams worked at PacTel (another telephone company), which provided him with experiences that feed into his Dilbert comic strip.  GTE exhibited nearly all the craziness on display in Dilbert, and sadly there is more truth behind it than you’d expect!   At GTE we had frequent reorganizations, during which little work got done.  We had management that seemed to hinder, rather than support our work.  There was a tendency to avoid doing things that could lead to “visibility” and/or “perceived failure” (no such thing as a successful negative result – in science finding out that something didn’t work can be a step of progress toward finding something that does work – but at GTE, if it didn’t work, you were a failure, so better not to try things).

My career at GTE Labs came to a crashing halt in 1996 when our entire 4-person team was laid off.   We had the option of seeking other positions, but most of us ended up leaving for other jobs.  GTE had been my “work home” for 11 years, and I was sad to leave.   On reflection, I feel that I (and we) could have done much better work given more supportive and encouraging circumstances.  My colleagues were all extremely smart, and I consider them long-term friends to this day.   I have come to believe that large corporations are often impediments to progress (scientific, technological, and social).  More on that another time, perhaps.

Marriage and Parenthood

While working at GTE, things got better in my marriage.  We had economic stability, a house we had purchased and fixed up, and our son, Aaron.   I loved being a father, and have fond memories of interacting & playing with him,  reading to (and later with) him, and playing video games.  The next major highlight of these years was the birth of our 2nd son, David …

Our Family Grows!  (David born July 7, 1987)

My wife and I welcomed our 2nd son, David, into our family in July of 1987.  This was a very happy time for all of us.  Aaron seemed to adapt well to his role as “big brother”, and we added a 2nd bedroom to the small ranch house we lived in.  I have very fond memories (and some favorite pictures) of carrying David around on my shoulder when he was an infant.

Interestingly,  David arrived at 2:06am, and our home address at the time was 206 Concord Ave.  Coincidence?  Probably!   Interesting?  I love it!

Around 1989 we sold our house in order to purchase and move to a larger residence, still in Lexington, MA.  Moving is always stressful, but we got through it.   I remember my father coming up from PA to help out with packing and moving (interesting side note:  for all my life I’ve lived in the MA and PA states ! ).   We moved in January 1990, I think, and I remember we had a horrible ice storm the day of the move — the driveway of the new place was a sheet of ice!   Despite the challenges, we got everything unloaded, and started to settle into our new house, which had much larger space (3 bedrooms).  Within a year, our family was to grow yet again!

Birth of a daughter!  (Rachel arrives Dec. 2, 1991)

On the first day of Hannukah, our family grew once again with the arrival of our 1st daughter, Rachel!  My wife and I, as much as we enjoyed our 2 boys, were grateful to have a daughter, too!  All 3 kids have been a special blessing, and I feel it has been my privilege and honor to be their father.   When I look back on my life,  being a parent (and, if I do say so myself, a rather good one) is my proudest accomplishment (of course it’s not done yet — I’m still their parent, and hopefully can still contribute a bit to their growth and development – and they all continue to contribute to mine, as well!).

I think I’ll end this note on that upbeat note !

Next time:  career transitions:  researcher -> software developer -> free-lance puzzle designer

… to be continued

Pittsburgh, PA  – CMU (1979-1981)

The first step in my career path was moving to Pittsburgh (with my new wife) in order to work as a programmer in the Psychology Department at CMU (Carnegie-Mellon University).  I turned down an industry offer from Texas Instruments at a higher salary.  I was always interested in academia, and I worried that transitioning from industry to academia (and taking a likely salary cut) would be more difficult than the opposite transition.  I was also curious to experience CMU, since it was another of the major U.S. centers of AI Research (along with MIT and Stanford).  The programming work was supported by an ONR contract and involved work on learning, so I was excited about it.

At CMU, the Psychology Department and Computer Science both worked on AI and machine learning, led by the famous duo of Herbert Simon (Psychology) and Allen Newell (Computer Science).  In addition to the faculty, I found the graduate students in both departments to be very friendly, welcoming, and stimulating to interact with.   A number of long-term friendships arose out of my time at CMU.

Pittsburgh had somewhat of a negative reputation, so I was prepared to be disappointed by it (relative to Boston/Cambridge which I loved!).  I was pleasantly surprised — the city was much cleaner than it was in the past, and the people were friendly.  Though it didn’t offer all that Boston did, there was still plenty to do.  I have fond memories of going white-water rafting on the Youghiogheny River, visiting Fallingwater (Frank Lloyd Wright house), and playing lots of tennis and racquetball with friends and colleagues.

One of the things I most enjoyed about CMU was the more accepting attitude toward research involving games and puzzles.  Newell and Simon studied (and wrote the book on) Human Problem Solving, and used puzzles such as Tower of Hanoi and Missionary and Cannibals as vehicles for exploration.  Richard Korf even wrote his Ph.D. thesis (in CS) on an algorithm to calculate macro-operators for solving Rubik’s Cube and 15-puzzle among others.  Hans Berliner studied and made contributions to computer chess playing.

During my time at CMU, I had the pleasure of meeting Pat Langley, then a new post-doc in Psychology, having written his PhD thesis on BACON, a machine approach to scientific-discovery.  He and I hit it off due to our mutual commitment to Machine Learning as a key to AI, and have become life-long friends.  I have vivid recollections of participating, along with Pat and many others, in a project to build a simulated world as a testbed for AI and ML research.  This group, called by the somewhat grandiose name of world-modelers, sparked numerous interesting discussions.  We all shared a commitment to the idea that simulating a testbed environment had numerous advantages compared with the “real world” as used in robotics work:

1. No worries about “hardware” breaking (a bane of robotics researchers)

2. Greater reproducibility or results

3. Ability to modify the (simulated) environment in carefully controlled ways

4. Software can be copied and shared, so simulation tools can easily be used by many other researchers

5. A simulated environment can be simple (if desired),  to allow focusing on critical issues.

The last point was actually controversial within the group.  I advocated for starting out with very simple “worlds”, because those could be more easily programmed, getting us to the actual AI researching business much more quickly.  There were others, especially with interests in machine vision, that argued for a realistic 3-D simulated physical environment.  I would have been quite content with a simple, abstract, 2-D grid-world. Unfortunately, this issue divided the group, and my recollection is that things never really “got off the ground”.  Nevertheless, I continue to this day, to believe that simulating simple grid-world environments is a valuable way to explore AI/ML.

Not much to say about my marriage during this time, though my wife expressed unhappiness about Pittsburgh (she complained that it wasn’t near the ocean), and I speculate that she may have harbored a touch of resentment at “following me there”.

Northampton, MA – Hampshire College (1981-1985)

Sadly, the funding ran out for my programming work at CMU, and after just under a year, I found myself unemployed.  I began a job search, including looking at private schools and some colleges.  I was thrilled to receive an offer to teach Computer Science at Hampshire College (in South Amherst, MA).  Hampshire College was (and is) an experimental college, created as a “5th college” to join Smith, Amherst, Mt. Holyoke, and U.Mass. Amherst.  It was designed and started in the late 60’s, during the same time period that ESG (see earlier post) was formed at MIT.   Hampshire and ESG have a number of similarities.  They are both committed to student-directed education, fostering a shared sense of community, interdisciplinary studies, and educational innovation.  I jumped at the opportunity to join the Hampshire faculty, and so in Summer 1981, my wife and I moved to Northampton, MA, where we lived in an apartment adjacent to the Smith campus.  My wife was happy to be back in Massachusetts and closer to her parents who lived north of Boston.

I loved the “5-college area”, which was like a smaller-scale Boston/Cambridge.  I enjoyed all that the 5 colleges had to offer in terms of both social and academic activities and stimulation. I made many friendships, both on and off campus, and at Hampshire with both faculty and students.  I found the students at Hampshire to be very motivated and energetic, and it was a pleasure to interact with them.  Students propose their own courses of study, so I had many meetings with students to discuss projects and areas of study.

I was part of the School of Language and Communication (called “L&C” for short).  Hampshire had 4 Schools, rather than departments, in part to encourage interdisciplinary interaction.  L&C (later re-named to Communication and Cognitive Science) had 2 primary foci:  cognitive science (psychology, linguistics, math, logic, computer science, and philosophy) and communications studies (media, history of technology, among others).  I was the faculty person representing AI and computer science.

While Hampshire encouraged individual student projects and studies, there were also courses taught by faculty.   Team-teaching was encouraged, and I have fond memories of co-teaching a number of courses with colleagues.  Perhaps my favorite was called Structures of Computation where we examined the different levels of organization involved in computation.  This covered the span from low-level hardware (transistors, gates and flip-flops) through high-level software such as compilers and interpreters (and all the levels in-between, such as ALU’s, microcoding, machine code, and assemblers).  I am fascinated with how complex structures can be built up out of simpler components (modules).  Perhaps this stems from all the time I spent playing with wooden blocks, lincoln logs, and bricks (pre-Lego) as a very young child.  I believe this powerful concept of modularity, and hierarchical (layered) structuring, is a cornerstone of most if not all areas of engineering.  I think it is fundamental, as well, to learning and skill acquisition (but more on that another time!).

In 1981,  I bought my first personal computer, an Apple II+, along with (dot-matrix) printer, modem (300-baud!), color monitor, and 2 external floppy disk drives.  It seems unbelievable today that this machine could do all it did with only 64K of RAM (and that was the “souped up” hardware configuration). I remember programming in LOGO and Apple BASIC.  LOGO was the turtle-graphics language pioneered by Seymour Papert for use in teaching children computational and mathematical thinking and problem-solving.   Later, I added a SoftCard (Microsoft’s early Z80 hardware plug-in board), so I could run CPM as well.   I have fond memories of playing around with this computer.  I spent more than $4000 on the computer, peripherals, add-ons, and software.  Amazing how over the years computer performance has increased so much, and prices have dramatically declined!

One of my initiatives at Hampshire was an attempt to create an ESG-like program for computer science students, which I called The Learning Community (TLC).   It was enthusiastically embraced by a number of students, and we had regular meetings, published a weekly newsletter, and engaged in learning and sharing about a variety of interesting topics.  The greatest impediment to greater success was that we lacked a dedicated physical space where our community could congregate to interact – this seemed to be a key ingredient in ESG’s success.  Nevertheless, I was encouraged by the student’s enthusiasm, and hoped to continue to grow the program.  I even explore seeking dedicated space in a dorm in order to “house” TLC.  This would have improved, in my opinion, on ESG, by further integrating living and learning which is an ideal I fully support.  Unfortunately, not all my faculty colleagues shared my enthusiasm for TLC, and I paid a political price for “forging ahead” with it (more later).

Parenthood!  (Aaron born June 18, 1983)

I always knew I wanted to be a parent.  My relationship with my own father was a mixture of positives and negatives (more on this another time).   I aspired to be the kind of “ideal” father that I had always wished for.  I got my chance to try when my first son, Aaron, was born on June 18, 1983.  This was a highlight of my life!  It was also a wonderful Father’s Day present, since Aaron arrived on Saturday morning at 6:18 a.m.(was he a budding numerologist?  6:18 on 6-18-83!), which was the day before Father’s Day that year.   I sat down (at my Apple II) that night, and wrote a long letter to my son, hoping he’d read it when he was older.  I remember expressing my excitement, and hopes and aspirations for our relationship, and encouraging him to grow and develop into the best person he could be.

Parenthood wasn’t easy, though.  There were many sleepless nights, diapers to change, feedings to do, etc.  The stresses exacted a toll on my marriage, unfortunately.  I was also still dealing with my teaching work at Hampshire, and facing a reappointment review during the 1983-84 academic year.

The positives were truly great, and I have absolutely no regrets!  I remember we bought a video camera (an early Olympus VCR cassette system) shortly before the birth, and I had a great time learning to use it, and then documenting Aaron’s early development!   Also, the part of me that is a “researcher into the nature of intelligence” was fascinated to observe Aaron’s development.  It was fascinating, for example, to see how the “simple” skill of turning over, actually is painstakingly learned through trial and error.  Aaron wanted to be on his stomach, since then he could move around a little.  When placed on his back he was “stuck” – but he tried and tried to figure out a way to turn over.  He would twist his back and extend his leg, and eventually (after many days and weeks of attempts) got his body flipped over, with the small residual problem that his arm would get tucked under his body, and he couldn’t get it out – this, too, required some learning to work around.  It’s fun to go back and watch this amazing learning process on the videotapes.

When Aaron was maybe a year old, I remember sitting him on my lap so he could “play” with a program I wrote for him on the Apple II+.  It was a relatively simple Basic program that would respond to any keypress by flashing random colored pixels on the monitor, and at the same time play random beep tones.  Aaron seemed to enjoy this, and soon he was whacking away at the keyboard!  Who knows how much influence this had on his developing into the highly-skilled software developer he is today !?

Not reappointed at Hampshire

My reappointment review did not go well.  My take on it is that my “stubbornness” in pursuing The Learning Community project was viewed as non-collegial, and irked my colleagues.  There may have also been some retaliation for things I candidly wrote during the earlier reviews of some other colleagues (but that’s only speculation on my part).  In general I attribute it to my political naivety and mis-handling interactions with my colleagues.  I was an idealist and maverick, and butted up against institutional conservatism (at Hampshire of all places) and “departmental politics”.

Not getting reappointed placed an excruciating stress on my marriage.  It nearly led to divorce.  My wife clearly was upset at the sudden removal of any long-term economic stability in our marriage, and things gradually worsened month by month.   I was not nearly as concerned – I had sought and found jobs before, and was reasonably confident I would do so again.  I had over a year of “cushion” to look for new opportunities, since my contract gave me a 4th year at Hampshire, even after the decision not to reappoint happened during my 3rd year.  I interviewed at several places, mostly in industry, as I recall, and by January 1985, had an offer from MITRE Corporation in Bedford, MA (at a salary more than twice what I received from Hampshire).  I’ll never forget my wife’s reaction to the news:  “Maybe I shouldn’t be so quick to divorce you”.   A mixed blessing at best.  It clearly indicated to me that the primary basis of our marriage was financial.  On the other hand,  I wanted to stay as closely involved with my son as possible, and dreaded the prospect of a divorce, so I was willing and satisfied to continue “working on the marriage”.

Moving to Lexington

I was scheduled to start work at MITRE on July 1, 1985.  My wife and I started looking for a place to live in the Bedford area.  She was naturally pleased to be moving even closer to her parents.  We initially looked at houses in Arlington, but were getting discouraged, and began thinking about renting.  Then “at the last minute” (June, I think) we looked at a house in Lexington.  We had considered Lexington to be out of our price range, but this house (a small ranch) seemed potentially manageable.  We put in an offer that was accepted, and were looking to move in August.   To complicate matters,  in the latter part of June, I attended a Machine Learning conference in Skytop, PA, where I was given a job offer to join GTE Laboratories as a Machine Learning researcher.  I had interviewed with GTE Labs back in January, and it was clearly my first choice, but they couldn’t extend an offer at that time.  So I faced a dilemma – but ultimately the choice was clear – I had to go with my 1st choice and accept the GTE offer, even though it meant backing out of my commitment to start work at MITRE. The Friday before July 1 (when I was to start work) was a rather momentous day:   I declined the MITRE offer (they were really nice about it!),  accepted the GTE offer (yeah!), and to top things off, my wife and I signed the Purchase & Sale agreement for the house in Lexington.  Wow!  So on Monday I started work at GTE Labs instead of MITRE.  Because we didn’t actually move until mid-August, I lived temporarily in a dorm on the campus of Bentley College in Waltham.

Things got better in my marriage during this period, and I was quite happy (at least initially) with my work at GTE Labs (more on this later), and I continued to be a very happy father!

to be continued …

In Fall of 1974 I entered MIT’s Graduate School.  This was the next step in my plan for a lifetime of research in AI/machine learning.  I was officially admitted through the Math Department, which served as my “host” department.  In fact, I was enrolled in an interdisciplinary PhD program through DSRE (the Division for Study and Research in Education).  The mechanics of this involved setting up an interdisciplinary committee to oversee my studies.  Actual requirements were then negotiated with my committee.  My committee included Seymour Papert (AI/ML, education, and developmental psychology), Susan Carey (cognitive psychology), and Dan Kleitman (mathematics, combinatorics).  My plan was to work directly for my PhD (skipping a Masters degree).

This setup seemed ideal!   In many ways it was like ESG at the graduate level.  I had tremendous freedom to define and pursue my own interests.  I was encouraged to explore multi-disciplinary interactions.  DSRE itself was set up as an interdisciplinary entity at MIT – including among its luminaries:  Seymour Papert (who was my graduate advisor),  Ben Snyder (psychiatrist), Don Schon (urban studies & creativity), and Jeanne Bamberger (music, education).   The unifying interest shared by all of these (and by me, too!) is in learning in all its forms, and how a deeper  understanding of learning can inform how we design education (create rich learning environments!).  I chose Seymour Papert as my advisor and mentor because we shared so many interests: understanding thinking and learning, math, computers, puzzles, and learning new skills. As part of the LOGO Learning Lab, Seymour encouraged everyone (both children and adults) to engage in novel and fun learning activities.  For example, circus arts such as juggling, pole-balancing, and bongo-board balancing were widely shared and explored.  We would not only learn these skills, we would analyze them and explore ways to teach them!  The same was true of puzzle-solving. Seymour, like me, was a puzzle enthusiast and collector.  We enjoyed sharing puzzles and discussing how we solved them.  One of Seymour’s memorable quotes is “You can’t think about thinking without thinking about thinking about something!”.  So basically anything at all that we spent any time thinking about became source material for thinking about how thinking worked.  I loved this kind of self-reflection.

Machine Learning was perhaps my central academic interest in pursuing my graduate studies.  It seemed clear to me that any true artificial intelligence must be able to learn, in order to adapt to new situations, as well as to extend it’s knowledge and skills.  Much of AI at the time worked in the paradigm of building a system to demonstrate a competence that was considered part of intelligence.  Examples included playing chess (Greenblatt), debugging programs(Sussman, et.al.), language understanding (Winograd).  The focus seemed to be on direct programming of skills and knowledge.  This approach, while certainly worthwhile for initial exploration, seemed too long and arduous a path to true machine intelligence, and if the resulting systems lacked learning capability, they would always be limited and brittle.  One exception was the thesis work by  Patrick Winston on machine concept learning  (the classic “Arch Program”).   This work was very influential on the direction of my research, and I ultimately added Winston as a co-Thesis Advisor (with Papert).

A Research Maverick

As I mentioned, pursuing machine learning ran counter to the dominant AI paradigm at the time.  Many people (faculty and fellow grad students) argued that it was “too difficult”.   Maybe it was difficult, but I was strongly convinced that it was the key to building AI.  If we could just build a general learning system, then we could educate it – let it learn the skills and knowledge we wanted it to have!  Of course, to make progress, it would be necessary to start with simple learning, and initially would not result in impressive performance.  Because most AI research was funded by DARPA (Defense Advanced Research Projects Agency), there was quite a strong pressure for researchers to generate impressive results.  I felt at the time (and still do in the present day!) that developing powerful AI systems required more of a basic research approach.  My thoughts on this were likely influenced by my mathematical training — I approached things from an abstract direction, wanted to understand basic core principles (Ross’s dictum: Think deeply about simple things!).  My ultimate intellectual goal was to develop a general and abstract theory of intelligence which would subsume both human and machine intelligence.  It occurs to me that my commitment to a learning approach to AI is analogous to the technique of mathematical induction (prove assertion true for n=1, and also if assumed true for a given arbitrary n then prove true for n+1).   The learning approach, admittedly challenging, seemed like a high-risk high-reward direction to pursue.  If successful, AI researchers would no longer have to work arduously to encode specific skills and competencies – the system could simply learn them!

Another dominant aspect of the prevailing AI paradigm was working on individual pieces of intelligence, for example planning, language understanding, game-playing, problem-solving, robotics, and even concept-learning.  These were all studied in relative isolation.  I heard little or no discussion regarding the overall architecture of intelligent systems.   The research approach was essentially bottom-up – build the pieces, and then figure out how to put them together.  I recall being struck by the research approach in nuclear physics to developing controlled fusion.  Yes, they focused on specific problems (attaining high temperatures, plasma containment, plasma density), but these sub-problems were studied the context of a set of alternative working models (e.g. Tokamak, and laser implosion).  AI didn’t have any working models for how an artificial intelligent system would be organized!   It struck me that there was tremendous heuristic value in having at least one working model — specifically to help focus research attention onto critical sub-problems, and at the same time help define the sub-problems by suggesting how the sub-problem solutions needed to interact with other (yet to be created) pieces.  One of the worst examples (to my mind) of the piece-meal approach was the work in Knowledge Representation, where there were numerous KRL (Knowledge Representation Language) proposals, but little attention to or work on the ways in which these systems would be used.  The CYC project also seems to favor this paradigm — let’s just encode lots of facts, and worry later about how to use them.  In knowledge representation work, a deep philosophical truth was (imho) overlooked — representation is a process!   Static symbols and data structures are not endowed with inherent meanings or representations.  It is the processes that interpret and work with those structures that are the key element in representation!   I sum this up in one of my favorite original slogans:

     No representation without interpretation!

My observation is that many philosophers don’t fully appreciate this.  I cringe when I hear discussions of meaning totally lacking any appreciation for all the processes (perception, interpretation) necessarily involved for meaning to exist at all.  It is seductive to imagine that words, for example, have inherent meaning, but the meaning cannot reside in the words themselves.  To have any real appreciation of meaning requires examining the social, cultural, perceptual, psychological, and learning processes that in effect attach meaning  to particular words and symbols.  But I’m straying from my topic (I plan to write at greater length on my philosophical thoughts at a future time).  Back to research strategies — whenever I suggested a top-down research approach (building integrated working models) The typical reaction I received was that “it’s just too hard and we don’t know enough at this point”.   I still think top-down is the “right way to proceed”, and I’m encouraged by the evolving sub-discipline of  cognitive architectures (examples include:  Langley’s ICARUS, and Laird and Rosenbloom’s SOAR architectures), but those weren’t developed until the 1980’s and later, and I think they still suffer a bit from “results pressure” from funding agencies [I wish there was more appreciation of and financial support for basic research].

One central personal learning goal for my graduate years was to develop my skills as a researcher.  It seemed essential to learn how to define  a research problem.  So when it came time to select a thesis topic, I used this as an opportunity to begin learning this skill.  I was not content to work on an “externally defined” problem — there were plenty of such problems that already had funding, and choosing one of those would have been the easy path.  Instead I generated a series of proposals, and the initial ones were overly-ambitious, and naturally I didn’t get very far with them.  One of my first ideas was to take Winograd’s SHRDLU (one of the great success of early AI, which demonstrated rudimentary language understanding), and work on a learning version of it.   This had the potential for a more integrated approach – it would integrate several sensori-moter modalities (hand-eye in manipulating a simulated blocks world, and language generation and understanding).  I even thought about having the system learn motor skills in the blocks world.  The problem with this is that it was way too difficult, and worse, tried to solve too many problems at once — it lacked focus.  It might serve well as a lifetime research project, but was not manageable as a  thesis (I hoped to finish my PhD before I retired or died).

I came to realize that I suffered from a serious “grandiosity” bug — I wanted whatever I did to be big, amazing, and spectacular, maybe even revolutionizing the field 🙂     What I needed was to simplify and focus on smaller, more manageable problems.  I think I also lacked the skill of working on large projects.  My training in mathematics and computer science had mostly consisted of working on smaller problems and projects.  The biggest project I had worked on was my Summer UROP research, but even that didn’t seem to scale up to a multi-year thesis project.  The thesis topic I finally settled on was “Extensions of Winston’s ARCH Concept Learner”.  I chose this because it was one of very few pieces of AI work that was centrally about learning, and also because I really liked the work itself (the way it used semantic nets to represent concepts, and the training paradigm of positive and negative examples).

A thesis is born

So I started out by writing a (friendly) critique (from an admirer’s point of view) of Winston’s concept learner.  I recall coming up with something like 6 directions in which the work could be extended, and my initial proposal was to work individually on each of these, and collect the results into my final thesis.  This had the heuristic advantage of dividing the “problem” into subproblems.   To further simplify, I selected just 3 of these extensions to work on:

1. Learning disjunctive concepts (Winston’s only learned conjunctive concepts)

2. Learning relational concepts (Winston had relational primitives, like ON & TOUCHES, but didn’t learn new ones)

3. Learning macro concepts (allowing any learned concept to be used as a primitive to represent and learn more complex concepts) (Winston’s work included some of this already, but I wanted to generalize it to cover disjunctive and relational concepts as well).

It was natural to have Winston as my (co)Thesis Advisor for this work, and I thank him for his patience, attention, and advice!

Masters thesis as “Consolation Prize”

By the end of my 5th year of grad school, I had only completed the 1st item (with a little preliminary work on item 2 as well).  It looked like another 2 or 3 years would be required for me to finish my planned thesis.  I was feeling frustrated, since my progress was much slower than I expected of myself, and I was losing self-confidence.   At the same time, my funding was running out.  To continue, I would have needed to start taking out loans.   I was very nervous about accumulating significant debt, and feared that even after a few more years I might still be unsuccessful at finishing.

So I decided to wrap up my work thus far as a Masters Thesis, collect my SM degree, and graduate and look for a job. My S.M. thesis was titled “Learning Disjunctive Concepts from Examples” and I think it was very solid piece of work.  I collected my SM degree in August 1979, and withdrew from my graduate program.  I had the intent of returning at some point to complete my PhD, but alas, that was not to be.

Non-academic threads of my life during the graduate years

During most of my graduate years I served as one of 3 graduate resident tutors in Bexley Hall (the undergraduate dorm I lived in when I was an undergrad myself).  I very much enjoyed both the social and mentoring aspects of this position, and have developed a number of lifelong friendships with students from those Bexley years!

I did not own a car during grad school, and don’t know where I could have parked it if I could have afforded one.  I did, however, purchase a motorcycle (a used Honda 350) which I learned to ride, and parked in the Bexley courtyard.  I had many interesting adventures riding to PA (to visit family) and New Hampshire, and also to Danbury CT to visit my first serious girlfriend when she moved back there.  I remember reading Zen and the Art of Motorcycle Maintenance and trying to apply some of its ideas to working on my bike.

I also purchased a used electric piano, which I enjoyed playing for many years.  Although I had written 1 or 2 songs in high school, I didn’t try more serious song-writing until I had my own piano.  I think I had fantasies of being in a rock band, and even auditioned at one point, but was turned down because the group felt my grad studies would prevent a full commitment to the band – I’m sure they were right. I still, to the present day, enjoy playing keyboards and writing songs.

My passion for puzzles continued unabated.  I added to my puzzle collection – my favorite puzzle store was (and still is) Games People Play located in Harvard Square.  I recall that it was around November 1979 when my wife got me perhaps the best puzzle gift I’ve ever received.  She went into Games People Play by herself and asked for “the hardest puzzle you have”!  Carol, the owner showed her a Rubik’s Cube, saying “we just got these in from Hungary, and it’s so hard he won’t be able to solve it”.   Of course, I couldn’t resist that kind of challenge, and after nearly a week of intensive work (I’d say roughly 15-20 hours over 5 days), I finally had developed my own solution method.  This was before Rubik’s Cube hit mega-popularity, and if anyone had suggested I should write a book on “How to Solve Rubik’s Cube” I would have laughed out loud at them!  This puzzle was so hard, that it would only appeal to a very small number of hard-core puzzle solvers (so I figured), and they are not the type to want to hear about anyone else’s solution (at least not until they had solved it themselves).  So I failed to cash in on the boom in Rubik’s Cube solution books — there were rough 4 or 5, I think, all simultaneously on the NYTimes top-10 non-fiction best seller lists for a time (1981?).  Just goes to show I’m a terrible market analyst!

I also had a number of relationships with women during these years, from which I learned a lot, and have mostly very positive memories!  In June of 1977 I met the woman I was to later marry.  We had a 2-year relationship which led to marriage in June 1979.  There was a lot going on in 1979 — In addition to getting married, I was writing up my SM thesis,  applying for jobs, accepting my first job, and moving to Pittsburgh — I’ll tell you more in the next installments on marriage and career.

… to be continued

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