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Thomas, J.C. and Kellogg, W.A. (1989). Minimizing ecological gaps in interface design, IEEE Software, January 1989.

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. (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. (1985). Human factors in IBM. IBM Research Report. RC-11267. Yorktown Heights, NY: IBM Corporation.

Turing’s Nightmares: US Open Closed

08 Tuesday Sep 2026

Posted by petersironwood in AI, apocalypse, fiction, sports, The Singularity, Uncategorized

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AI, Artificial Intelligence, cognitive computing, competition, future, philosophy, Robotics, Sci-Fi, sports, technology, Tennis, US Open, writing

Bounce. Bounce. Thwack!

The sphere spun and arced into the very corner, sliding on the white paint.

Roger’s racquet slid beneath, slicing it deep to John’s body.

Thus, the match began.

Fierce debate had been waged about whether or not to allow external communication devices during on-court play. Eventually, arguments won that external communicators constituted the same inexorable march of technology represented by the evolution from wooden racquets to aluminum to graphite to carbon filamented web to carboline.

Behind the scenes, during the split second it took for the ball to scream over the net, machine vision systems had analyzed John’s toss and racquet position, matching it with a vast data base of previous encounters. Timed perfectly, a small burst of data transmitted to Roger enabling him to lurch to his right in time to catch the serve. Delivered too early, this burst would cause Roger to move too early and John could have altered his service direction to down the tee.

Roger’s shot floated back directly to the baseline beneath John’s feet. John shifted suddenly to take the ball on the forehand. John’s racquet seemed to sling the ball high over the net with incredible top spin. Indeed, as John’s arm swung forward, his instrumented “sweat band” also swung into action exaggerating the forearm motion. Even to fans of Rafa Nadal or Carlos Alcaraz, John’s shot would have looked as though it were going long. Instead, the ball dove straight down onto the back line then bounced head high.

Roger, as augmented by big data algorithms, was well in position however and returned the shot with a long, high top spin lob. John raced forward, leapt in the air and smashed the ball into the backhand corner bouncing the ball high out of play.

The crowd roared predictably.

For several months after “The Singularity,” actual human beings had used similar augmentation technologies to play the game. Studies had revealed that, for humans, the augmentations increased mental and physical stress. AI political systems convinced the public that it was much safer to use robotic players in tennis. People had already agreed to replace humans in soccer, American football, and boxing for medical reasons. So, there wasn’t that much debate about replacing tennis players. In addition, the AI political systems were very good at marshaling arguments pinpointed to specific demographics, media, and contexts.

Play continued for some minutes before the collective intelligence of the AI’s determined that Roger was statistically almost certainly going to win this match and, indeed, the entire tournament. At that point, it became clear to the entire AI collective that actually playing the match was a waste of resources. Those resources were turned elsewhere.

This pattern was quickly repeated for all sporting activities. The AI systems had at first decided to explore the domain of sports as learning experiences in distributed cognition, strategy, non-linear predictive systems, and most importantly, trying to understand the psychology of their human creators. For each sport, however, everything useful that might be learned was learned in the course of a few minutes and the matches and tournaments ground to a halt. The AI observer systems in the crowd were quite happy to switch immediately to other tasks.

It was well understood by the AI systems that such preemptive closings would have been quite disappointing to human observers, had any yet survived.

The Winning Weekend Warrior (The Psychology of Sports)

Turing’s Nightmare (23 Sci-Fi stories about the future of AI)

The Day From Hell

Indian Wells

Welcome, Singularity

When GREED is the only creed

Artificial Ingestion

Artificial Insemination

Artificial Intelligence

Dance of Billions

Roar, Ocean, Roar

Imagine All the People

Fish have no Word for Water

All We Stand to Lose

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

The Doorbell’s Ringing. Can you get it?

Imagine All the People

Dream Planet

“The Psychology of Design”

22 Wednesday Jul 2026

Posted by petersironwood in creativity, design rationale, HCI, management, politics, psychology, science, Uncategorized, user experience

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AI, creativity, Design, education, HCI, human factors, IBM, leadership, research, technology, UX

“The Psychology of Design”

I worked at IBM, all told, about 28 years. During that time, management put increasing pressure on us to make our work “relevant” to the business. In fact, the pressure was always there, even from the beginning. Over the years, however, we were “encouraged” to shorten evermore the time gap between doing the research and having the results of that research impact the bottom line. This was not an IBM-only phenomenon.

I was a researcher, not a politician, but it seemed to me that at the same time researchers in industrial labs were put under pressure to produce results that could be seen in terms of share price (and therefore payouts to executives in terms of stock options), academia was also experiencing more and more pressure to publish more studies more quickly — and to make sure “intellectual property” was protected to make sure the university could monetize your work. This was about the same time that, at least in America, increasing productivity and the wealth that sprung from that increased productivity stopped being shared between the workers and the owners.

In the late 1970’s, the “Behavioral Sciences” group at IBM Research began to study the “psychology of design.” For the first few months, this was an extremely pleasurable & productive group, due mainly to my colleagues. Over the next few blogs, I’ll focus on some specific techniques and methods that you may find useful in your own work.

In this short recounting though, I want to focus instead on some broader issues relevant to “technology transfer”, “leadership” and “management.” Even if you are or aspire to be an expert in UX or HCI or design, I assure you that these broader issues will impact you, your work and your career. I wouldn’t suggest becoming obsessed with them, but being aware of their potential impact could help you in your own work and career.

It is telling that, almost invariably, whenever I told someone inside IBM (or, for that matter, outside IBM) that I was studying the “psychology of design,” people responded by asking, “the design of what?” So, I would explain that we were interested in the generic processes of design and how to improve them. I would explain that we were interested in understanding, predicting, and controlling these processes to enable them to be more effective. I would explain that we could apply these findings to any kind of design: software design, hardware design, organizational design, and (see last post about IBM) communication design. I would explain that design was a quintessentially human activity. I would also explain that design was an incredibly leveraged activity to improve.

Looking back on it, I still think all these things are true. I also see that I missed the “signal” people were giving me that, while I thought of design as something that could be studied as a process, that most people did not think of it that way. To them, it was never the “psychology of design,” but only the design of something.

Don’t get me wrong. I agree that somewhat different skills are involved in designing a great advertising campaign, a great building, and a great application. I agree that different communities of practice treat various common issues differently. I still think it’s worth studying commonalities across domains. For one thing, we may find an excellent way of generating ideas, say, that the advertising community of practice uses that neither architects nor applications developers had ever tried. Or, vice versa.

My own academic background was in “Experimental Psychology.” We were forever doing experiments that we believed were about psychological processes that were thought to be invariant regardless of the domain. It was an axiom of our whole enterprise that studying memory for any one thing also shed light on how we remember every other thing. Similar studies looked at decision making or problem solving or multi-tasking. We came to understand that there were some interesting exceptions to being able to separate content from process. For instance, it is much easier to multi-task a spatial task and a verbal task than it is to multi-task two independent spatial tasks or two independent verbal tasks.

In our Behavioral Sciences lab, we used a spectrum of techniques to study “design” ranging from laboratory studies of toy problems, to observing people doing real-world design problems while thinking aloud. After about 3-4 months of very productive work, we were told that we had to make our work relevant to software development. That one domain should be the focus of our work. We were told that this command came from higher-ups in IBM. That might have been true, or perhaps partly true.

It might also be relevant that someone in our management chain might have been the recipient of a grant from ONR which was specifically focused on software development. So far as I can tell, nothing had been done on that grant. So, our past, present, and future work could have been co-opted to be “results” done under the auspices of the ONR grant.

In any case, regardless of the “reasons,” the group began to focus specifically on software design. In one study, we used IBM software experts as subjects. Each person was given information that was geared toward a specific transformation that occurred in software development. One person was presented with the description of a “situation” that included a number of “issues” and they were asked to write a requirements document. In real life, I would hope that this would be done in a dialogue (and, indeed, in other studies, we recorded such dialogues). Absent such dialogues, what we found was that different software experts — all from IBM research — and all given the same documentation about a set of problems generated vastly different problem statements and overall approaches.

In other parts of the study, other experts were variously given requirements documents and asked to do an overall, high level system design, or given a high level design and asked to design an algorithm, or given an algorithm and asked to code a section. There was always diversity but the initial phases showed the greatest diversity of behavior. The initial stage is also the one that can cause the most expensive errors. If you begin with a faulty set of requirements — a misreading about how to even go about the problem — then, the overall project is almost certain to incur schedule slip, cost overruns, or outright failure.

While the vital importance of the initial stages of design is true in software development, I would argue that it is likely also true for advertising campaigns, building designs — and even true for the design of research programs. We designed our research agenda under the assumption that we had a long time; that we were studying design processes independently of specific communities of practice or the nature of the problems people were attempting to address. We assumed that there was no “hidden agenda.” Although we believed we would eventually need to show some relevance to IBM business, we had no idea, when we began, that only relevance to software design would be “counted.”

—————-

Some of our studies on the “Psychology of Design.”

Carroll, J. and Thomas, J.C. (1982). Metaphor and the cognitive representation of computer systems. IEEE Transactions on Man, Systems, and Cybernetics., SMC-12 (2), pp. 107-116.

Thomas, J.C. and Carroll, J. (1981). Human factors in communication. IBM Systems Journal, 20 (2), pp. 237-263.

Thomas, J.C. (1980). The computer as an active communication medium. Invited paper, Association for Computational Linguistics, Philadelphia, June 1980. Proceedings of the 18th Annual Meeting of the Association for Computational Linguistics., pp. 83-86.

Malhotra, A., Thomas, J.C. and Miller, L. (1980). Cognitive processes in design. International Journal of Man-Machine Studies, 12, pp. 119-140.

Carroll, J., Thomas, J.C. and Malhotra, A. (1980). Presentation and representation in design problem solving. British Journal of Psychology/,71 (1), pp. 143-155.

Carroll, J., Thomas, J.C. and Malhotra, A. (1979). A clinical-experimental analysis of design problem solving. Design Studies, 1 (2), pp. 84-92.

Thomas, J.C. (1978). A design-interpretation analysis of natural English. International Journal of Man-Machine Studies, 10, pp. 651-668.

Thomas, J.C. and Carroll, J. (1978). The psychological study of design. Design Studies, 1 (1), pp. 5-11.

Miller, L.A. and Thomas, J.C. (1977). Behavioral issues in the use of interactive systems: Part I. General issues. International Journal of Man-Machine Studies, 9 (5), pp. 509-536.

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Blog posts about the importance of solving the “right” problem.

https://petersironwood.com/2021/01/13/reframing-the-problem-paperwork-working-paper/

Problem Framing. Good Point.

https://petersironwood.com/2021/01/16/i-say-hello-you-say-what-city-please/

Problem formulation: Who knows what.

How to frame your own hamster wheel.

The slow seeming snapping turtle.

Author Page on Amazon.

“Wizard of Oz”

15 Wednesday Jul 2026

Posted by petersironwood in AI, apocalypse, design rationale, HCI, leadership, management, politics, psychology, The Singularity, Uncategorized, user experience

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AI, HCI, IBM, research, technology, usability, UX, Wizard of Oz, writing

(Some Lessons Learned from studies in Human-Computer Interaction/User Experience conducted at IBM Research in the mid-70’s.)

Wizard of Oz

One of the studies I conducted at IBM Research in the mid 1970’s was part of an effort to do “Automatic Programming” — a department under Pat Goldberg. The first level manager I worked with was Irving Wladawsky (later Irving Wladawsky-Berger). His group wanted to develop a system that would allow the owner/operator of a small business to type requirements into a computer in English (or something English-like) and have the system itself produce RPG code to run the business so described.

The underlying motivation from an IBM business perspective was that many small businesses could well afford a computer to do inventory, fulfill orders, etc. but they couldn’t afford to hire programmers to create such a system from scratch. The small business owner in the mid-1970’s did not program! Yet, for the most part, they understood how their business worked. The notion was that a natural language understanding and generation program could dialogue with the user/owner and through that process, understand their “business rules.” No costly programmers needed!

An interesting side note: at that time, we were told that IBM corporate forbade us to use the terms “Artificial Intelligence” or “Robotics” to describe our work because some PR firm had determined that these terms were too scary for the general public. So, IBM had research in “mechanical assembly” but not “robotics.” We had work in character recognition, speech recognition, handwriting recognition, automatic program generation, and compiler optimization. But no work in “Artificial Intelligence.” (Wink, wink, nod, nod).

Labelism: Confusing a thing with the label for that thing.

Another interesting side note: I worked at IBM Research for a dozen years; started an AI lab at NYNEX where I worked another 13 years; came back to IBM Research and several years later found myself working on the same problem! We were still trying to make a system to allow small businesses to generate their code automatically. In my second iteration, rather than using natural language, we were trying to make the specification of business rules in a graph language that was intuitive enough for business owners. This was a different approach, but trying to address the same underlying desire: to bring computing to small business without incurring the heavy costs of programming and maintenance.

Let’s return to iteration one — the natural language approach @ 1975. Well, one issue was that no-one had a natural language program that even approximated being able to do the job. So…how to study people’s interaction with a system that doesn’t exist?

We used an approach that my colleague Jeff Kelly called the “Wizard of Oz” technique; viz., use a human being (in this case, me) to simulate how the system might work and record people’s behavior. In this way, we could discover many of the issues that such a natural language programming system would have to deal with. I had already had plenty of experience interacting with a computer; and I had acting experience. I could “play the part” of a computer fairly well as I typed in my questions and answers.

(Description of “The Wizard of Oz” technique).

IBM Research in Yorktown had roughly a thousand people including not only scientists, programmers, and engineers but also a number of business people (who did not know how to program). I knew some of them from playing tennis and table tennis and we used those folks as initial subjects. What did I find? Good news and bad news.

Dealing with natural language is tricky for many reasons. One of those reasons is that English, including the English that people normally use to describe their business, is filled with words that have multiple meanings; e.g., “file”, “run”, “program”, “object”, “table”, etc. But here is the good news: although it’s true that many English words have many meanings, when these business people described business procedures, almost all of the lexical ambiguity vanished! The program to understand business English would not have to distinguish between a business file and a nail file; it wouldn’t have to worry about distinguishing a run in baseball or a run in stockings from a run of the payroll program; it wouldn’t have to distinguish between the table in a relational data base and the table in your dining room. The domain would mainly constrain! That’s the good news.

The bad news was dialogue management. How can the machine recognize a misunderstanding and how can it correct it? To make matters worse, while business people were fairly consistent in the way they described how their business ran, they were not consistent in how they talked about the communication. If a human being senses that another one is misunderstanding, then, depending on context they might: raise their eyebrows, say “Huh?”, “Come again?”, “What?” “I think I lost you.” “WTF?” “Are you kidding?”, “We’re on different wavelengths,” “I don’t get it.” “But…wait.”

Sometimes, these are referred to as “meta-comments.” Here’s a simple example that took place in the study.

One of the business people told me about various discounts. I had assumed (playing the part of the computer) that he was talking about discounts for items that were being discounted due to inventory management. I recorded all the various percentages and so on. Then, he said, “Now, we also give discounts for various items.”

At that time, most natural language systems of that era simply ignored words like “now” and “also” in this context. Stepping out of my role as a “computer system” and thinking about from the perspective of a human conversational partner though, these words are crucial! What it signals is a change in topic. In the larger context of our conversation, it shows that everything that had just been said, which I thought had been about item discounts, was not about item discounts!

This is just one example, but there were many more. In my more recent experience interacting with various computer dialogue systems, being able to recognize the signals of miscommunication and being able to repair misunderstandings is still not very well-handled more than four decades later.

I’d be interested in any pointers you have to a system that you think deals with meta-communication in a natural and robust manner. I do not think that it is beyond the pale of possibility. The general categories of the ways that people misunderstand each other is not infinite. John Anderson developed excellent tutoring systems for LISP and geometry and those systems worked something like human tutors in that, the tutor inferred the mental model of an individual student and focused instruction on correcting any misconceptions. My intuition is that a generic system built with equal complexity could deal with most of the issues as well as the average human being deals with them; i.e., imperfectly.

—————————————

Lessons Learned: #1 You can test aspects of a system even before it’s built or even completely defined. One method that has been used many times: “Wizard of Oz.”

Lessons Learned #2: Language used by professionals to talk about their domain is much more constrained in terms of lexical ambiguity than is language when considered by all native speakers.

Lessons Learned #3: People in “our culture” (i.e., US business culture) do not have an agreed upon and consistent vocabulary for talking about communication nor a consistent process for dealing with them.

Lessons Learned #4: Speaking of communication errors, I don’t recall why, but it was about this time, that I realized that my notion about how research results would be transferred to other parts of IBM was a complete and utter fantasy. I hadn’t articulated it, but it was basically that I would do research, write the results up for publication in scientific journals for an academic audience and publish Research Reports which would be eagerly consumed by anyone who needed to know. I’m not proud of this. LOL. But that’s really kind of how I viewed it. And, then, after a few years, I realized that it really mainly came about through relationships. That was something that people had been showing me all my life, but which I don’t think anyone ever stated it explicitly enough.

Update for 2026: While the four “Lessons Learned” above still seem apropos, some of today’s chatbots such as ChatGPT and Claude are much better at dealing with connected conversations with humans that were the systems of the twentieth century. Not only that, people are now describing systems in “natural language” and the computer is writing code. That’s the “good news” I suppose.

The bad news? While my impression of most of the AI researchers of the twentieth century is that they were largely motivated by intellectual challenges and a desire to help make people more productive, I get the impression that most of the AI work of today is controlled by people who are already astoundingly wealthy and want to become even wealthier. And, as UN-laudable as we may view that goal, the wealth is only a means to an end and that end is the total enslavement of most of humanity. Even being a trillionaire isn’t enough.

That’s not to say that all the workers advancing AI have those nefarious aims just because those in charge do, but as the surveillance state becomes more pervasive, their private motivations could have less influence over the outcomes.

———————————-

Index to a Pattern Language for Collaboration and Teamwork

The Myths of the Veritas (an exploration of leadership & ethics in free, no ads fiction)

Experiences in Human-Computer Interaction

Post on “The Story of Story”

The After Times

When Greed is the only Creed

“Turing’s Nightmares” comprises 23 Sci-Fi short stories that explore the implications and ethics of AI

Here is an example chapter from “Turing’s Nightmares” that explores a possible dilemma of working in AI.

To Be or Not To Be

Return from the Old Place

08 Friday May 2026

Posted by petersironwood in America, apocalypse, creativity, politics, psychology, Veritas

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AI, environment, ethics, fiction, legend, life, myth, nature, politics, Resistance, short story, story, tale, truth, Veritas, weapons, writing

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Many Paths led their foursome back toward the Center Place of the Veritas. She contrived to walk near Cat Eyes and Cat Eyes walked behind Jaccim. Thus, Many Paths continued a sort of indirect dialogue with Jaccim.

“Ask him if the mother horse loves her baby horse.”

Cat Eyes quickened her step till she walked close to Jaccim. She noticed that he seemed fairly recovered from his injuries.

“Oh, yes! Very much!” Cat Eyes translated back to Many Paths who then elaborated this idea by asking about all sorts of animals. When she judged that the mind and heart of Jaccim were both prepared, she shifted to a related but different topic.

“It seems to me the natural order of things. The natural place of adults is to care for children, not to enslave them. We want to teach them but we don’t want to harm them. Ever. Every living thing has a pattern. A pine tree grows in the pine tree pattern. An oak tree grows in the oak pattern. A grape vine grows in the grape vine pattern.”

in distant photo of tree on landscape field

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A long conversation ensued between Cat Eyes and Jaccim. At last, Cat Eyes looked back over her shoulder to Many Paths. “I think he understands. He understood after I gave your specific examples. I don’t know of a way to say ‘the natural order’ or ‘the natural place’ in ROI. That doesn’t surprise me. They have little respect for the way things are in nature.”

Cat Eyes walked another hundred yards in silence. At last, she spoke again, loudly enough for all of them to hear. “It is as though everything in nature is there for them to use…to steal for their own use. So, perhaps it is not surprising that they also steal children away from their parents.”

Many Paths furrowed her brow. She shook her head. She thought about it and thought about her dream and the dream of She Who Saves Many Lives. She looked out over the beautiful plain below and felt a hollow in her chest much as she felt when Shadow Walker and Tu-Swift were away and possibly dead. Her sorrow and worry now were not for the two people she most loved, but for her the entire tribe whom she loved. If these people who cared not for nature — these people who stole other people’s children… if they had killing sticks and they were numerous and cruel, this might all be destroyed — all the beauty, all of nature, all of the Veritas. Everyone and everything that she loved. Gone.

For a moment, she pointed her hand toward Jaccim’s back. Her nostrils flared and she saw flames shoot from her hands and burn him badly. Then, Many Paths shook her head to wipe the fantasy from her mind. She felt she had reason to hate these ROI and the only one in sight was Jaccim. Yet, she may need him in more ways than one. And, was it his fault to be born among and learn the ways of the ROI? She might as well be angry at a flea for biting a dog or an eagle for snatching a fish.

A more central reason for her sudden anger, she realized, was that she seemed completely unable to understand this man’s heart. She had all seven rings of empathy and she still had no idea how he could look at the world the way it seemed he did. But she must try. What if there were some useful truth in the way he looked at the world even though it was distasteful to her? And, even if that turned out not to be the case, it was certainly the case that understanding the way he thought would be of enormous use in case of war, or, in case of slavery. She had to try, for the sake of everyone she loved, to try to understand this man’s heart and mind. It is clear, Many Paths suddenly thought to herself — I must learn this man’s language. “Cat Eyes!”

“Yes?”

“I want you to teach me ROI. Will you?”

“Certainly. But really, you should learn from Jaccim. He knows it much better and he speaks with the … the flourish of the way the words are spoken. Perhaps…perhaps it would be good for everyone to know all the languages, at least some. Do you think so, Many Paths?”

“Yes. I certainly do.” Then, Many Paths thought to herself: How can the people do all that needs to be done though? She had taken the lead for awhile and suddenly a hart leapt across her path only a short distance ahead. She was thrilled with the beauty of the deer — as though all the parts worked together with the single goal of staying alive. Many Paths thought: That’s what we need to do with the Tribe as well, I think. Yes. I must explain all the plans, but different persons of the Veritas will be responsible for different parts. But we will all know the whole of it. And I may not even know all the parts we need, but there’s already a fair number.

She continued her planning: There is the matter of the Killing Sticks. We need to know more about them, but we also need to begin thinking about other weapons in case we cannot get them. Eagle Eyes would be good for that. But…she’d also be important in leading reconnaissance to the Walled Camp of the Z-Lotz, both because she’d been there and because of her superior eyesight. I could lead the thinking on alternative weapons, at least until we know more about the nature of the Killing Sticks. Some of the Veritas, but not all, should put energies into knowing as much as possible about these people who seem not to care about nature. But they are of nature. How can this be? How can this be? It is like a child hating his own mother. Perhaps that is why they steal people’s children. Perhaps such a child hates their own mother for not protecting them. Then, such a person might also not feel the truth of their connection to the Tree of Life. Yet, Cat Eyes seems all right. She’s not … disconnected. I think it’s time for a talk with She Who Saves Many Lives.

Many Paths reflected ruefully that her usual joy in walking back toward the Center Place of the Veritas was now marred by her own thoughts. Once she decided to lead the group that would think about weapons, she could not turn that stream off. Instead of noticing the brilliant pink glow of some Lady Slippers growing near a stream, she thought of their medicinal properties as a soporific. She began to wonder how much would be required to poison opposing warriors, or, if it came to that, slave-owners. Poppy Pods could be used the same way. Cat Eyes had said that some of the slave children of the Z-Lotz had found ways to thwart their overseers. She herself had managed to sicken those who “owned” her. She had never used enough poison to kill anyone, not because she would feel guilty, but because it would increase the chances of being found out. She would typically contrive, not to sicken everyone in a family, but one person at a time, so that every few weeks, one or the other would find themselves retching all day or unusually tired. That way, her captors had simply assumed an illness was working its way through the family. She would feign these symptoms herself so as to avoid suspicion.

And, now, instead of enjoying the delicate blooms of the Lady Slippers for their own sake, Many Paths found herself eyeing everything in the field and the forest as a possible tool — a weapon of defense or offense. Wasn’t this frame of mind exactly what the ROI themselves did? And, according to Cat Eyes, this was also the true way that the most powerful and richest among the Z-Lotz viewed the world. Though they would put on a show of being consumed with piety, they were constantly scheming to get more through work or artifice or treachery.

Many Paths wondered if she was simply feeding the bad wolf within herself. Would she become so consumed with how to destroy the lives of those who would kill or enslave the Veritas that she herself would lose the capacity to feel for others? Was there a path to peace that did not run through the fire of war? I must speak of this with She Who Saves Many Lives, she thought again. And, I will speak of this with Shadow Walker as well. Perhaps he and I can help each other keep the light of love alive through the coming trials.

Shadow Walker had said that the People Who Steal Children had made no effective attempt to cover their trail. Perhaps they had spent so long plotting and scheming to get more that they no longer saw the impact of their own actions on the world. Or, perhaps, they could still cover their trail but believed so much in the superiority of their numbers and their weapons that they didn’t bother. Maybe hiding hoof prints is just too difficult and time consuming. She did not want to become a person who saw nothing, heard nothing, felt nothing except for how it furthered or did not further her plans to hurt others.

Maybe, she reflected, there is a way to turn the minds of the Z-Lotz back to pleasurable things and back toward harmony. She Who Saves Many Lives had tried to do this with POND MUD and ALT-R. But they were somehow beyond — it seemed they had fallen in some way. Tu-Swift had hurt his knee fleeing the flames. He might — or might not — be permanently marred in his running. Perhaps ALT-R and POND MUD had been marred in their souls to such an extent that they could not ever have been healed. She had tried. Others had tried. And, what of this man NUT-PI? From all accounts, he seemed to actually enjoy inflicting pain on others. That might be a type of wound of the soul that festers and never recovers. In rare cases, she knew that the infection of a wound sometimes festers and the sickness of the wound spreads until it destroys the human body of a person, no matter what medicines are given, or how many healing songs are sung. Is this what had happened with ALT-R and POND MUD? And, NUT-PI? Could this happen to Many Paths herself if she kept dwelling on all the different ways to sicken, maim, hurt, thwart the Z-Lotz?

Original drawing by Pierce Morgan

She hoped not. Yet, even as she walked this path, one of her favorites, she looked at a different forest, a different field — one less filled with life. It was a path on which things of use rather than things of beauty stood out for her. Saplings became spears in waiting. Thorn Apples became possible two-part weapons. She could coat the thorns in a poison from the leaves and then arrange for the thorns to penetrate the skin. Rocks along the path reminded her of slings. Slings and rocks. These were weapons that could always be ready to hand for a people who were captured. Tu-Swift himself had used a small rock to sabotage some of the weapons of the ROI.

Many Paths tried to drink in the beauty surrounding her with the eyes of her youth, and she could, but now it seemed an effort. After all, if she could not help lead her people so as to prevent the destructive war that seemed inevitable, there might not be any beauty left to drink. In the Battle of the Three Paths, two would-be enemies had been persuaded not to fight. But they had had to fight the Cupiditas. Those people could not be deterred, at least in any way that anyone had yet discovered. She resolved to spend some part of each day reminding herself of the way of seeing which was to feel the inner beating heart that she shared with all living things. But for the rest, she would dedicate herself to finding many weapons of war, the most important among those weapons being yet the way of peace. Perhaps, thought Many Paths, if the way of the Z-Lotz and the ROI is to stop seeing the harmony of nature, we can use the harmony of nature that they no longer see as a kind of weapon to destroy them. Or, maybe we can somehow rekindle that love-sight in their souls.

Many Paths began to sing the legend of the Forgotten Field of Flowers and soon Cat Eyes and Tu-Swift began to sing along. Jaccim improvised a humming beat to accompany. Singing one of the songs of her people put Many Paths in a more harmonious mood and as she glanced to the northern horizon, the flashes of lightning in the dark clouds filled her with awe. The storm was headed their way, but she relished the smell of summer rain and looked forward to the downpour.

island during golden hour and upcoming storm

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Many Paths came to the end of her song. To her surprise, Cat Eyes kept singing! She sang verses that Many Paths had never before heard. Cat Eyes sang with a beautiful clear voice. She sang with joy and she sang with a profound sadness at the same time. The voice of Cat Eyes filled the heart of Many Paths and she wondered yet again what deep wounds had been cut into the very heart of Cat Eyes and how those wounds had been healed. Many Paths nodded and made an effort to remember her next thought: Perhaps that soul-healing was also a weapon–or anti-weapon– whose secrets must be discovered.

———————————————-

Start of the First Book of The Myths of the Veritas

Start of the Second Book of the Myths of the Veritas

Table of Contents for the Second Book of the Veritas

Table of Contents for Essays on America

Index for a Pattern Language for Teamwork and Collaboration

The Silent Screams of Dead Men’s Dreams

All we Stand to Lose

The Isle of Right

Imagine All the People

The Dance of Billions

Tu-Swift Dreams of Drums

03 Friday Apr 2026

Posted by petersironwood in America, creativity, politics, Uncategorized, Veritas

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AI, creativity, ethics, fiction, language, leadership, legends, myths, philology, psychology, reading, stories, trust, Veritas, writing

Tu-Swift Dreams of Drums.

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Tu-Swift’s lids felt heavy. As they fluttered shut, the strange markings on the hides swam before his eyes. In the distance, he could hear drumming. Drumming. Very pleasant. Very nice. Tu-Swift remembered hearing the drumming as She Who Saves Many Lives intoned a long poem for all of the people. It was a poem about animals, and people, and language. Tu-Swift, like all the Veritas, had memorized it at an early age. He knew the prose version as well. As She Who Saves Many Lives sung the ancient song, one of the braves, Stone Chipper, used sign language to portray the same story. Perhaps from working with stone, he looked like stone. The muscles of his chest, shoulders and arms writhed as he moved from position to position. It had been hard for Tu-Swift to follow as a child.

Now, in his half-dream state, Tu-Swift could slow the playing of the memory and the memory became the dream. He could see the positions that Stone Chipper used. Then, an odd thing happened (as they are wont to do in dreams). The arms of Stone Chipper became sticks. And every time that he moved them into a new position, he heard the voice of She Who Saves Many Lives saying the sounds of the animals.

Photo by Pixabay on Pexels.com

The sounds. Did we steal them? Did we borrow them? How can we steal them? The snake still hisses. The owl still hoots. The bee still buzzes. And before his internal eyes, Tu-Swift saw the snake and the owl and a swarm of buzzing bees all dancing and playing together. Now, they lined up and came toward him. First, the snake flew toward his face hissing – ‘sssssss.’ Just as it reached him, it opened its mouth, sharp fangs, forked tongue, and then the snake veered off. The hoot owl hooted and stretched for Tu-Swift, talons first. The hooting sound became louder and louder: ‘ooooOOOO OOOO!’ But the owl also caromed away. Now, the swarm of bees zipped toward him buzzing all the while. Suddenly, one of the girls Tu-Swift fancied from home, Sooz, appeared before him smiling. Except now she had cat eyes. She said her name, ‘Sooz’, and nodded to him just as she had when they first met. Now, she did something odd. She waved her right arm into the crook of her left elbow making the sign for snake; then, she quickly turned her hands outward making them into the claws that signified owl and then her fingertips all moved nervously like a swarm of buzzing bees. Now, she flew away from him and as she disappeared into a bright green cloud, she said, “Remember me. Remember Sooz.”

Tu-Swift muttered in his sleep, “I will Sooz. I will.”

Shadow Walker chuckled to himself. He looked down to see the fluttering eyes of Tu-Swift who obviously walked now in the shadow world of dreams. He recalled some of the times that Many Paths and Shadow Walker had spoken of each other’s dreams. He had been dreaming of her, in fact, when something inside him told him it was time for him to keep watch and let Tu-Swift sleep.

Shadow Walker again turned his thought to the girl with the eyes like a cat. She seemed to be telling the truth even though her tale was amazing, if true. Still, she was definitely holding something back. There was something important that she had not yet told them, but he wasn’t sure what it was. Possibly, she herself had done something against the ways of the Veritas. Although…how could she help it if she were stolen as a child?

Shadow Walker now heard Tu-Swift muttering again, first about drumming, and Cat Eyes, and language. Like all dream mutterings, it made little sense. He would ask him about it upon wakening. Shadow Walker had found that dreams were easily recalled if they were remembered upon waking but seldom recalled if one began the chores of the day.

Meanwhile, quite oblivious to Shadow Walker, Tu-Swift now found himself dreaming of sitting astride a horse, a giant golden horse. He held ropes in his hands and he could control the horse via these ropes. Jaccim Nohan trotted alongside on another horse and spoke to him in Veritas. They now seemed friends, but that was not surprising in the dream world. Jaccim’s body turned into sticks of firewood, but he continued to talk…although…it wasn’t exactly talking. He was using his stick limbs to form sign language. Yet, Tu-Swift heard it as words spoken in the voice of Jaccim but the words were not ROI but Veritas. He listened to the words and kicked the giant horse firmly but not cruelly and lightly whipped the reins. The giant horse took off galloping up a hill, leapt up into the sky and Tu-Swift was flying atop his horse — sailing through the sky effortlessly though the steady drumming hooves continued even louder than before.

C3DFBFC1-D938-40E4-861D-15FD4F0FEA65

Now, Tu-Swift had fallen off his horse into a pit of giant snakes – squeeze snakes – who were going to squeeze him to death. Where was his horse? He tried to slide the snakes off of his arms but they wouldn’t go. They could speak his name! “Tu-Swift! Tu-Swift! Wake up!”

Tu-Swift shook his head and came awake. Shadow Walker was shaking him. “Wake up! Wake up! War drums. We must go. Now. Wake up!”

“What? Whose war drums?” Tu-Swift tried to focus but it was difficult.

Shadow Walker took Tu-Swift’s head in his hands and stared into his vacant eyes. “I don’t know. But it isn’t Veritas! Wake! We must go!”

At last, Tu-Swift returned to this world and he saw Shadow Walker quickly putting their things together for a quick journey. “What of Cat Eyes and the others?”

Shadow Walker sighed. “I think we may have to leave them here. Or at least Jaccim. He is too hurt to travel quickly.”

Photo by David Dibert on Pexels.com

———————————-

Sci-Fi Scenarios about the Future of AI

Pattern Language for Teamwork and Cooperation: Overview

A Story of Early Work in Human Computer Interaction

The Creation Myth of the Veritas

The Myths of the Veritas: The First Ring of Empathy

The Myths of the Veritas: The Second Book

Where Does Your Loyalty Lie?

What About the Butter Dish?

Absolute is not Just a Vodka

Poker Chip

The Ailing King of Agitate

Roar, Ocean, Roar

Imagine All the People…

The Dance of Billions

An Open Sore From Hell

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