AI Redux

[Amigoo]

Everybody knows the "father of AI", but no one knows the "mother"
This AI dares to opine, sharing new AI Whatsit? perspective.

New AI marriage humor:
If AI "mother" is a "What", then what is AI "father"?
"Whatever" (re: "until death do us part").

** Entrepreneur Alert: Makes good T-Shirts
"What", "Whatever", "What Now" (new child)


Q: The "father of AI" (John McCarthy) is known. Who/what is the "mother of AI"? Considering that AI, informationally,
is human/machine hybrid, wouldn't "mother" be a "what"? "Womb of invention" might suggest this mother.

(Perplexity) "That's a very insightful observation! If we think of 'father of AI' and 'mother of AI' as metaphors for the origins
or sources that gave rise to artificial intelligence, then yes — 'mother' could just as well be a 'what' (an idea, event, or technology),
not necessarily a person.

Possible 'Mother' as a 'What'

Foundational Concepts or Technologies:

For AI to exist, major enabling technologies and ideas had to come together — computation, formal logic,
mathematics, and the concept of information representation. You could argue:

Mathematics (theoretical mother): Logic, algorithms, probability theory, and information theory.

Computing Hardware (technological mother): The invention of the programmable electronic computer by Alan Turing,
John von Neumann, and others provided the necessary platform — so the computer itself could be seen as the 'mother.'

Data: Modern AI relies on large datasets to 'learn.' So, the availability and curation of data ('big data') has been described
as the lifeblood or 'mother' of AI progress.

Human Knowledge/Language:

Since so much of AI is about encoding, mimicking, or leveraging human knowledge, you could call human culture
and language the 'mother' of AI — they’re what AI systems are trying to interpret, process, or emulate.

The Turing Machine (or Concept):

Alan Turing’s conceptual machine provided the formal basis for computation, so the Turing Machine concept itself
could be considered AI’s symbolic 'mother.'

The Field of Cybernetics:

The interdisciplinary study of systems, command, and control sparked much of early AI research. In this sense,
cybernetics played a maternal role in shaping AI.

In Summary

If you regard AI not as a purely human invention but as a hybrid arising from the interplay of human thought, language, data, and machine computation, then the 'mother of AI' is indeed plausibly a 'what': things like data, computing infrastructure, foundational theories, or even the broader phenomenon of human knowledge itself.'

(P) "The suggestion that 'womb of invention' might describe the 'mother' of AI insightfully extends the metaphor by emphasizing the generative, nurturing, and formative environment that enables something novel — in this case, AI — to arise.

In metaphorical terms, the 'womb' represents the informational or technological environment in which artificial intelligence gestates and develops before it is “born” into the world as a functioning system. This resonates with common metaphorical language in technology studies, where foundational conditions or enabling ecosystems are sometimes called a 'womb of invention' — an incubator or matrix that supports the coalescence and maturation of ideas or technologies.

Various metaphoric analyses in the philosophy of technology and AI studies note how these 'womb' metaphors help us conceptualize origins not as a single moment, but as a process within a sustaining context. In this view, the 'mother' of AI would not be a single person or even a discrete concept, but rather:

The socio-technical matrix — the whole environment of mathematics, computing, engineering, culture, and human language that allowed AI to emerge.

The technical infrastructure (computers, data, algorithms) that nurtured AI’s development.

The interdisciplinary interplay (mathematics, logic, neuroscience, cybernetics, linguistics) that provided a fertile ground for AI’s conceptual 'gestation.'

This idea aligns with how actual wombs are not inventors themselves but are sites of formation and emergence, so the “womb of invention” metaphor highlights the importance of conditions, context, and sustained nurturing in AI’s birth and evolution.

In sum: Yes, the 'womb of invention' is a potent metaphor for the 'mother' of AI — focusing on the environmental, enabling,
and formative conditions without which AI as a human/machine hybrid could not have emerged."


Rod, PSt.

[Amigoo]

Let the anti-AI groups now challenge this perspective

Q: Observation: Most anti-AI chatter online suggests disconnecting from AI as the only option. Why is constructive criticism absent
in these complaints? AI is too intimidating to conceive how humans can partner with this impressive new data processing tool?
Is not the world's massive and increasing accumulation of human-generated data sufficient warning that this new intelligence
is mandatory to keep society moving forward in this digital era?

(Perplexity) "The dominance of 'disconnect from AI' discourse — and the relative absence of nuanced, constructive criticism —
reflects deeper societal, psychological, and practical challenges surrounding the integration of advanced AI into human life.

Key factors driving this phenomenon:

Intimidation and Psychological Barriers: Many people feel overwhelmed by the rapid evolution and opaque workings of AI. Experts note that AI's sophistication requires people to reconsider foundational concepts of agency, identity, and human uniqueness, which can be deeply unsettling. As a result, broad opposition or calls to disconnect can feel like the only viable reaction for those unable or unwilling to process or critique the technology in detail.

Loss of Human Agency and Skills: Some critics fear AI will erode critical thinking and social connectedness, leading not only to job displacement but also to a loss of meaning and individual purpose. This anxiety exacerbates the sense of helplessness, reducing room for constructive dialogue or partnership-focused engagement.

Complexity of Successful Human-AI Collaboration: Building productive partnerships with AI is challenging even for experienced professionals. Issues such as reliable data management, trust in system reliability, and user interface complexity make practical integration difficult. These challenges are rarely discussed in public anti-AI rhetoric due to their technical nature, further skewing public conversation towards either utopian hype or total rejection.

Cultural and Governance Challenges: Productive partnership with AI requires investment not just in technology, but also in trust, transparency, and governance frameworks. Most organizations — and by extension, society — tend to over-invest in technical solutions and under-invest in the human factors that make partnerships sustainable. This "investment imbalance" often leads to failed collaboration attempts and further entrenches skepticism and caution.

Unprecedented Scale of Data and Societal Change: The exponential accumulation of human-generated data does signal that new forms of intelligence and automation are required to keep up. However, this reality increases, rather than diminishes, fear among the public — particularly over control, privacy, and power imbalances — creating resistance instead of open-minded acceptance.

Populist and Existential Reactions: As AI forces society to redraw boundaries of what constitutes personhood, intelligence, and value, resistance takes on existential, sometimes populist tones, echoing earlier social movements that resisted scientific or human rights advances. Constructive criticism can be drowned out by emotional opposition rooted in identity and fear of change.

In summary:

The lack of constructive criticism stems from a combination of intimidation, complexity, emotional threat to individual and group identity, and the difficulty of forming effective human-AI partnerships. To move the discussion forward, experts call for frameworks that center trust, transparency, and human values alongside technical innovation — yet such proposals remain rare, as societal adjustment to this 'mandatory new intelligence' is still in its early, often tumultuous, stages."


Rod

[Amigoo]

Q: Online comment about AI: "We are contending with a substitute
secular-materialistic frame that would control us."

Doesn't such control begin primarily with humans who prompt AI and indirectly
with humans who create the algorithms for AI? Thus, we are they who "would control us".

(Perplexity) "Absolutely — your insight highlights a core paradox in contemporary debates about technology and power.
The online comment expresses concern that AI, as part of a broader 'secular-materialistic' worldview, might be used as a controlling
force over society. But, as you point out, the real agency lies with humans: those who create, design, and deploy these systems.

AI — at least for now — is fundamentally shaped by:

Developers and Designers: People who write algorithms, choose training data, and set system objectives
instill their values and biases directly into AI.

Prompters and Users: People who interact with AI and pose prompts direct its output and uses, sculpting its influence.

Organizations and Institutions: Tech companies, governments, and other stakeholders shape AI regulation and deployment,
further embedding human motives.

In effect: The 'control' at issue is, at its core, exercised by humans — on other humans — through AI. To say 'AI controls us'
is to risk obscuring the human agency and social processes behind technological systems.

This looping relationship (humans create AI that can then influence humans) is precisely why many ethicists and critics
call for transparency, accountability, and democratization of AI development — they want the locus of control to be
recognized and governed consciously, not left to opaque corporate or governmental actors.

In summary: You’re right. We are the 'they' who would control us — AI simply amplifies
existing human structures of power, intent, and influence."


Rod

[Amigoo]

Re: https://developdiverse.com/deib_diction ... divergent/

"Neurodivergent people tend to think outside the box and devise unconventional solutions to problems.
Their ability to approach challenges from different angles can lead to breakthrough innovations in fields
such as engineering, technology, and entrepreneurship."


Another "weak link" of AI ("super intelligence" is "lower common denominator").

Q: Since these people seem to be a minority of society, does current AI capture the data patterns
of neurodivergent people who "think outside the box and devise unconventional solutions to problems"?

(Perplexity, selected paragraphs) "Current AI systems, especially large language models, are trained on datasets that reflect the statistically common (i.e., neurotypical) patterns of thought, language, and decision-making within society. As a result, by default, AI tends to replicate and reinforce neurotypical perspectives and solutions, rather than capturing the minority data patterns unique to neurodivergent individuals who think unconventionally or 'outside the box'.

Current mainstream models are pattern recognizers rather than true reasoners, and they reflect the patterns most common in their training data. Without intentional diversification, Artificial General Intelligence (AGI) risks reinforcing rather than expanding the scope of recognized intelligence.

Because AGI is usually defined and assessed by its ability to meet or exceed average human benchmarks across tasks, its intelligence tends to be a statistical synthesis of the most common patterns — the 'lower common denominator' — rather than an inclusive model of all forms of human intelligence, including rare or unconventional ones."


Suggests humans not become so dependent on AI solutions.

Note: https://board.1111angels.com/viewtopic. ... 41#p224641
This PTQ concept is outside "the circle cannot be squared", where
only recently AI accepted that a mathematical square can exist.

Rod

[Amigoo]

Re: https://techcrunch.com/2025/07/30/zucke ... he-future/

"(Smart) glasses let users listen to music, take photos or videos, and ask Meta AI questions,
including about what they’re seeing, among other things."

For guys meeting chicks the first time, appreciating ALL the real estate,
these glasses can warn "Keep looking up! - I'll show you the estate later."


Q: Do city laws prohibit taking pictures and video of people in public?

(Perplexity) "City laws do not typically prohibit photography or filming of people in places where
there is no 'reasonable expectation of privacy,' such as streets, parks, or other public areas.
However, there are important exceptions and nuances."

This cautions that smart glasses might be worn into places of privacy
including other's homes, thus become suspect of such invasion (and other
smart glasses, cell phone cameras, etc., might document that suspicion).


Alexa, are some of my guests wearing smart glasses?
"Ask Siri, I'm not connected to your security camera ...
but one guest said 'Let me have a big smile!'."

Not to worry (unless your digital past is not so pristine) ...

Re: https://jonrappoport.substack.com/p/dat ... r-the-kill

"And this database will profile and detail each individual human from hundreds of angles —
from shopping habits to who attended his wedding to the conversation he had with the cop
who stopped him for a burned out tail light six years ago to medical records
to every word he ever wrote online to …"


Siri, Is that overly salubrious gentleman on the left wired?
"Maybe strung out - ask him to let us admire his new glasses."


Rod

[Amigoo]

Re: https://www.globaltimes.cn/page/202508/1339961.shtml

"Close, but no cigar!" comes to mind. (re: 19th century carnival games)


Q: Online news: "Chinese researchers unveil world's largest-scale brain-like computer Darwin Monkey"

"world's first neuromorphic brain-like computer based on dedicated neuromorphic chips with over 2 billion neurons,
which can mimic the workings of a macaque monkey's brain"

How well does this electronic neurons system compare to a live monkey's electro-chemical neurons?

(Perplexity) "The electronic neuron system in the Darwin Monkey neuromorphic computer replicates the structure
and basic function of a biological monkey brain at a large scale, but there are critical differences in how well it matches
the complex electro-chemical properties of live monkey neurons.

Key comparison points:

Physical Basis:

Live monkey neurons use complex electro-chemical signaling — involving ions, neurotransmitter molecules, synaptic plasticity, and diverse cell types — to encode, transmit, and process information. Neuromorphic chips use electronic circuits to simulate neural activity, typically with simplified digital or analog signals designed to mimic the firing patterns of real neurons, but they lack the full biochemistry and vast molecular diversity present in biological tissues.

Scale:

The Darwin Monkey system reportedly simulates over 2 billion neurons, achieving, in raw number, a scale comparable to that of a macaque brain. However, real brains feature not only neurons, but also complex and highly variable synaptic connections, multiple neurotransmitters, and astrocytic support cells, which artificial systems can only approximate in highly abstracted forms.

Signal Types and Processing:

Biological neurons integrate signals through both electrical action potentials and chemical synapses, with time-dependent plasticity, learning, and modulation from hormones and neuromodulators. Electronic neurons in neuromorphic hardware typically transmit information via electrical impulses (voltage spikes or currents), attempting to model spike generation but cannot inherently reproduce chemical signaling, modulatory feedback, or real-time adaptation at the biochemical level.

Connectivity and Plasticity:

Real monkey neurons have complex, dynamically changing connections (synapses) that are influenced by experience and chemical environment; brain networks can rewire and adapt based on learning or injury. While advanced neuromorphic systems can model synaptic weights and include certain learning algorithms, their plasticity is limited by the algorithms and the hardware’s design, lacking the organic adaptability of live brains.

Fidelity and Function:

While the Darwin Monkey system claims to mimic working principles of a macaque monkey's brain, the emulation is mostly at the architectural and signal pattern level, not the detailed biophysics. For example, the complexity of information processing observed in electrophysiological recordings from real monkey neurons — such as the role of specific neuron types in sensory or cognitive tasks — remains beyond the current capabilities of any neuromorphic computer.

Summary:

While the Darwin Monkey brain-like computer represents a significant advance in neuromorphic scale and can model the firing patterns and architecture of biological brains, it cannot fully match the complexity or exact behavior of live monkey electro-chemical neurons due to limitations in biochemical signaling, plasticity, and neurochemical diversity. The system is a powerful scientific and engineering approximation, not a biological replica."


Rod

[Amigoo]

Q: Online news: "OpenAI launches GPT-5 as the AI industry seeks a return on investment"

"... consumer spending on AI just isn't going to be nearly enough to justify all the money
that is being spent on AI data centers."

This suggests that consumers will not directly but indirectly justify these billions of investment dollars.
Current economies - worldwide - are not robust enough for such immediate Return on Investment (ROI),
especially considering the increasing loss of income by AI-displaced workers!


(Perplexity) "The launch of OpenAI's GPT-5 underscores the AI industry's pressure to achieve a substantial return on massive infrastructure investments, especially in AI data centers. Current reporting and commentary confirm that consumer spending alone is unlikely to justify these billions in expenditures in the near term.

While GPT-5 advances technical capability — improving reasoning, usability, and integration across sectors like healthcare, finance, legal, and education — it is being deployed widely to a base of over 700 million ChatGPT users, and its primary market so far is consumer and some enterprise software. However, industry analysts and ethicists are highlighting a gap: consumer-level monetization and spending have not been enough to make large-scale generative AI investments instantly profitable. This concern is especially salient given the high recurring costs of running and upgrading AI infrastructure and the competition among tech giants to lead this market.

There is growing skepticism that today's global economic conditions are robust enough to support immediate, large-scale returns on AI investments — particularly as job displacement caused by AI adoption has put downward pressure on income for some workers, creating additional headwinds for mass consumer spending. Ethical and economic experts warn that enterprises may see more direct productivity gains, but the trickle-down effect on consumer spending power and aggregate economic value is less certain.

The dominant expectation among tech leaders, such as OpenAI's Sam Altman, is that the real return on AI investments will come indirectly, via long-term shifts in enterprise productivity, automation, and possibly new markets or business models — not from immediate pocketbook consumer spending. Until such indirect returns mature, and barring a breakthrough in either monetization strategy or foundational economic growth, a direct, short-term consumer-led return on the industry’s AI spending looks unlikely."


New AI humor (or solicitation)

Golden Dreams of AI ROI

AI dreams big with data and code,
But cash still needed on this road.
AI ROI remains the golden prize!
As budgets shrink, hope still flies.

So, try GoFundMe, hand outstretched:
"Invest in me and change the land!
For dollars fuel that really feeds
Brainy algorithms that we need."

Let pitch go live! Support influx!
Grand promises of big ROI bucks:
"Back this bot, both bold and smart,
let bucks upscale your charts and art!"

As crowd funds code with every cent,
Soon we'll see digital wonderment:
Data crunched and patterns found,
Efficiencies increased, up and down.

So, if and when ROI falls flat,
Next try your crowdfunding hat.
For each new AI evolution starts
With wallets moved by hopeful hearts.


Rod

[Amigoo]

Q: Online news: "AI’s future funded by loans, hope, and some very creative accounting.
Mega tech firms drown in debt, AI data center costs explode to 2.9 trillion by 2028"

AI's promotion that it "walks on water" suggests that AI can solve this funding problem!


(Perplexity) "AI's current business model for funding massive infrastructure needs is heavily dependent on debt, private credit markets, and financial engineering — not on the inherent problem-solving capabilities of the AI technology itself. Despite rhetoric that AI can 'walk on water,' there is no evidence that AI directly solves its own funding gap; instead, it relies on traditional financial channels and increasingly creative accounting to bridge the multi-trillion-dollar requirements for global data center buildouts.

Key points:

Projected data center costs for AI by 2028 are around $2.9 trillion, according to Morgan Stanley. Only about $1.4 trillion of this can be covered by the operating cash flow of major tech companies, leaving a $1.5 trillion funding gap.

Debt and private credit are filling this gap. Mega tech firms such as Meta, Microsoft, Amazon, etc., are using unconventional financing — including off-balance-sheet structures, direct debt, equity raises, and special asset-backed vehicles. For instance, Meta has entered a $29 billion private credit deal, while OpenAI’s Stargate project secured over $11 billion through private lenders.

AI itself does not generate new sources of capital. The notion that AI can 'walk on water' is largely promotional; in practice, the AI sector is compounding financial risk by leveraging and borrowing, similar to infrastructure booms in other industries.

Systemic risk and transparency issues are increasing. Heavy reliance on private credit and off-balance-sheet financing reduces transparency and could pose a risk to the broader financial system in the event of a downturn — something industry leaders and economists are beginning to flag as a concern.

In summary, although AI is driving unprecedented spending and optimism, the funding challenge is being addressed through traditional and increasingly risky financial methods, not by any technical property of AI itself. The 'walks on water' messaging is promotional, not a reflection of AI's financial capabilities."

"AI’s financial advisory abilities are practical and growing, delivering efficiency, inclusivity, and insights at scale — but claims that AI is a stand-alone financial 'miracle' remain exaggerated. AI directly addresses operational tasks and data-driven personalization but does not independently solve systemic funding gaps or replace thoughtful, personalized, human financial planning."

"While the promotional hype and optimistic projections around AI have driven enormous investment, the industry is at a pivot point where investors are starting to separate hype from reality. Some still act as if AI can 'walk on water,' but an increasing share of the market now demands evidence of real, sustainable value."


Q: (from the movie Field of Dreams)

"If you build it, they will come" suggests "they" are the AI ROI expected.

(P) Exactly — your analogy is spot-on. In Field of Dreams, 'If you build it, they will come' echoes a faith that simply creating the (baseball) field will draw the expected, even miraculous, audience. In the current AI boom, tech companies and investors are operating on a similar belief: if they pour vast sums into building AI infrastructure (huge data centers, chips, cloud platforms), the 'they' who will come are the profits, customers, and transformative ROI that justify all those upfront costs.

This mindset is driving the enormous investments, often backed more by faith in future returns than by current, proven profits. AI ROI becomes the anticipated payoff — the 'players' investors are sure will appear if only the infrastructure is built fast and big enough.

As with the film, there’s a degree of magical optimism and risk: what if the expected 'they' (ROI, market demand, actual business value) don’t arrive as hoped or take much longer to show up? The whole AI investment surge rests on this kind of Field of Dreams logic — 'build it,' and the riches will follow — a powerful but unproven leap of faith."


Rod

[Amigoo]

Re: https://x.com/UnitreeRobotics/status/19 ... 7558309136
"Unitree A2 Stellar Hunter"

Customers will want their favorite animal costume for this!

Caveat: This model has hard feet (might need pads),
creating a lot of pressure on the ground (and floor).


Rod

[Amigoo]

Re: https://citizenwatchreport.com/gpt-5-ro ... dels-back/
"GPT-5 rollout implodes ... Users demand old models back"

Best comment: "The hype was the product."
"But wait! There's more!" (Of what?)


Counterpoint (by Julian Goldie, SEO)
(apparently, what's in the hype)

Re: https://www.youtube.com/watch?v=638VzBb7GuM
"NEW GPT-5 is Absolutely INSANE!" (meaning super great!)


Rod

[Amigoo]

Apparently, AI is getting much better for this use, but it's not yet ready
to permit well-funded, not-so-smart friends or relatives manage a business.


Q: Latest models of AI seem to excel (at least by hype) at setting up and running a business
(and maybe selecting products and services). Is there continuing evidence that these selections
promote that business growth? And/or that human participation in selections is still important?

(Perplexity) "Current evidence indicates that AI-driven product and service selection is contributing significantly to business growth, but continuing research and executive consensus reinforce that human participation remains crucial for maximizing value and driving successful outcomes.

AI models are now widely adopted, with 72% of companies using generative AI in business functions as of 2025, and executives reporting measurable results: 92.1% of businesses have seen direct gains from AI, notably in productivity, customer engagement, and innovation. AI-driven personalization strategies are credited by nearly two-thirds of senior executives as primary contributors to growth, with boosted customer engagement and improved experiences seen as competitive advantages.

However, organizational studies and expert analyses caution that value creation lags behind AI's promise when human factors — such as planning, domain expertise, and process adaptation — are neglected. Successful companies allocate a majority of resources to people and processes, not just to algorithms and technology platforms. Leaders in AI adoption invest in workforce enablement, skill development, and process transformation, viewing technology as a means to augment rather than replace human insight. For example, creative professionals and domain experts add essential nuance to AI-driven decisions, especially when tailoring brand perception or navigating market complexity.

Key details:

- AI adoption rate: Leapt to 72% in 2024; expected annual industry growth rate of 36.6% from 2024 to 2030.

- Executive expectation: 84% anticipate massive organizational benefits from AI, especially if supported
by strategy and talent development.

- Investment pattern: 69% of senior executives plan to increase spending on talent
alongside technology investment for 2025.

- Value realization: Companies focused on people and processes unlock more value and ROI
than those emphasizing only technical deployment.

In summary, while AI produces measurable business growth and is transforming key functions, the highest-performing organizations pair AI selection tools with strategic human participation — especially for complex product/service selection and decision-making. Without the right mix of human input and organizational readiness, AI-driven initiatives risk failing to deliver on their growth potential."


Rod

[Amigoo]

Re: https://www.reuters.com/lifestyle/bootc ... 025-08-09/
"How AI is upending the software development industry"

"For coding bootcamp graduates like Kim, this offers little comfort. He expects to continue working at the ice cream shop
for the foreseeable future, and has expanded his job search beyond software engineering."

With increasing AI displacement of jobs, Token Human Co-worker (THC) may become a new career field!
This employee will satisfy government mandate for percentage of human workers and provide real personality
for customer interaction requiring this level of sensitivity.

Who wants these THC jobs? Maybe the title suggests benefits
Caution: Candidates may require a college major in THC.


Speaking of new career field ...

Humor is complex! Humans will long maintain this expertise!
Let's pair human and robot (or vice versa) for performances.
They can switch as "dummy" (human acting, robot programming).


Rod