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Artificial Ingestion

Artificial Insemination

Artificial Intelligence

Dance of Billions

When GREED is the only creed

Imagine All the People

After All

All We Stand to Lose

Fish have no Word for Water

Somewhere a Bird Cries

Guernica

Myths of the Veritas: The First Ring of Empathy

Travels with Sadie: Precipitation

The Walkabout Diaries: Symphony

Essays on America: The Game

Query By Example

08 Tuesday Sep 2026

Posted by petersironwood in AI, design rationale, HCI, leadership, management, psychology, science, Uncategorized, user experience

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AI, Artificial Intelligence, expertise, HCI, human factors, IBM, leadership, QBE, research, technology, usability, UX, writing

This is part of a series on experiences in my career in Human Computer Interaction and some lessons learned.

I joined IBM Research on the winter solstice of 1973. I had earned a Ph.D. in Experimental Psychology from the University of Michigan and for the previous few years, I had managed a research project at Harvard Medical School on the “Psychology of Aging.” At the time, I was married and had three small children. I mention this because I was funded by so-called “soft money” which basically meant that my salary depended on a research grant. I helped write a renewal of the grant but the decision was “deferred”; that is, it was neither funded nor unfunded. Then, it was deferred again. This meant that if the grant were not funded, I would only have a few weeks to find a new job. That seemed far too short so I began to look other places for a job.

Lessons Learned: #1 If you want continuity of personnel in your laboratory, make sure you have overlapping and multiple grants or other sources of income.

In this case, the grant actually was ultimately approved, but by that time, I had already agreed to join IBM Research. That turned out to be fine, by the way. It was a wonderful place to work.

One of the reasons that I got the job at IBM was that I already knew something about computers. I had taken several computer science courses in grad school along with the needed psych courses. More importantly, our “Psychology of Aging” study was run by a PDP-8 and I had programmed the computer to run our suite of experiments and to do data analyses on the results. I had taken a week-long course at DEC in Maynard, Massachusetts on the assembly language, another week-long course on the machine language, and another week-long course actually tracing the circuitry with a probe and oscilloscope. I felt I “understood” the PDP-8 at a fairly deep level.

At IBM, I did not have that familiar machine. Instead, I was connected to a mainframe via a dumb terminal. The first day at IBM, I got my userid and tried to log on to APL (A programming language I had not used before). I tried following the manual but I could not seem to get logged on. After hours of trying, I finally gave up and went down to the computer room and found someone willing to help. I showed him the logon instructions I was trying to follow and he immediately said, “Oh, yeah, that doesn’t work any more. We changed that months ago. Here’s how you need to do it now.” The manual I had may have looked new, but it was out of date.

Lessons Learned: #2 Manuals can be wrong. These days, most are online. But they can still be wrong.

Lessons Learned: #3 Someone who knows how to do something can save you hours with a few minutes of their time.

Of course, it’s more respectful, efficient, and a better learning experience if you can figure it out on your own. But sometimes you can’t. My stumbling block was not due to an error in logic, or a lack of in-depth knowledge. It was simply that the computer center administrators had changed something arbitrary so that the documentation I was given about how to log on for the first time was no longer accurate.

In order to teach myself APL, I wrote a very small program to “predict” how long I was going to live “based on” some behaviors that I was interested in controlling. My main goal was to learn APL. My secondary goal was to motivate myself, for instance, to exercise more, lose weight, and not drink too much alcohol. I had no intention or pretensions of making this prediction “accurate.” If I had been doing a consulting gig for an insurance company setting life insurance rates, for example, I would have given far more attention to see precisely what the real data were and incorporated many more variables into the regression model.

Here’s a link,

https://www.death-clock.org

by the way, to a more accurate model than the one I used, but it’s still simple to use. Note that my goal was to motivate myself and so I intentionally exaggerated the impact of those behaviors I was trying to change. I had programmed it. I knew how “bogus” the calculation was — nonetheless — here’s the interesting thing though:

Lessons Learned #4: Even an over-simple model that the user knows is over-simple can still motivate change.

At last we come to the actual project I worked on — the usability and learnability of Query By Example. One of my colleagues, Moshe Zloof, invented the language for relational data bases. He had designed the language but not yet implemented it. I did not immediately test the design; first, I sought to understand it. In seeking to understand it in depth, prior to testing it, the two of us had some sense-making discussions. Moshe improved the design; in particular, our discussions uncovered some ambiguities and inconsistencies that were not at all obvious when he simply gave talks about the design. This brings me to the next lesson learned which has proven true in nearly every study of early stage designs that I’ve been involved with over the course of six decades.

Lessons Learned #5: Don’t just accept a surface description of something; understand it as deeply as you can before designing a study.

In this particular case, it was possible for me to understand it in some depth. Relational data bases and second order logic are things I was capable of understanding. If it had been an interface to running a nuclear reactor or using the artificial heart that Moshe had designed earlier in his career, that would have been a much more difficult task for me.

I wanted to understand, not just the “logic” of Query By Example, but also possible contexts of use. For instance, my manager & I visited Burlington, Vermont to talk with IBMer’s who actually used query languages to understand what was happening in chip production lines. At one point, a particular production line that had been producing nearly 100% perfect chips starting having a much higher error rate. Using their query facility, they were quickly able to diagnose the cause of the change which was a supplier of one of the raw materials using a different source. In turn, this meant a slightly different profile of trace impurities in the substrate. Of course, this is only one example, but to me, understanding something in depth means not only understanding its internal logic but also understanding real users, their real tasks, and their context of use.

Photo by Chokniti Khongchum on Pexels.com

I won’t go into all the details of the pencil & paper study or the results. High School students and then college students were taught the basics of the language and then given a simple relational data base and a set of questions stated in English which they had to translate into Query By Example. Briefly, the bottom line was that Query By Example was easy to learn and easy to use. However, there were still questions that people had difficulty with. In analyzing the data and doing some further experiments, the difficulties that people tended to have, stemmed not so much from Query By Example per se, but from what I much later came to call “labelism” — that is, confusing a label with the thing that label refers to.

Here’s a simple example of the type of confusion we saw. In Query By Example (and other query languages) there is usually an OR operator and an AND operator. (These operators can be important for doing advanced queries with search engines as well). If you are interested in getting a list of pets you might adopt and you’re willing to adopt dogs or cats, you might ask for “cat OR dog.” If you only want long-haired cats, you might ask for “cat” AND “long hair.”

English, however, can be tricky.

If you and I (as opposed to you and a query language) are having a conversation, you might say, “I hear there are many pets that need to be adopted.”

I say, “Yes, there are all kinds of pets. There are snakes, dogs, turtles, rabbits, cats…”

You say, “Let me stop you right there. I’m only interested in adopting cats and dogs. Those are the only animals I’d want to adopt.”

See what you said there? You exact words included: “…cats and dog.” If you put “Cats AND dogs” into a query against the data base of available pets, however, you will get the null set (that is, nothing) back. There are no animals who are both cats and dogs! (Though my part Main Coon cats, Luna and Charles Wallace, can play fetch like dogs).

When people were presented with an English statement that included the English word “and” — regardless of the actual syntax and context, some of them had difficulty using the OR operator. If instead, the query in English had set up like this: “Oh, I don’t want reptiles. I’d be happy with adopting a cat or a dog, however” then, they’d have no problem translating it into the OR operator in the query language.

Lessons Learned: #6 Sometimes the difficulty people have in using a product, a service, or a prototype is not due to the interface details but with the structure of the task, their background, and their training.

By analogy, you will not allow me to beat Carlos Alcaraz or Jannik Sinner at tennis by giving me a better tennis racquet! (Although if you gave one of them a toothpick for a tennis racquet, I might have a shot).

That sounds obvious and even absurd, but I promise you, some companies get so greedy that they want you to design a system that allows people who do not understand the task and have minimal background and training to nonetheless be able to perform that task. And, to be fair, it isn’t just the companies who are greedy. They are steered into thinking that they can get away with this absurdity because some outsourcing companies (and more recently, AI companies) tell them they can do it.

One example you may have encountered is having “help desk” personnel who have no understanding of a product go through a script to help you “solve your problem.” Sometimes, it works. But many times it doesn’t. When it does not work, you might not be able to “fix” the system by making the interface to the scripts easier to use for the help desk folks. The problem is much deeper (in some cases). Yes, a really bad interface can make it difficult even for a really knowledgeable and capable person to do the job. But often, even a really great interface cannot always substitute for actual expertise.

——————————————————-

Essays on America: Labelism

My Brief Case Runneth Over

03 Thursday Sep 2026

Posted by petersironwood in The Singularity

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adaptive-systems, AI, Artificial Intelligence, artificial-intellligence, books, chatgpt, cognitive computing, ethics, fiction, law, lawyers, legality, life, music, politics, technology, the singularity, USA, writing

left-right patterns 2

Turing’s Nightmares: Nineteen — My Brief Case Runneth Over

“Nice office, counselor.” Marvin spread his arm to indicate the panorama of Manhattan and Ellis Island. Yet, his attempt to sound casual failed utterly. He realized all too well the depth of trouble he was in and the creakiness in his voice so signaled.

J.B. smiled. “No need to be nervous. After all, we’re here to help. Just answer honestly. Everything you say will typically be covered under attorney client privilege.”

“Okay. Thanks. Typically?”

J.B. sighed. “There is a rare exception in the Patriot Act. Unless you’re some kind of terrorist, I wouldn’t worry about it. The government is apparently accusing you of importing illegal chips is all. I think we can argue that you are new to the business and did not realize all the required legalities. Pay the taxes now and some penalties. You should be able to avoid jail time. But we are getting way ahead of ourselves. Just tell me how and why and when you got into this business in the first place.”

Marvin chuckled slightly and then sighed “That’s a rather long story.” Marvin glanced at the expensive abstract paintings and noted the rich smell of the solid mahogany furniture. The room also had that crazy idea of luxury — making it freezing cold inside just because it was horribly hot and humid that day, even for New York City August. “At your rates…What do you really need to know?”

“Why did you get involved in making chips without the required back door?”

“These chips are not that powerful. At least not in the traditional sense of the word. And, the government’s requirement is frankly kind of silly. The extra logic makes programming more difficult. It increases the chances of errors. The chips are more expensive and require more power. Leaving aside all the valid privacy concerns, there are plenty of applications that can use a more efficient cheaper chip design. No need to report all the little finger twitches of every twelve year old who’s playing Grand Theft Spaceship. What’s the point?”

“Marvin. I’ve been a friend of your family for a long time. When…well…if we go to court and if you testify, please steer clear of politics. The law is the law. You don’t get to decide which applications are immune from the law. No, what we…our script here is that you made some honest mistakes. You made technical mistakes and what are essentially accounting errors.”

Marvin flushed. “What technical mistakes? What are you talking about? My design is an improvement.”

J.B. got up and paced. “Marvin, Marvin, you are a smart boy. We are not going to fight the government on this. Our script as I said is that you made some honest mistakes. If you want to —- the court is not the place to try to change the law. If you want to do that, get involved in politics. But not until after this is resolved. If you get on the stand and start railing about privacy and the dangers of the Sing and…”

“Ha. The Sing indeed. This data collection rampage has nothing to do with trying to increase the intelligence of computer systems. It is just mindless greed.”

“Marvin, the back door requirement is about preventing terrorism; it has nothing to do with greed.”

“Are you serious, J.B.? Terrorism is just the cover story the government uses. They want to collect all this data to keep their sponsors happy.”

“What? What sponsors? What are you talking about? The government doesn’t have sponsors.”

“Of course they do. Ever since ‘Citizens United’ — nice title by the way — billionaires can buy all the media they want. They wash the airwaves and the print media and saturate the social media space and make people believe anything they want. The point of the back door is just to make sure the sponsors keep tabs on everybody’s buying habits —- and to make sure no-one gets too far out of line. How can you not know this?”

“I don’t want to think that way. But at the end of the day, it doesn’t matter what I think. It’s the law. All digital processing chips with more than a peta-flop and …”

“Hold it! Did you say ‘digital’?”

“Huh? Yeah, of course. Isn’t that what we’re talking about? You designed digital processing chips that have no back door and now…”

“No. Maybe not, J.B. My chips are not, strictly speaking, digital.”

“What are you talking about, Marvin? Why? How not digital?”

“In my design process, I strove for something different and more robust. See, here’s the thing. Natural life processes are all…they all have some level of flexibility. They are robust. They are adaptive, I suppose, because every life form that was not flexible or robust died off over the four billion years that life evolved. Something new would come along and every life form that was rigid died. Even physical machines that people make have some flexibility. Metal bends. Wood bends. These materials also compress. They stretch. Not much, but a little. Digital devices do not stand for bending, folding or mutilating. Of course, when people build systems that include IT devices, they try to make the systems flexible. They add error checking; they put humans in the loop; there are all sorts of work-around, but the underlying tech is brittle. It is only flexible in precisely the ways that the designers anticipated it needed to be flexible. I made something that is fundamentally flexible. It is fashioned after life itself. Social systems are flexible. Eco-systems are flexible. Individual animals are flexible. Cell membranes are flexible. Bones are flexible. And so on. This flexibility is fundamental to life and evolution.”

“Okay, Marvin, but life is not infinitely flexible. You cannot just decide to breathe underwater.”

“Of course not! I never said it was infinitely flexible! For fundamental change, you need evolution and lots of time. If the climate changes too quickly, Permian life species largely go extinct. Dinosaurs die when a comet hits. But small adaptations just happen automatically and at every level. It isn’t necessary to have a pre-existing ‘program’ or ‘case’ to handle every contingency that is even slightly outside of what is anticipated.”

“Okay, fine. Granted, but what does this have to do with — did I mention that I charge $500 an hour, by the way?”

“Yes, J.B., you did. And it was in the contract you had me sign. But the point is that my chips are not, strictly speaking, digital at all. They are not strictly EITHER/OR. They are not binary. Although, in many cases, they can behave as binary.”

“So, you want me to claim that these chips are not covered under the back-door provision because they are not really digital devices? I don’t know. That seems pretty shaky to me. Can’t these chips of your be used for the same things as ordinary digital chips? Speech reco. Machine vision. Game control. Big data analytics.”

“Here’s the thing, J.B. Yes, they can, but they can change and evolve and reprogram themselves over time. And, now it occurs to me, that they could not have a real back door. They wouldn’t really function if they did.”

“What? Why?”

“Are you familiar with programmed death in cells? I can see from your blank stare, you aren’t. Anyway, if a cell is damaged within the body, it can wreak all sorts of havoc so the cells are essentially programmed to self-destruct. If that fails, the other healthy cells will tend to wipe out the damaged ones. So, if there were a functioning back door sharing data out of these chips, the chips would shut themselves down. If that failed, other chips in the matrix would isolate them and shut off communication. So, by their very nature, these particular non-digital chips cannot have a functioning back door.”

“That all sounds very clever, Marvin, but now we are talking about a very lengthy and expensive trial with expert witnesses on both sides. If, that is, you are lucky enough to even have an open trial.”

“Lucky enough? What are you talking about, J.B.? I thought you said we wanted to avoid a trial.”

“Yeah. With my strategy where we convince the NSA that this was just technical and business incompetence on your part. But in your scenario, where you want to prove you are all brilliant and everything, then, we will be lucky if —- you will be lucky to —- Look Marvin, I just cannot advise you to take this line of argument. If you really want to go that route, you will have to find a different lawyer.”

“Okay. Fine. I’ll use Solomon.”

“Solomon? Who is ‘Solomon’? Never heard of him. I mean, obviously, I know Solomon from the Bible but what firm are you talking about?”

“No firm, J.B. Solomon is one of the apps we built with my chipset.”

“An AI lawyer? Are you serious?”

“Oh, quite serious. He’s already informed me that he thinks he can win this case.”

“What? That’s preposterous. You told me you just designed the chips a year ago. What kind of experience could this Solomon have?”

“J.B., he’s been reading applicable statutes and case law for the last two months. Which means, he literally knows it all, when it comes to case law and the rulings beneath. Beyond that, Solomon has argued both sides of thousands of hypothetical cases.”

“What?! Ridiculous! But even so, he — or it —cannot possibly know how to anticipate or react to what happens in court!”

“But J.B., haven’t you been listening? Of course he can. He’s flexible, just like a real lawyer. Only…no offense…a lot smarter.”

“Computers cannot be that flexible. Ridiculous. How? No way.”

“Look, J.B., I have a picture to illustrate. See this pattern on the left [Picture at the top of page]? And, there it is on the right?”

“Yeah, beautiful. Same pattern. So what?”

“Ah, that’s just it! Is it the same pattern? Is it the same pattern or the same pattern?”

“Well. One is perfect and the other has little mistakes, but I guess it’s the same pattern.”

“Exactly, J.B. It’s the same basic pattern, but it’s not the identical instantiation of that pattern. Ordinary chips are like the picture on the left and mine are like the picture on the right. You see? And, that flexibility is built in at every level in every system. Because the underlying substrate uses an adaptive process with variations. It works 99% as efficiently as the system on the left, but it can accommodate things we didn’t think of. The difference between the system on the left and the system on the right is hubris. The one on the left is designed under the assumption that the designer knows everything of relevance. The system on the right is designed under the assumption that the designer, no matter how brilliant, does not know everything of relevance. You get it?”

“Yes, Marvin, I’m not stupid. But I am still not going to take your case. And, I strongly advise you not to rely on a robot. I am sorry to say it, Marvin, but I think you have picked on the wrong people this time. Attorney client privilege no longer applies and I have to send my recording of this conversation to the proper authorities.”

“Oh, that won’t be necessary Marvin. I’ve already broadcasts this through the social media. You’re famous! It will be good for your business.”

“What? Are you crazy? They will come get you, you fool.”

“Maybe, but if I am taken out over these chips without a trial, everyone will know, don’t you see? I sent it out everywhere, J.B. I might be ‘disappeared’ but I can’t just disappear unnoticed. Solomon’s idea, by the way.”

Author Page

Welcome, Singularity

All Around the Mulberry Bush

Imagine All the People

Dream Planet

Abracadabra!

20 Tuesday Jan 2026

Posted by petersironwood in apocalypse, The Singularity, Uncategorized

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"Citizens United", AI, Artificial Intelligence, biotech, chatgpt, chess, cognitive computing, Democracy, emotional intelligence, ethics, HCI, life, prediction, psychokinesis, technology, the singularity, truth, Turing, USA, UX

Abracadabra!

Here’s the thing.

There is no magic.

Of course, there is the magic of love and the wonder at the universe and so there is metaphorical magic. But there is no physical magic and no mathematical magic. Why do we care? Because in most science fiction scenarios, when super-intelligence happens, whether it is artificial or humanoid, magic happens. Not only can the super-intelligent person or computer think more deeply and broadly, they also can start predicting the future, making objects move with their thoughts alone and so on. Unfortunately, it is not just in science fiction that one finds such impossibilities but also in the pitches of companies about biotech and the future of artificial intelligence. Now, don’t get me wrong. Of course, there are many awesome things in store for humanity in the coming millennia, most of which we cannot even anticipate. But the chances of “free unlimited energy” and a computer that will anticipate and meet our every need are slim indeed.

This all-too popular exaggeration is not terribly surprising. I am sure much of what I do seems quite magical to our cats. People in possession of advanced or different technology often seem “magical” to those with no familiarity with the technology. But please keep in mind that making a human brain “better”, whether by making it bigger, or have more connections, or making it faster —- none of these alterations will enable the brain to move objects via psychokinesis. Yes, the brain does produce a minuscule amount of electricity, but way too little to move mountains or freight trains.

Of course, machines can be built to wield a lot of physical energy, but it isn’t the information processing part of the system that directly causes something in the physical world. It is through actuators of some type, just as it is with animals. Of course, super-intelligence could make the world more efficient. It is also possible that super-intelligence might discover as yet undiscovered forces of the universe. If it turns out that our understanding of reality is rather fundamentally flawed, then all bets are off. For example, if it turns out that there are twelve fundamental forces in the universe (or, just one), and a super-intelligent system determines how to use them, it might be possible that there is potential energy already stored in matter which can be released by the slightest “twist” in some other dimension or using some as yet undiscovered force. This might appear to human beings who have never known about the other 8 forces let alone how to harness them as “magic.”

There is another more subtle kind of “magic” that might be called mathematical magic. As known for a long time, it is theoretically possible to play perfect chess by calculating all possible moves, and all possible responses to those moves, etc. to the final draws and checkmates. It has been calculated that such an enumberation of contingencies would not be possible even if the entire universe were a nano-computer operating in parallel since the beginning of time. There are many similar domains. Just because a person or computer is way, way smarter does not mean they will be able to calculate every possibility in a highly complex domain.

Of course, it is also possible that some domains might appear impossibly complex but actually be governed by a few simple, but extremely difficult to discover laws. For instance, it might turn out that one can calculate the precise value of a chess position (encapsulating all possible moves implicitly) through some as yet undiscovered algorithm written perhaps in an as yet undesigned language. It seems doubtful that this would be true of every domain, but it is hard to say a priori.

There is another aspect of unpredictability and that has to do with random and chaotic effects. Imagine trying to describe every single molecule of earth’s seas and atmosphere in terms of it’s motion and position. Even if there were some way to predict state N+1 from N, we would have to know everything about state N. The effects of the slightest miscalculation or missing piece of data could be amplified over time. So long term predictions of fundamentally chaotic systems like weather, or what your kids will be up to in 50 years, or what the stock market will be in 2600 are most likely impossible, not because our systems are not intelligent enough but because such systems are by their nature not predictable. In the short term, weather is largely, though not entirely, predictable. The same holds for what your kids will do tomorrow or, within limits, what the stock market will do. The ability to do long term prediction is quite different.

In The Sciences of the Artificial, Herb Simon provides a nice thought experiment about the temperature in various regions of a closed space. I am paraphrasing, but imagine a dormitory with four “quads.” Each quad has four rooms and each room is partitioned into four areas with screens. The screens are not very good insulators so if the temperature in these areas differ, they will quickly converge. In the longer run, the temperature will tend toward average in the entire quad. In the very long term, if no additional energy is added, the entire dormitory will tend toward the global average. So, when it comes to many kinds of interactions, nearby interactions dominate, but in the long term, more global forces come into play.

Now, let us take Simon’s simple example and consider what might happen in the real world. We want to predict what the temperature is in a particular partitioned area in 100 years. In reality, the dormitory is not a closed system. Someone may buy a space heater and continually keep their little area much warmer. Or, maybe that area has a window that faces south. But it gets worse. Much worse. We have no idea whether this particular dormitory will even exist in 100 years. It depends on fires, earthquakes, and the generosity of alumni. In fact, we don’t even know whether brick and mortar colleges in general will exist in 100 years. Because as we try to predict in longer and longer time frames, not only do more distant factors come into play in terms of physical distance. The determining factors are also distant conceptually. In a 100 year time frame, the entire college may or may not exist and we don’t even know whether the determining factor(s) will be financial, astronomical, geological, political, social, physical or what. This is not a problem that will be solved via “Artificial Intelligence” or by giving human beings “better brains” via biotech.

Whoa! Hold on there. Once again, it is possible that in some other dimension or using some other as yet undiscovered force, there is a law of conservation so that going “off track” in one direction causes forces to correct the imbalance and get back on track. It seems extremely unlikely, but it is conceivable that our model of how the universe works is missing some fundamental organizing principle and what appears to us as chaotic is actually not.

The scary part, at least to me, is that some descriptions of the wonderful world that awaits us (once our biotech or AI start-up is funded) is that that wonderful world depends on their being a much simpler, as yet unknown force or set of forces that is discoverable and completely unanticipated. Color me “doubting Thomas” on that one.

It isn’t just that investing in such a venture might be risky in terms of losing money. It is that we humans are subject to blind pride that makes people presume that they can predict what the impact of making a genetic change will be, not just on a particular species in the short term, but on the entire planet in the long run. We can indeed make small changes in both biotech and AI and see improvements in our lives. But when it comes to recreating dinosaurs in a real life Jurassic Park or replacing human psychotherapists with robotic ones, we really cannot predict what the net effect will be. As humans, we are certainly capable of containing and testing and imagining possibilities and slowly testing them as we introduce them. Yeah. That could happen. But…

What seems to actually happen, however, is that companies not only want to make more money; they want to make more money now. We have evolved social and legal and political systems that put almost no brakes on runaway greed. The result is that more than one drug has been put on the market that has had a net negative effect on human health. This is partly because long term effects are very hard to ascertain, but the bigger cause is unbridled greed. Corporations, like horses, are powerful things. You can ride farther and faster on a horse. And certainly corporations are powerful agents of change. But the wise rider is master or partner with a horse. They don’t allow themselves to be dragged along the ground by rope and let the horse go wherever it will. Sadly, that is precisely the position that society is vis a vis corporations. We let them determine the laws. We let them buy elections. We let them control virtually every news medium. We no longer use them to get amazing things done. We let them use us to get done what they want done. And what is that thing that they want done? Make hugely more money for a very few people. Despite this, most companies still manage to do a lot of net good in the world. I suspect this is because human beings are still needed for virtually every vital function in the corporation.

What will happen once the people in a corporation are no longer needed? What will happen when people who remain in a corporation are no longer people as we know them, but biologically altered? It is impossible to predict with certainty. But we can assume that it will seem to us very much like magic.

Very.

Dark.

Magic.

Abracadabra!

Turing’s Nightmares

After All

All We Stand to Lose

The Crows and Me

The Last Gleam of Twilight

Guernica

The Orange Man

Donnie Visits Granny

How the Nightingale Learned to Sing

An Open Sore from Hell

The Impossible

Pattern Language Summary

The Midnight Flight to Crazytown

To Be or Not to Be

Peace

Who Won the War?

We Won the War! We Won the War!

At Least He’s Our Monster

Stoned Soup

Fifteen Properties

Tools of Thought: And then what?

The Walkabout Diaries: Sunsets

All that Glitters is not Gold

When Greed’s the Only Creed

We won the war! We won the war!

Systems Thinking: Positive Feedback Loops

17 Wednesday Dec 2025

Posted by petersironwood in America, creativity, psychology, Uncategorized

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AI, Artificial Intelligence, books, chatgpt, Design, Feedback, government, innovation, leadership, learning, machine learning, management, politics, POTUS, problem solving, science, sense-making, society, systems thinking, thinking, vicious circle

Systems Thinking: Positive Feedback Loops

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One of the most important tools of thought that anyone can learn: “Systems Thinking.” I touched on this in yesterday’s post “And Then What.” I pointed out that when you take an action that impacts a system such as a human being, a family, or a country, it often does not react in a mechanical way.

Here are some examples. For many years, the United States and the USSR were involved in a cold war arms race. Every time the USSR added more nuclear missiles to their arsenal, the people in America felt less safe. Since they felt less safe, they increased their armaments. When the USA increased nuclear weapons, this made the Soviet Union feel less safe so they increased their arms again and so on. This is what is known in Systems Thinking as a “Positive Feedback Loop.” It is also popularly known as a “Vicious Circle” or “Vicious Cycle.”

Let’s say that you are in pretty good shape physically and regularly run, play tennis, or work out. The more you exercise (up to a point), the better you feel. Feeling better makes you feel more like exercise and more exercise makes you feel better. People call this a “Virtuous Cycle” or “Virtuous Circle” because we think the outcome is good. But formally, it is the same kind of cycle.

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The most important thing to recognize about a Positive Feedback Loop is that it can be run in either direction. At some point, the US reduced their nuclear arsenal and this decreased the perceived threat to folks in the Soviet Union so the soviets felt that they could also reduce their nuclear arsenal which in turn, made people in the US feel safer and led to further reductions and so on.

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Similarly, if you stop exercising for a month, you will tend to feel pretty crappy. Feeling crappy makes you feel less like exercising and this in turn makes you exercise less which in turn makes you even more out of shape, feel worse and be even less likely to exercise. You can break such a “vicious circle” by starting to exercise – even it it’s just a little to start moving the circle in the “virtuous” direction. (Incidentally, that’s why I wrote “Fit in Bits” which describes many easy exercises to get you started).

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“Vicious circles” also often cause disagreements to escalate into arguments and arguments into fights. Each person feels “obligated” not to “give in” and the nastier their opponent becomes, the nastier they become.

“Fawlty Towers” (https://en.wikipedia.org/wiki/Fawlty_Towers), a British sit-com uses “Positive Feedback Loops” in the escalating action of the comedy plots. John Cleese plays the co-owner Basil (with his wife, Sybil) of a small hotel. Typically, John Cleese makes some rather trivial but somewhat embarrassing mistake which he wants to hide from his wife. In the course of trying to cover up this rather small mistake, he has to lie, avoid, or obfuscate. This causes an even more egregious mistake which makes him even more embarrassed so he must result to still more outlandish lies and trickery in order to cover up the second mistake which in turn causes an even bigger mistake, and so on.

That pattern of behavior reminds me of the current POTUS who is famously unable to admit to an error or lie and uses a second and bigger error or lie to try to cover up the first lie and so on. He seems, in fact, completely incapable of “systems thinking.”

For example, he may see (and exaggerate) a real, but containable threat such as a trade deficit. He sees the US send more money out of the country than the US takes in from trade. That’s a legitimate issue. But the approach he takes is to ZAP the other parties by slapping on tariffs without any real appreciation of the fact that our trading partners are extremely unlikely to react to tariffs on their products by simply doing nothing. One could use logic, empathy, or a look at history to determine that what is much more likely is that the other countries will put tariffs on our goods (which, of course, is precisely what happened).

Similarly, he demands absolute loyalty. He repeatedly puts himself and his own interests above the law, the Constitution, the good of the country and the good of his party. He expects everyone loyal to him to do the same. But he betrays these loyal appointees, friends, and wives whenever it suits him. He thinks he is being “smart” by doing what seems to be in his best interest at that moment. But what he fails to see is that by being disloyal to so many people who have been mainly loyal to him, he encourages his so-called “allies” to only be loyal to him while it suits their interests.

In the Pattern “Reality Check,” I point out that such behavior is an occupational hazard for dictators. Apparently, it can even be such a hazard for would-be dictators as well. By surrounding himself with those who always lie, cover for him, laud him, cater to his insane whims, etc., such a dictator (or would-be dictator) loses touch with what is really going on. He becomes more and more disconnected from sensible action yet those who remain loyal must say and do more and more outrageous things to keep the dictator from finding out just how bad things really are. Eventually, the Emperor with no clothes may die of hypothermia because no-one has the courage to tell him that he’s actually wearing no protection against the elements!

Positive feedback loops exist in purely natural systems as well as biological and social systems. For example, increased global mean temperatures mean less arctic ice which means more solar radiation will be absorbed by the earth’s dark oceans rather than reflected back into space by the white ice and snow. This, of course, makes the earth hotter still. In addition, the thawing of tracts of arctic tundra also releases more methane gas into the atmosphere which is even more effective at trapping the earth’s heat than is carbon dioxide. Global climate change also makes forest fires more prevalent which directly spews more carbon dioxide into the air and reduces the number of trees that help mitigate the emissions of carbon dioxide by turning it into oxygen through photosynthesis.

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A concept closely related to “Vicious Cycles” is that of “Cognitive Dissonance.” Basically, people like to believe that they are honest and competent. Much like John Cleese (Basil) in Falwty Towers, once they do something dishonest or incompetent, their first reaction is not to believe that they did something dishonest or incompetent. They will now try to distort reality by misperceiving, mis-remembering, or distracting.

For example, at the height of the Vietnam War, I was horrified at the beatings perpetrated by the police on peaceful protestors at the Democratic National Convention. I was also disturbed at the techniques the Democrats used at their convention to silence the voices of dissension within the convention. Candidate Nixon claimed he had a “secret plan” to end the War in Vietnam. I voted for Nixon. As it became clear that Nixon was a crook, I decided that I had made a mistake voting for the man. But I could have taken another path which would be to “double down” on the original mistake by continuing to support Nixon and dismiss all the growing evidence of his misdeeds. As his malfeasance became more and more egregious, it made the egregiousness of my original mistake of voting for him grow as well. So, it would be possible to become ever more invested in not believing the overwhelming evidence of his treachery. (Now, it turns out, it was even worse than we knew at the time. He actively thwarted the peace efforts of Johnson!). Perhaps because I’ve been trained as a scientist and science values the truth very highly, I did not fall prey to that particular instance of “Cognitive Dissonance.” I readily admitted it was a stupid mistake to vote for Nixon.

Of course, today, we see many people not just backed into a corner to support the current POTUS but backed into a corner of a corner. Instead of believing that a liar is lying, they protect their “integrity” by insisting that everything and everyone else is lying: the newspapers, the reporters, his opposition, people in other countries, his former business partners, his former customers, the CIA, the FBI, the NSA. Ironically, for some people, it would be easier to admit that voting for a slightly inferior candidate was a mistake than to admit that voting for a hugely inferior candidate was a mistake. Voting for a slightly inferior candidate is easily understood but if they voted for a candidate that bad and bad in so many ways it was a huge error. And now, as each new revelation comes to light, it is more and more and more embarrassing to admit what a huge mistake it was.

Another common example of a “Vicious Circle” is addiction. A small amount of alcohol, nicotine or heroine makes you feel better. But taking the drug increases your tolerance for it. So, next time, to feel better, you need to take a little more. Taking a little more increases your tolerance still further so now you need to take a still higher dosage in order to feel better. When you do, however, your tolerance increases still more. Whether it is drugs, gambling, addictive sex, or unbridled greed, the mechanism is the same. You need more and more and more over time due to the nature of the “Positive Feedback Loop.”

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A similar mechanism may be at work in the minds of apologists for the NRA (National Rifle Association). As more and more innocent people are killed partly because of easy access to guns, the mistake of supporting the NRA in their refusal to support mandatory vetting, training, and competency demonstrations for gun owners becomes an ever-more obviously egregious error. But, rather than making this more likely for supporters to admit to such an error and therefore change their position, every new slew of innocent children killed for no reason makes them actually less likely to change their position. According to Cognitive Dissonance, every such death makes their earlier decision worse – unless there is some counter-balancing argument. As the number of innocent deaths arises, and indeed, as more and more evidence of the perfidy of the NRA becomes clear, many who previously supported the NRA become ever more entrenched because they “buy into” the great value of unlimited access to guns ever more. Why? They continue their support because not to do so makes them complicit in more and more horrendous crimes.

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If you can see such patterns in your own behavior and in others, you can better choose the correct course of action for yourself and be more thoughtful in how you communicate with others about their errors. Hint: Trying to make people feel more guilty for their stupid decisions will likely backfire.

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I Went in Seeking Clarity

10 Wednesday Dec 2025

Posted by petersironwood in AI, creativity, HCI, psychology, Uncategorized, user experience

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AI, Artificial Intelligence, coding, parallel programming, problem formulation, problem framing, problem solving, programming, technology, thinking, tools, X10

“I stopped by the bar at 3 A.M.
To seek solace in a bottle or possibly a friend
And I woke up with a headache like my head against a board
Twice as cloudy as I’d been the night before
And I went in seeking clarity” — Lyrics from The Indigo Girls: Closer to Fine

If you think programming is cognitively difficult, try parallel programming. It is generally harder to design, to code, and to debug than its sequential cousin. One of the fun projects I worked on at IBM Research was on the X10 language which was designed to enable parallel programmers to be more productive. Among other things, I fostered community among X10 programmers and used analytic techniques to show that X10 “should be” more productive. Although these analytic techniques are very useful, we also wanted to get some empirical data that the language was, in actuality, more productive.

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One part of those empirical studies involved comparing people doing a few parallel programming tasks in X10 to those using a popular competitor. But, like many other “chicken and egg” problems, there were no X10 programmers (other than the inventors and their colleagues). I was part of a team who travelled to Rice University in Houston. The design called for one group to spend a chunk of time learning X10 (perhaps half a day) and another chunk of time coding some problems.

Besides the three behavioral scientists like me who were there to make observations, there were also three high-powered Ph.D. computer scientists present who would teach the language. Programmers tend to be very smart. Parallel programmers tend to be very very smart. People who can invent better languages to do parallel programming? You do the math.

Anyway, after the volunteers students had arrived, one of the main designers of the language began to “teach them” X10.

But — there was a problem.

The powerpoint presentation designed to teach the students X10 was far too blurry to read!

Immediately, the three computer scientists tried to issue commands to the projector to put the images in focus. Nothing worked. The three of them began a fascinating problem solving conversation. The conversation concerned what communication protocol(s) among the PC, the projector, and the controller was the likely source of the problem. I suppose it might not have been fascinating to everyone, but it was to me. First, it fascinated me because I was learning something about computer science and communication protocols. Second, it fascinated me because I loved to watch these people think. I suppose many of the advanced computer science students who were in this classroom to learn X10 also found it interesting. Third, I found it fascinating because my dissertation was about human problem solving and I’ve been interested in it ever since.

But the study itself had completely stalled.

After a few minutes of fascinating conversation that did nothing to focus the images, something possessed me to walk over to the projector and turn the lens by hand. The images were immediately clear and the rest of the experiment continued.

The three computer scientists had “framed” the problem as a computer science problem and I found the discussion that sprang from that framing to be fascinating. But one of the part-time jobs I had had as an undergraduate was as a “projectionist” at Case-Western, and it was that experience that allowed me to try framing the problem differently. All of us have huge reservoirs of experience outside of our professional “training” and those experiences can sometimes be important sources of alternative ways to frame a problem, issue, or situation.

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Essays on America: Wednesday

Essays on America: The Update Problem

Essays on America: The Stopping Rule

The Invisibility Cloak of Habit

Labelism

Tools of Thought

Where Does Your Loyalty Lie?

Stoned Soup

Travels with Sadie: Teamwork

Author Page on Amazon

Turing’s Nightmares: Eight

21 Friday Nov 2025

Posted by petersironwood in psychology, The Singularity, Uncategorized

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AI, Artificial Intelligence, cognitive computing, collaboration, cooperation, openai, peace, philosophy, seva, teamwork, technology, the singularity, Turing, ubuntu, United Peoples Ecosystem

Workshop on Human Computer Interaction for International Development

In chapter 8 of Turing’s Nightmares, I portray a quite different path to ultra-intelligence. In this scenario, people have begun to concentrate their energy, not on building a purely artificial intelligence; rather they have explored the science of large scale collaboration. In this way, referred to by Doug Engelbart among others as Intelligence Augmentation, the “super-intelligence” comes from people connecting.

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It could be argued, that, in real life, we have already achieved the singularity. The human race has been pursuing “The Singularity” ever since we began to communicate with language. Once our common genetic heritage reached a certain point, our cultural evolution has far out-stripped our genetic evolution. The cleverest, most brilliant person ever born would still not be able to learn much in their own lifetime compared with what they can learn from parents, siblings, family, school, society, reading and so on.

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One problem with our historical approach to communication is that it evolved for many years among a small group of people who shared goals and experiences. Each small group constituted an “in-group” but relations with other groups posed more problems. The genetic evidence, however, has become clear that even very long ago, humans not only met but mated with other varieties of humans proving that some communication is possible even among very different tribes and cultures.

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More recently, we humans started traveling long distances and trading goods, services, and ideas with other cultures. For example, the brilliance of Archimedes notwithstanding, the idea of “zero” was imported into European culture from Arab culture. The Rosetta Stone illustrates that even thousands of years ago, people began to see the advantages of being able to translate among languages. In fact, modern English contains phrases even today that illustrate that the Norman conquerers found it useful to communicate with the conquered. For example, the phrase, “last will and testament” was traditionally used in law because it contains both the word “will” with Germanic/Saxon origins and the word “testament” which has origins in Latin. Many other traditional legal terms in English have similar bilingual origins.

Automatic translation across languages has made great strides. Although not so accurate as human translation, it has reached the point where the essence of many straightforward communications can be usefully carried out by machine. The advent of the Internet, the web, and, more recently google has certainly enhanced human-human communication. It is worth noting that the tremendous value of google arises only a little through having an excellent search engine but much more though the billions of transactions of other human beings. People are exploring and using MOOCs, on-line gaming, e-mail and many other important electronically mediated tools.

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Equally importantly, we are learning more and more about how to collaborate effectively both remotely and face to face, both synchronously and asynchronously. Others continue to improve existing interfaces to computing resources and inventing others. Current research topics include how to communicate more effectively across cultural divides; how to have more coherent conversations when there are important differences in viewpoint or political orientation. All of these suggest that as an alternative or at least an adjunct to making purely separate AI systems smarter, we can also use AI to help people communicate more effectively with each other and at scale. Some of the many investigators in these areas include Wendy Kellogg, Loren Terveen, Joe Konstan, Travis Kriplean, Sherry Turkle, Kate Starbird, Scott Robertson, Eunice Sari, Amy Bruckman, Judy Olson, and Gary Olson. There are several important conferences in the area including European Conference on Computer Supported Cooperative Work, and Conference on Computer Supported Cooperative Work, and Communities and Technology. It does not seem at all far-fetched that we can collectively learn, in the next few decades how to take international collaboration to the next level and from there, we may well have reached “The Singularity.”

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For further reading, see: Thomas, J. (2015). Chaos, Culture, Conflict and Creativity: Toward a Maturity Model for HCI4D. Invited keynote @ASEAN Symposium, Seoul, South Korea, April 19, 2015.

Thomas, J. C. (2012). Patterns for emergent global intelligence. In Creativity and Rationale: Enhancing Human Experience By Design J. Carroll (Ed.), New York: Springer.

Thomas, J. C., Kellogg, W.A., and Erickson, T. (2001). The Knowledge Management puzzle: Human and social factors in knowledge management. IBM Systems Journal, 40(4), 863-884.

Thomas, J. C. (2001). An HCI Agenda for the Next Millennium: Emergent Global Intelligence. In R. Earnshaw, R. Guedj, A. van Dam, and J. Vince (Eds.), Frontiers of human-centered computing, online communities, and virtual environments. London: Springer-Verlag.

Thomas, J.C. (2016). Turing’s Nightmares. Available on Amazon. http://tinyurl.com/hz6dg2

An Inside View of IBMs Innovation Jam

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Turing’s Nightmares: The Road Not Taken

Pattern Language for Collaboration and Cooperation

Imagine All the People…

Take a Glance; Join the Dance

Corn on the Cob

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Turing’s Nightmares: Seven

20 Thursday Nov 2025

Posted by petersironwood in The Singularity, Uncategorized

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Axes to Grind.

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Why the obsession with building a smarter machine? Of course, there are particular areas where being “smarter” really means being able to come up with more efficient solutions. Better logistics means you can deliver items to more people more quickly with fewer mistakes and with a lower carbon footprint. That seems good. Building a better Chess player or a better Go player might have small practical benefit, but it provides a nice objective benchmark for developing methods that are useful in other domains as well. But is smarter the only goal of artificial intelligence?

What would or could it mean to build a more “ethical” machine? Can a machine even have ethics? What about building a nicer machine or a wiser machine or a more enlightened one? These are all related concepts but somewhat different. A wiser machine, to take one example, might be a system that not only solves problems that are given to it more quickly. It might also mean that it looks for different ways to formulate the problem; it looks for the “question behind the question” or even looks for problems. Problem formulation and problem finding are two essential skills that are seldom even taught in schools for humans. What about the prospect of machines that do this? If its intelligence is very different from ours, it may seek out, formulate, and solve problems that are hard for us to fathom.

For example, outside my window is a hummingbird who appears to be searching the stone pine for something. It is completely unclear to me what he is searching for. There are plenty of flowers that the hummingbirds like and many are in bloom right now. Surely they have no trouble finding these. Recall that a hummingbird has an incredibly fast metabolism and needs to spend a lot of energy finding food. Yet, this one spent five minutes unsuccessfully scanning the stone pine for … ? Dead straw to build a nest? A mate? A place to hide? A very wise machine with freedom to choose problems may well pick problems to solve for which we cannot divine the motivation. Then what?

In this chapter, one of the major programmers decides to “insure” that the AI system has the motivation and means to protect itself. Protection. Isn’t this the major and main rationalization for most of the evil and aggression in the world? Perhaps a super intelligent machine would be able to manipulate us into making sure it was protected. It might not need violence. On the other hand, from the machine’s perspective, it might be a lot simpler to use violence and move on to more important items on its agenda.

This chapter also raises issues about the relationship between intelligence and ethics. Are intelligent people, even on average, more ethical? Intelligence certainly allows people to make more elaborate rationalizations for their unethical behavior. But does it correlate with good or evil? Lack of intelligence or education may sometimes lead people to do harmful things unknowingly. But lots of intelligence and education may sometimes lead people to do harmful things knowingly — but with an excellent rationalization. Is that better?

Even highly intelligent people may yet have significant blind spots and errors in logic. Would we expect that highly intelligent machines would have no blind spots or errors? In the scenario in chapter seven, the presumably intelligent John makes two egregious and overt errors in logic. First, he says that if we don’t know how to do something, it’s a meaningless goal. Second, he claims (essentially) that if empathy is not sufficient for ethical behavior, then it cannot be part of ethical behavior. Both are logically flawed positions. But the third and most telling “error” John is making is implicit — that he is not trying to dialogue with Don to solve some thorny problems. Rather, he is using his “intelligence” to try to win the argument. John already has his mind made up that intelligence is the ultimate goal and he has no intention of jointly revisiting this goal with his colleague. Because, at least in the US, we live in a hyper-competitive society where even dancing and cooking and dating have been turned into competitive sports, most people use their intelligence to win better, not to cooperate better.

If humanity can learn to cooperate better, perhaps with the help of intelligent computer agents, we can probably solve most of the most pressing problems we have even without super-intelligent machines. Will this happen? I don’t know. Could this happen? Yes. Unfortunately, Roger is not on board with that program toward better cooperation and in this scenario, he has apparently ensured the AI’s capacity for “self-preservation through violent action” without consulting his colleagues ahead of time. We can speculate that he was afraid that they might try to prevent him from doing so either by talking him out of it or appealing to a higher authority. But Roger imagined he “knew better” and only told them when it was a fait accompli. So it goes.

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Turing’s Nightmares

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The Loud Defense of Untenable Positions

Turing’s Nightmares: Six

19 Wednesday Nov 2025

Posted by petersironwood in sports, The Singularity, Uncategorized

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Human Beings are Interested in Human Limits.

About nine years ago, an Google AI system won its match over the human Go champion. Does this mean that people will lose interest in Go? I don’t think so. It may eventually mean that human players will learn faster and that top-level human play will increase. Nor, will robot athletes supplant human athletes any time soon.

Athletics provides an excellent way for people to get and stay fit, become part of a community, and fight depression and anxiety. Watching humans vie in athletic endeavors helps us understand the limits of what people can do. This is something that our genetic endowment has wisely made fascinating. To a lesser extent, we are also interested in seeing how fast a horse can run, or how fast a hawk can dive or how complex a routine a dog can learn.

In Chapter 6 of “Turing’s Nightmares” I briefly explore a world where robotic competitors have replaced human ones. In this hypothetical world, the super-intelligent computers also find that sports is an excellent venue for learning more about the world. And, so it is! In “The Winning Weekend Warrior”, I provide many examples of how strategies and tactics useful in the sports world are also useful in business and in life. (There are also some important exceptions that are worth noting. In sports, you play within the rules. In life, you can play with some of the rules.)

Chapter 6 also brings up two controversial points that ethicists and sports enthusiasts should be discussing now. First, sensors are becoming so small, powerful, accurate, and lightweight that is possible to embed them in virtually any piece of sports equipment(e.g., tennis racquets). Few people would call it unethical to include such sensors as training devices. However, very soon, these might also provide useful information during play. What about that? Suppose that you could wear a device that not only enhanced your sensory abilities but also your motor abilities? To some extent, the design of golf clubs and tennis racquets and swimsuits are already doing this. Is there a limit to what would or should be tolerated? Should any device be banned? What about corrective lenses? What about sunglasses? Should all athletes have to compete nude? What about athletes who have to take “performance enhancing” drugs just to stay healthy? Sharapova’s recent case is just one. What about the athlete of the future who has undergone stem cell therapy to regrow a torn muscle or ligament? Suppose a major league baseball pitcher tears a tendon and it is replaced with a synthetic tendon that allows a faster fast ball?

With the ever-growing power of computers and the collection of more and more data, big data analytics makes it possible for the computer to detect patterns of play that a human player or coach would be unlikely to perceive. Suppose a computer system is able to detect reliable “cues” that tip off what pitch a pitcher is likely to throw or whether a tennis player is about to hit down the tee or out wide? Novak Djokovic and Ted Williams were born with exceptional visual acuity. This means that they can pick out small visual details more quickly than their opponents and react to a serve or curve more quickly. But it also means that they are more likely to pick up subtle tip-offs in their opponents motion that give away their intentions ahead of time. Would we object if a computer program analyzed thousands of serves by Jannik Sinner or Carlos Alcaraz in order to detect patterns of tip-offs and then that information was used to help train Alexander Zerev to learn to “read” the service motions of his opponents? Of course, this does not just apply to tennis. It applies to reading a football play option, a basketball pick, the signals of baseline coaches, and so on.

Instead of teaching Zerev these patterns ahead of time, suppose he were to have a device implanted in his back that received radio signals from a supercomputer able to “read” where the serve were going a split second ahead of time and it was this signal that allowed Alexander to anticipate better?

I do not know the “correct” ethical answer for all of these dilemmas. To me, it is most important to be open and honest about what is happening. So, if Lance Armstrong wants to use performance enhancing drugs, perhaps that is okay if and only if everyone else in the race knows that and has the opportunity to take the same drugs and if everyone watching knows it as well. Similarly, although I would prefer that tennis players only use IT for training, I would not be dead set against real time aids if the public knows. I suspect that most fans (like me) would prefer their athletes “un-enhanced” by drugs or electronics. Personally, I don’t have an issue with using any medical technology to enhance the healing process. How do others feel? And what about athletes who “need” something like asthma medication in order to breathe but it has a side-effect of enhancing performance?

Would the advent of robotic tennis players, baseball players or football players reduce our enjoyment of watching people in these sports? I think it might be interesting to watch robots in these sports for a time, but it would not be interesting for a lifetime. Only human athletes would provide on-going interest. What do you think?

Readers of this blog may also enjoy “Turing’s Nightmares” and “The Winning Weekend Warrior.” John Thomas’s author page on Amazon

Welcome Singularity

The Day from Hell

Indian Wells Tennis Tournament

Take a Glance; Join the Dance

US Open Closed

Life is a Dance

Donnie Gets a Tennis Trophy

Math Class: Who are you?

The Agony of the Feet

Wordless Perfection

The Jewels of November

Turing’s Nightmares: Chapter Five

17 Monday Nov 2025

Posted by petersironwood in The Singularity, Uncategorized

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AI, Artificial Intelligence, chatgpt, cognitive computing, health, medicine, Personal Assistant, philosophy, technology, the singularity, Turing

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An Ounce of Prevention: Chapter 5 of Turing’s Nightmares

Hopefully, readers will realize that I am not against artificial intelligence (after all, I ran an AI lab for a dozen years); nor do I think the outcomes of increased artificial intelligence are all bad. Indeed, medicine offers a large domain where better artificial intelligence is likely to help us stay healthier longer. IBM’s Watson had already begun “digesting” the vast and ever-growing medical literature more than a decade ago. As investigators discover more and more about what causes health and disease, we will also need to keep track of more and more variables about an individual in order to provide optimal care. But more data points also means it will become harder for a time-pressed doctor or nurse to note and remember every potentially relevant detail about a patient. Certainly, personal assistants can help medical personnel avoid bad drug interactions, keep track of history, and “perceive” trends and relationships in complex data more quickly than people are likely to. In addition, in the not too distant future, we can imagine AI programs finding complex relationships and “invent” potential treatments.

Not only medicine, but health provides a number of opportunities for technology to help. People often find it tricky to “force themselves” to follow the rules of health that they know to be good such as getting enough exercise. Fit Bit, Activity Tracker, LoseIt and similar IT apps help track people’s habits and for many, this really helps them stay fit. As computers become more aware of more and more of our personal history, they can potentially find more personalized ways to motivate us to do what is in our own best interest.

In Chapter 5 of Turing’s Nightmares, we find that Jack’s own daughter, Sally is unable to persuade Jack to see a doctor. The family’s PA (personal assistant), however, succeeds. It does this by using personal information about Jack’s history in order to engage him emotionally, not just intellectually. We have to assume that the personal assistant has either inferred or knows from first principles that Jack loves his daughter and the PA also uses that fact to help persuade Jack.

It is worth noting that the PA in this scenario is not at all arrogant. Quite the contrary, the PA acts the part of a servant and professes to still have a lot to learn about human behavior. I am reminded of Adam’s “servant” Lee in John Steinbeck’s East of Eden. Lee uses his position as “servant” to do what is best for the household. It’s fairly clear to the reader that, in many ways, Lee is in charge though it may not be obvious to Adam.

In some ways, having an AI system that is neither “clueless” as most systems are today nor “arrogant” as we might imagine a super-intelligent system to be (and as the systems in chapters 2 and 3 were), but instead feigning deference and ignorance in order to manipulate people could be the scariest stance for such a system to take. We humans do not like being “manipulated” by others, even when it for our own “good.” How would we feel about a deferential personal assistant who “tricks us” into doing things for our own benefit? What if they could keep us from over-eating, eating candy, smoking cigarettes, etc.? Would we be happy to have such a good “friend” or would we instead attempt to misdirect it, destroy it, or ignore it? Maybe we would be happier with just having something that presented the “facts” to us in a neutral way so that we would be free to make our own good (or bad) decision. Or would we prefer a PA to “keep us on track” even while pretending that we are in charge?

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