The eyes have it/Rev II

Credit: CC0 Public Domain

The eyes have it - Rev 1, is pretty extensive as the notion of sight is rather interesting because the instrument enabling it to happen first appeared in the Ediacaran over 555 million years ago. 

Why all this palaver? Well, it seems front loading applies when the entity in question sees something interesting to investigate, in this case, a person calculating 2 + 2 = 4 by starting the calc when seeing the first number before the calculation is finished because priming the pump of thinking is necessary before doing anything of consequence, something all life forms do when dealing with the vagaries of reality. 

To whit.

People often solve simple arithmetic problems, such as basic addition, subtraction, multiplication or division, in their minds. The precise mental processes they rely on to solve these problems, however, are not entirely clear. Researchers at Université de Bordeaux and UCLouvain recently tried to better understand how humans tackle simple math mentally by tracking the size of their pupils.

Their findings, published in Proceedings of the Royal Society B: Biological Sciences, suggest that the human brain starts solving these problems before it receives all the information and relies on Bayesian inference. This is a reasoning approach used to make decisions under varying levels of uncertainty, which entails combining prior beliefs with new information.

"Our core hypothesis was that when people solve an addition problem, they don't wait passively for all the numbers before starting to compute," explained Zénon. "Instead, as soon as they hear the first number, the brain begins updating a kind of internal probability map of likely answers, eliminating implausible results. The more informative the first number, the greater this 'information gain,' and the more cognitive work the brain does at that early stage."

The researchers' observations were consistent across all three experiments. Specifically, they found that people's pupils dilated more when the first number they were presented with carried more information; or in other words, whether it offered more clues about what the likely solution to the problem was.

"This happened even before participants heard the second number, showing that the brain was already updating its expectations at that early stage," said Zénon. "Interestingly, participants who showed larger pupil responses to the first number were subsequently faster at producing the correct answer. This shows that this early cognitive work that used only the first number was very helpful in simplifying the problem and getting faster to the answer once the second number was available."

Now think a lioness going after a zebra. The processing is not arithmetic, it's dinner. Her pupils will dilate while doing the calc of how to get the zebra. This amazing research relates to how all life tackles problems as the eyes have it without question. :)

Major stages in the evolution of the eye in vertebrates

Factoid

The first possible fossils of eyes found to date are from the Ediacaran period (about 555 million years ago),[5] while the oldest certain fossilized eye is from a Schmidtiellus reetae fossil from 530 mya, collected in Saviranna in northern Estonia. The structure is similar to the compound eyes of modern-day dragonflies and bees, but with (~100) ommatidia spaced further apart, and without a lens.[6][7] The lower Cambrian had a burst of apparently rapid evolution, called the "Cambrian explosion". One of the many hypotheses for "causes" of the Cambrian explosion is the "Light Switch" theory of Andrew Parker: it holds that the evolution of advanced eyes started an arms race that accelerated evolution.[8] Before the Cambrian explosion, animals may have sensed light, but did not use it for fast locomotion or navigation by vision.

How a person drives ...

Photo by Quynh Do on Unsplash

How a person drives tells you exactly what kind of person the driver truly is. Does the individual signal? Does she drive slow and hog the left lane? Is speed, tail gating and incessant lane shifting the modus operandi for the obnoxious cowboy driving a beater or is speed up, slow down the way to travel the environs of America? The variations are endless as the thought process is inevitably revealed in the kinetic art of driving. One must be in the flow to drive safely without the goddamn cell phone enticing you while happy motoring on god forbid I-95 because driving is serious business without question. 

Stream of conscious and then some.

After the service, yours truly was a chauffeur for 6 months driving people, in their cars, to and from destinations in CT & NYC. I was good at it. In NYC, you knew the guy saw you when he cut you off. It was ok as NYC drivers, for the most part, are sane and know when you know when they cut you off. It was the code. The unwritten law in the city ... you cannot be a wuss. In Boston, it's incompetency 101. They do not know how to drive. Maybe it's the layout. NYC/Manhattan's a grid, Boston, chaos incarnate. Lastly, try to buy a stick shift car in America. In SF, automatic's mandatory but in just about any other reasonable terrain, stick is ok. Besides, it's the perfect anti theft device known
to man. :)

Quiescence 

Commonsense for a change

Credit: King's College London

Quantum mechanics proves there is no certitude so shit happens, always. The way to deal with this is to employ commonsense, flexibility and pragmatism to, in this instance, work with possible emerging AGIs (Artificial General Intelligence(s)) to insure proper alignment with us rubes becomes the norm and not the exception.

Public concern about AI safety has grown significantly in recent years. As AI systems become more powerful, a key question is how we make sure they do what we actually want. Now, researchers suggest that rather than trying to eliminate misalignment between AI and humans, we should embrace and manage it through a diverse ecosystem of AI systems that can balance and correct one another.

"While we have shown that sufficiently strong AI cannot be fully controlled or predicted, we also demonstrate that agents can be influenced by other agents without central control, and that greater diversity and openness influence their behavior. As these systems get more powerful, ensuring they remain beneficial to and aligned with humanity becomes more important," said Dr. Hector Zenil, senior author and Senior Lecturer/Associate Professor at King's Institute for AI and the School of Biomedical Engineering & Imaging Sciences/

Published in PNAS Nexus, the paper uses mathematical principles to demonstrate that an AI system powerful enough to exhibit artificial general intelligence will inevitably explore behaviors we didn't predict or plan for, making perfect guaranteed alignment impossible.

Exactly. One cannot predict the future so ... by doing the diverse ecosystem approach
regarding AIs makes eminent sense. This is akin to the Butterfly Effect whereby the
flapping of a butterfly's wings in Brazil becomes the start point for generating a tornado in Kansas. 

Speaking of math ...

Credit: Image generated by the editorial team using AI for illustrative purposes.

Perfect AI alignment with human values and interests is mathematically impossible, according to a study, but behavioral diversity among AI agents offers the promise of some control. Published in PNAS Nexus, Hector Zenil and colleagues used Gödel's incompleteness theorem and Turing's undecidability result for the Halting Problem to show that any LLM complex enough to exhibit general intelligence or superintelligence will also be computationally irreducible and produce unpredictable behavior, making forced alignment impossible.

The Halting Problem

In computability theory, the halting problem is the decision problem of determining, from a description of an arbitrary computer program and an input, whether the program will eventually halt (finish running) or continue to run forever.[1][2][3] Alan Turing proved in 1936 that the halting problem is undecidable, meaning that no general algorithm exists that can correctly solve the problem for all possible program–input pairs.[4] The problem comes up often in discussions of computability since it demonstrates that some functions are mathematically definable but not computable.

Gödel's Incompleteness theorems

The first incompleteness theorem states that no consistent system of axioms whose theorems can be listed by an effective procedure (i.e. an algorithm) is capable of proving all truths about the arithmetic of natural numbers. For any such consistent formal system, there will always be statements about natural numbers that are true, but that are unprovable within the system. Equivalently, there will always be statements about natural numbers that are false, but that cannot be proved false within the system.

The second incompleteness theorem, an extension of the first, shows
that no such system can demonstrate its own consistency.

To solve the two theorems, one must go outside the system. Hence, OODA

If one looks closely, one sees how to not only solve Gödel's theorems but also
to understanding how all life uses the constructs of OODA to cope with the vagaries of reality. :)

Unkillable

Image Edited on Canva Pro

Back in the day, yours truly used Fortran 77 to create fine art on the MAGI SynthaVision system.
My code was basic to the extreme but it produced what I needed back in the 1980's. :)

Assembly 101/MAGI CadCam Modeler - 1980

Wonderful Medium article on legacy code that never dies. :)

1. C — The Language That Still Runs the World

Every year, someone writes a blog post titled “Is C finally dead?”

Meanwhile, C is busy

• Powering operating systems
• Running embedded devices
• Driving networking stacks
• Holding together performance‑critical systems no one wants to rewrite

I’ve watched teams try to replace C with “safer” languages. The idea sounds great in a slide deck.

Then reality hits.

2. C++ — Too Complex to Die, Too Powerful to Ignore

People love to hate C++. Usually right after they misuse it.

I’ve seen modern C++ code that’s cleaner than many “simple” scripting languages.

I’ve also seen horrors that should be classified as historical artifacts.

But here’s why developers won’t quit C++

• High performance systems still need it
• Game engines depend on it
• Financial systems demand its control

The language evolved. The internet didn’t update its memes.

Smart pointers, RAII, move semantics, used correctly, they solve real problems.
Used badly, they create nightmares.

C++ doesn’t forgive laziness. That’s why experienced developers respect it.

7. Fortran — The Silent Giant of Scientific Computing

People assume Fortran died decades ago. Scientists never got the memo.

High‑performance computing clusters still use Fortran because

It’s optimized for numerical computation

Compilers are incredibly mature

Scientific codebases are huge and validated

I’ve talked to engineers who trust a 20‑year‑old Fortran model more than a brand‑new rewrite.

When results must be mathematically correct,
familiarity and validation matter more than syntax aesthetics.

Cybernetic Serendipity/MAGI SynthaVision 1980

Fractals yet again :)

Fractals yet again. This time, in 3D. Mind blowing without question. :)

 

They're everywhere ...

 

Endlessly fascinating

Yet another meander

Blue on Green ...

Channeling Ozymandias

And so it goes - K. Vonnegut

Tsar Bomba ...

Channeling Strangelove, Tsar Bomba

A Tsar Bomba-type casing on display at the Sarov atomic bomb museum, Sarov

The blast

The nuclear arms race that originated in the race for atomic weapons during World War II reached
a culminating point on October 30, 1961, with the detonation of the Tsar Bomba,
the largest and most powerful nuclear weapon ever constructed.

It could have been 100 megatons, not 50.

Lifespans ...

Not completely accurate but very cool indeed. Some animals never die but we do and that's ok. :)

If this is NOT done ...

Getty / Futurism

Not to beat a dead horse but, when reading yet another gem from Futurism regarding AI and the corporate workforce, one thing seems to always apply. Said corporation does not do due diligence in terms of how to properly integrate AI into business operations as it takes real analysis and an in depth plan on how to do it intelligently. Yours truly did this with clients with some success, before AI, to improve corporate workflows and to help companies do their jobs better and in doing this, I found out this is not a trivial task. The bible used, as reference, is, of course,
The Mythical Man Month, the best book, IMHO, in terms of how to correctly do the job just described in this short blurb. If this is not done, a CF of biblical proportion occurs, ALWAYS.

CEOs have eagerly grabbed onto AI as a tool to make offices more efficient,
and often to reduce headcount via brutal layoffs.

There’s a problem, though: the workers who remain often say they now have to fix a flood of error-ridden AI-generated “workslop” that’s burdening them, paradoxically, with more work than ever.

All this pointless busywork to correct AI-generated output results in hidden costs for companies that embrace the tech, according to The Guardian. One recent survey of 1,150 desk jockeys found that the 40 percent had encountered workslop — defined as “AI-generated content that looks good, but lacks substance” — in the course of their duties, forcing them to waste 3.4 hours per month dealing with it. At scale, that’s significant: all those hours wasted tally up to an estimated $8.1 million of lost productivity for a workplace with 10,000 workers.

The hypothesis is supported by previous research that found that computer programmers become slower when using AI. A widely-cited MIT study found that 95 percent companies that deployed AI don’t see any added revenue from its adoption, despite massive enthusiasm among CEOs.

Adding more people (and AI) to a late project makes it later.  Mythical Man Month.

Time to jump out ...

Illustration by Tag Hartman-Simkins / Futurism. Source: Getty Images

The boiling frog is an apologue describing a frog being slowly boiled alive. The premise is that if a frog is put suddenly into boiling water, it will jump out, but if the frog is put in tepid water which is then brought to a boil slowly, it will not perceive the danger and will be cooked to death. The story is often used as a metaphor for the inability or unwillingness of people to react to or be aware of sinister threats that arise gradually rather than suddenly.

Fortunately, this is false. The frog jumps out when it's time to leave but for humans
connecting to AI, this logical move might not apply. 

In a new study, researchers claim to provide the first causal evidence that leaning on AI to assist with “reasoning-intensive” cognitive labor — mental tasks ranging from writing to studying to coding to simply brainstorming new ideas — can rapidly impair users’ intellectual ability and willingness to persist despite difficulty.

“We find that AI assistance improves immediate performance, but it comes at a heavy cognitive cost,” the study declares of its findings. “After just [about] 10 minutes of AI-assisted problem-solving, people who lost access to the AI performed worse and gave up more frequently than those who never used it.”

The study, which was conducted by a multidisciplinary cohort of scientists from across the United States and United Kingdom, has yet to be peer-reviewed. But it builds on a growing body of research suggesting that extensive AI use can distort and dampen users’ thinking and independence, and as experts work to understand the impacts of widely-used chatbots on people as they unfold in real-time, they’re warning that outsourcing cognitive tasks to AI tools could put humans in a “boiling frog” conundrum — in which an unwitting, bit-by-bit erosion of our cognitive “muscles” leads to formidable challenges in the long-term.

A frog sitting on the handle of a saucepan on a hot stove

Tarantulas have smarts. :)

Rick West

Tarantula's are so cool. I've always wanted one but the wife says no way. Females live for as long as 30 years and their bite, as painful as it is, is not lethal. Seems they have smarts and innate spatial sense as well.

Researchers have witnessed several cases of spatial orientation in tarantulas – indicating that they may be capable of learning and memory.

They studied nine cases of the movement and hunting behaviour of arboreal (tree-dwelling) and fossorial (subterranean) tarantulas across North and South America, producing rare field-based observations of the creatures.

On two occasions, the arboreal Aviculariinae tarantulas repeatedly left their retreats to hunt at more favourable areas up to two metres away – particularly at locations near artificial light sources that attracted more flying insects.

Species that usually live in burrows within lowland floodplain areas also demonstrated unusual climbing behaviour. They scaled trees at heights of up to 12 metres, with the researchers concluding that they may have been foraging in the tree canopy.

While this has been previously observed to occur in the floodplain’s rainy season to avoid flooding, this behaviour was recorded during the dry season.

Gooty tarantula Poecilotheria metallica, Blue Tarantula - Getty Images

The key for these guys is to avoid, if in the southwest, the Tarantula Hawk,
a formidable hunter of tarantulas without question. 

Pepsis grossa, alongside a golden paper wasp for scale

Intense without question. Once in a great while, the spider wins. :)