Author: joel@waterfallhouse.net

One of my favorite politicians is Senator Kennedy from Louisiana who has just published a book by that name. The subtitle is provocative as well: And why Washington never will. Watching him on YouTube is always interesting since he asks such good questions and makes such obvious points. Here are some of the “one liners” from his book:

• “If you trust government, you obviously failed history class.”
• “I believe our country was founded by geniuses but is being run by idiots.”
• “Always follow your heart . . . but take your brain with you.”
• “The water in Washington won’t clear until you get the pigs out of the creek.”
• “I have the right to remain silent but not the ability.”
• “Common sense is illegal in Washington, D.C.”

My prior blogs about trying to fix stupid all point to the same conclusion: don’t bother because you can’t … no one but God can do that and, in most cases, God doesn’t seem to care or be concerned.

I recently lead a Sunday School class on the Book of Jonah which included a puppet show performed by my wife Susan on the comparison of the story of Pinocchio and Jonah. You can watch that on the video and the podcast section of this website https://geektheology.net/podcasts (The podcast is at the bottom of the page and titled Sarah the Puppetarian.)

My point was that the Book of Jonah is proof you can’t fix stupid. Jonah is instructed by God to preach repentance to the archrival of the Jews, the Ninevites. Jonah decides that it is futile and attempts to flee in the opposite direction by sea. God sends a storm which alerts the ship crew that someone onboard had sinned (so nonbelieving crew had more faith than Jonah) and Jonah finally admits he is the culprit, so they throw him overboard upon which he is supposedly swallowed by a big fish … call it a whale … after all, who knew a whale was not a fish way back then.

After three days, this smelly prophet is cast up on the beach and reluctantly starts to preach to the Ninevites who almost immediately agree to humble themselves before God and repent … he didn’t get more than about a third of the way through the city … and even the King orders everyone to get right with God.

What does Jonah do? He gets pissed off that God didn’t punish these evildoers and proceeds to have a pity party under a tree provided by God to protect him from the sun, so God then sends a worm to eat the roots of the tree so it withers and dies. We are left at the end of this brief book with a prophet who witnesses the power of God but is so disappointed at God’s mercy he can’t do anything other than wallow in his despair.

Why is this book in the Bible library? I think it was to prove to everyone not only that we can’t fix stupid, but that God can make things right even with stupid people.  Simple advice: pray for them and let it go.

I finished out the Sunday School lesson with a review of the movie Forest Gump where the message that hit me after seeing this movie again was that our natural emphasis on intellectual ability misleads us. While some may object to the characterization of Forrest as demeaning, on balance, the movie emphasizes that the truth in life is in the simple acts of kindness and generosity.

True happiness is summed up in his life: love your neighbor and seek their wellbeing above your own. Forrest never lost his love for Jenny since the first time they met on the school bus and she offered him a seat. Forrest never lost his love for Lt. Dan even though Dan repeatedly wanted to die in the family tradition of wartime heroes.

Most importantly, Forrest never became obsessed with success or any earthly treasure. He lived his life simply and lovingly. The world might define his intelligence as inferior, but perhaps that is the point. I grew to see Forrest as brilliant for the way he lived his life.

His mother continually tried to tell him that he was no different than anyone else. She was wrong … Forrest was very different, and specifically because he didn’t have the distractions others have. Forrest was a far better and a far more godly person than anyone else in the movie or than many of us have met.

You could say he had a simple faith … that of a child. Nope, sorry … I think he had a profound faith because he could always see a path where he could make a difference. He didn’t wait for others to take care of him. He took care of himself and others, even when they didn’t want or respect him for that.

Reward was not his motive … at least not in the sense of earthly reward. It is so ironic that with all his success, his finishes out his life cutting the grass … for free. What a life well lived.  A good lesson for us all, especially at this time.

Once upon a time, long ago, I found myself in a counseling session with a psychotherapist trying to save a failing marriage. My engineering brain had concluded that, even though my spouse was “making me crazy” with her erratic behaviors and tantrums of rage that I needed to get my own emotional life in order. So, I braved the encounters and tried to do the soul searching in earnest.

While I discovered a great deal about myself, I became rather irritated paying someone to repeatedly ask the same question: How did that make you feel? Yes, I know why he did, but I almost never heard a question that moved beyond the surface and reflected on what I had been saying with any level of empathy.

What prompted this blog was an article in Nautilus on whether computers can learn to express emotions: Artificial Emotions – Nautilus. As I read it, those experiences from five decades ago resurfaced. That therapist made me feel he was an automaton.

When I asked AI to explain why that question is so often used, I got this:

The question “How did that make you feel?” is a common one, especially in therapeutic contexts, and is often used to encourage self-reflection and emotional awareness. It prompts individuals to connect their internal emotional experiences to external events or situations, helping them identify and understand their feelings. Therapists may also use it to help clients explore their emotions, express them, and potentially learn new ways of coping with them. 

So, there we have proof … a computer can indeed seem to express emotions. But, just like humans, these can be surface interactions rather than deep connections. The marriage I mentioned ended with her grounds for dissolution that I was boring and unintellectual. Yes, you read that correctly and you can ask my wife Susan whether I am making it up … but no, I am not. That was her grounds for divorce. But I digress.

Susan participated in a psychological experiment when she attended college where she was paid to listen to incoming freshmen and at first try to help them but halfway through the experiment to not offer any advice … only ask the same question: how did that make you feel?

The results of her research project were startling perhaps. The students initially hated her and thought she was stupid, but then felt she was wonderful when all they did was listen and ask that question.

As I ponder what I am learning from AA’s Twelve Step meetings, I see a parallel. Simply offer people a safe venue to express how they feel and what they are coping with and share with them how you have had the same challenges … validating them.

The Serenity Prayer is part of these meetings: “God, grant me the serenity to accept the things I cannot change, the courage to change the things I can, and the wisdom to know the difference.” Ironically, IMHO there is definitely room for a robot to replace counselors, especially when only superficial relationships are needed.

Given most of us live our lives on that surface … counselors be afraid … be very afraid!  AI will replace you.  So … go deeper.  Listen more intently and reflect that listening by asking better questions.

Everyone today is talking about Artificial Intelligence, yet few understand how it works. The average person on the street thinks it is somehow programmed in … giving it actual instructions of what to say and do in a given situation. 

That might make intuitive sense, but it is not how it works. Instead, the computer is given a large training set of inputs and outputs and algorithms to process them … kind of like the way you may have done your statistical testing back in college.  One of the first applications of AI was OCR: optical character recognition.  The idea was to take a printed sheet of paper and produce a text file of the characters. 

This is a relatively well-defined training experience: there are only a limited number of characters and context can give the computer hints as to whether it is the letter “I” or the character “1”.  These systems are now reliable.  Speech recognition was next, with huge business implications for legal and medical professions.  While speech recognition is pretty good and in so many systems, it also depends upon a “training set” to learn.  If the software was trained on British voices, it would have trouble understanding Americans.  Accents from the deep south are humorous examples.

Facial recognition was next and while China used it to keep track of their citizens, MIT researchers discovered that it did not work reliably on people of color.  Once again, the problem was the training set did not include enough people of color.

But AI has its challenges when a new situation does not “map” to some part of the training set or when those designing the training algorithms never anticipated the use case.  In these cases, AI produces general nonsense and probably can’t be trusted.  My wife Susan asked ChatGPT for a list of public swimming pools in our area and it offered a list of the nearby private hotel pools. 

Modern prognosticators seem obsessed with the prediction that AI will develop a sense of itself and result in some level of revenge against its creators.  Movies have long suggested this theme.

Those of you who care about how AI learns should watch the movie Ex Machina where the AI is a beautiful young lady robot named AVA.  She is being tested by a young programmer to see if she can pass for being human … aka the Turing Test … but she learns by being tested and in short order realizes she is being tested and then logically deduces that there might be something bad that happens if she fails the test.

I have watched this movie about a dozen times and each time I discover more about how the process of the Turing Test taught her more and more about her situation and eventually led to her asking two key questions: First, “What will happen if I fail your test?” He replies, “I don’t know.”   She then asks the second question, “Why should anything happen to me based upon a test?”

Do you remember that wonderful movie War Games and how the computer learned that the path it was on lead nowhere?  It was the youngster played by Matthew Broderick who thought by learning the trick behind playing Tic Tac Toe, the computer would learn the game of thermonuclear war would also have no winners … which then stopped the escalation with the computer’s famous line: “Strange game … the only winning move is not to play.”

Spoiler alert … the movie Ex Machina does not end well.  I personally believe everyone today needs to watch this movie and think about where we are all going with AI. 

Yes, of course, there will be some good outcomes with AI, but more often than not there will be some very disruptive ones too.

 

 

We all know technology is evolving.  Clocks went from sundials to pendulum-driven clocks, then spring wound, and on to smart watches or phones.  Music went from phonographs, to radio, to vinyl records, to cassettes, to CD, to iPods and now streaming apps with smart speakers.  Much of this occurred in my lifetime.  Today, I am intrigued by the evolution of something seemingly simple: Laundry Detergents. 

My first memory of laundry detergents was in the 70s when they were powder you would scoop from a container or pour from a box.  They were messy and sometimes left clumps of congealed power on your clothes. The 80s brought liquid formulas prized for their ability to dissolve quickly and remove grease stains.

However, that popularity came with a cost: large plastic packaging and heavy bottles made them less than ideal for storage, transportation, or the environment. I remember lugging the heavy jug to the basement of our dorm where the washers were located.  Maybe the benefit was some good exercise, but it did not contribute to a good laundry day experience.  Nor did they cart the hefty plastic jugs home from the grocery store. 

An ‘80s Amway advertisement for their detergent piqued my interest. It claimed that other liquid laundry detergents contained enormous amounts of water to make you feel you were getting more for your money, while theirs had little water. Therefore, you could use less, extending your value and lightening your load. I never knew if the claims were true, but they made sense.

In the 2010s, pods revolutionized detergent delivery, offering convenience, single-use, mess-free, lightweight, and easy storage.  The premeasured doses in dissolvable films eliminated the guesswork on how much to use.  However, they were not perfect – early safety concerns arose as children were attracted to the candy-like appearance of the colorful pods.  However, over time, packaging and public awareness have improved, and pods have become a staple in many homes. 

This year, I discovered the latest evolution in laundry: lightweight, eco-friendly detergent sheets. These thin, biodegradable sheets, which resemble paper napkins, contain concentrated detergent and completely dissolve in water during the wash cycle. Being lightweight with minimal cardboard packaging, no plastic-bottle waste, and a significantly smaller carbon footprint, they are environmentally friendly. They’re also ideal for small spaces, travel, or for anyone who doesn’t enjoy carrying heavy plastic jugs. Currently, you may have to search the shelves to find the small packages of sheets, but I believe over time, they will take over the detergent section and free up a lot of space currently devoted to bulky jugs of laundry detergent.

As sustainability, simplicity, convenience, and effectiveness take center stage in household products, we can expect to see even more innovations in detergents. But for now, laundry sheets represent the latest chapter in a long story of soapy ingenuity.

I have been using this term in recent conversations about AI and drawing blank stares.  Perhaps that is because the word has only recently become a frequent term as the graph shows.  This coincides with my professional career so that could be why I use it to categorize the kind of data needed to get the maximum benefits from AI, and the kind of problems you have if the data is not curated.

Going back to basics, the dictionary defines curation as the action or process of selecting, organizing, and looking after the items in a collection or exhibition.  Each of these steps is deliberate and REQUIRES qualified professional judgement. There are of course still matters of opinion, but the removal of unqualified or aberrant candidates is critical to the quality of the collection.

The current frenzy over the latest craze in AI ignores this step and assumes the code can generate only curated candidates for later analysis.  That may be true if the candidates come from a nested set of known equations and you simply are trying to bound or predict the solution space … but no!  Stop the insanity … more bad data does not help!

Medical records can be an extremely beneficial dataset because almost all the information in them has been curated.   You still have the vagaries of human complexity and the randomness of the healing process, but at least you seldom get data that should be thrown out because it is nonsense. 

Let me illustrate my point with a common area in which I use AI: preparing my lessons for Sunday School.  I am trying to go back to the Hebrew, search for the cultural and historical contexts, and also surface scholarly arguments about almost everything including authenticity, meaning, and modern interpretation.  What I most often run into is party lines within religious denominations … each trying to defend an article of faith in that tradition.

It is almost as if each denomination recognizes that if you pull just one block out of their stack of theological arguments and resulting dogmas the stack will fall for their congregants.  The idea of continual interpretive relevance falls to defending past statements and beliefs.  Stagnant, rigid, and virtually universal points of view are the only things you see.

Therefore, when you use any modern AI tool to ask theological or contextual questions you get the codified, curated (according to their perspective) talking points.  These may still be relevant to today … but maybe not.

Please stay with me.  Can you see the crucial curation step here?  There was a deliberate step to curate what people should know and believe about something.  Does that curation step reflect a modern mind and or the context of the person asking the question?

Probably not … so AI just spits out the traditional answers to the question and in all too many cases misleads the user.

This is why I have been warning my friends in the military to beware of their AI systems usefulness because it is likely trained on data curated by our perspectives of combat ethics and tactics. 

I love the movie Down Periscope where the admiral asks Kelly Grammer to think like a pirate, and he does and as a result wins the war game exercise where his old diesel sub outflanks the modern nuclear attack subs which are faster and more powerful.

Curation … is it in the data we are using?  And, if it is there, who and for what purpose was that data curated?  These questions will surface whether the AI can be trusted at all.