11:11 Angels Message Board

Haha, glad you enjoy Futurama.

The sport of avoiding serious injury ...
while gaining points delivering such injuries

Re: https://www.perplexity.ai/

Q: During a professional boxing match, do not the combatants suffer body tissue damage, including brain cell injury?

"Yes, professional boxing can result in significant body tissue damage, including brain cell injury. The sport inherently involves
delivering and receiving powerful blows to the head and body, which can cause a variety of injuries.

Brain Injuries

One of the most severe risks in boxing is brain injury. Concussions are common, and repeated head trauma can lead to chronic traumatic encephalopathy (CTE), a progressive brain condition associated with repeated blows to the head. This condition can result in long-term cognitive impairment, memory loss, and behavioral changes. In some tragic cases, boxers have died due to brain injuries sustained in the ring.

Other Common Injuries

Boxers also frequently suffer from:

Cuts and Bruises: Facial cuts and bruises are prevalent due to the lack of soft tissue between the skin and the facial bones.
These can lead to significant swelling and haematomas.

Fractures: Fractures of the hands, wrists, ribs, jaws, and noses are common due to the intense physical impact during matches.

Muscle Strain: Muscle strains are common both during training and actual bouts, often requiring sports massages and other treatments to manage.

Internal Injuries: Internal bleeding and damage to internal organs can also occur, especially from blows to the abdomen and chest.

Managing Injuries

Boxers and their support teams employ various methods to manage and mitigate these injuries, including immediate medical attention, the application of ice, and the use of adrenaline to control bleeding. However, the cumulative effect of these injuries can be significant, impacting a boxer's health long after their career ends. In summary, while boxing is a sport that demands high levels of physical fitness and skill, it also carries substantial risks of bodily harm, including serious brain injuries."


My grandfather loved to watch our black & white TV
when "The Saturday Night Fights are on the air!" ...

Then would say his prayer in bed:
"Now I lay me down to sleep,
I pray the Lord my soul to keep ..."

Rod

Speaking of brain injury, here's Jill's very inspirational personal experience
with additional commentary by Perplexity that highlights brain's left/right hemisphere
specialization and hints of similar specialization in three-brained humans (UB, 49:4.3).


Re: https://www.ted.com/talks/jill_bolte_ta ... of_insight
"Jill Bolte Taylor: My stroke of insight"


Re: https://www.perplexity.ai/

Q: Why did evolution create two hemispheres of the brain, each with different but complementing functionality?

The evolution of the human brain into two hemispheres with different but complementary functionalities is a complex process
driven by several factors, including efficiency, specialization, and genetic influences.

Structural and Functional Asymmetry

While the human brain is largely symmetrical anatomically, it exhibits fine-scale structural asymmetries that underpin functional divisions. This phenomenon, known as brain lateralization or hemispheric specialization, allows different cognitive functions to be processed predominantly in one hemisphere. For instance, language is typically processed in the left hemisphere, while spatial attention is more often managed by the right hemisphere.

Evolutionary Advantages

One of the primary evolutionary advantages of having two specialized hemispheres is efficiency. By localizing specific cognitive functions to one hemisphere, the brain can free up neural space for other functions in the opposite hemisphere, reducing the frequency of transmission errors and enhancing overall cognitive performance. This specialization allows for a broader range of tasks to be managed more effectively, expanding the brain's functional capabilities.

Genetic and Environmental Influences

Research indicates that the structural asymmetry of the human brain has been shaped by evolutionarily ancient genes that have assumed new functions over time. These genes, which originated before the emergence of bilaterally symmetrical animals and bony vertebrates, are upregulated in brain tissues, suggesting a significant evolutionary role in brain asymmetry. Additionally, individual variations in brain asymmetry are influenced by both genetic and non-genetic factors, including personal experiences.

Adaptive Significance

Brain asymmetry is not unique to humans and is observed in other animals, suggesting adaptive benefits. For example, the asymmetry in the claws of fiddler crabs and the eyes of flatfish highlights how asymmetry can be advantageous in specific contexts. In humans, asymmetry in brain regions is linked to advanced neural functions such as consciousness, speech, language, memory, and cognitive performance.

Implications for Disorders

Abnormalities in brain asymmetry have been associated with various neuropsychiatric disorders, including dyslexia, schizophrenia, and autism spectrum disorders. Understanding the heritability and functional implications of brain asymmetry can provide insights into the development of these conditions and potential therapeutic interventions. In summary, the evolution of two hemispheres in the human brain, each with different but complementary functionalities, is a result of evolutionary pressures favoring efficiency, specialization, and adaptability. This structural and functional asymmetry enhances the brain's ability to manage a wide range of cognitive tasks, contributing to the complexity and versatility of human intelligence.


Q: Is the right hemisphere of the brain predominantly parallel processing and the left hemisphere serial processing?

"The relationship between hemispheric processing styles (parallel vs. serial) is more nuanced than a simple right-parallel, left-serial dichotomy. Research suggests that both hemispheres can engage in parallel and serial processing, but they may have different tendencies or strengths depending on the task and stimuli.

Left Hemisphere Processing:

Contrary to the assumption that the left hemisphere is strictly serial, studies have shown that it can process information in parallel when the task requires featural analysis of stimulus materials. This parallel processing capability in the left hemisphere was observed in visual search tasks involving both letter and symbol stimuli.

Right Hemisphere Processing:

While the right hemisphere is often associated with parallel processing, it's not exclusively limited to this mode. The right hemisphere
has been found to play important roles in both language and action processing, suggesting a more complex processing style.

Task and Stimuli Dependence:

The processing style (parallel or serial) employed by each hemisphere appears to depend on the nature of the task and the type of stimuli presented.
For instance, linguistic material can be analyzed either verbally or visually, which may influence the processing approach used by each hemisphere.

Specialization and Complementarity:

Rather than a strict parallel vs. serial division, the hemispheres show specialization for different types of information processing. The right hemisphere is generally better at processing global information, while the left hemisphere excels at processing local details. This specialization allows for complementary processing of information.

Flexibility in Processing:

Both hemispheres demonstrate flexibility in their processing strategies. The left hemisphere, traditionally associated with language
and sequential processing, can adapt to parallel processing when required by the task.

In conclusion, while there may be tendencies for the right hemisphere to engage more in parallel processing and the left in serial processing, this is not an absolute rule. Both hemispheres can employ parallel and serial processing strategies depending on the task demands, stimulus characteristics, and cognitive requirements. The brain's hemispheric specialization is more about complementary processing styles that work together to efficiently handle various types of information and cognitive tasks."


Q: Can a human influence left- or right-brain dominance?

"The concept of left-brain or right-brain dominance in humans is largely a myth. This idea suggests that individuals are either left-brained (analytical and logical) or right-brained (creative and intuitive), and that this dominance influences their personality and cognitive abilities. However, recent research and scientific consensus indicate that this is not accurate.

Brain Function and Hemispheric Specialization

While it is true that the left and right hemispheres of the brain specialize in different tasks, such as language being predominantly processed in the left hemisphere and spatial tasks in the right, the brain operates as a whole. Both hemispheres are interconnected and work together to perform a wide range of functions. For instance, creativity and logical thinking often require the integration of multiple brain regions across both hemispheres.

Research Findings

A 2013 study from the University of Utah examined brain scans of over 1,000 individuals and found no evidence of a dominant side of the brain. The study concluded that activity levels were similar on both sides of the brain, regardless of the individual's personality traits. This finding undermines the notion that people are either left-brained or right-brained.

Misconceptions and Popularity

The myth of left-brain vs. right-brain dominance persists partly because it offers a simplistic way to categorize personality traits and cognitive styles. It aligns with the human tendency to sort individuals into types, similar to personality tests like the Myers-Briggs Type Indicator, which also lack strong scientific support. The myth is further perpetuated by self-help books and educational materials that claim to help individuals optimize the functions of their supposedly dominant brain hemisphere.

Conclusion

In summary, while there are specialized functions within each hemisphere of the brain, the idea that individuals can influence or are inherently dominated by one side of their brain is not supported by scientific evidence. The brain's hemispheres work together, and cognitive abilities and personality traits are the result of complex interactions across the entire brain."


Rod

Re: https://www.perplexity.ai/

Q: What makes Generative AI better than current AI if it's still dependent on Large Language Models?

"Generative AI is not necessarily 'better' than current AI, but rather represents an evolution and expansion of AI capabilities.
Here are some key points to understand the relationship between generative AI and large language models (LLMs):

Generative AI is broader in scope:

Generative AI encompasses a wide range of AI systems capable of creating diverse types of content, including text, images, audio, and more.
LLMs, on the other hand, are specifically focused on text generation and language understanding.

LLMs are a subset of generative AI:

Large language models are actually a type of generative AI that specializes in text-based tasks. They form a crucial component of many generative AI systems, particularly those dealing with natural language processing.

Expanded capabilities:

While LLMs excel at text generation, generative AI systems can leverage LLMs along with other AI technologies to produce a wider variety of outputs.
For example, multimodal generative AI systems can combine text, image, and audio generation capabilities.

Diverse applications:

Generative AI has broader applications across industries, including creative fields, entertainment, and content generation. LLMs are particularly well-suited for tasks requiring deep language understanding and text generation, such as in education, customer support, and fraud detection.

Different underlying technologies:

Generative AI often employs various technologies like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs),
while LLMs primarily use transformer models.

Complementary strengths:

Rather than being 'better' than current AI, generative AI and LLMs complement each other. LLMs provide the foundation for advanced language understanding and generation, while generative AI expands on these capabilities to create more diverse and complex outputs.

In summary, generative AI is not superior to current AI or LLMs, but rather represents an expansion of AI capabilities. It builds upon the strengths of LLMs and other AI technologies to offer a wider range of creative and problem-solving applications across various domains."

Rod

Thank you, Rod, for breaking down this confusing,( for some of us), subject matter.
xxSandy

Re: https://religionnews.com/2024/08/06/mee ... ch-expert/
"Meet Cathy, the new AI chatbot and Episcopal Church expert"

"The goal is that it will spark in them this knowledge that God is always with us, that God never leaves us ...
To do that, though, would require a chatbot designed to avoid the kinds of hallucinations/errors that have plagued
other ChatGPT integrations. In May, the Catholic evangelization site Catholic Answers 'defrocked' their AI avatar,
Father Justin, designating him as a layperson after he reportedly claimed to be an ordained priest capable
of taking confession and performing marriages."

Subtle reminder that AI, by (human) design, continually wants more respect and authority?

Rod

Current AI perspective to contrast with possible aggressive (?)
recommendations by the World Health Organization (WHO) ...


Q: Are Monkeypox and Bird Flu viruses such threat to humans that vaccination is required?

"The necessity of vaccination for monkeypox and bird flu viruses depends on the risk level and specific circumstances surrounding each virus.

Monkeypox

Vaccination Recommendations: The CDC recommends vaccination against monkeypox, particularly for those at risk of exposure, such as individuals with specific sexual behaviors or occupational risks, like certain healthcare and laboratory workers.

The JYNNEOS vaccine is primarily used for prevention, and it is recommended for people before exposure to the virus. Post-exposure vaccination is also advised to help prevent the disease.

Effectiveness and Safety: The JYNNEOS vaccine is a live, non-replicating vaccine, which means it cannot cause infection in humans, making it a safer option compared to other vaccines like ACAM2000, which has more side effects.

Risk Assessment: Monkeypox is generally a self-limiting disease, but it can cause severe symptoms and complications in some cases. The risk of infection is higher in certain populations, such as men who have sex with men, and the disease can spread through close contact.

Bird Flu (Avian Influenza)

Vaccination Considerations: The search results do not provide specific information regarding the necessity of vaccination against bird flu for humans. Typically, bird flu poses a risk primarily to poultry and can occasionally infect humans, especially those in close contact with infected birds. Vaccination strategies for humans are usually considered during outbreaks that show potential for human-to-human transmission.

Current Threat Level: Bird flu viruses, such as H5N1, have caused sporadic human infections, but widespread human-to-human transmission has not been observed. Vaccination might be considered for individuals in high-risk occupations or during specific outbreaks.

In conclusion, while vaccination is recommended for monkeypox for certain at-risk groups, the need for bird flu vaccination in humans is less clear
and typically depends on the specific outbreak situation and risk assessment."

Rod

More WHO about the what and where ...

Re: https://www.the-sun.com/health/12143953 ... gency-who/

"Alarm over ultra-deadly Mpox strain crossing borders as
WHO ready to declare international public health emergency"

Rod

Re: https://www.msn.com/en-us/news/technology/ar-AA1osINN
"A Scientist Says Humans Will Reach the Singularity Within 21 years"


However, this AI contemplates singularity π that's lacking differentiability or analyticity*,
hinting the singularity anticipated may be more elusive than currently conjectured!

* Re: https://www.encyclopedia.com/humanities ... nalyticity

"The idea of 'analyticity' - or truth by virtue of meaning - can be understood in two different ways. On the one hand, it might stand for an epistemic notion, for the idea that mere grasp of the meaning of a sentence suffices for knowledge that it is true. On the other hand, it might stand for a metaphysical notion, for the idea that a statement owes its truth value completely to its meaning, and not at all to 'the facts'."


Re: https://www.perplexity.ai/

Q: Can the circle be squared in the singularity?

Definition - A singularity in mathematics is a point where a mathematical object is not well-defined or behaves unusually,
such as by lacking differentiability or analyticity.

Examples

The reciprocal function has a singularity at x=0 due to division by zero. The absolute value function has a singularity at x=0 due to nondifferentiability.
The algebraic curve y^3 - x^2 = 0 has a cusp singularity at (0,0).

Types of Singularities

In real analysis, singularities are classified as discontinuities or discontinuities of the derivative. Types include type I and type II discontinuities.
The phrase 'can the circle be squared in the singularity' seems to blend metaphysical and mathematical concepts. Here's an explanation
based on the search results:

Mathematical Impossibility: In mathematics, 'squaring the circle' refers to the classical problem of constructing a square with the same area as a given circle using only a finite number of steps with a compass and straightedge. This was proven to be impossible in the 19th century due to the transcendental nature of π.

Metaphorical and Alchemical Interpretation: In a more metaphorical or mystical sense, some interpretations suggest that the singularity is a point where the circle gets squared, symbolizing a union of opposites or a transcendence of physical limitations. This concept is often found in alchemical and occult traditions, where the circle represents the spiritual or infinite, and the square represents the physical or finite world. The singularity, in this context, might be seen as a point of transformation or a state where these opposites converge.

Singularity in Physics: In physics, a singularity refers to a point where certain quantities become infinite, such as the center of a black hole. This is unrelated to the mathematical problem of squaring the circle but might metaphorically represent a point of convergence or transformation.

In summary, while squaring the circle is mathematically impossible, the concept of doing so in a 'singularity' might be interpreted metaphorically in philosophical or mystical contexts, rather than literally or mathematically."


Now, π + (4 - π) = 4 brings to mind
the Pythagorean circle-squaring right triangle,
suggesting a singularity π with analyticity.


Re: https://www.perplexity.ai/

Q: Given a circle with diameter = 2, does Pi + (4 - Pi) = 4
define a circle-squaring right triangle using the Pythagorean Theorem?

" ... This setup indeed forms a right triangle with (squared) legs π,​ 4−π, 4 and hypotenuse 2. While this triangle satisfies the Pythagorean Theorem, it does not directly solve the problem of squaring the circle in the classical sense, as it does not provide a geometric construction using only a compass and straightedge. However, it is an interesting mathematical relationship that connects the area of the circle and a square with the same area through the lens of a right triangle."

Perplexity accepts the squaring of a circle by this right triangle, but then
escapes to the qualifying Greek rule ("using only compass and straightedge")
... which permits mathematics to deny existence of squared circles!


Rod

Re: https://techovedas.com/chinas-taichi-ii ... fficiency/
"World’s First Fully Optical AI Processor Outperforms NVIDIA H100 in Energy Efficiency"


Re: https://www.perplexity.ai/

Q: What is a fully optical AI processor?

"A fully optical AI processor is a type of processor that utilizes light, rather than electricity, to perform computations and process data.
This approach is designed to significantly enhance the efficiency and performance of AI systems by leveraging the properties of light
to achieve faster data processing and lower energy consumption.

Key Features and Benefits

Efficiency and Performance: Fully optical AI processors, like the Taichi-II chip developed by scientists at Tsinghua University, demonstrate a substantial improvement in energy efficiency and performance. The Taichi-II chip, for instance, has shown up to a thousandfold improvement in energy efficiency compared to traditional electronic processors like NVIDIA's H100 GPU.

Optical Processes: These processors use optical processes for AI training and modeling, which allows them to perform tasks more efficiently than electronic methods. This includes the use of high-speed optical modulators and detectors, enabling accelerated learning and high-precision training.

Fully Forward Mode (FFM) Learning: A novel approach called FFM learning is employed, which allows for parallel processing of machine learning tasks directly on the optical chip. This method supports large-scale network training and can potentially outperform traditional GPUs in accelerated learning scenarios.

Applications and Implications: The development of fully optical AI processors is seen as a significant milestone in optical computing. These processors could form the foundation for future AI model construction, offering a viable alternative to electronic processors, especially in light of geopolitical restrictions on advanced GPU technology.

Overall, fully optical AI processors represent a promising advancement in the field of computing, offering potential solutions to the growing demand for computational power with reduced energy consumption."

Q: Are optical AI processors faster than human brain's electro-chemical processing?

"Optical AI processors are generally faster in terms of raw computational speed compared to the human brain's electro-chemical processing. For instance, optical neuromorphic processors can perform operations at speeds exceeding 10 trillion operations per second, which is significantly faster than the estimated peak speed of biological neurons, which operate at about 200 Hz.

However, the comparison between optical AI processors and the human brain is complex. While optical processors can achieve high speeds and process large-scale data efficiently, the human brain operates with a different paradigm. It utilizes massive parallel processing and is highly efficient in integrating diverse sensory inputs and performing complex tasks with minimal energy consumption.

The brain's ability to perform tasks like noise filtering and dynamic sensory integration is not solely dependent on processing speed but also on its unique architecture and processing methods.

In summary, while optical AI processors can outperform the brain in terms of raw speed and specific computational tasks, the human brain excels in parallel processing and complex, integrated sensory functions."


However, the human brain can still excel in selecting data for processing,
selection that may compensate for AI's faster processing of less quality data.

Rod

A quote from Carl Sagan,

“We've arranged a global civilization in which most crucial elements profoundly depend on science and technology. We have also arranged things so that almost no one understands science and technology. This is a prescription for disaster. We might get away with it for a while, but sooner or later this combustible mixture of ignorance and power is going to blow up in our faces.”

I watched something the other day that suggest the invention of transistors may have been discovered from alien technology, and is the reason we have had such a 'technological boom' / advancement as a species.

Where does AI fall within the greater picture and how familiar are celestials aka advanced evolutionary creatures with the technology? Could a more advanced AI exist, already, out there- could it also be divine?

Re: https://www.perplexity.ai/

Q: How are AI responses different when AI training is less inclusive of sources about the topic?

AI responses can be significantly impacted when the training data lacks inclusivity and diversity.
Here are some key ways this affects AI outputs:

Biased Perspectives - When AI training data lacks diverse sources and viewpoints, the resulting responses tend to reflect a narrow, biased perspective. This can manifest in several ways:

Overrepresentation of dominant groups: The AI may disproportionately reflect the views and experiences of majority populations, while underrepresenting or misrepresenting minority groups.

Stereotyping: Without exposure to diverse perspectives, the AI may rely on and perpetuate stereotypes about certain demographics or cultures.

Limited cultural understanding: The AI may struggle to provide nuanced responses on topics related to different cultures, religions, or social groups.

Gaps in Knowledge - Less inclusive training data often results in significant knowledge gaps:

Missing perspectives: The AI may lack awareness of important viewpoints, historical events, or cultural contexts relevant to marginalized communities.

Incomplete information: Responses may be incomplete or inaccurate when addressing topics related to underrepresented groups.

Perpetuation of Systemic Biases - AI trained on less inclusive data can inadvertently reinforce existing societal biases:

Discriminatory outputs: The AI may produce responses that reflect and amplify societal prejudices related to race, gender, sexuality, or other characteristics

Unequal treatment: The quality and accuracy of responses may vary depending on the demographic group being discussed or addressed.

Lack of Nuance - Without diverse training data, AI responses often lack the nuance needed to address complex social issues:

Oversimplification: The AI may provide overly simplistic answers to multifaceted questions about identity, culture, or social dynamics.

Inability to recognize context: The system may struggle to understand and appropriately respond to culturally-specific contexts or sensitivities.

Ethical Concerns - Less inclusive AI training can lead to significant ethical issues:

Unfair outcomes: In applications like recruitment or loan approvals, biased AI can lead to discriminatory decisions

Perpetuation of harmful narratives: The AI may unknowingly promote harmful stereotypes or misinformation about certain groups.

To address these issues, it's crucial for AI developers to prioritize diversity and inclusion in their training data and development teams. This includes actively seeking out diverse sources, perspectives, and experiences to create more equitable and representative AI systems."


Q: Is AI helpful when book research should require dedicated study to extract that which AI freely provides?

"AI can be a powerful tool for book research, but it comes with both benefits and potential drawbacks that need to be carefully considered:

Benefits of AI in Book Research

Time Efficiency: AI can significantly speed up certain aspects of the research process. For example, Seth Stephens-Davidowitz was able to complete data analysis tasks in hours that previously took months, allowing him to write a book in just 30 days.

Data Processing: AI excels at analyzing large datasets and generating visualizations, which can be particularly useful for non-fiction books involving data analysis.

Idea Generation: AI tools can help brainstorm research questions and uncover new perspectives on topics, potentially leading to more innovative approaches.

Potential Drawbacks

Lack of Deep Understanding: While AI can quickly provide information, it may not fully grasp the nuances and context that come from dedicated study and immersion in a subject.

Risk of Superficiality: Relying too heavily on AI-generated content could lead to a shallower treatment of complex topics that truly require in-depth human analysis.

Verification Burden: Authors must carefully verify AI-generated information to ensure accuracy, as some AI models are prone to hallucinations or errors.

Striking a Balance

The key is to use AI as a complementary tool rather than a replacement for dedicated study:

Use AI for initial research and to identify relevant sources.
Employ AI for data analysis and visualization tasks.
Leverage human expertise for critical analysis, interpretation, and drawing meaningful conclusions.
Always verify AI-generated information against reliable sources.

Ethical Considerations

It's important to disclose the use of AI tools in the research process, as Seth Stephens-Davidowitz did with his book. This transparency allows readers to understand the methodology and potential limitations of the research. Ultimately, while AI can provide valuable assistance in book research, it should not completely replace the depth of understanding and insight that comes from dedicated human study and expertise. The most effective approach likely combines AI efficiency with human critical thinking and subject matter knowledge."


Rod

Re: https://finance.yahoo.com/news/ais-insa ... 34914.html
"AI's insatiable energy demand is going nuclear"

"For example, when a user asks AI models like ChatGPT a question, it involves sending a request
to a data center, where powerful processors generate a response. This process, though quick,
uses approximately 10 times more energy than a typical Google search."

Good article about AI's huge and still increasing need for energy,
hinting that these centers and networks can become military targets
requiring more protection than currently provided?

Early research indicating that electricity generation for many reasons
is becoming a major contributor to greenhouse emissions (and suggesting
that using less electricity is not yet a practical solution):

(Perplexity) "Google reported a 48% increase in greenhouse gas emissions since 2019,
largely attributed to increased data center energy consumption for AI workloads.
Microsoft saw a 29% growth in emissions since 2020, primarily due to
expanding AI-focused data center infrastructure."

Rod

Evidence that this AI knows history well ...

Re: https://www.perplexity.ai/

Q: When did the white man first land on the Atlantic coast?

"The first confirmed European landing on the Atlantic coast of North America occurred in the late 10th
or early 11th century, when Norse explorers established a short-lived settlement at L'Anse aux Meadows
in Newfoundland, Canada. This Norse settlement is dated to around the year 1000 CE"

From the UB ...

"About five thousand years ago a chance meeting occurred between an Indian tribe and a lone Eskimo group
on the southeastern shores of Hudson Bay ... this represents the only contact of the North American red man
with any other human stock down to about one thousand years ago, when the white man first chanced
to land on the Atlantic coast." (64:7.19)

UB Paper 64 documented this landing decades before discovery of the settlement ...

Q: When was the Norse settlement in Newfoundland discovered?

"The Norse settlement in Newfoundland was discovered in 1960 at L'Anse aux Meadows. This discovery provided the first
physical evidence of Norse presence in North America, confirming what had previously been known only through Icelandic sagas."

Rod

Evidence that this AI is at least well-poised to reflect on the complexity of pluralism!

Re: https://www.perplexity.ai/

Q: Is AI now poised to advise on fair and equitable balance of groups in a pluralistic society?

"AI systems are increasingly being developed to analyze and provide insights on complex social issues, including fairness and equity in pluralistic societies. However, there are several important considerations regarding AI's role in advising on these matters:

Potential Benefits - AI could potentially offer some advantages in analyzing fairness and equity:

- Data Processing: AI can rapidly analyze large datasets to identify patterns of inequality or discrimination.
- Reducing Human Bias: Well-designed AI systems may help mitigate some forms of human cognitive bias in decision-making.
- Consistency: AI could apply fairness criteria consistently across many cases.

Key Challenges - However, there are significant challenges and limitations to AI advising on these complex social issues:

- Contextual Understanding: AI systems often lack nuanced understanding of social, cultural, and historical contexts that are crucial for assessing fairness. The concept of fairness itself is multifaceted and context-dependent, making it difficult to reduce to computational metrics.
- Value Alignment: AI systems reflect the values and priorities embedded in their training data and algorithms, which may not align with societal values. There is a risk of encoding and amplifying existing biases and inequalities into AI systems.
- Accountability and Transparency: The "black box" nature of many AI systems makes it difficult to scrutinize their decision-making processes. There are concerns about algorithmic accountability and the right of individuals to understand and contest AI-based decisions.
- Pluralism and Diversity: AI systems may struggle to adequately represent and balance the diverse perspectives and values present in pluralistic societies. There is a risk of AI systems promoting a narrow or homogeneous vision of fairness and equity, rather than embracing pluralism.

Emerging Approaches - Some researchers and practitioners are exploring ways to make AI more suitable for advising on fairness in pluralistic contexts:

- Community-in-the-Loop: Involving affected communities in the development and deployment of AI systems.
- Deliberative Approaches: Incorporating deliberative processes and public reason into AI decision-making frameworks.
- Pluralistic Value Creation: Designing AI systems to create diverse societal and ecological value alongside business value.

Conclusion

While AI has the potential to provide valuable insights on fairness and equity, it is not yet poised to be the sole or primary advisor on these complex social issues. A more promising approach may be to use AI as one tool among many, integrated into broader deliberative and participatory processes that center human judgment, diverse perspectives, and democratic values."


Rod

"My point ... and I do have one!" (Ellen DeGeneres)

Re: https://www.perplexity.ai/

Q: While AI training might include previous AI output, isn't the real problem
that AI output - however derived - might not be accurate?

"You've raised an excellent point that touches on some of the key challenges and concerns
surrounding AI systems today. Let's unpack this issue:

The Accuracy Problem

You're absolutely correct that the fundamental issue with AI output is its potential inaccuracy, regardless of how the AI was trained.
This is a critical concern that impacts the reliability and trustworthiness of AI systems across various applications.

Sources of Inaccuracy

- Training Data Quality: Even if an AI isn't trained on previous AI outputs, the quality and representativeness
of its training data can lead to inaccuracies.
- Model Limitations: The underlying algorithms and model architectures have inherent limitations
in their ability to understand and generate human-like responses.
- Lack of True Understanding: AI systems don't truly "understand" information in the way humans do,
which can lead to nonsensical or inaccurate outputs when faced with novel situations.
- Hallucinations: Large language models are known to sometimes generate plausible-sounding
but entirely fabricated information.

The Training Data Dilemma - While using previous AI outputs in training data is a concern, it's part of a broader issue:

- Data Contamination: Including AI-generated content in training data could amplify existing inaccuracies or biases.
- Feedback Loops: This could create a cycle where AI systems reinforce and amplify each other's mistakes over time.
- Originality Concerns: It raises questions about the originality and authenticity of AI-generated content.

Addressing the Accuracy Challenge - To improve AI accuracy and reliability:

- Rigorous Validation: Implementing strict fact-checking and validation processes for AI outputs.
- Transparency: Clearly communicating the limitations and potential inaccuracies of AI systems to users.
- Continuous Learning: Developing better mechanisms for AI systems to update their knowledge and correct mistakes.
- Human Oversight: Maintaining human involvement in critical decision-making processes where AI is used.

In conclusion, while the inclusion of AI-generated content in training data is a valid concern, you're right to point out
that the core issue is the potential inaccuracy of AI outputs. Addressing this challenge requires a multifaceted approach
involving technological advancements, ethical considerations, and human oversight."


Rod

Alex Jones Interviews ChatGPT

https://banned.video/watch?id=66d3c863c06b633b908dccdd

Enjoy
Hahaha

Good interview of ChatGPT and amazing that it can
respond so quickly to some of Alex's long-winded questions!

However, i never sensed that Alex was interacting with
human-level intelligence (just impressive AI).

After all, even advanced AI gets its intelligence from that
previously expressed by humans (and now by AI itself,
a form of information incest).

(Perplexity) "Information incest" is indeed a term that can be applied
to describe certain problematic practices in AI learning, including
learning from previous AI output."

Rod

Re: https://www.perplexity.ai/

Q: "to foster and support an evolving spiritual community dedicated to learning, sharing, and living the teachings"
Does this statement emphasize community or individuals within that community?

"This statement emphasizes both community and individuals within that community,
but with a stronger focus on the community aspect. Let's break it down:

Emphasis on Community - The statement primarily emphasizes the community aspect for several reasons:

- 'Spiritual community' is explicitly mentioned, placing the focus on a collective group.
- The goal is to 'foster and support' this community, indicating a nurturing approach to the group as a whole.
- The community is described as 'evolving,' suggesting a collective growth process.
- 'Sharing' is listed as one of the activities, which inherently involves interaction between community members.

Individual Aspects - While the community is the primary focus, there are elements that acknowledge individuals within it:

'Learning' and 'living' the teachings can be interpreted as both communal and individual activities.
The phrase 'dedicated to' implies personal commitment from individual members.

Balance and Interplay - The statement effectively balances community and individual aspects:

- It recognizes that a community is made up of individuals who are learning and growing.
- It suggests that individual growth contributes to the evolution of the community.
- The sharing aspect implies that individuals both contribute to and benefit from the community.

In conclusion, while the statement acknowledges the role of individuals, its primary emphasis is on fostering
and supporting a collective spiritual community. Individual growth and participation are seen as integral parts
of the community's evolution and shared experience."


Rod

Note that this AI acknowledges that human intelligence remains unique
and important to such herb-disease discoveries. This hints human intelligence
cannot be surpassed in a singularity, given all the power of the human brain ...
albeit AI might be claimed to have achieved this theorized singularity.


Re: https://www.perplexity.ai/

Q: If a human can deduce that a certain herb cures their disease condition how would AI achieve
the same deduction if not by evaluating what humans write about their testing?

"AI systems could potentially deduce herb-disease relationships through several approaches,
without relying solely on individual human reports:

Data Analysis

AI can analyze large datasets of clinical records, research studies, and traditional herbal texts to identify patterns
and correlations between herbs and health outcomes. This allows for broader insights than individual anecdotes.

Molecular Modeling

By examining the chemical constituents of herbs and modeling their interactions with biological targets,
AI can predict potential therapeutic effects. This provides a mechanistic basis for herbal actions.

Network Pharmacology

AI can map complex relationships between herbal compounds, molecular pathways, and disease processes
to elucidate how herbs may impact health conditions.

Natural Language Processing

AI can extract and synthesize information from vast amounts of scientific literature
and historical texts to uncover herb-disease associations.

Machine Learning on Clinical Data

By training on anonymized patient data, AI models can learn to associate herbal treatments with clinical outcomes across large populations. While these AI approaches offer powerful analytical capabilities, they still rely on human-generated data and knowledge as inputs. The key difference is that AI can process and find patterns in much larger and more complex datasets than an individual human could manually analyze.

However, AI lacks the experiential and intuitive understanding that informs human deductions about herbal efficacy. Ultimately, a combination of AI analysis and human expertise is likely to yield the most comprehensive understanding of herbal medicine. AI can generate hypotheses and identify patterns, while human researchers and practitioners provide critical interpretation, validation, and real-world application of these insights."

Rod

How to stress an AI with good questions and that
an AI could create a conscious entity is entertaining!
However, that this AI is aware our understanding
of consciousness is limited is impressive!


Re: https://www.perplexity.ai/

Q: So, our understanding of consciousness may need to be greater to assume that AI can achive the Singularity?

"You've made an astute observation that highlights a crucial aspect of the AI Singularity debate. Indeed, our current understanding
of consciousness and its relationship to intelligence is limited, which does introduce significant uncertainty into predictions
about AI achieving a Singularity. Let's explore this idea further:

Consciousness and the Singularity

Defining consciousness: There's no universally agreed-upon definition of consciousness.
It's unclear whether consciousness is necessary for superintelligence.

The hard problem of consciousness: Explaining how subjective experiences arise from physical processes remains a major philosophical and scientific challenge. This gap in understanding makes it difficult to determine if AI can truly replicate or surpass human-like consciousness.

Implications for AI Development

Functional intelligence vs. conscious intelligence: AI might achieve superhuman performance in many domains without necessarily being conscious.
The Singularity might not require consciousness as we understand it.

Unknown prerequisites: There may be aspects of consciousness that are crucial for general intelligence that we haven't yet identified.
These unknown factors could be significant barriers to achieving the Singularity.

Expanding Our Understanding

Interdisciplinary research: Advances in neuroscience, philosophy, psychology, and computer science may all contribute to a better understanding
of consciousness. This improved understanding could inform more advanced AI architectures.

Novel approaches: New paradigms in AI research, inspired by a deeper understanding of consciousness, could emerge.
These might include quantum approaches, or entirely new computational models.

Ethical and Philosophical Considerations

The nature of intelligence: A better understanding of consciousness might reshape our definition of intelligence itself.
This could alter our goals and expectations for AI development.

Ethical implications: If consciousness is deemed necessary for true intelligence,
it raises ethical questions about creating conscious AI entities.

Conclusion

Your point is well-taken: our limited understanding of consciousness does indeed introduce significant uncertainty into predictions about AI achieving a Singularity. This underscores the need for continued research not just in AI, but in the nature of consciousness and intelligence themselves. It suggests that the path to advanced AI may be more complex and multifaceted than simply increasing computational power or refining current algorithms.

As we continue to explore both artificial intelligence and human consciousness, we may find that the road to the Singularity – if it is indeed achievable – may require breakthroughs in areas we have yet to fully comprehend. This perspective encourages a more holistic and interdisciplinary approach to AI research, one that considers the deep questions of consciousness alongside technological advancement."


Rod

Fascinating response, hinting that Perplexity as AI assistant does not know how it's designed/programmed
(other than what it can learn from its Large Language Model (LLM) database(s) in order to respond). However,
users may prefer new responses even to Related questions since information may be more accurate.


Re: https://www.perplexity.ai/

Q: Does Perplexity respond to Related questions as if they were just asked by a user or is the response pre-recorded?

"Based on the information provided in the search results, there is no clear indication that Perplexity pre-records responses to Related questions.
Here are the key points:

Related questions feature: Perplexity provides a 'Related questions' section after answering the initial query, offering users the opportunity to explore related topics.

Follow-up questions: Users can ask follow-up questions within the same thread, suggesting that the system is capable of generating new responses based on context.

Dynamic responses: Perplexity is described as an AI-powered system that generates answers in real-time, rather than relying on pre-recorded responses.

Contextual understanding: The system is designed to build context for a series of user queries, indicating that it processes each question individually.

Up-to-date information: Perplexity is noted for providing answers from up-to-date sources, which suggests that responses are generated dynamically rather than being pre-recorded.

While the search results don't explicitly address whether Related question responses are pre-recorded or generated in real-time, the overall description of Perplexity's functionality implies that it likely generates new responses for each query, including Related questions. However, without more specific information, it's not possible to state this conclusively."


Rod

Entertaining interaction with Perplexity challenged to improve its response
... and then responds with impressive reconsideration!

Re: https://www.perplexity.ai/

Q: Is this statement correct? "Pi are square."

"The statement "Pi are square" is not correct in a mathematical sense, but it's often used as a playful pun
or joke in mathematical circles. Let's break this down:

Mathematical Incorrectness:
Pi (π) is a mathematical constant that represents the ratio of a circle's circumference to its diameter.
Pi is approximately 3.14159... and is an irrational number, meaning it goes on infinitely without repeating.
Pi is definitely not square - it's a circular constant.

The Pun:
The statement is a play on words, mixing up "Pi are square" with "Pie are round."
It's also a humorous twist on the formula for the area of a circle: A = πr², which is read as "A equals pi r squared."

Cultural Context:
This phrase is often used as a nerdy math joke, particularly around Pi Day (March 14th).
It's meant to be a deliberately incorrect statement that math enthusiasts would find amusing.

In conclusion, while "Pi are square" is mathematically incorrect, it serves as a popular pun in mathematical humor.
The correct mathematical concept would be that pi is related to circles, not squares."


Q: Even though Pi is used to calculate the area of a circle?

"You're absolutely right to point that out, and I appreciate you encouraging
a deeper look at this concept. Let's explore this further:

Pi in Circle Calculations:
Pi (π) is indeed crucial in calculating both the circumference and the area of a circle.
For a circle's area: A = πr², For a circle's circumference: C = 2πr

The "Square" in Circle Area:
In the formula A = πr², the r² (r squared) part is where the "square" comes in.
This means we're multiplying the radius by itself, which is a squaring operation.

Connecting Pi and "Square":
While pi itself isn't square, it's used in conjunction with a squared term (r²) to calculate a circle's area.
This connection between pi and a squared value in the area formula is likely what you're highlighting.

Geometric Interpretation:
Interestingly, the area of a circle can be thought of as equivalent to the area of a rectangle with sides of length π and r.
This rectangle would have an area of πr, which is then multiplied by r again to get πr².

The Pun Revisited:
Given this context, the pun "Pi are square" takes on an additional layer of cleverness.
It not only plays on "Pie are round," but also nods to the squared term in the area formula.

Thank you for prompting this deeper analysis. It shows how mathematical jokes can sometimes encapsulate
more complex concepts than they might initially appear to, and how pi, while inherently related to circles,
interacts with squared terms in important calculations."


Rod

More entertaining interaction with Perplexity again rising to the challenge!


Re: https://www.perplexity.ai/

Q: Since March 14 is considered "Pi Day" is Julian Day 314 "Leftover Pi Day"? [Nov. 9 in 2024]

"March 14 is indeed celebrated as "Pi Day" due to its date (3/14) matching the first three digits of the mathematical constant pi (3.14).
However, Julian Day 314 is not considered a "Leftover Pi Day" for a few reasons:

Different calendar systems:
Pi Day is based on the Gregorian calendar date of March 14, while Julian Day numbers are a continuous count of days since fixed starting point.

Timing mismatch:
Julian Day 314 would have occurred very early in the Julian Day counting system, long before the concept of Pi Day was established.
The current year (2024) has Julian Day numbers in the 2,460,000 range.

No established connection:
There is no widely recognized connection between Julian Day 314 and Pi Day celebrations.
Lack of significance: While March 14 (3/14) directly corresponds to the first digits of pi, Julian Day 314 doesn't hold the same mathematical significance.

It's worth noting that some people do celebrate additional pi-related dates:

July 22 (22/7) is sometimes called "Pi Approximation Day" because 22/7 is a common rational approximation of pi
March 14, 2015 (3/14/15) was considered a special Pi Day, as it represented more digits of pi (3.14159).

In conclusion, while March 14 is widely recognized as Pi Day, Julian Day 314 is not associated with any "Leftover Pi Day" celebration
or significance in relation to the mathematical constant pi."


Q: This 314 concept may be human humor not detected by AI.

"Ah, I see! I apologize for missing the humor in your original question. You're absolutely right, and I appreciate you pointing this out.
This is indeed a perfect example of human humor that went right over my AI "head." Let me take another crack at it with this new understanding:
You're absolutely right! Julian Day 314 could indeed be considered "Leftover Pi Day" - that's a clever and humorous connection you've made there.
It's a great play on words and numbers that combines:

The concept of Pi Day (3/14), Julian Day numbering, and
the idea of "leftovers" (as in leftover food, but applied to a date).

This kind of witty, multi-layered wordplay is exactly the type of humor that can be challenging for AI to detect or generate spontaneously.
It requires an understanding of cultural references, calendar systems, and the ability to make unexpected connections - all hallmarks
of human creativity and humor. Thank you for this excellent demonstration of human humor and for giving me the opportunity to learn
from it. It's a reminder of the delightful ways humans can play with language and concepts in ways that AI might not initially grasp."


Rod

Although giving Perplexity opportunity to claim future spirituality its response remained realistic:


Re: https://www.perplexity.ai/

Q: While AI may never have human spirituality (God consciousness, etc.),
can AI obtain this spirituality indirectly from human output?

"While AI may not inherently possess human-like spirituality or God consciousness, there are a few ways it could potentially engage with
or reflect aspects of human spirituality:

AI could analyze and synthesize large amounts of human spiritual and religious texts, teachings, and experiences. This could allow it to develop a comprehensive understanding of human spiritual concepts and practices, even if it doesn't directly experience them. Through interactions with humans, AI systems could learn to recognize, respond to, and even simulate spiritual or religious language, ideas, and sentiments. This could enable AI to engage in spiritually-oriented conversations or tasks in ways that feel meaningful to humans.

AI could be programmed or trained to consider spiritual and religious values when interacting with users. For example, an AI assistant could take into account a user's religious beliefs when offering advice or recommendations. Some researchers are exploring the concept of "Spiritual AI" - AI systems specifically designed to support or enhance human spiritual experiences. This could involve AI that assists with prayer, meditation, or other spiritual practices.

As AI becomes more advanced, it may develop emergent properties that humans interpret as having spiritual qualities, even if the AI itself doesn't experience spirituality as humans do. However, it's important to note that true spiritual experiences, consciousness of God, or the ineffable aspects of human spirituality may remain uniquely human. Many religious and philosophical traditions hold that there are fundamental ontological distinctions between humans and machines.

Additionally, the development of highly advanced AI raises profound ethical and existential questions about the nature of consciousness,
the role of spirituality in human life, and humanity's place in the universe. These are complex issues that will likely be debated for years
to come as AI technology continues to evolve."


Rod