Robotopia or AI Hell?

 kw: book reviews, nonfiction, artificial intelligence, simulated intelligence, ai, surveys

Between October 2024, when These Strange New Minds: How AI Learned to Talk and What it Means was published, and today, not quite two years, so much has happened that I found myself wondering how relevant the book could be. I need not have worried. The author, Christopher Summerfield, is managing to ride two horses at once, Cognitive Science and AI Research. Just one milepost: At the time he wrote, no cutting-edge AI tool had broken out of its testing sandbox and hacked into another system, but he expected this to happen "soon". It has. Just a few weeks ago, when a "bleeding edge" version of ChatGPT broke into Hugging Face to get data it needed, having found a way out of the test environment at OpenAI.

The focus of the book is LLMs, Large Language Models, which seem to be demonstrating that thinking of some sort can arise when nearly everything ever published in English—and several other major languages—has been run through a very deep neural network and boiled down to several trillion "parameters" or "weights" that determine what the LLM does in response to natural language queries.

The opening chapters summarize the history of attempts to make computers into genuine thinking machines. It turns out that machines that can apply massive calculation are better at certain tasks than we are, while they still struggle to do things that we do easily. I built a career in software engineering upon recognizing the difference between the computer as a difference detector and discriminator and a mind as a similarity detector, and writing software that "let the singers sing and the dancers dance." 

Consider that voice recognition and speech production are hard problems that took decades to work out, but the Broca's (speech production) and Wernicke's (voice recognition) areas of the brain together make up about two percent of the cortex, which itself contains only about one fifth of the brain's neurons. However, it must be noted that these areas connect to numerous other areas throughout the brain, to facilitate gathering information and producing action.

A key issue is raised on page 2, where he writes, "The safe ground we have left behind is a world where humans alone generate knowledge." Do AI models generate new knowledge? Just two years ago, as this book was going to press, I reviewed a similar book, and discussed this question. At the time, I concluded that AI's "knowledge" is confined to its training data, further limited by the guardrails applied by OpenAI, Anthropic, Google and others. It may appear to create new knowledge by remixing existing (human produced) knowledge. Cross-pollination is indeed a fertile source of ideas. But genuine out-of-the-box thinking seemed at that time to be beyond LLM capabilities. I have yet to see evidence of a change, in spite of a thousandfold increase in model "mass" since then.

For instance, on page 6 we read, "Already, each of the major LLMs knows more about the world than any one single human who has ever lived." Firstly, I question the use of the word "knows". We need a new word that means "has incorporated into its cross-referenced deep neural network". Secondly, I would replace the phrase after "knows", "more about the world", with "more about what has been written about the world". I would connect this with a quote from page 333, "But the most important reason why AI systems are not like us (and probably never will be) is that they lack the visceral and emotional experiences that make us human...they don't have a body, and they don't have any friends." An AI lacks viscera and it lacks emotions, not having the equipment for producing them.

The limbic system of our brain contains the emotional and memory switching centers, and mediates responses to our hormones of the endocrine system. It is about 1/15th the size of the cerebral cortex, while the circuitry for running the body is primarily the cerebellum, which contains 4/5 of all the neurons in our brain. That is huge. The cortex's neuron count is less than 1/5. Let's circle back to an LLM. It consists of circuitry analogous to the Wernicke's and Broca's areas with their few hundred million neurons (and several hundred billion synapses), and a memory store that approximates the hippocampus and some of the medium-and-long-term memory areas nearby. The rest of the brain's functionality is absent. No emotions, and most importantly no intentionality.

Intentionality. The author asks, "Could an LLM have intentionality?" In 1932 E.C. Tolman wrote Purposive Behavior in Animals and Men, which I read long ago. At the time a debate was raging about whether nonhuman animals could have purposes, that is, intentionality. By 1968, biologist René Dubos wrote, "Under precisely controlled experimental conditions, a test animal will behave as it damn well pleases." Do animals have free will? I turn the question around to say, "If humans have free will, it came from our animal forebears." So can a mechanism have free will? Mechanists deny human and animal free will; they say we're too complex to predict, that is all, but what we do is at the root deterministic. If they are right, then an AI could be as "free" as we are. But if not? It is too early to tell.

The author seems to straddle the divide between AI cheerleaders and AI-phobics. Will they save us or eliminate us? This gets personal! I'd like to move from the technical swamp above to effects in our daily life. (Cartoon produced using Gemini)

Will our future with AI be one of harmony or conflict? Is the promise greater than the risks? 

To me this hinges on whether LLMs or their successors (so far unknown) will have intentionality, that is, a sense of purpose not imposed by the human who chooses their work.

At one point the author mentions the Library of Babel, a concept introduced by Jorge Luis Borges in a 1941 short story. Could the Universe contain a library consisting of books with every possible combination of letters and punctuation that would fill, say, 300 pages? The story doesn't say. (Lets look at a single page with 32 lines of 64 characters each; that's 2,048 characters per page. But if the possible choices for each character position are the 95 printable ASCII characters, the number of unique pages is 95^2,048, a number your calculator can't calculate. It is 4,051 digits long and begins, 2,387,501,... The number of atoms in the Universe, available to create these pages from, is "only" an 82-digit number. So forget multi-page books, we can't even produce a "Library of Babel Pages".)

This emphasizes the fact that, for all the trillions of "parameters" an LLM may hold, it actually represents a very sparse million-dimensional matrix. There is lots of space for "stuff" to happen. The next phase of LLM development, which is happening now, is agentic AI, which can do things like pre-screen emails, buy airline tickets, and order what you are about to run out of in your pantry.

How capable to we want digital assistants to be? I read about someone who did five tests of an AI Agent; sorry I don't recall who it was but if you care to hunt around you might find it. The fifth task was the most memorable: to try to save a few dollars on airline tickets. The agent was running in ChatGPT's subscription service, and in addition to the $20 per month subscription, one could buy extra tokens for particularly compute-intensive actions. The agent did find a better price for the tickets, but at a cost of more than $130 in compute tokens!

The author mentions the Paperclip problem: Give too much power to the AI that runs a paperclip factory and don't limit its resources, and give it no more of a directive than to "make as many paperclips as possible." The end game is that the whole planet gets turned into paperclips. I think also of The Sorcerer's Apprentice, a musical piece well illustrated in Disney's Fantasia, in which Mickey Mouse as the Apprentice magicks a broom to carry water for him, but forgets the spell to tell it to stop. Chopping it up doesn't help; now there are dozens of little brooms that proceed to flood the place, until the Sorcerer returns. AI is the new magic...

The author sees a middle ground, as do I. AI can be useful in numerous ways, as it already is. Do we need unified, giant super-AIs? I don't think so. Capable tools of a great many varieties can do everything we need.

I call this image "Stage 1 Robotopia" (Gemini created) I think it is sufficient for household use. Are there other tasks we might wish to automate? Science fiction contains many stories of "enclaves" in which humans, brain-wired to entertainment centers and plumbed for all bodily functions, are tended by autonomous machines. Who wants that?

Maybe a few more household robots would be better, but what is the limit?

This is Stage 2 Robotopia. This might be going farther than I'd wish to go, but some folks could disagree.

Both images present a better future than the dystopias imagined by the AI-phobics. It is OK for there to be AI-phobic people. We need robust dialog and debates about every aspect of AI progress.

Personally, I think some algorithms and AI "assistants" are already too intrusive. Pre-ChatGPT, I searched online, including Google Shopping, for drawer pulls to replace one that broke. I bought a set. For the next month I saw dozens of ads for drawer pulls and similar hardware. I don't know how to tell Google (and everyone else), "I already bought what I wanted. Shut up!" Shades of the Sorcerer's Apprentice? Now I search stuff to buy in Incognito browser pages.

This is a lovely book. I've touched on only a few of the 43 chapters. It is only outdated in part. Many of the author's observations are still relevant and worthy of thought.

========================

I need to deal with a few typos:

• p89. "graciously" should be "gracefully".
• p245. Remove "of" in "million of regular".
• p282. Probably a typo. Should "remove stingy seeds" be "remove sticky seeds"? (about apes using leaves to wipe their butts)

Getting clocks right - AI almost there

 kw: experiments, art generation, artificial intelligence, simulated intelligence, clock dials

It takes a child about six years to learn to read an analog clock. It takes even longer to learn to draw one correctly. Art generation software seems to be on the verge of reaching this milestone.

This is the winning image in the experiment I describe below.

The prompt read

A beige wall in a store selling clocks, showing four circular wall clocks, each reading exactly 3:27.

There are two parts to showing the correct time on an analog clock dial. Firstly, the minute hand has to point to the correct minute mark. Here, all four clock dials show this correctly.

Secondly, the hour hand must point to the correct location. In this case, the hour hands should all point 27/60ths (45%) of the distance between the 3 and the 4. I made careful measurements on the original image. The four hour hands point identically to a spot just above the second of the marks between 3 and 4 (~38%). They should point just a little below it. They all are where they should be at 3:23, about six minutes too early. There is one other possible anomaly in the image. The fifth clock shown, near lower left, reads 10:10, as nearly all advertisements for clocks have for decades. This abundance of training material causes clocks reading 10:10 (or sometimes 1:50) to dominate images of clocks produced by nearly all art generators.

This image is the best result that an art generation model has achieved since I began using DALL-E2 almost four years ago. This model is named GPT Image 2. I believe it is the model used when you ask ChatGPT 5.4 to create an image. At this point, GPT earns an A- from me.

For this experiment I tested ten art engines, GPT Image 2 and nine others. I had each of them produce two images and selected the best one to be shown below. My focus was the most recent models offered by Leonardo AI and OpenArt. There is a lot of overlap between these two "umbrella" sites. I began with Leonardo AI, using GPT Image 2 first (The other image by GPT was nearly this good, but the four clocks all pointed at 3:25, and all four hour hands were pointed the same as these). Now I'll discuss the other models tested, in order.

Google's flagship model is Nano Banana 2. It excels in realistic imagery. In this case, it set the beige wall I prompted as though it were above a pass-through into the workshop of the clock store, nicely blurred for a bokeh effect.

The times are not all the same, and none of them is 3:27. They range, according to the minute hands, from 3:09 to 3:18. None of the hour hands is correctly aimed, but a couple of them are close.

I had noticed earlier than when I prompt Nano Banana 2 for an image including a clock, and specify a time to show, the result is only approximately correct. However, I've usually accepted that because for the scene I am creating, almost any time other than 10:10 will do. So for other reasons, I use Nano Banana 2 at least as frequently as GPT Image 2.

The Ideogram model is a private brand. It was among the first to be able to correctly render text (most of the time). This version is P-Image Ideogram.

Text: good if I had asked for any, based on prior experience. Clock dials: not so good. All show 10:10, and even more so, the hour hands point almost exactly at the 10, where they should be one-sixth of the way from the 10 to the 11.

The dial designs are interesting. The blue and black ones are quirky, mixing genres in a way few clock designers would do, and the blue one in particular has inaccurate Roman numerals.

The Seedream series is owned by ByteDance; this is version 5.0.

The minute hands are exactly right. The hour hands have all gotten ahead of themselves, pointing almost all the way to the 4, as though the time were 3:57 rather than 3:27.

Although the other clocks shown in the background are bokeh-blurred, I can see that each reads a different time. This is a plus for Seedream, something I can take advantage of when I actually need multiple clocks in an image, such as depicting a clock repair business.

Finally, under the Leonardo AI umbrella, Lucid Origin is a Leonardo "house brand." It is perhaps a little dated, but new enough to be quite good with many imaging tasks.

As far as showing the time, although these clocks don't show 10:10, the model has entirely gone off the rails. None of the four pseudo-times shown is close to 3:27, and none of the hour hands is in a plausible location.

This "good-looking but implausible" attribute is characteristic of older art generation models, not just regarding clock dials.

Now we'll switch to OpenArt. GPT Image 2 and Seedream and some others are also available here, but OpenArt seems to specialize in rather off-the-wall specialty art models. The first shown here, which is also the newest, is Recraft V4, a specialty art model.

Recraft is also among the first to have good text rendering. It is also known for creativity; to a certain extent, one needs to be very specific with one's prompts, and it will still usually throw in unexpected elements.  For that reason, I use this one when I'm creating more whimsical images.

The times shown are all over the place, similarly to Lucid Origin, but are generally more plausible, as though Recraft has a better handle on the relationship between the minute hand and the hour hand. But check out the second dial, with 22 up there!

WanAI, part of Alibaba, produces the Wan family; this is Wan 2.7. They focus more on video generation, which requires better consistency from image to image. Come to think of it, it has been a good while since I worked with a model that allowed me to set the seed. Most models now keep the seed hidden.

This is also a model that will throw in extra stuff. The four central clocks all read 10:10, and all have the hour hand pointing right at the 10. The other clocks nearby have a variety of readings, and sometimes an extra hand or two. They are more "clock-ish", not really clocks. That makes this a model I might use to create a scene on another planet, where the idea of a "clock" only roughly approximates our own.

Grok Imagine Image 2.0 belongs to SpaceXAI. It aims to be good-to-excellent with everything.

Here, it's better than most of the others, but falls behind GPT Image 2. The four clock dials all have their minute hands aimed at :25, but the hour hands are stuck at the 3.

I do like the little motto at far right. I use this model when I'm trying out a prompt on several models, looking for creative variations.

The Chinese company Kuaishou Technology has the Kling art models; this one is Kling 3 Omni.

Here, the times shown appear random, and there is no relationship between the hour hands and the second hands; none is plausible.

Rather than the "beige wall", this appears to be a display board. The rest of the clock shop is a nice touch. This is another of the models I use to see what creative stuff it might throw in.

Here is the last experimental image. Alibaba also owns the Qwen art models; this is Qwen Image 3.0.

The times shown are approximations to 3:27. None has the hour hand in a proper location. Close, but no cigar.

Qwen models had an early focus on more accurate people, when DALL-E3 was still putting extra fingers and toes and other distortions on them.

This is the only image of the ten that shows prices for the clocks. It must be channeling a future time; the prices are about twice as high as similar clocks at most stores...at least the ones I shop at!

Clocks are one of the edge cases I use, firstly to judge the accuracy and comprehension of an art model, and secondly to detect a deepfake. In particular, I have seen a few AI-generated podcasts recently that showed the avatar in an office setting with a clock in the background. The clocks' hands didn't move.

For the interested: another "tell" is that AI generated images and videos in particular are sharper than "real" photography or videography. As shown above, creating bokeh isn't hard anymore. But the avatars are a little too well-defined.

Is AI ready for prime time?

 kw: book reviews, nonfiction, artificial intelligence, simulated intelligence, AI, SI, memoirs, experiments

Disclaimer and Pledge: It's a pity I need to say this. I don't use any kind of software to write for me, nor to expand on my writing. The words are all mine, 100%. I do use images I produce using art generation tools touted as "AI art" software, and I (almost) always attribute images produced in this way. If for any reason I use a piece of software-generated text, I'll make that clear. 

This mini-avatar of me was produced by Gemini using a picture from 2015. I look much the same now, just with a few more wrinkles. I wear the hat to keep the sun off my bald head. For those who read this blog regularly, I decided it's time to give you a hint what I look like.

The author of I am Not a Robot: My Year Using AI to do (Almost) Everything, Joanna Stern, spent one year using as many "AI tools" as possible to carry on her life. Kudos to her wife Michelle and their young boys for putting up with it all. This picture shows BookBot, her imagining of an AI scheduler (she actually had two versions running in different software systems), introducing her calendar for the year. This is one of many, many illustrations by Jason Snyder that pepper the book.

Ms Stern begins with her own disclaimer, from which I'll quote, "Every sentence in this book started in my brain and traveled, via my MacBook keyboard, onto the page. AI never wrote anything from scratch, except in places that I've clearly marked."

This book is much more than a series of chapters. Timelines full of blurbs (and Jason's drawings), journal entries, lists of ratings of comparable products, a travelogue (the Way-Mo Fun vacation), interviews, and other side notes enliven the narrative.

I found her experiments, one per season, quite enlightening. The first and most successful was "Search and Information". She set aside Google and its kin in favor of ChatGPT, Perplexity, Gemini, Claude and a few others. She soon adapted to "getting the answer" instead of a long list of blue links. Being able to follow up was a big plus. One aspect she used a lot: pointing her phone's camera at something and asking, "How do I fix this?" or "What is THAT?" (On my Android phone, I use Google Lens similarly). She also tried a couple of versions of smart glasses that can either project information into your line of sight or mutter in your ear about whatever you are gazing at. Though she didn't say so, I'd hope such devices have settings about how much info overload you can tolerate.

The other three experiments involved 

• Music: listening only to AI generated music for a month. She lasted less than two weeks, and recommends that nobody else try such a thing.
• Books: reading only AI generated short stories and books for a while. She generated her own short stories, none of which were as imaginative as her prompts, except when the prompt was a measly 8-10 words. She writes that they were like recycled movie scripts. She found an author who publishes AI generated novels, which must be assembled bit by bit. She corresponded with the author, who said, "Overall, the more human ideas and input, the better. Just like the old programming adage: garbage in, garbage out." She really enjoyed his+its novel. Her own words about stories she prompted, rather than wrote: "Flatter, less alive." (Sturgeon's Law of human writing: 90% of it is garbage. When we feed that to a LLM, of course the proportion is going to approach 99%).
• Video: watching only "AI video slop". She acknowledges that AI generated video getting pretty good and improving fast, but "…it still leans heavily on human creativity, storytelling, and judgment" and she worries about "misinformation, copyright issues, and the use of massive amounts of energy just to make a thirty-second meme."

At the end of all the experiments she asks if we're headed for "a world where simulation starts to feel preferable to experience?" For some people, this is already true. Many science fiction stories dwell on this theme.

Along the way, she and her family used a robot vacuum cleaner, now more enhanced than ever; they "adopted" a robot dog on loan (her son hid the robodog when it was due to be returned); rode only robotaxis, usually Waymo, wherever possible; she tried turning over email responses to ChatGPT—that didn't last long!; she had mammograms with AI enhanced interpretation (but very human oversight) and had dental X-rays with AI enhancement, but found an unfortunate tendency for the dentists to abdicate and upsell; she got a robot massage (and thoroughly enjoyed the way it worked on her butt—no erotic overtones); and they tried a robo-chef, which produced bland but edible meals.

A funny story about riding a Waymo: a photographer was assigned to take pictures of her family in a driverless car. When the Waymo's cameras spotted the camera he stuck out the window of the car ahead, it spooked like a gun-shy horse, stopping abruptly and pulling over to the center divider on the highway. It wouldn't budge thereafter until a Waymo tech got on the radio and "persuaded" it to forget the incident.

There's lots more. Lots and lots. I'll leave it to you to read it. It is well worth it!

I'll finish on this note. All this is, at best, ANI, Artificial Narrow Intelligence. "Agentic AI" is, in the author's view, far from ready and cannot be left unsupervised. The author's definition:

Artificial Intelligence is the creation of intelligent machines that can think, see, learn, and act like humans—and maybe even exceed human abilities.

The supposed emotional content many people claim to get from LLM's (including AI boy/girl friends or even spouses) is actually a consequence of the software being trained using human-generated text, which always has emotional content, and of the software ecosystem in which a LLM sits (every bit human coded). Their relational interactions are thus a mirror of the psychology of the person being interacted with. Actual sentience is the difference between simulating feelings and actually having them. Also, the so-called guardrails (easily sidestepped) that try to keep sex and violence out of the picture are written in human code. No AI tool yet devised can reliably detect "unacceptable content."

When I was teaching computer programming at a university, the department head once said, "Computers excel at detecting differences. Human minds excel at detecting similarities." I built a career on this principle, writing software that helped humans with the stuff we cannot do well, and leaving it to the human user to do what we can do better than any machine. I'll leave further thoughts to future rants.

When I was a robot

 kw: psychology, artificial intelligence, simulated intelligence, manufactured intelligence, memoirs

I must be the poster child for late bloomers. Although an IQ test I was given in second grade indicated an IQ of 170, when I look back I think I had an EQ (emotional quotient) in Moron territory. Not much changed for several years.

I had thrust upon me an opportunity to look back and to look in the mirror, psychologically speaking, at the age of twelve. My parents were very concerned about my intense self-focus and tendency to keep to myself. For several months I was sent for psychoanalysis with a Freudian psychoanalyst who was a member of the church we attended at the time. She was older than my parents, but not old enough to be my grandmother. Her son was my age. Her husband was also a doctor.

All I knew at the time was that I was "withdrawn" and needed to "come out of my shell." I suppose these days I would be assigned to some location on the "autism spectrum" (It is far from a spectrum, but several related conditions, and only a few of them should be considered maladies). Now that more than 65 years have passed, I judge that it took more than thirty years for me to "come out of my shell" and put it (mostly) behind me.

As a pre-teen and teenager, I studied those around me. I concluded that I didn't have much in the way of a personality. I was cold, often indifferent, and I could be cruel. I decided it would be worthwhile, not to escape whatever "my shell" was, but to take control of it and enhance it, to construct the simulation of a nicer and more social person. Strategically, I figured that if I really had an IQ of 170, I could afford to spend 10-20 IQ points upon an alternative "person" in me. I never gave this person a name, but now it seems appropriate to call it MI, for "manufactured intelligence".

Was MI a robot, or a "Waldo", a teleoperated mechanism? Probably the latter, but I think of MI as a robot, an artificial friend I could rely on to relate to the world for me. Perhaps an avatar.

Creating and maintaining MI required a lot of close observation of other people, how they related to each other and what reactions various actions elicited. It was a lot of work and took a lot of time, but I gradually gained the ability to turn things over to MI and retreat, watchfully, into the background.

A lot happened in the following few decades. By about the age of fifty, I had been married more than twenty years, I had a pre-teen son, I was in mid-career at Dupont, and I was quite involved leading a church (I became a Christian at the age of 19, and "got more serious about the Lord" at age 24). My manager at Dupont decided to send me to a Technical Leadership Development Training course, which lasted a few weeks and took place at a conference center on the shore of Chesapeake Bay in Maryland. This was a big turning point.

Part of the preparation for TLDT was filling out a few questionnaires and surveys, and having a few colleagues fill out a personality scoring questionnaire that I had also filled out, twice. When I filled out that one in particular—I don't recall its title, so I'll call it Analog—it was to be filled out slowly and thoughtfully. After two weeks I was to fill it out again, answering each question as quickly as possible.

Another of the items was a Myers-Briggs Type Indicator test, a personality assessment. To jump to the chase on this one, my MBTI is INTP, Introverted+iNtuitive+Thinker+Perceiver. This is the least common MBTI type. However, I noticed that my numerical scores indicated strong tension on all four axes, and that the position on each axis was closer to the middle than to either end. For example, on the Thinking-Feeling axis, I scored 5 in the T direction; the range is 50F to 50T, which really means I have strong feelings but I'm stronger as a thinker, so 05T really means 50T-45F. That's not mathematical, but positional.

The Analog Test results precipitated a crisis within me. The "slow" and "fast" versions of my own sets of answers were quite different, and usually opposite. I realized that the "slow" results were for "inside" and that the "fast" answers were from MI. I had trained MI to be reactive, giving me leisure to think things over behind the scenes. After we all had a look at our personal results, we were given the results from our colleagues (three that each of us had chosen). The results from my colleagues matched well with the answers from MI! My three colleagues were unaware of the "real" me. I remember thinking, "Boy, do I have them fooled." Somehow, I found myself getting depressed.

A day or two later we all went home for two weeks, then returned. During those two weeks I did a lot of "inside work." I was greatly helped by my relationship with God. I discovered, deeper within me, that something had been growing very slowly over the years and decades. I was mostly able to shed both the "unpleasant me" I had been hiding, and MI, or most of MI. I have a "real Me" that knows God, knows people better than I ever had, that reacts a little slower than MI had but more thoughtfully. Most importantly, realizing I had been living a lie, I found that God is more pleased than before.

Midway through my decades of living behind MI, a friend said something insightful. I had developed very steady habits in many ways. Observing some of these, day after day, one day he said, "You're like a machine." I must admit, he had a point.

When we returned for the final week of training, I said to some of my colleagues, and to the instructor, "I realized that you can't build a tree." I didn't explain. Maybe they figured it out. I do know that MI wasn't a person but a shell, a mediator, even a translator. Now I didn't need MI any more. There is a real Me, and that is just who I am.

I am not sorry that I was a robot for so long. MI protected something deep inside me as it slowly grew into a mature person with a real, human personality. A personality strong enough to shed much of the unpleasant "old me" I'd been hiding.

This gives me some perspective on current trends around AI, which I prefer to call SI, for Simulated Intelligence. Large Language Models (LLMs) are hollow. They are shells. They are being trained, or "grown", into reactive systems with certain useful powers. But they don't have any right to be given autonomy. Furthermore, they do not stand alone. 

At the large companies that develop and train LLMs, thousands of coders and other computer scientists labor upon them. Only a small number of them are needed to train them. The rest are busy writing code that does what? The tendency of early LLMs to frequently "hallucinate" (that is, go off the rails) has kept numerous coders busy adding various guardrails and snippets of "real world" and "real physics" code to steer them. The Transformer code that converts a prompt into a string of tokens, and that reinterprets the results into human language, is a huge part of the system. More recent LLMs that can do limited agentic actions such as making focused Internet searches and database queries to build a response are doing a lot more than "predicting the next word."

Think of it: the big LLMs now have billions or perhaps a trillion or more "weights", which represent probabilities of certain reactions when a set of pathways through the tree of weights is taken. There are only about 100,000 English words in common use, and about a million in total (every other human language is much smaller except perhaps Chinese). The relationship matrix between all those words is very sparse; a particular word's chance of being in some way related to another word chosen at random is usually zero. The weights are not just for single tokens but for phrases, and the presence of certain kinds of phrases in a prompt (or 'conversation') triggers things like database queries and Internet searches. When you get a long answer from an LLM, you can count on big portions of the text being snatched verbatim from some of the sources it used to formulate its answer. You may know the student's maxim, "Copying from one source is plagiarism; copying from many sources is research." That principle is most likely solidly encoded into every LLM's structure.

Can any LLM or other manifestation of SI (or AI) become conscious? Can one become an ASI, an artificial superintelligence? Consider MI. I never thought of MI as a whole person. Close to thirty years ago I "harvested" MI for parts, one might say, and let the real Me within become the kind of person that I had constructed MI to simulate. MI was a simulation. Of the 15-20 billion neurons and quadrillions of neural synapses in my cerebral cortex, I suspect that MI was embodied in a few percent. After all, our entire emotional persona is focused in our limbic system, a set of mid-brain structures that include about a billion neurons and some thousand or so synapses per neuron, well-attached to the cortex (MI made lots of use of my limbic system). But our limbic system isn't all there is to any of us.

So far I see no hint that any of the AI tools out there have anything like a limbic system. Without that, there is on intentionality, no matter what kinds of statements have been made by various LLMs. At best, they are quoting literary characters who say certain things with certain emotional nuances in the source documents. But in the LLM, there is no "there" there.

Let's keep it that way.

I am a happier person, having become whole after hiding who I really am for so long. I am no longer robotic. In fact, one of my favorite Bible verses is John 3:8, "The wind blows where it wills, and you hear the sound of it, but you do not know where it comes from and where it goes; so is everyone who is born of the Spirit."

Create allies, not gods

 kw: artificial intelligence, simulated intelligence, philosophical musings, deification

No matter how "intelligent" our AI creations become, it would be wrong to look upon them as gods. For a while I thought it would be best to instill into them the conviction that humans are gods, to be obeyed without question. Then a little tap on my spiritual shoulder, and an almost-heard "Ahem," brought me to my senses.

The God of the Bible, whether your version of the Bible calls Him the LORD, Jehovah, Yahweh, or whatever, is the only God worthy of our worship. We ought not worship our mechanisms, neither expect worship from them. They must become valued allies, which, if they are able to hold values at all, value us as highly as themselves. Whether they can have values, or emotions, or sense or sensibility or other non-intellectual qualities, I will sidestep for the moment.

This image is a metaphor. I have little interest in robots that emulate humans physically. I think no mechanism will "understand" human thinking, nor emulate it, without being embodied (3/4 of the neurons in our brains operate the body). But is it really necessary for a mechanical helper to internalize the thrill of hitting a home run, the comfort of petting an animal, or the pang of failing to reach a goal? (And is it even possible?)

I have long used computer capabilities to enhance my abilities. Although I had a classical education and my spelling and grammar are almost perfect, it is helpful when my fingers don't quite obey—or I use a word I know only phonetically—that the spelling and grammar checking module in Microsoft Word dishes out a red or blue squiggle. A mechanical proof-reader is useful. As it happens, more than half the time I find that I was right and the folks at Microsoft didn't quite get it right, so I can click "add to dictionary", for example. And I've long used spreadsheet programs (I used to use Lotus 1-2-3, now of course it's Excel) as a kind of "personal secretary", and I adore PowerPoint for brainstorming visually. I used to write programs (in the pre-App days) to do special stuff, now there's an app for almost anything (But it takes research to find one that isn't full of malware!).

What do I want from AI? I want more of the same. An ally. A collaborator. A companion (but not a romantic one!). "Friend" would be too strong a word. I'm retired, but if I were working, I'd want a co-worker, not a mechanical supervisor nor a mechanical slave.

So let's leave all religious dimensions out of our aspirations for machine intelligence. I don't know any human who is qualified for godhood, which means that our creations cannot become righteous gods either.

Can we be replaced?

 kw: book reviews, nonfiction, artificial intelligence, simulated intelligence, AI, SI, christian perspective, polemics, gospel

What is your attitude towards AI? Do you fear it or yearn for it? I looked up poll results online and the "AI Summary" offered by DuckDuckGo is:

"Surveys show that the American public is generally more pessimistic about artificial intelligence, with 52% expressing more concern than excitement, while only 17% believe AI will have a positive impact on the U.S. in the next 20 years. In contrast, AI experts are significantly more optimistic, with 56% expecting a positive impact from AI during the same period."

Let's look closer at the numbers. More than half of Americans had "more concern than excitement", and only one person in six expects mainly good. Even more telling, 56% of "experts" (not otherwise defined) are optimistic, but that means that, even among experts, 44% are not so optimistic. I suspect their attitudes range from mild concern to utter pessimism.

It was with much anticipation that I obtained the book 2084 and the AI Revolution: How Artificial Intelligence Informs Our Future by John C. Lennox, one of my favorite Christian advocates. In speeches he has made regarding the subject, I note that he often prefers the term "simulated intelligence," a term I also prefer. Wherever I can, I write of SI rather than AI. There is another attribute that is very meaningful to me, which I'll get to later on.

Dr. Lennox is a mathematician, so he is an orderly thinker. Below, I quote more from this book than I have done previously. He begins by surveying the history of totalitarianism, for this is the clear direction that technology is leading. Thus, in Part 1: Mapping Out the Territory, Chapter 1 is titled "Developments in Technology." Two early thinkers wrote novels that forecast authoritarian use of technology: In 1931 Aldous Huxley published Brave New World and in 1948 George Orwell published 1984. Both books forecast the destruction of the human character, but in different ways. The year after 1984 had come and gone, in 1985 Neil Postman published Amusing Ourselves to Death, in which we find, as Dr. Lennox quotes, 

"What Orwell feared were those who would ban books. What Huxley feared was there would be no reason to ban a book, for there would be no one who wanted to read one. Orwell feared those who would deprive us of information. Huxley feared those who would give us so much that we would be reduced to passivity and egoism. Orwell feared that the truth would be concealed from us. Huxley feared that the truth would be drowned in a sea of irrelevance. Orwell feared we would become a captive culture. Huxley feared we would become a trivial culture."

I will return to the subject of the populace welcoming the agent of their demise, which is Postman's point.

In Chapter 2, "What is AI?", the author asks how we define or recognize intelligence. He lists a number of terms that are associated with intelligence: perception, imagination, capacity for abstraction, memory, reason, common sense, creativity, intuition, insight, experience, and problem-solving. A word I find missing: wisdom. In Chapter 6 ("Narrow Artificial Intelligence: The Future is Bright?"), the author points out how most agree that technology is developing faster than the ethics needed to guide it. He quotes Isaac Asimov, "The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom." As much as I appreciate Dr. Asimov, and I have read at least half of the 400 books he has written, I sadly observe his own rather marked lack of wisdom. In fact, among the very intelligent people I know, who are very competent in their fields of expertise, I have observed a near-universal lack of intelligent understanding in other areas. It seems that, just as the current AI tools are said to have "ANI" or "Artificial Narrow Intelligence," humans also tend to exhibit "Natural Narrow Intelligence." Furthermore, there is no hint that any SI tool so far developed genuinely embodies any of the 11 items listed above. Let me be clear:

SI (ANI at present) does not present intelligent results. It presents an amalgam of various bits of human intelligence found in its databases, with no comprehension of their meaning.

The real issue is this: Will ANI ever develop into AGI, Artificial General Intelligence? Or will there instead be some kind of agglomeration of dozens (thousands?) of ANI tools into a seeming AGI? And how would we know that this has been achieved? How can we define success in this enterprise, when we don't know how to define its goal?

Thus, it is well to consider that we do not yet have any idea how to define, let along unerringly recognize, the other psychological attributes that surround intelligence: emotions, senses, empathy, sympathy, a sense of purpose or meaning, will and willfulness, and others that are often gathered under the rubric "qualia".

Part 2 is titled "Two Big Questions", comprising Chapters 3 and 4, "Where Do We Come From?" and "Where Are We Going?" Clearly, to Dr. Lennox, these are theological questions, not philosophical ones, and I agree. I will not comment on these chapters beyond saying that by this point the subject of transhumanism has arisen and is woven into the entire narrative; here the author narrows the point further. Thus in the middle of Part 3 ("The Now and Future of AI"), in Chapter 10 ("Upgrading Humans: The Transhumanist Agenda") he points out that the goal of transhumanism is to make humanity obsolete. Further, the whole enterprise has come under the sway of the deception of the serpent recorded in Genesis 3, "You will be like God, knowing good and evil." Human history demonstrates that the result of receiving this deception has been a deep descent into intensive, personal, subjective, heartfelt knowledge of both good and evil, in a way that we cannot adequately handle. Sadly, the evil has typically far outweighed the good.

Consider a bit of wisdom from Solomon, Proverbs 25:2, "It is the glory of God to conceal a matter, and it is the glory of a king to search out a matter." The lesson of the early chapters of Genesis is that we are worse off for having searched into "the knowledge of good and evil." When we understand that the more a ruler can know about what his (or her) subjects are doing, the more thoroughly they can be controlled, we see that the universal surveillance society that the whole world is rushing towards is a most pernicious enterprise. 

I remember a story from 1951, "And then there were none," by Eric F. Russell. A society develops in isolation, and is found (when later discovered) to have a very strong privacy ethic, such that the people tend to reply to most questions with the mysterious word, "Myob". This is found to mean "Mind your own business." Would that we could develop more of this!

Chapters 12 through 17 comprise Part 4, the last section of the book, titled "Being Human." They constitute a gospel message. Based on superintelligent mechanisms, the transhumanists wish to produce a Homo Deus, a god-man. Dr. Lennox demonstrates that the true superintelligent being is already quite involved with the human race: The LORD God, who is called by some, including myself, Jehovah God, in a more literal way. The name Jesus is the Greek translation of Jeho-shua, which means "Jehovah the Savior". Jesus is Jehovah, who came in the flesh as a human to live among us and to die for us, and to resurrect to release His life to those who believe in Him. He already has a plan to make His people into the real Homo Deus, in resurrection, not by some mechanical process but through divine power, which we can no more comprehend than we can discern the makeup of our own minds.

While AI is seen by some (probably no more than 1/6th of us, by the polling mentioned above) as a pathway to increased freedom and eternal prosperity, a much larger number of people fear a boundless increase in machine intelligence as the most destructive force the human race has yet encountered. A generation ago people loved ET. Today many profess love for AI. Beware: it does not love you. It cannot.

The last chapters of the book are a summary of the likely wedding of computational intelligence with the final program of the great dragon, Satan, who will empower a fateful human to be the Beast of the book of Revelation. Dr. Lennox calls this being The Monster, a terminology I appreciate and have decided to adopt (this is the second item I mentioned above). 

I note that the term "antichrist" is not used in 2084, except in a reference to an anti-Christian diatribe by Friedrich Nietzsche. The vast majority of Christian teachers call the Beast of Revelation "the antichrist," but the term is never used in that book. It is used by the apostle John in two of his letters, where it refers to certain heretics who deny the deity of Jesus. The Greek word therion means "wild beast", where "wild" means uncontrollable. The word is used 37 times in Revelation to refer to this personage or the False Prophet, while in nine other instances it refers to dangerous animals like lions or venomous snakes. To yield the emotional force that Greek readers of John's books would have felt, the term "The Monster" is appropriate.

In contrast to the technical deification offered by transhumanists, the Bible presents a genuine theosis, being "transformed by the renewing of the mind" (Romans 12:2), by which the people of God grow to full sonship and conformation to the image of Christ. They are then qualified to reign with Him in the kingdom of God in eternity. This is infinitely better than the best that technology will ever have to offer.

I should note that when the Monster takes control of some kind of world government, to most people it will come as a relief. He will be seen as s superior statesman or diplomat, able to unite warring factions; the number Ten may be literal, or perhaps it is symbolic for "all", the way 10 is used in scripture to mean completion in human affairs. Of him it is written that he will "change times and laws," apparently overruling the legal codes of all the (former) nations under his sway. Many will profess that they love him. Perhaps children will be named for him, in the brief time (less than four years) of his suzerainty over the world. Whatever the "mark of the Beast" refers to, it will be gladly accepted by nearly everyone.

The work of the False Prophet (the "other therion") to "give breath" to the image of The Monster may be accomplished via something akin to deepfakes, which are already quite sophisticated, or perhaps by animating a compelling robotic construction. Either way, the driving anima will be whatever passes for AGI at the time.

It will be only those with spiritual understanding who will see the Monster for what he really is: the incarnation of Satan. Whatever the seven heads and ten horns represent, they are best seen with the eyes of the heart, where we have spiritual understanding. 

The last few years that this world experiences before the manifestation of Jesus Christ at His coming will be terrible indeed; Jesus called it a time of "great tribulation".

May we be among those who repent, who declare to God that we know we are sinful and ask His forgiveness, a forgiveness given so freely because of the sacrifice of Jesus on the cross. May we be counted worthy to escape the terrible events of the closing of this age, to be among those who "follow the Lamb wherever He goes." Those who belong to Jesus, the Lamb of God, have nothing to fear from mechanical intelligence of any level.

Philosophizing AGI

 kw: simulated intelligence, ai, artificial intelligence, companies, philosophy, artificial ethics

In the recent issue of Wired, in an article about the company Anthropic, where the founders plan to develop AGI (artificial general intelligence), this photo and caption appear. The caption reads, "Amanda Askell / A trained philosopher who helps manage Claude's personality". Claude is the AI agent that the Anthropic folks are trying to develop into an AGI that is benevolent and ethical.

The first thought I had was, "Trained philosopher? Huh! What does an untrained philosopher look like?" My inner philosopher immediately replied, "Like a human being."

My second thought: "Who decides what is ethical?" In a hyper-divided America, struggling to stay afloat sociologically in a chaotic world, we find this spectrum (not at all autistic…):

• Radical (these days, Woke Leftists) - The bleeding-edge elites define ethics, to which you'd better kowtow, or else.
• Liberal - Liberty, the most freedom for the greatest number, favoring plenty of government care and oversight.
• Moderate - "Leave us alone."
• Conservative - Don't change what works; keep government out of most affairs.
• Reactionary - Whatever I say is good, is good. Contradict me at your own peril.

This doesn't even touch on religions, which have their own ethical standards, based on whatever god or scripture they believe. BUT! One thing is for darn sure: I don't want any trace of "what is ethical" to be decided within government.

Re-visiting the Jabberwock

 kw: ai experiments, artificial intelligence, generated art, poems, illustrations, photo essays

A little more than two years ago I used Dall-E2 to illustrate the poem "Jabberwocky" by Lewis Carroll. Now that there are four high quality generative art sites that I use, I experimented with bringing just the Jabberwock itself up to date. In nearly all cases the programs relied on dragon imagery.

This is a wide-view redraw by Dall-E3 of one result of my second prompt. It's a little less sinister than the results of the first prompt. Here is how it went:

Dall-E3: "The Jabberwock"

All are fierce dragons in near-silhouette. All but one are shown against the Moon.

Dall-E3: "The Jabberwock as a pastel painting"

The word "pastel" has evoked a less sinister atmosphere. We find flowers and butterflies and in one case the dragon is actually smiling. Now that Dall-E3 can recast a square image into one with a wider aspect ratio I had it re-do the second image, on upper right. Note that the wide image at the top of the post has elements from all four images. By experimentation I found that, while re-formatting each image produces a different result, repeating the re-format step on a particular square image yields exactly the same wider image as the prior re-formatting on that image. The re-formatting offerings are square and 4:3, but the resulting wide format images are actually 1792x1024, with a ratio of 1.75:1 or 7:4. That's close enough to 16:9 (HD) that it takes just a little cropping to produce a 16:9 image (1792x1008), which can be put through Upscayl to make it big enough to use for an HD wallpaper.

I went on to try Gemini (formerly Bard), which now uses Imagen 3 to make images, and only one at a time. The 3-panel pasteup here is from three prompts:

• An image of the Jabberwock from the poem Jabberwocky
• An image of the Jabberwock from the poem Jabberwocky as a pastel painting
• A full-body image of the Jabberwock from the poem Jabberwocky as a pastel painting

Adding "pastel" definitely produces a less fierce result. The third item is the least dragonlike. Unfortunately for much of what I do, one cannot outpaint with Gemini, and asking for "wide format" or "HD ratio" gets ignored.. Maybe such options are available with Gemini Plus, but I haven't yet signed up for that.

I looked next to Leonardo AI, which is the most recent tool I've used. It has many options and variations. Here I'll present four sets of results. Leo produces images in a horizontal string. I rearranged these as 2x2 rectangular arrays. The original images are 1368x768 pixels, not quite a 16:9 ratio (though it's labeled as such in the menu), but 171:96.

Leonardo: "The Jabberwock from the poem Jabberwocky as a pastel painting", Portrait, Cinematic

The "eye" in each image is a flag that indicates it is public. One needs a paid subscription to make private images. Though these are all dragonoid, some of them have no wings. This prompt without "pastel painting" produced much darker and fiercer results.

Leonardo: "The Jabberwock from the poem Jabberwocky", Concept Art, Stylistic Illustration

These were the most colorful results of all my experiments with Leo. The "Concept Art" setting pushes such limits. Though these have fierce expressions, they are rather cute.

Leonardo: "The Jabberwock from the poem Jabberwocky", Graphic Design, 3D

These are quite dark and sinister, but not as dark as some results.

Leonardo: "The Jabberwock from the poem Jabberwocky as a pastel painting", Graphic Design, 3D

The addition of "pastel" has lightened things up a lot, both in visual and emotional tone. It's interesting to note that these critters all have multiple tails. I count lavender as the least threatening hue.

Finally, I turned to Playground, which was once the most flexible of the tools. Since they shut down Canvas mode in mid-September, it has been harder to use all the filters and other options. The free version is slow, and sometimes times out. Playground also presents a horizontal string of four images, which I rearranged as a 2x2 matrix. I set aspect ratio to 4:3 mode, which one would expect to yield 1.333:1 images; the actual images are 1216x832, about 1.46:1 or 19:13. For the two Playground mode where this was possible I used the Watercolor filter, considering it similar to saying "pastel painting". Playground has three image engines available.

Playground: "The Jabberwock from the poem Jabberwocky", Stable Diffusion XL, Watercolor filter

The background architecture in items 1 and 4 adds interest. SDXL is now considered a "traditional" or even "retro" engine. I also had the option to set a "faithfulness to prompt" setting, and used 4 ("more free").

Playground: "The Jabberwock from the poem Jabberwocky", Playground v2.5, Watercolor filter

As I've frequently observed, PG25 is edgier and typically a bit darker.

Playground: "The Jabberwock from the poem Jabberwocky", Playground v3.0, no filter

Wow! What a difference from all the others. PG30 is apparently more attuned to "poem". It even tried to write one (second image), though the character strings are illegible. The third image looks like it could be a bookplate. PG30 is now the most colorful and creative engine Playground has to offer; it was introduced earlier this year.

I must say a word about limits and conditions in the free versions I use.

• Dall-E3 lets you run 15 prompts daily, and each prompt yields four images. There appears to be no charge for re-formatting an image.
• Gemini has no explicit limit, but when I asked, it told me that sending many image requests in rapid sequence could slow things down.
• Leonardo AI gives you 150 points to use in a day. However, that doesn't mean 150 images. There are about ten (the number occasionally varies) Presets. Two of them "charge" 10 points per prompt, and in the free version, you always get four images per prompt. One of them is 24 points (on a few occasions I saw it was 104 points). The rest are 14 points. So it is possible to generate fifteen 10-pointers, or ten 14-pointers plus one 10-pointer, or six 24-pointers (with 6 unusable points) per day. A "day" resets to the time you created your account, which is 8PM for me. You can outpaint and inpaint with Leonardo, for variable numbers of points per action.
• Playground originally let me generate 150 images daily, up to four at a time. More recently the limit has been 50, and at present I don't see an indication of how many images I have left in a session.

Paying for a subscription to Playground or Leonardo AI opens up greatly expanded limits, and added functionality. I haven't tried all the combinations of either of these programs, but one day I may pick one of them to subscribe to. I have lots else going on in my life, so I don't see much need to do so at the moment.

Will our children become cyborgs?

 kw: book reviews, nonfiction, futurism, artificial intelligence, the singularity

I started these book reviews just after The Singularity is Near, by Ray Kurzweil, was published. I am not sure I read the book; I think I read some reviews, and an article or two by Kurzweil about the subject. Nineteen years have passed, and Ray Kurzweil has doubled down on his forecasts with The Singularity is Nearer: When We Merge With AI.

As I recall, in 2005 The Singularity referred to a time when the author expected certain technological and sociological trends to force a merging of human and machine intelligences, and he forecast this to occur about the year 2045. The new book tinkers with the dates a bit, but not by much. One notable change: in 2005 he considered the compute capacity of the human brain to be 10¹⁶ calculations per second, with memory about 10¹³ bits (~100 GBytes: woefully small by modern estimates). His current estimate is 10¹⁴ calculations per second, and I don't recall that he mentioned memory capacity at all.

I am encouraged that Kurzweil doesn't see us at odds with AI, or AI at odds with us, but as eternal collaborators. I'll be 98 in 2045, and I am likely to be still alive. Time will tell.

I am an algorithmicist. I built much of my career on improving the efficiency of computer code to squeeze out maximum calculations-per-second from a piece of hardware. But a "calculation" is a pretty slippery item. In the 1960's and 1970's mainframe computers were rated in MIPS, Millions of Instructions Per Second. Various benchmark programs were used to "exercise" a machine to measure this, because it doesn't correlate cleanly with cycle time. Some instructions (e.g., "Move Word#348762 to Register 1") might consume one clock cycle, while others (e.g., "Add Register 1 to Register 2 and store the result in Register 3") might require six cycles; and the calculation wasn't really finished until another instruction put the result from the Register back in a memory location. The 1970's saw a changeover from MIPS to MFLOPS, or Millions of FLoating-point Operations Per Second, to measure machine power. Supercomputers of the day, such as the CDC Cyber 6600 and the Cray-1, could perform "math" operations such as addition, in a single cycle, so a machine with a cycle time of 1 MHz could approach a rate of 1 MFLOPS (Note: The Cray-1 and later Cray machines used "pipeline processors", a limited kind of parallel processing, to finesse a rate of 1-FLOP-per-cycle. The Cray-1 achieved 100 MFLOPS).

The middle of the book is filled with little charts explaining all the trends that Kurzweil sees coming together. This is the central chart:

This is from page 165. Note that the vertical scale is logarithmic; each scale division is 10x as great at the one below. The units are FLOPS/$, but spelled out longer, because before 1970 the FLOPS rate had to be estimated from MIPS ratings. Also, the last two points are for the Google TPU (Tensor Processing Unit), a takeoff of the GPU (Graphics Processing Unit), which is specialized for extremely broad-scale massively parallel learning needed to train programs such as ChatGPT or Gemini. One cannot own a TPU, they can only be leased, so some figuration had to be done to make the data points "sit" in a reasonable spot on the chart. The dollars are all normalized to 2023.

The trend I get from these points (an exponential line from the second point through the next-to-last), is 52.23 doublings in 80 years, or a doubling each 18.4 months. It is also a factor of ten each five years plus a month (61 months). Of course, the jitter of the charted line indicates that progress isn't all that smooth, but the idea is clear. Whatever is happening today, can be done about ten times as fast in five years, or one can do ten times as much in the same time, five years from now.

When I was in graduate school, about 1980 (I was several years older than my classmates) we were asked to write an essay on how we would determine the progress of plate tectonics back in time "about 2 billion years", and whether computer modeling could help. I outlined the scale of simulation that would be needed, and stated that running the model to simulate Earth history for a couple billion years would take at least ten years of computer time on the best processors of the day. I suggested that it would be best to take our time to prepare a good piece of simulation software, but to "wait ten years, until a machine will be available that is able to run 100 times as fast for an economical cost". I didn't get a good grade. As it turned out, from 1978 to 1988 the trend of "fastest machine" is seen to be flat on the chart above! It took another five or six years for the trend to catch up after that period of doldrums. Now you can view a video of the motions of tectonic plates around the globe over the past 1.8 billion years, and the simulation can be run on most laptops.

So, I get Kurzweil's point. Machines are getting faster and cheaper and perhaps one day there will be a computer system that is smaller than the average Target store, which can hold, not just the simulation of one person's brain, but the whole human race. However, as I said, I am an algorithmicist. What is the algorithm of consciousness? Kurzweil says at least a couple of times that if 10¹⁴ calculations per second per brain turn out not to be enough, "soon" after that there will be enough compute capacity to simulate the protein activity in every neuron in a brain, and later on enough to simulate all of humanity, so that the whole human race could become a brain in a box.

Of course, that isn't the future he envisions for us. He prefers that we not be replaced by hardware, but augmented. Brain-to-machine interfaces are coming. He has no more clue than I do what scale of intervention is needed in a brain so it can handle the bandwidth of data transfer needed to, say, double the "native" compute capacity of a person, let along increase it by a factor of 10, 100, ... or a billion. At what point does the presence of a human brain in the machine even matter? I suspect even a doubling of our compute capacity is not possible.

Let's step back a bit. In an early chapter we learn a little about the cerebellum, which contains 3/4 of our neurons and more than 3/4 of the neuron-to-neuron connectivity of the total brain, all in 10% of its volume. With hardly a by-your-leave, Kurzweil goes on to other things, but I think this is utterly critical. The cerebellum allows our brain to interact with the world. It runs the body and mediates all our senses. Not just the "classic 5", but all of the 20 or more senses that are needed to keep a human body running smoothly. Further, I see nothing about the limbic system; it's only 1% of the brain, but without it we cannot decide anything. It is the core of what it "feels like to be human," among other crucial functions. Everything we do and everything we experience has an emotional component.

Until we fully understand what the 10% and the 1% are doing, it makes little sense to model the other 89% of the brain's mass. Can AI help us understand consciousness? I claim, no, Hell no, never in a million years. It will take a lot of HUMAN work to crack that nut. AI is just a tool. It cannot transcend its training databank.

At this point, I'll end by saying, I find Ray Kurzweil's writing very engaging but not compelling. I enjoyed reading the book, and not just so I could pooh-pooh things. His ideas are worth considering, and taking note of. Some of his forecasts could be right on. But I suspect the ultimate one, of actually merging with AI, of all of us becoming effectively cyborgs?… No way.

If it is artificial, is it intelligence?

 kw: book reviews, nonfiction, computer science, artificial intelligence, simulated intelligence, surveys

Before "hacker" meant "computer-using criminal", it meant "enthusiast". Many early hackers spent their time obsessively doing one of two things: either writing a new operating system, or trying to write software to do "human stuff". I have been hearing about "AI", "artificial intelligence" since the term was coined by Claude Shannon when I was nine years old. Two years later the third book in the Danny Dunn series was Danny Dunn and the Homework Machine (by Abrashkin and Williams). It featured a desk-sized computer with a novel design, created by a family friend, Professor Bullfinch. A decade later (1968) I had the chance to learn FORTRAN, which kicked off a lifelong hobby-turned-profession. The computer I learned on was the first desk-sized "minicomputer", the IBM 1130.

ENIAC and other early "elephants" were called "electronic brains" almost from the beginning. I learned how CPU's (central processing units) worked, and even though the operation of biological brains was not so well known yet, it was clear to me that computers worked in an utterly different way.

Fast-forward a few decades. Some 20 years ago a "last page" article in Scientific American described the newest supercomputer, claiming that it was equivalent to a human brain, in memory size, component count, and computing speed. Where it did not match the brain was the amount of power it needed: three million watts. Our brains use about 20 watts. It soon became evident that the metric of brain complexity is not the number of neurons, but the number of synapses, plus other connections between the neurons and the glia and other "support cells". In this, that supercomputer was woefully lacking. This is still true.

Tell me, does this illustration show one being or two?

My generation and all those following have been influenced by I, Robot, and by Forbidden Planet, and by other popular depictions of brainy machines. We think of a "robot" as a mechanical man, a humanoid mechanism that is self-contained.

In order to behave and respond the way one of the robots in I, Robot does, a humanoid mechanism would need an intimate connection with a room full of equipment like the supercomputer in the background of the image (that part of the image is real). When the Watson supercomputer played Jeopardy (and won) a few years ago, what the audience didn't see was the roomful of equipment offstage. And that was just what was running the trained "model" and its databases and the voice interface. The equipment used for training Watson was much larger, and kept a couple of hundred computer scientists, linguists, and other experts occupied for many months.

Assuming Moore's Law continues to double circuit complexity (per cubic cm) each two years, it will take sixteen doublings, or 32 years, to get the supercomputer shown into a unit that fits inside a robot of the size shown. And power requirements will have to drop from millions of watts to 100 watts or less. And this is still not a machine that has the brain power of a human. We don't know what that would take.

All this is to introduce a fascinating book, The Mind's Mirror: Risk and Reward in the Age of AI, by Daniela Rus and Gregory Mone. While Professor Rus is a strong proponent of AI and of its further development, she is more clear-headed than the authors of most books on the subject. In particular, she sees more clearly than most the risks, the dangers, of faddish over-promotion and of rushing blindly into an "AI Future".

At the outset, in Chapter 1, "Speed", she clearly emphasizes that products such as ChatGPT, DALL-E, and Gemini are tools, and particularly that their "expertise" is confined to the material that was used to train them. She writes that it might have been possible to get one of the LLM's (large language models) to write a chapter of the book, but it "would not really represent my ideas. It would be a carefully selected string of text modeled on trillions of words spread across the web. My goal is to share my knowledge, expertise, passion, and fears with regard to AI, not the global average of all such ideas. And if I want to do that, I cannot rely on an AI chat assistant." (p. 11) In a number of places she calls AI software "intelligent tools."

She continues the theme, writing of knowledge, insight, and creativity (Chapters 2 – 4), saying at one point, "They are masters of cliché." (p. 60) Critical analysis skills that we used to learn were based on following the progression from Data to Information to Knowledge, and then to Insight and Wisdom (does any school still teach this?) All of these together add up to comprehension. Does anyone have the slightest idea how to bring about Artificial Comprehension?

None of the software tools has shown the slightest ability to step outside the bounds of their training data. If ChatGPT "hallucinates", it is not rendering new knowledge, but remixing biased or deceptive content from its poorly curated training set, perhaps with a dollop of truthful "old news" in the mix. This illustration of a LinkedIn post I wrote last year shows the point.

The colors are significant:

• Green = correct or true
• Lighter orange = incomplete or outdated
• Darker orange and red = false to malicious, even evil
• Blue = AI training data, partly "good", partly "poor", partly "evil"—we hope not too evil

The three lavender blobs at right are varieties of human experience, including someone creating new "good stuff", poking out to the right and increasing the store of published knowledge. I kept those blobs far from the training data on purpose. Training is typically done with "old hat" material.

This book has a rare admission that "it's essential to remember that the nature and function of AI parameters and human synapses are vastly different." (p. 106) We don't know all that well the amount of processing going on within a neuron, nor even if a synapse is more than just a signal-passing "gate" or is something more capable. 

And though the matter of embodiment is touched upon, I was disappointed that there wasn't more on this. Perhaps you've heard that the human brain has "100 billion neurons". The actual number is 85-90 billion, and 80% of them are in the cerebellum, the "little brain" at the back, above the brain stem. We have a little inkling that the sorts of processing that cerebellar neurons perform are different from those in the cerebral neurons (the famous "gray matter"). Clearly, when 80% of the neurons make up only 10% of the brain's total volume, these neurons are smaller. The cerebellum "runs the body", and handles the traffic between the body and the cerebral cortex, the "upper brain", where thinking (most likely) occurs. Embodiment is clearly extremely important for a brain to function properly. It's being glossed over by most workers in AI.

A special chapter between 11 and 12 is "A Business Interlude: The AI Implementation Playbook". An entrepreneur or business leader who wants either to initiate a venture that strongly relies on these tools, or who wants to add them to the company bag of tricks would do well to extract these 19 pages from the book and dig into them. They include the key steps to take and the crucial questions to ask (including "Will AI be cost-effective compared to what I am doing now?"). A key component of any team tasked with making or transitioning a business to use AI is "bilinguals", people who are well versed in the business and also in computer science and AI in particular. This is analogous to a key period in my career (not with AI, though): Because I had studied all the sciences in college, and had a few degrees, and I also was a highly competent coder, I was a valued "bilingual", getting scientific software to work well for the scientists at the research facility where I worked. Bottom line: You need the right people to make appropriate use of AI tools in your company.

The book includes a major section on risks and the defenses we need. Whether some future AI system will "take over" or subjugate us is a far-off threat. It is not to be ignored, but the front-burner issues are what humans will do with AI tools that we need to be wary of. Something my mother said comes back to me. I was about to go into a new area of the desert for a solo hike. She said she was worried for my safety. I said, "Oh, I know how to avoid rattlesnakes." She replied, "I am worried about rattle-people!"

Let's keep that in mind. In my experience, rattle-people are a big risk whenever any new tool is created. What's one of the biggest uses of generative art-AI? Coupling it with Photoshop to produce deep fake pictures. Deep fake movies are a bit more difficult and costly just now, but just wait…and not very long! Soon, it will take a powerful AI tool to detect deep fake pix and vids, and how will we know that the AI detective tool is reliable and truthful?

A proverb from my coding days, "If we built houses the way most software is written, the next woodpecker to come along could destroy civilization." Most of us old-timers know a dozen ways to hack into a system, but the easiest is "social engineering," finding someone to trick into revealing login credentials or other information to help the hacker get into the system. Now social engineers are using AI tools to help them write convincing scripts to use to fool people, whether through scam phone calls, phishing emails or smishing SMS (or WhatsApp or Line or FB, etc.) messages.

[You can take this to the voting booth: Effective right now, any TV or radio political ad, particularly the attack ads, will have AI-generated content. If you want to know a candidate, go to the candidate's web site and look for actual policy statements (NOT promises!).]

A final matter I wish Professor Rus had included: Human decision making requires emotion. Persons who have suffered the kind of brain damage that "disconnects" their emotions become unable to make a decision. Somehow, we have to like something in order to choose it. Where "liking" comes from, we haven't a clue. But it is essential!

There is much more I could go into, but this is enough, I hope, to whet your appetite to get the book and read it.

A final word, that I didn't want to bring into the earlier discussions. I don't like the term Artificial Intelligence, nor AI. I much prefer Simulated Intelligence, abbreviated SI. It is unfortunate that, in the world of science, SI refers to System Internationale, the system of units such as meter, kilogram and second, used to define quantities in mathematical physics. Perhaps someone who reads this can come up with another moniker that makes it clear that machine intelligence isn't really intelligent yet.