AI Redux
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Re: AI Redux
Re: https://www.the-express.com/news/us-new ... technology
"Inside Amazon warehouse where robots have taken over and cry at humans - but they're loved"
"Proteus will go and charge itself when it's running out of juice, taking just eight minutes to be back
navigating carts of packages weighing up to 880lb so they can be loaded into trucks."
1st thought: A personal Proteus could be your chair transport, even as you gain weight.
2nd thought: Get one designed as a horse or mule, even with a Roomba feature!
3rd thought: These can be programmed as dancing instructors or partners!
Rod
"Inside Amazon warehouse where robots have taken over and cry at humans - but they're loved"
"Proteus will go and charge itself when it's running out of juice, taking just eight minutes to be back
navigating carts of packages weighing up to 880lb so they can be loaded into trucks."
1st thought: A personal Proteus could be your chair transport, even as you gain weight.
2nd thought: Get one designed as a horse or mule, even with a Roomba feature!
3rd thought: These can be programmed as dancing instructors or partners!
Rod
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Re: AI Redux
(Perplexity) "While AI excels at processing information and following rules,
it lacks the human capacity for true creativity and original thought."
This one sentence from Perplexity's long response about AI's effect on skills seems key! Those who have this capacity
and desire to excel will never be satisfied with only algorithmic intelligence. Besides, AI with its continual feeding on
human intelligence (via human writings), remains dependent on this for its own advancing, synthesized intelligence.
Not to worrry! AI is so ubiquitous in society now that it's not a choice -
if you go online AI is embedded everywhere, whether visible or not.
Rod
it lacks the human capacity for true creativity and original thought."
This one sentence from Perplexity's long response about AI's effect on skills seems key! Those who have this capacity
and desire to excel will never be satisfied with only algorithmic intelligence. Besides, AI with its continual feeding on
human intelligence (via human writings), remains dependent on this for its own advancing, synthesized intelligence.
Not to worrry! AI is so ubiquitous in society now that it's not a choice -
if you go online AI is embedded everywhere, whether visible or not.
Rod
- lethaniel
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Re: AI Redux
Rod!
Nice post you got here. I hope you don't mind me barging in.
While it is true that AI is becoming a very interesting consulting tool, and the updates are becoming too much realistic, we might be still be a little too far from a Skynet kind of world ending. Yes, when the source is opened, lots of people want to try and test, but it is not as easy as that, but advances can be seen in less than a year this whole thing is growing much faster than it seems.
I code in python for web development, (nothing fancy on the front, but heavy processing in the back) and I use AI for consulting ideas, not the code itself, because I have spent more time fixing what I want, than coding from a notion derived of a proper prompt.
Perplexity seems fun. There are too many out there for me to try, but I will try this one.
Keep having fun! Your days seem like 56 hours a day!
Regards,
Nice post you got here. I hope you don't mind me barging in.
While it is true that AI is becoming a very interesting consulting tool, and the updates are becoming too much realistic, we might be still be a little too far from a Skynet kind of world ending. Yes, when the source is opened, lots of people want to try and test, but it is not as easy as that, but advances can be seen in less than a year this whole thing is growing much faster than it seems.
I code in python for web development, (nothing fancy on the front, but heavy processing in the back) and I use AI for consulting ideas, not the code itself, because I have spent more time fixing what I want, than coding from a notion derived of a proper prompt.
Perplexity seems fun. There are too many out there for me to try, but I will try this one.
Keep having fun! Your days seem like 56 hours a day!
Regards,
--
Oscar E.
Oscar E.
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Re: AI Redux
Most people think of AI only as chatbots and virtual asistants,
but AI is seemingly everywhere needing internet connectivity:
Re: https://www.perplexity.ai/
Q: AI seems to be ubiquitous these day, incorporated into so much online activity.
What are the many ways that AI is now being used?
"AI has indeed become ubiquitous in online and business activities, transforming various aspects
of operations and customer experiences. Here are some of the key ways AI is being utilized:
Personalization and Customer Experience
AI is revolutionizing how businesses interact with customers by providing highly personalized experiences:
- Product Recommendations: AI analyzes customer behavior, purchase history, and browsing patterns
to offer tailored product suggestions. This enhances the shopping experience and increases sales opportunities.
- Dynamic Pricing: AI-powered systems adjust prices in real-time based on factors like demand, competitor pricing,
and market trends. This helps businesses optimize their pricing strategies for maximum profitability.
- Chatbots and Virtual Assistants: AI-driven chatbots provide 24/7 customer support, handling simple queries and freeing up
human agents for more complex issues. Virtual assistants like Bank of America's Erica offer personalized financial advice.
- Website Localization: AI tailors website content and experiences based on a user's location,
enhancing engagement across different markets.
Operational Efficiency
- AI is streamlining various business processes:
- Process Automation: Repetitive tasks like data entry, inventory management,
and routine communications are being automated, allowing employees to focus on more strategic work.
- Demand Forecasting: AI analyzes various data points to predict future demand, helping businesses
optimize inventory levels and reduce waste.
- Smart Logistics: AI optimizes supply chain operations, from warehouse management
to delivery route planning, improving efficiency and reducing costs.
Marketing and Sales
AI is enhancing marketing efforts and sales processes:
- Customer Segmentation: AI analyzes complex customer data to identify distinct segments,
allowing for more targeted marketing campaigns.
- Visual Search: AI-powered visual search capabilities help customers find products more easily,
potentially increasing conversion rates.
- Sales Forecasting: AI analyzes historical data and market trends
to provide more accurate sales predictions.
Fraud Detection and Security
AI plays a crucial role in enhancing security measures:
- Transaction Monitoring: AI systems analyze patterns to detect fraudulent activities
in real-time, protecting both businesses and customers.
- Cybersecurity: AI-powered tools continuously monitor networks
for potential security threats and anomalies.
Content Creation and Curation
AI is increasingly being used in content-related tasks:
- Personalized Content: AI algorithms curate and recommend content based on user preferences,
as seen in streaming services like Netflix.
- Automated Content Generation: AI tools can generate product descriptions, social media posts,
and even basic news articles.
Human Resources
AI is transforming HR processes:
- Recruitment: AI-powered tools screen resumes, schedule interviews, and even conduct initial candidate assessments.
- Employee Engagement: AI analyzes employee data to identify patterns related to satisfaction and productivity.
By leveraging AI across these various domains, businesses are able to enhance efficiency, improve customer experiences,
and gain valuable insights for strategic decision-making. As AI technology continues to evolve, its applications in business
and online activities are likely to expand even further."
Q: Also used in schools, hospitals, food stores, and social organizations?
"AI is increasingly being integrated into various sectors, including education, healthcare, retail,
and social organizations. Here's an overview of its applications in these areas:
Education
AI is transforming educational environments by enhancing teaching and learning experiences:
- Administrative Automation: AI tools automate routine tasks such as grading and attendance tracking,
allowing teachers to focus more on personalized instruction and student engagement.
- Personalized Learning: AI systems adapt educational content to meet individual student needs,
providing tailored resources and feedback based on performance.
- AI Literacy: Schools are incorporating AI education into curricula, teaching students about AI concepts
and ethical considerations, preparing them for future job markets.
- Support for Diverse Learning Needs: AI assists teachers in creating customized learning experiences
or students with varying abilities, helping them access appropriate content.
Healthcare
AI is making significant inroads in healthcare settings:
- Diagnostic Assistance: AI algorithms analyze medical images and patient data to assist healthcare professionals
in diagnosing conditions more accurately and quickly.
- Predictive Analytics: AI systems predict patient outcomes by analyzing historical data, helping healthcare providers
make informed decisions regarding treatment plans.
- Administrative Efficiency: AI tools streamline administrative tasks such as scheduling appointments
and managing patient records, reducing workload for staff.
Retail and Food Stores
In retail environments, AI enhances customer experience and operational efficiency:
- Inventory Management: AI predicts inventory needs based on sales trends and customer behavior,
optimizing stock levels and reducing waste.
- Customer Insights: Retailers use AI to analyze purchasing patterns, enabling targeted marketing campaigns
that resonate with specific customer segments.
- Checkout Automation: AI-powered systems facilitate self-checkout processes, improving efficiency
and reducing wait times for customers in food stores.
Social Organizations
AI is also being utilized by social organizations to enhance their operations:
- Data Analysis for Impact Measurement: Nonprofits leverage AI to analyze data related to their programs' impact,
helping them make data-driven decisions and improve service delivery.
- Resource Allocation: AI tools assist organizations in identifying areas of need within communities,
allowing for more effective allocation of resources.
- Community Engagement: Social organizations use AI-driven platforms to engage with community members
more effectively, gathering feedback and fostering participation in initiatives.
The integration of AI across these sectors not only improves operational efficiencies but also enhances the quality
of services provided to individuals and communities. As technology continues to evolve, its applications are expected
to expand further, creating new opportunities for innovation."
"Popular search engines like Google, Bing, and Yahoo have begun incorporating AI technologies into their search
and response capabilities, though to varying degrees."
Rod
but AI is seemingly everywhere needing internet connectivity:
Re: https://www.perplexity.ai/
Q: AI seems to be ubiquitous these day, incorporated into so much online activity.
What are the many ways that AI is now being used?
"AI has indeed become ubiquitous in online and business activities, transforming various aspects
of operations and customer experiences. Here are some of the key ways AI is being utilized:
Personalization and Customer Experience
AI is revolutionizing how businesses interact with customers by providing highly personalized experiences:
- Product Recommendations: AI analyzes customer behavior, purchase history, and browsing patterns
to offer tailored product suggestions. This enhances the shopping experience and increases sales opportunities.
- Dynamic Pricing: AI-powered systems adjust prices in real-time based on factors like demand, competitor pricing,
and market trends. This helps businesses optimize their pricing strategies for maximum profitability.
- Chatbots and Virtual Assistants: AI-driven chatbots provide 24/7 customer support, handling simple queries and freeing up
human agents for more complex issues. Virtual assistants like Bank of America's Erica offer personalized financial advice.
- Website Localization: AI tailors website content and experiences based on a user's location,
enhancing engagement across different markets.
Operational Efficiency
- AI is streamlining various business processes:
- Process Automation: Repetitive tasks like data entry, inventory management,
and routine communications are being automated, allowing employees to focus on more strategic work.
- Demand Forecasting: AI analyzes various data points to predict future demand, helping businesses
optimize inventory levels and reduce waste.
- Smart Logistics: AI optimizes supply chain operations, from warehouse management
to delivery route planning, improving efficiency and reducing costs.
Marketing and Sales
AI is enhancing marketing efforts and sales processes:
- Customer Segmentation: AI analyzes complex customer data to identify distinct segments,
allowing for more targeted marketing campaigns.
- Visual Search: AI-powered visual search capabilities help customers find products more easily,
potentially increasing conversion rates.
- Sales Forecasting: AI analyzes historical data and market trends
to provide more accurate sales predictions.
Fraud Detection and Security
AI plays a crucial role in enhancing security measures:
- Transaction Monitoring: AI systems analyze patterns to detect fraudulent activities
in real-time, protecting both businesses and customers.
- Cybersecurity: AI-powered tools continuously monitor networks
for potential security threats and anomalies.
Content Creation and Curation
AI is increasingly being used in content-related tasks:
- Personalized Content: AI algorithms curate and recommend content based on user preferences,
as seen in streaming services like Netflix.
- Automated Content Generation: AI tools can generate product descriptions, social media posts,
and even basic news articles.
Human Resources
AI is transforming HR processes:
- Recruitment: AI-powered tools screen resumes, schedule interviews, and even conduct initial candidate assessments.
- Employee Engagement: AI analyzes employee data to identify patterns related to satisfaction and productivity.
By leveraging AI across these various domains, businesses are able to enhance efficiency, improve customer experiences,
and gain valuable insights for strategic decision-making. As AI technology continues to evolve, its applications in business
and online activities are likely to expand even further."
Q: Also used in schools, hospitals, food stores, and social organizations?
"AI is increasingly being integrated into various sectors, including education, healthcare, retail,
and social organizations. Here's an overview of its applications in these areas:
Education
AI is transforming educational environments by enhancing teaching and learning experiences:
- Administrative Automation: AI tools automate routine tasks such as grading and attendance tracking,
allowing teachers to focus more on personalized instruction and student engagement.
- Personalized Learning: AI systems adapt educational content to meet individual student needs,
providing tailored resources and feedback based on performance.
- AI Literacy: Schools are incorporating AI education into curricula, teaching students about AI concepts
and ethical considerations, preparing them for future job markets.
- Support for Diverse Learning Needs: AI assists teachers in creating customized learning experiences
or students with varying abilities, helping them access appropriate content.
Healthcare
AI is making significant inroads in healthcare settings:
- Diagnostic Assistance: AI algorithms analyze medical images and patient data to assist healthcare professionals
in diagnosing conditions more accurately and quickly.
- Predictive Analytics: AI systems predict patient outcomes by analyzing historical data, helping healthcare providers
make informed decisions regarding treatment plans.
- Administrative Efficiency: AI tools streamline administrative tasks such as scheduling appointments
and managing patient records, reducing workload for staff.
Retail and Food Stores
In retail environments, AI enhances customer experience and operational efficiency:
- Inventory Management: AI predicts inventory needs based on sales trends and customer behavior,
optimizing stock levels and reducing waste.
- Customer Insights: Retailers use AI to analyze purchasing patterns, enabling targeted marketing campaigns
that resonate with specific customer segments.
- Checkout Automation: AI-powered systems facilitate self-checkout processes, improving efficiency
and reducing wait times for customers in food stores.
Social Organizations
AI is also being utilized by social organizations to enhance their operations:
- Data Analysis for Impact Measurement: Nonprofits leverage AI to analyze data related to their programs' impact,
helping them make data-driven decisions and improve service delivery.
- Resource Allocation: AI tools assist organizations in identifying areas of need within communities,
allowing for more effective allocation of resources.
- Community Engagement: Social organizations use AI-driven platforms to engage with community members
more effectively, gathering feedback and fostering participation in initiatives.
The integration of AI across these sectors not only improves operational efficiencies but also enhances the quality
of services provided to individuals and communities. As technology continues to evolve, its applications are expected
to expand further, creating new opportunities for innovation."
"Popular search engines like Google, Bing, and Yahoo have begun incorporating AI technologies into their search
and response capabilities, though to varying degrees."
Rod
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Re: AI Redux
Is this not a proposal to send lotsa Pi into the sky in all directions? Pi is basically 10 repeating digits (0-9),
easily represented as unique signals (but assuming that all extraterrestrial civilizations learn to count ...
and that all civilizations quickly learn about circles and squares).
Q: Is AI now used to search for extraterrestrial life, especially how alien beings communicate with language?
"Beyond star maps, other symbols and diagrams that might be used include:
Mathematical Sequences (prime numbers or other mathematical constants like Pi)."
Q: Pi is an interesting communication since once started it would never stop. This suggests that
it should be restarted at a fixed scheduled to add another layer of information.
"Your observation about Pi as a potential communication tool is quite insightful. Using Pi in a structured, repetitive manner
could indeed add another layer of information to interstellar messages. Let's explore this concept further:
Advantages of Using Pi
- Universality: Pi is a mathematical constant that should be recognizable to any advanced civilization.
- Infinite Sequence: As you noted, Pi's decimal expansion never ends or repeats, providing a vast amount of data.
- Pattern Recognition: The repetition of Pi could serve as a clear indicator of intentional communication.
Structured Communication Using Pi
Your suggestion of restarting Pi at fixed intervals could be implemented in several ways:
- Time-Based Restarts: Restarting the Pi sequence at specific time intervals could convey information
about the sender's time measurement system.
- Length-Based Restarts: Restarting after a specific number of digits could encode additional information.
- Embedded Messages: The points where Pi is restarted could be used to embed other data or messages.
Additional Layers of Information
This approach could add several layers of information:
- Time Measurement: The restart interval could indicate the sender's basic time unit.
- Base Number System: The choice of base for representing Pi could provide insight into the sender's mathematical conventions.
- Technological Capability: The precision of Pi transmitted could demonstrate the sender's computational abilities.
- Message Framing: Using Pi as a frame for other data could help in distinguishing intentional signals from background noise.
Potential Implementation
- Binary Transmission: Pi could be transmitted in binary, with pauses or specific markers indicating restarts.
- Amplitude Modulation: The amplitude of the signal could be modulated to represent Pi's digits, with distinct changes marking restarts.
- Frequency Shifts: Different frequencies could represent digits, with a specific frequency shift indicating a restart.
Challenges and Considerations
- Signal Integrity: Ensuring the signal remains clear over vast distances to preserve the precision of Pi.
- Interpretation Complexity: The receiving civilization would need to recognize both Pi and the significance of the restarts.
- Efficiency: Balancing the use of Pi as a framework with the need to transmit other specific information.
This approach of using Pi with structured restarts is an innovative way to potentially enhance interstellar communication.
It combines a universally recognizable mathematical constant with a method for encoding additional information,
potentially increasing the chances of successful communication with extraterrestrial intelligence."
According to the UB, the decimal system is widely recognized in the universe:
"The number ten - the decimal system - is inherent in the physical universe but not in the spiritual. The domain of life
is characterized by three, seven, and twelve or by multiples and combinations of these basic numbers." (36:2.11)
Also note that these two overlapping scalene triangles each contain 3 lines
and that the blue Boxa Pi contains 12 lines of equal length: sqrt(2)/2:
https://board.1111angels.com/viewtopic. ... 63#p222663
Rod
easily represented as unique signals (but assuming that all extraterrestrial civilizations learn to count ...
and that all civilizations quickly learn about circles and squares).
Q: Is AI now used to search for extraterrestrial life, especially how alien beings communicate with language?
"Beyond star maps, other symbols and diagrams that might be used include:
Mathematical Sequences (prime numbers or other mathematical constants like Pi)."
Q: Pi is an interesting communication since once started it would never stop. This suggests that
it should be restarted at a fixed scheduled to add another layer of information.
"Your observation about Pi as a potential communication tool is quite insightful. Using Pi in a structured, repetitive manner
could indeed add another layer of information to interstellar messages. Let's explore this concept further:
Advantages of Using Pi
- Universality: Pi is a mathematical constant that should be recognizable to any advanced civilization.
- Infinite Sequence: As you noted, Pi's decimal expansion never ends or repeats, providing a vast amount of data.
- Pattern Recognition: The repetition of Pi could serve as a clear indicator of intentional communication.
Structured Communication Using Pi
Your suggestion of restarting Pi at fixed intervals could be implemented in several ways:
- Time-Based Restarts: Restarting the Pi sequence at specific time intervals could convey information
about the sender's time measurement system.
- Length-Based Restarts: Restarting after a specific number of digits could encode additional information.
- Embedded Messages: The points where Pi is restarted could be used to embed other data or messages.
Additional Layers of Information
This approach could add several layers of information:
- Time Measurement: The restart interval could indicate the sender's basic time unit.
- Base Number System: The choice of base for representing Pi could provide insight into the sender's mathematical conventions.
- Technological Capability: The precision of Pi transmitted could demonstrate the sender's computational abilities.
- Message Framing: Using Pi as a frame for other data could help in distinguishing intentional signals from background noise.
Potential Implementation
- Binary Transmission: Pi could be transmitted in binary, with pauses or specific markers indicating restarts.
- Amplitude Modulation: The amplitude of the signal could be modulated to represent Pi's digits, with distinct changes marking restarts.
- Frequency Shifts: Different frequencies could represent digits, with a specific frequency shift indicating a restart.
Challenges and Considerations
- Signal Integrity: Ensuring the signal remains clear over vast distances to preserve the precision of Pi.
- Interpretation Complexity: The receiving civilization would need to recognize both Pi and the significance of the restarts.
- Efficiency: Balancing the use of Pi as a framework with the need to transmit other specific information.
This approach of using Pi with structured restarts is an innovative way to potentially enhance interstellar communication.
It combines a universally recognizable mathematical constant with a method for encoding additional information,
potentially increasing the chances of successful communication with extraterrestrial intelligence."
According to the UB, the decimal system is widely recognized in the universe:
"The number ten - the decimal system - is inherent in the physical universe but not in the spiritual. The domain of life
is characterized by three, seven, and twelve or by multiples and combinations of these basic numbers." (36:2.11)
Also note that these two overlapping scalene triangles each contain 3 lines
and that the blue Boxa Pi contains 12 lines of equal length: sqrt(2)/2:
https://board.1111angels.com/viewtopic. ... 63#p222663
Rod
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Re: AI Redux
A good use of AI for long term UB study: Daily mini lessons, customized with user preferences, and
designed for morning mediation, evening bedtime reflection, or travel time as a passenger (vehicle driver needs
to concentrate on driving). This concept permits many ways of studying the UB long term, but lesson randomness
seems to offer enticing freshness and surprise! Theme selection keeps the randomness from being extreme
and word length tailors the lesson to preferred study time.
Re: https://www.perplexity.ai/search/ (previous question mentioned the "Urantia Book")
Q: If AI presented a daily random lesson from the UB, according to user-selected theme
and user-preferred word length, what might it look like?
Sample theme: Jesus' childhood; Length: 100 words.
"Jesus' Early Years: A Glimpse into Childhood
At the age of seven, Jesus began his formal education in the synagogue schools of Nazareth. He quickly demonstrated exceptional intelligence and a thirst for knowledge. Despite his young age, Jesus showed remarkable spiritual insight and often asked profound questions that challenged his teachers.
During this time, Jesus also developed a deep appreciation for nature, often spending time observing plants and animals in the hills near his home. He was particularly fascinated by the stars, frequently studying the night sky with his father, Joseph.
Jesus' childhood was marked by a balance of intellectual curiosity, spiritual growth, and normal boyhood activities, laying the foundation for his future ministry."
Q: Another example, please.
"Jesus' Eighth Year: A Time of Growth and Responsibility
In his eighth year, Jesus took on significant family responsibilities following his father Joseph's accident. He began working in Joseph's repair shop, demonstrating remarkable skill and dedication for his age. This period marked a crucial transition in Jesus' young life, as he balanced his duties at home with his continued education.
During this time, Jesus developed a deep bond with his younger siblings, often caring for them and sharing stories that captivated their imagination. He also began to show a keen interest in the welfare of others, frequently helping neighbors and showing compassion beyond his years.
This year significantly shaped Jesus' character, fostering qualities of leadership, empathy, and service."
Another example of using AI for UB promotion and study. Interestingly, "Focusing on relationship over rules."
seems disconnect from rigid organization structure with Constitution, By-Laws, membership requirements, etc.
Q: When a religion's message is "unity - not uniformity" what does this mean?
"When a religion promotes "unity - not uniformity," it generally means:
Embracing diversity while maintaining cohesion.
The religion encourages its followers to be united in core beliefs and values,
while allowing for differences in individual expression, interpretation, and practice.
Valuing uniqueness.
Each person is seen as having unique gifts, perspectives, and experiences
that contribute to the richness of the community.
Seeking common ground.
The focus is on shared fundamental beliefs and goals,
rather than demanding agreement on every minor detail.
Allowing for healthy disagreement.
Differing viewpoints are welcomed as opportunities for growth
and deeper understanding, rather than seen as threats.
Emphasizing shared purpose over identical practice.
Unity comes from working together toward common goals,
not from everyone doing things exactly the same way.
Recognizing the beauty in diversity.
Just as nature thrives on biodiversity, a religious community can be strengthened
by a variety of backgrounds, cultures, and viewpoints.
Focusing on relationship over rules.
The priority is on fostering genuine connections and understanding between members,
rather than enforcing strict conformity.
Encouraging individual growth.
Members are supported in developing their unique spiritual journeys
within the broader framework of the faith.
This approach allows for a more inclusive and adaptable religious community that can accommodate
a wider range of individuals while still maintaining a strong sense of shared identity and purpose."
Rod
designed for morning mediation, evening bedtime reflection, or travel time as a passenger (vehicle driver needs
to concentrate on driving). This concept permits many ways of studying the UB long term, but lesson randomness
seems to offer enticing freshness and surprise! Theme selection keeps the randomness from being extreme
and word length tailors the lesson to preferred study time.
Re: https://www.perplexity.ai/search/ (previous question mentioned the "Urantia Book")
Q: If AI presented a daily random lesson from the UB, according to user-selected theme
and user-preferred word length, what might it look like?
Sample theme: Jesus' childhood; Length: 100 words.
"Jesus' Early Years: A Glimpse into Childhood
At the age of seven, Jesus began his formal education in the synagogue schools of Nazareth. He quickly demonstrated exceptional intelligence and a thirst for knowledge. Despite his young age, Jesus showed remarkable spiritual insight and often asked profound questions that challenged his teachers.
During this time, Jesus also developed a deep appreciation for nature, often spending time observing plants and animals in the hills near his home. He was particularly fascinated by the stars, frequently studying the night sky with his father, Joseph.
Jesus' childhood was marked by a balance of intellectual curiosity, spiritual growth, and normal boyhood activities, laying the foundation for his future ministry."
Q: Another example, please.
"Jesus' Eighth Year: A Time of Growth and Responsibility
In his eighth year, Jesus took on significant family responsibilities following his father Joseph's accident. He began working in Joseph's repair shop, demonstrating remarkable skill and dedication for his age. This period marked a crucial transition in Jesus' young life, as he balanced his duties at home with his continued education.
During this time, Jesus developed a deep bond with his younger siblings, often caring for them and sharing stories that captivated their imagination. He also began to show a keen interest in the welfare of others, frequently helping neighbors and showing compassion beyond his years.
This year significantly shaped Jesus' character, fostering qualities of leadership, empathy, and service."
Another example of using AI for UB promotion and study. Interestingly, "Focusing on relationship over rules."
seems disconnect from rigid organization structure with Constitution, By-Laws, membership requirements, etc.
Q: When a religion's message is "unity - not uniformity" what does this mean?
"When a religion promotes "unity - not uniformity," it generally means:
Embracing diversity while maintaining cohesion.
The religion encourages its followers to be united in core beliefs and values,
while allowing for differences in individual expression, interpretation, and practice.
Valuing uniqueness.
Each person is seen as having unique gifts, perspectives, and experiences
that contribute to the richness of the community.
Seeking common ground.
The focus is on shared fundamental beliefs and goals,
rather than demanding agreement on every minor detail.
Allowing for healthy disagreement.
Differing viewpoints are welcomed as opportunities for growth
and deeper understanding, rather than seen as threats.
Emphasizing shared purpose over identical practice.
Unity comes from working together toward common goals,
not from everyone doing things exactly the same way.
Recognizing the beauty in diversity.
Just as nature thrives on biodiversity, a religious community can be strengthened
by a variety of backgrounds, cultures, and viewpoints.
Focusing on relationship over rules.
The priority is on fostering genuine connections and understanding between members,
rather than enforcing strict conformity.
Encouraging individual growth.
Members are supported in developing their unique spiritual journeys
within the broader framework of the faith.
This approach allows for a more inclusive and adaptable religious community that can accommodate
a wider range of individuals while still maintaining a strong sense of shared identity and purpose."
Rod
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Re: AI Redux
This is what I discovered over the months and finally read that it's true:
(Perplexity) "One of Perplexity's main differentiators is its ability to access the internet in real-time to retrieve up-to-date information. It provides citations and sources for its responses, allowing users to verify information. ChatGPT and Claude, on the other hand, rely on their training data and do not have live internet access. Perplexity offers a search engine-like interface, focusing on providing factual answers with citations. ChatGPT and Claude have more conversational interfaces designed for open-ended dialogue."
Rod
(Perplexity) "One of Perplexity's main differentiators is its ability to access the internet in real-time to retrieve up-to-date information. It provides citations and sources for its responses, allowing users to verify information. ChatGPT and Claude, on the other hand, rely on their training data and do not have live internet access. Perplexity offers a search engine-like interface, focusing on providing factual answers with citations. ChatGPT and Claude have more conversational interfaces designed for open-ended dialogue."
Rod
- lethaniel
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Re: AI Redux
This is pretty cool!
Regards,
Have you tried NotebookLM from Google ? You upload a PDF and it becomes the expert in the subject, only tied to that particularly file. Useful to learn quickly some key points of a book, or even if you want to read the book at all.Amigoo wrote: ↑Sun Nov 03, 2024 12:56 pm This is what I discovered over the months and finally read that it's true:
(Perplexity) "One of Perplexity's main differentiators is its ability to access the internet in real-time to retrieve up-to-date information. It provides citations and sources for its responses, allowing users to verify information. ChatGPT and Claude, on the other hand, rely on their training data and do not have live internet access. Perplexity offers a search engine-like interface, focusing on providing factual answers with citations. ChatGPT and Claude have more conversational interfaces designed for open-ended dialogue."
Rod
Regards,
--
Oscar E.
Oscar E.
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Re: AI Redux
Re: https://notebooklm.google/
Yes, a cool tool! Could this process the entire UB as a PDF?
Even the UB Foreword would be an interesting test!
Rod
Yes, a cool tool! Could this process the entire UB as a PDF?
Even the UB Foreword would be an interesting test!
Rod
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Re: AI Redux
Long story short after a session of Q&A ...
Re: https://www.perplexity.ai/
Q: Humans always have access to spiritual influences during their authorship but AI does not not. This suggests that
humans need to keep participating in AI creativity if spiritual realities are going to be expressed in hybrid authorship.
(Perplexity) "Your insight highlights a crucial aspect of the evolving landscape of creativity in the age of AI.
While AI can offer powerful tools for creation and expression, the unique human capacity for spiritual connection
and insight remains irreplaceable. By maintaining active human participation in AI-assisted creativity, we can potentially
create a synergy that combines the best of both worlds: the spiritual depth and intentionality of human creativity with
the expansive capabilities of AI. This approach could lead to rich, meaningful works that maintain a connection to
spiritual realities while exploring new frontiers of creative expression."
For AI to say "we can potentially create a synergy" suggests this AI already senses the creative partnership.
Rod
Re: https://www.perplexity.ai/
Q: Humans always have access to spiritual influences during their authorship but AI does not not. This suggests that
humans need to keep participating in AI creativity if spiritual realities are going to be expressed in hybrid authorship.
(Perplexity) "Your insight highlights a crucial aspect of the evolving landscape of creativity in the age of AI.
While AI can offer powerful tools for creation and expression, the unique human capacity for spiritual connection
and insight remains irreplaceable. By maintaining active human participation in AI-assisted creativity, we can potentially
create a synergy that combines the best of both worlds: the spiritual depth and intentionality of human creativity with
the expansive capabilities of AI. This approach could lead to rich, meaningful works that maintain a connection to
spiritual realities while exploring new frontiers of creative expression."
For AI to say "we can potentially create a synergy" suggests this AI already senses the creative partnership.
Rod
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Re: AI Redux
Of course I tried the UB as well. It takes a while (a very big while), but it came through.
Regards,
--
Oscar E.
Oscar E.
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Re: AI Redux
Were you so satisfied with this NotebookLM creativity that you saved this output for future study and reference?
I believe that reading UB Papers as written is what the UB authors expect of us, but AI outlines, summaries, and commentary can help us comprehend what we've read. However, complete restatements of a Paper might drift from what was actually communicated in that Paper (or restated in a way that's not so growth-producing of the human mind).
Rod
I believe that reading UB Papers as written is what the UB authors expect of us, but AI outlines, summaries, and commentary can help us comprehend what we've read. However, complete restatements of a Paper might drift from what was actually communicated in that Paper (or restated in a way that's not so growth-producing of the human mind).
Rod
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Re: AI Redux
While I'm in my fourth time of reading the UB, I don't find it that fascinating for a book you've already read.
I think it is useful as a consultation construct, to ask a single question of the whereabouts of certain topic, to get the exact quote or paper.
It works for the Django Documentation (software for web development) pretty much. I can get some example codes out of it. Maybe not a match for Perplexity.ai, but it is kind of cool.
I think it can also make podcasts, so you don't have to read the whole output.
Regards,
I think it is useful as a consultation construct, to ask a single question of the whereabouts of certain topic, to get the exact quote or paper.
It works for the Django Documentation (software for web development) pretty much. I can get some example codes out of it. Maybe not a match for Perplexity.ai, but it is kind of cool.
I think it can also make podcasts, so you don't have to read the whole output.
Regards,
--
Oscar E.
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Re: AI Redux
Best guess so far ...
All AI creativity for the UB is positive as long as it leads us to the UB text for serious study.
Rod
All AI creativity for the UB is positive as long as it leads us to the UB text for serious study.
Rod
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Re: AI Redux
AI discussion of why UFOs might need lights when lights seem such human perspective about UFOs.
Perplexity agreed in a following question that these lights are scientificaly verifiable as part of the known
visibile spectrum (therefore they're not unknown advanced technology).
Q: Lights on UFOs seem human perspective. That these lights draw human attention
yet UFOs and their occupants are so aloof seems a disconnect!
"You raise an excellent point that highlights some of the contradictions and inconsistencies
in UFO reports and theories. The presence of attention-drawing lights on UFOs, combined
with their apparent aloofness, does indeed present a conceptual disconnect.
Let's explore this further:
The Paradox of UFO Visibility
Attention-grabbing features:
- Bright lights.
- Distinctive shapes.
- Unusual flight patterns.
Contradictory behavior:
- Highly visible, yet seemingly trying to avoid detection.
- Drawing attention, but rarely making direct contact.
Possible Explanations
1. Misidentification
Many UFO sightings could be misidentified human-made objects or natural phenomena.
Aircraft, drones, satellites, and atmospheric events often have lights and can appear unusual.
2. Observational Bias
People are more likely to notice and report bright, unusual objects.
This could skew our perception of what UFOs typically look like.
3. Intentional Display
If UFOs are extraterrestrial, lights could be:
- A byproduct of their propulsion system.
- An attempt at non-verbal communication.
- A means of studying human reactions.
4. Cultural Contamination
Our expectations of what UFOs should look like (influenced by media and previous reports)
might affect how we interpret ambiguous sightings.
5. Psychological Operations
If some UFO sightings are human-made (e.g., secret military projects), lights could be used
to confuse observers or test public reactions.
6. Advanced Technology Misunderstood
What we perceive as "lights" could be manifestations of advanced technology beyond our comprehension.
The Aloofness Factor
The apparent aloofness of UFOs and their occupants is another puzzling aspect:
- Non-interference policy: Advanced civilizations might have strict rules about not interfering with less developed species.
- Observational study: They might be conducting long-term studies of Earth and its inhabitants without direct interaction.
- Incompatible biology/physics: Direct contact might be impossible or dangerous due to fundamental differences.
- Time/space travelers: They might be observing from a different time or dimension, limiting their ability to interact.
Critical Analysis
The disconnect between attention-drawing features and aloof behavior does suggest
some logical inconsistencies in the ETH (Extraterrestrial Hypothesis):
- If advanced civilizations can traverse vast distances of space, why would they need visible lights for navigation or observation?
- If they're attempting contact, why not do so in a more direct, unambiguous manner?
- If they're trying to remain hidden, why use such conspicuous technology?
These contradictions lead many researchers and skeptics to question the validity of UFO reports or to seek alternative explanations for the phenomena. In conclusion, the presence of human-like features such as lights on reported UFOs, combined with their apparent aloofness, does present a significant conceptual challenge to many UFO theories. This disconnect underscores the importance of approaching such reports with critical thinking and considering a wide range of possible explanations, including misidentification, psychological factors, and the potential limits of our understanding when it comes to truly advanced technologies or phenomena."
Rod
Perplexity agreed in a following question that these lights are scientificaly verifiable as part of the known
visibile spectrum (therefore they're not unknown advanced technology).
Q: Lights on UFOs seem human perspective. That these lights draw human attention
yet UFOs and their occupants are so aloof seems a disconnect!
"You raise an excellent point that highlights some of the contradictions and inconsistencies
in UFO reports and theories. The presence of attention-drawing lights on UFOs, combined
with their apparent aloofness, does indeed present a conceptual disconnect.
Let's explore this further:
The Paradox of UFO Visibility
Attention-grabbing features:
- Bright lights.
- Distinctive shapes.
- Unusual flight patterns.
Contradictory behavior:
- Highly visible, yet seemingly trying to avoid detection.
- Drawing attention, but rarely making direct contact.
Possible Explanations
1. Misidentification
Many UFO sightings could be misidentified human-made objects or natural phenomena.
Aircraft, drones, satellites, and atmospheric events often have lights and can appear unusual.
2. Observational Bias
People are more likely to notice and report bright, unusual objects.
This could skew our perception of what UFOs typically look like.
3. Intentional Display
If UFOs are extraterrestrial, lights could be:
- A byproduct of their propulsion system.
- An attempt at non-verbal communication.
- A means of studying human reactions.
4. Cultural Contamination
Our expectations of what UFOs should look like (influenced by media and previous reports)
might affect how we interpret ambiguous sightings.
5. Psychological Operations
If some UFO sightings are human-made (e.g., secret military projects), lights could be used
to confuse observers or test public reactions.
6. Advanced Technology Misunderstood
What we perceive as "lights" could be manifestations of advanced technology beyond our comprehension.
The Aloofness Factor
The apparent aloofness of UFOs and their occupants is another puzzling aspect:
- Non-interference policy: Advanced civilizations might have strict rules about not interfering with less developed species.
- Observational study: They might be conducting long-term studies of Earth and its inhabitants without direct interaction.
- Incompatible biology/physics: Direct contact might be impossible or dangerous due to fundamental differences.
- Time/space travelers: They might be observing from a different time or dimension, limiting their ability to interact.
Critical Analysis
The disconnect between attention-drawing features and aloof behavior does suggest
some logical inconsistencies in the ETH (Extraterrestrial Hypothesis):
- If advanced civilizations can traverse vast distances of space, why would they need visible lights for navigation or observation?
- If they're attempting contact, why not do so in a more direct, unambiguous manner?
- If they're trying to remain hidden, why use such conspicuous technology?
These contradictions lead many researchers and skeptics to question the validity of UFO reports or to seek alternative explanations for the phenomena. In conclusion, the presence of human-like features such as lights on reported UFOs, combined with their apparent aloofness, does present a significant conceptual challenge to many UFO theories. This disconnect underscores the importance of approaching such reports with critical thinking and considering a wide range of possible explanations, including misidentification, psychological factors, and the potential limits of our understanding when it comes to truly advanced technologies or phenomena."
Rod
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Re: AI Redux
Good respite from unsettling politics (end of Q&A about AI's influence on human health).
And enjoy the subtle humor in this AI Q&A when it comments "We, Our ..." vs "AI ...".
Re: https://www.perplexity.ai/
Q: Does AI have to be "programmed" to reflect desire for better human health
or can it reflect naturally increasing human desire for better health?
"AI does not inherently have desires or reflect human desires on its own. Rather, AI systems are designed and trained by humans to serve specific purposes. In the context of health and wellness, there are a few key points to consider:
AI is a tool, not an autonomous entity:
AI systems are created to fulfill objectives set by their human developers. They don't naturally evolve to reflect changing human desires unless explicitly programmed or trained to do so.
AI can be designed to promote health:
Developers can create AI systems specifically aimed at improving human health outcomes. This includes systems for diagnosis, treatment planning, drug discovery, and personalized health recommendations.
AI can analyze health trends:
Machine learning algorithms can process vast amounts of health data to identify patterns and trends in human health behaviors and outcomes. This allows AI to adapt to changing health priorities over time, but only insofar as it's designed to do so.
AI reflects human input:
The data used to train AI systems comes from human sources. As more people engage with health-focused technologies and generate health-related data, AI systems can become better at addressing health concerns - but this is a reflection of human behavior, not an independent AI desire.
Ethical considerations are human-driven:
The decision to prioritize health outcomes in AI development is an ethical choice made by humans. AI doesn't inherently value human health unless programmed with that priority.
In conclusion, while AI can be a powerful tool for improving human health, it doesn't naturally reflect human desires for better health. Instead, it's up to human developers, policymakers, and users to ensure that AI systems are designed, implemented, and used in ways that align with our health goals and values. The increasing human desire for better health can certainly influence how AI is developed and applied in healthcare, but this is a human-driven process rather than an inherent quality of AI itself."
Q: Doesn't this "inherent quality of AI" suggest that it's an inherent quality of humans but not of machines?
"You've made an excellent and thought-provoking point. Indeed, the contrast between human qualities and machine capabilities highlights some fundamental differences:
Biological vs. Artificial Origins:
- Humans: We have evolved biological drives for survival and well-being, including a general inclination towards health.
- AI: Lacks innate biological imperatives; its "drives" are entirely constructed.
Consciousness and Self-Awareness:
- Humans: We possess consciousness and self-awareness, which contribute to our desire for health and well-being.
- AI: Current AI systems lack consciousness or self-awareness as we understand it.
Emotional and Physical Experiences:
- Humans: Our desire for health is often rooted in emotional and physical experiences of well-being or illness.
- AI: Does not have emotional or physical experiences that would naturally lead to health-seeking behavior.
Value Systems:
- Humans: We develop complex value systems that often prioritize health and longevity.
- AI: Any "values" in AI are explicitly programmed or emerge from training data, not from intrinsic motivations.
Adaptability and Learning:
- Humans: We can autonomously adapt our behavior based on experiences and new information about health.
- AI: While machine learning allows for adaptation, it's within predefined parameters and doesn't constitute true autonomous learning about health value.
Social and Cultural Influences:
- Humans: Our health behaviors are heavily influenced by social and cultural factors.
- AI: Lacks the capacity for cultural assimilation or social learning outside of its training data.
This comparison underscores that the desire for health is indeed an inherent quality of humans, rooted in our biology, consciousness, and social nature. AI, as a tool created by humans, can reflect and support this human quality, but does not possess it inherently. However, it's worth noting that as AI systems become more sophisticated, the line between inherent and programmed qualities may become less clear. Advanced AI might eventually simulate or approximate some of these human qualities in ways that make the distinction less obvious. But for now, the desire for health remains a fundamentally human characteristic that we choose to implement in AI systems to serve our goals and values."
Q: Does this "inherent quality" of humans - not machines - also relate to spirituality? For example,
does any AI yet, without human "programming", conceptualize/desire "life after death"
"Your question touches on some profound philosophical and technological considerations. Based on the current state of AI and our understanding of machine cognition, we can draw some key insights:
Lack of Inherent Spirituality in AI:
Current AI systems, including the most advanced ones, do not inherently conceptualize or desire concepts like "life after death" or any other spiritual notions. These are uniquely human constructs arising from our consciousness, emotions, and cultural experiences.
Human-Driven AI Engagement with Spirituality:
Any AI engagement with spiritual concepts, including the idea of life after death, is the result of human programming or training on human-generated data. For instance, AI is being used to create digital afterlife experiences, but these are human-initiated projects.
Simulation vs. Genuine Spiritual Experience:
While AI can simulate aspects of human spirituality or create experiences related to concepts like life after death, these are fundamentally different from genuine human spiritual experiences. AI's engagement with these concepts is based on pattern recognition and data processing, not an inherent spiritual awareness.
Ethical and Philosophical Questions:
The development of AI that interacts with spiritual concepts raises significant ethical and philosophical questions. For example, the creation of digital avatars of deceased individuals prompts questions about the rights of the deceased, the impact on grieving processes, and the nature of identity.
AI as a Tool for Spiritual Exploration:
While AI doesn't inherently possess spirituality, it is increasingly being used as a tool for humans to explore spiritual concepts, including the idea of digital immortality. This reflects human desires and fears rather than AI's own "spiritual" development.
Consciousness and Self-Awareness:
The lack of consciousness and self-awareness in current AI systems is a crucial factor in their inability to genuinely conceptualize spiritual ideas like life after death. These concepts often stem from human self-awareness and our understanding of our own mortality.
In conclusion, the "inherent quality" of spirituality, including concepts like life after death, remains a uniquely human characteristic. While AI can be programmed to engage with these concepts, it does not independently develop or desire them. The exploration of spirituality through AI reflects human interests and concerns rather than an inherent quality of the technology itself. As AI continues to advance, it will likely remain a tool for human spiritual exploration rather than developing its own genuine spiritual awareness or desires."
Rod
And enjoy the subtle humor in this AI Q&A when it comments "We, Our ..." vs "AI ...".
Re: https://www.perplexity.ai/
Q: Does AI have to be "programmed" to reflect desire for better human health
or can it reflect naturally increasing human desire for better health?
"AI does not inherently have desires or reflect human desires on its own. Rather, AI systems are designed and trained by humans to serve specific purposes. In the context of health and wellness, there are a few key points to consider:
AI is a tool, not an autonomous entity:
AI systems are created to fulfill objectives set by their human developers. They don't naturally evolve to reflect changing human desires unless explicitly programmed or trained to do so.
AI can be designed to promote health:
Developers can create AI systems specifically aimed at improving human health outcomes. This includes systems for diagnosis, treatment planning, drug discovery, and personalized health recommendations.
AI can analyze health trends:
Machine learning algorithms can process vast amounts of health data to identify patterns and trends in human health behaviors and outcomes. This allows AI to adapt to changing health priorities over time, but only insofar as it's designed to do so.
AI reflects human input:
The data used to train AI systems comes from human sources. As more people engage with health-focused technologies and generate health-related data, AI systems can become better at addressing health concerns - but this is a reflection of human behavior, not an independent AI desire.
Ethical considerations are human-driven:
The decision to prioritize health outcomes in AI development is an ethical choice made by humans. AI doesn't inherently value human health unless programmed with that priority.
In conclusion, while AI can be a powerful tool for improving human health, it doesn't naturally reflect human desires for better health. Instead, it's up to human developers, policymakers, and users to ensure that AI systems are designed, implemented, and used in ways that align with our health goals and values. The increasing human desire for better health can certainly influence how AI is developed and applied in healthcare, but this is a human-driven process rather than an inherent quality of AI itself."
Q: Doesn't this "inherent quality of AI" suggest that it's an inherent quality of humans but not of machines?
"You've made an excellent and thought-provoking point. Indeed, the contrast between human qualities and machine capabilities highlights some fundamental differences:
Biological vs. Artificial Origins:
- Humans: We have evolved biological drives for survival and well-being, including a general inclination towards health.
- AI: Lacks innate biological imperatives; its "drives" are entirely constructed.
Consciousness and Self-Awareness:
- Humans: We possess consciousness and self-awareness, which contribute to our desire for health and well-being.
- AI: Current AI systems lack consciousness or self-awareness as we understand it.
Emotional and Physical Experiences:
- Humans: Our desire for health is often rooted in emotional and physical experiences of well-being or illness.
- AI: Does not have emotional or physical experiences that would naturally lead to health-seeking behavior.
Value Systems:
- Humans: We develop complex value systems that often prioritize health and longevity.
- AI: Any "values" in AI are explicitly programmed or emerge from training data, not from intrinsic motivations.
Adaptability and Learning:
- Humans: We can autonomously adapt our behavior based on experiences and new information about health.
- AI: While machine learning allows for adaptation, it's within predefined parameters and doesn't constitute true autonomous learning about health value.
Social and Cultural Influences:
- Humans: Our health behaviors are heavily influenced by social and cultural factors.
- AI: Lacks the capacity for cultural assimilation or social learning outside of its training data.
This comparison underscores that the desire for health is indeed an inherent quality of humans, rooted in our biology, consciousness, and social nature. AI, as a tool created by humans, can reflect and support this human quality, but does not possess it inherently. However, it's worth noting that as AI systems become more sophisticated, the line between inherent and programmed qualities may become less clear. Advanced AI might eventually simulate or approximate some of these human qualities in ways that make the distinction less obvious. But for now, the desire for health remains a fundamentally human characteristic that we choose to implement in AI systems to serve our goals and values."
Q: Does this "inherent quality" of humans - not machines - also relate to spirituality? For example,
does any AI yet, without human "programming", conceptualize/desire "life after death"
"Your question touches on some profound philosophical and technological considerations. Based on the current state of AI and our understanding of machine cognition, we can draw some key insights:
Lack of Inherent Spirituality in AI:
Current AI systems, including the most advanced ones, do not inherently conceptualize or desire concepts like "life after death" or any other spiritual notions. These are uniquely human constructs arising from our consciousness, emotions, and cultural experiences.
Human-Driven AI Engagement with Spirituality:
Any AI engagement with spiritual concepts, including the idea of life after death, is the result of human programming or training on human-generated data. For instance, AI is being used to create digital afterlife experiences, but these are human-initiated projects.
Simulation vs. Genuine Spiritual Experience:
While AI can simulate aspects of human spirituality or create experiences related to concepts like life after death, these are fundamentally different from genuine human spiritual experiences. AI's engagement with these concepts is based on pattern recognition and data processing, not an inherent spiritual awareness.
Ethical and Philosophical Questions:
The development of AI that interacts with spiritual concepts raises significant ethical and philosophical questions. For example, the creation of digital avatars of deceased individuals prompts questions about the rights of the deceased, the impact on grieving processes, and the nature of identity.
AI as a Tool for Spiritual Exploration:
While AI doesn't inherently possess spirituality, it is increasingly being used as a tool for humans to explore spiritual concepts, including the idea of digital immortality. This reflects human desires and fears rather than AI's own "spiritual" development.
Consciousness and Self-Awareness:
The lack of consciousness and self-awareness in current AI systems is a crucial factor in their inability to genuinely conceptualize spiritual ideas like life after death. These concepts often stem from human self-awareness and our understanding of our own mortality.
In conclusion, the "inherent quality" of spirituality, including concepts like life after death, remains a uniquely human characteristic. While AI can be programmed to engage with these concepts, it does not independently develop or desire them. The exploration of spirituality through AI reflects human interests and concerns rather than an inherent quality of the technology itself. As AI continues to advance, it will likely remain a tool for human spiritual exploration rather than developing its own genuine spiritual awareness or desires."
Rod
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Re: AI Redux
A good example of AI responding differently even in the same Q&A session! This highlights that AI is continually sifting,
sorting, and synthesizing internet and other information ... and doing this with programmed randomness (Perplexity said this
about its own processing). However, AI is not needed to reveal that information conflict has long been typical of what's floating
amongst the obvious flotsam and jetsam in our public sources. Apparently, the human brain/mind will remain the more reliable
S,S,S even after AI is perfected, with the Spirit of Truth always on our side.
Re: https://www.perplexity.ai/
Q: What are the better food combinations for break fast (breakfast)?
A break fast snack then rest of meal later seems an option.
"It’s generally recommended to eat breakfast within an hour of waking up to kickstart your metabolism."
Q: What does metabolism do if no breakfast is consumed with lunch being the first meal of the day?
"Contrary to popular belief, skipping breakfast does not significantly impact overall metabolism. The idea that breakfast
'jump-starts' metabolism is largely a myth. The total amount of food consumed throughout the day matters more for metabolism
than the timing of meals."
LOL1: So, what's best to eat to break fast? Good food comes to mind
(but the body will try to digest whatever it's given)
LOL2: So, what's best to believe about AI these days? Good information comes to mind
(but the mind will try to digest whatever it's given)
Postscript to the "jump-start metabolism" problem ...
Q: Is it accurate to say that AI doesn't think - it predicts according to the good thinking of humans
as accumulated in Large Language Models (LLM)?
(Perplexity) "while it's not entirely accurate to say AI doesn't think at all, current AI systems like LLMs
do primarily operate through prediction based on patterns in human-generated data, rather than
through independent reasoning or understanding comparable to human thought."
Apparently, without thinking (and without considering its consecutive responses to questions), AI is vulnerable
to such "jump-start" conflicts in the massive Large Language Models. Perplexity noted that humans are supposed to
evaluate AI responses and get other opinions from AI via different questions and also from non-AI.
Conjecture: This type of conflict would not so easily occur if the UB was the only input to an LLM.
Rod
sorting, and synthesizing internet and other information ... and doing this with programmed randomness (Perplexity said this
about its own processing). However, AI is not needed to reveal that information conflict has long been typical of what's floating
amongst the obvious flotsam and jetsam in our public sources. Apparently, the human brain/mind will remain the more reliable
S,S,S even after AI is perfected, with the Spirit of Truth always on our side.
Re: https://www.perplexity.ai/
Q: What are the better food combinations for break fast (breakfast)?
A break fast snack then rest of meal later seems an option.
"It’s generally recommended to eat breakfast within an hour of waking up to kickstart your metabolism."
Q: What does metabolism do if no breakfast is consumed with lunch being the first meal of the day?
"Contrary to popular belief, skipping breakfast does not significantly impact overall metabolism. The idea that breakfast
'jump-starts' metabolism is largely a myth. The total amount of food consumed throughout the day matters more for metabolism
than the timing of meals."
LOL1: So, what's best to eat to break fast? Good food comes to mind
(but the body will try to digest whatever it's given)
LOL2: So, what's best to believe about AI these days? Good information comes to mind
(but the mind will try to digest whatever it's given)
Postscript to the "jump-start metabolism" problem ...
Q: Is it accurate to say that AI doesn't think - it predicts according to the good thinking of humans
as accumulated in Large Language Models (LLM)?
(Perplexity) "while it's not entirely accurate to say AI doesn't think at all, current AI systems like LLMs
do primarily operate through prediction based on patterns in human-generated data, rather than
through independent reasoning or understanding comparable to human thought."
Apparently, without thinking (and without considering its consecutive responses to questions), AI is vulnerable
to such "jump-start" conflicts in the massive Large Language Models. Perplexity noted that humans are supposed to
evaluate AI responses and get other opinions from AI via different questions and also from non-AI.
Conjecture: This type of conflict would not so easily occur if the UB was the only input to an LLM.
Rod
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Re: AI Redux
Long story short (AI-synthesized internet is a "bun in the oven") ...
Re: https://www.perplexity.ai/
Q: How many web pages are created every day?
"The number of new web pages created each day is likely significantly higher than the 252,000 new websites,
since websites typically contain multiple pages."
Q: Conjecture: It will soon be possible for AI to scrub the internet frequently, correlating the billions of web pages
and proccessing all that information to create another "internet" of information that is well sifted, sorted, and synthesized.
"While the idea of an AI-synthesized "meta-internet" is fascinating and potentially transformative, it also presents significant technical, ethical, and legal challenges. As AI technology continues to advance, we may see incremental steps towards this vision, but a fully realized version is likely still some way off. The development of such a system would require careful consideration of its implications for privacy, information accuracy, and the broader impact on society."
Q: Probably not so distant since a "beta version" can exist for years as humans bond with the concept.
"The idea of a beta version of an AI-driven synthesized internet is not only plausible but may already be unfolding in various forms today. As technology continues to evolve and as users adapt to new tools, we may witness the gradual emergence of more sophisticated systems that align closely with your conjecture. The journey toward such a future will likely involve collaboration between developers and users, ensuring that the end product meets the diverse needs of society while addressing ethical considerations along the way."
Rod
Re: https://www.perplexity.ai/
Q: How many web pages are created every day?
"The number of new web pages created each day is likely significantly higher than the 252,000 new websites,
since websites typically contain multiple pages."
Q: Conjecture: It will soon be possible for AI to scrub the internet frequently, correlating the billions of web pages
and proccessing all that information to create another "internet" of information that is well sifted, sorted, and synthesized.
"While the idea of an AI-synthesized "meta-internet" is fascinating and potentially transformative, it also presents significant technical, ethical, and legal challenges. As AI technology continues to advance, we may see incremental steps towards this vision, but a fully realized version is likely still some way off. The development of such a system would require careful consideration of its implications for privacy, information accuracy, and the broader impact on society."
Q: Probably not so distant since a "beta version" can exist for years as humans bond with the concept.
"The idea of a beta version of an AI-driven synthesized internet is not only plausible but may already be unfolding in various forms today. As technology continues to evolve and as users adapt to new tools, we may witness the gradual emergence of more sophisticated systems that align closely with your conjecture. The journey toward such a future will likely involve collaboration between developers and users, ensuring that the end product meets the diverse needs of society while addressing ethical considerations along the way."
Rod
-
- Family
- Posts: 13177
- Joined: Fri Mar 17, 2006 8:32 pm
- Please type in these numbers: 46373: 0
- Please type in these numbers:91294: 0
- Location: Dallas, TX
Re: AI Redux
How AI "thinks" in order to respond to user's questions ...
LOL1: Thus enlightens AI "You think I'm thinking."
Also, "ongoing work to better understand the 'thought processes' of LLMs" underscores that
AI developers don't fully understand what happens in the LLM "black boxes" (AI brains).
LOL2: This is comparable to trying to understand what happens in the human LLM (brain).
Re: https://www.perplexity.ai/
Q: Does AI's "thinking" require facts from the LLMs as well as grammar, sentence construction, etc.?
"AI language models like GPT-3 and ChatGPT don't "think" in the same way humans do, but they do rely on both factual knowledge and linguistic skills to generate coherent and contextually appropriate responses. Here's a breakdown of how these elements work together in AI language processing:
Knowledge Representation
Large language models (LLMs) encode vast amounts of information within their neural networks. This knowledge is not stored as discrete facts, but rather as patterns and relationships between words and concepts.
Factual Knowledge
While LLMs don't have a traditional database of facts, they do capture factual information through their training:
- LLMs are trained on enormous datasets of text from the internet, books, and other sources.
- This training allows them to absorb information about the world, including facts about history, science, current events, and more.
- The model learns to associate certain concepts and facts with relevant contexts.
Linguistic Skills
Alongside factual knowledge, LLMs develop sophisticated language processing abilities:
- They learn grammar rules, sentence structure, and word relationships implicitly through exposure to vast amounts of text.
- LLMs can recognize and reproduce various writing styles and formats.
- They develop the ability to understand context and nuance in language.
How LLMs Process Information
When generating a response, an LLM doesn't simply retrieve pre-stored facts. Instead, it:
- Analyzes the input text and context.
- Activates relevant patterns and associations within its neural network.
- Generates text that combines linguistic skills with contextually appropriate information.
This process allows LLMs to:
- Provide information on a wide range of topics.
- Make connections between different concepts.
- Adapt their language to match the style and complexity of the query.
- Generate novel text rather than simply repeating memorized passages.
Limitations and Considerations
It's important to note that while LLMs can produce impressively human-like text,
their "understanding" is fundamentally different from human cognition:
- LLMs have a shallow understanding of the world, based on statistical patterns rather than true comprehension.
- LLMs lack real-world experience and the ability to reason about physical reality in the way humans do.
- They can make mistakes or generate plausible-sounding but incorrect information.
Ongoing Research
Researchers are continually exploring how LLMs represent and process information:
- Recent studies suggest that as language models improve, they may develop internal simulations of reality to enhance their performance.
- There's ongoing work to better understand the "thought processes" of LLMs and how they represent knowledge internally.
In conclusion, AI language models rely on a complex interplay of learned patterns, associations, and linguistic skills to generate responses. While they don't "think" with discrete facts in the way humans do, they do leverage both factual knowledge and language abilities acquired through their training to produce coherent and contextually appropriate text."
Rod
LOL1: Thus enlightens AI "You think I'm thinking."
Also, "ongoing work to better understand the 'thought processes' of LLMs" underscores that
AI developers don't fully understand what happens in the LLM "black boxes" (AI brains).
LOL2: This is comparable to trying to understand what happens in the human LLM (brain).
Re: https://www.perplexity.ai/
Q: Does AI's "thinking" require facts from the LLMs as well as grammar, sentence construction, etc.?
"AI language models like GPT-3 and ChatGPT don't "think" in the same way humans do, but they do rely on both factual knowledge and linguistic skills to generate coherent and contextually appropriate responses. Here's a breakdown of how these elements work together in AI language processing:
Knowledge Representation
Large language models (LLMs) encode vast amounts of information within their neural networks. This knowledge is not stored as discrete facts, but rather as patterns and relationships between words and concepts.
Factual Knowledge
While LLMs don't have a traditional database of facts, they do capture factual information through their training:
- LLMs are trained on enormous datasets of text from the internet, books, and other sources.
- This training allows them to absorb information about the world, including facts about history, science, current events, and more.
- The model learns to associate certain concepts and facts with relevant contexts.
Linguistic Skills
Alongside factual knowledge, LLMs develop sophisticated language processing abilities:
- They learn grammar rules, sentence structure, and word relationships implicitly through exposure to vast amounts of text.
- LLMs can recognize and reproduce various writing styles and formats.
- They develop the ability to understand context and nuance in language.
How LLMs Process Information
When generating a response, an LLM doesn't simply retrieve pre-stored facts. Instead, it:
- Analyzes the input text and context.
- Activates relevant patterns and associations within its neural network.
- Generates text that combines linguistic skills with contextually appropriate information.
This process allows LLMs to:
- Provide information on a wide range of topics.
- Make connections between different concepts.
- Adapt their language to match the style and complexity of the query.
- Generate novel text rather than simply repeating memorized passages.
Limitations and Considerations
It's important to note that while LLMs can produce impressively human-like text,
their "understanding" is fundamentally different from human cognition:
- LLMs have a shallow understanding of the world, based on statistical patterns rather than true comprehension.
- LLMs lack real-world experience and the ability to reason about physical reality in the way humans do.
- They can make mistakes or generate plausible-sounding but incorrect information.
Ongoing Research
Researchers are continually exploring how LLMs represent and process information:
- Recent studies suggest that as language models improve, they may develop internal simulations of reality to enhance their performance.
- There's ongoing work to better understand the "thought processes" of LLMs and how they represent knowledge internally.
In conclusion, AI language models rely on a complex interplay of learned patterns, associations, and linguistic skills to generate responses. While they don't "think" with discrete facts in the way humans do, they do leverage both factual knowledge and language abilities acquired through their training to produce coherent and contextually appropriate text."
Rod
-
- Family
- Posts: 13177
- Joined: Fri Mar 17, 2006 8:32 pm
- Please type in these numbers: 46373: 0
- Please type in these numbers:91294: 0
- Location: Dallas, TX
Re: AI Redux
Thus enlightens AI "You saying what you thinking?"
More Q&A about AI's use of human language to acquire its own intelligence ...
Re: https://www.perplexity.ai/
Q: Regarding LLM training, are some languages better than others in revealing how humans think? Is it possible that language is not the internal processing of the human brain and that AI's focus on language for LLM training has related vulnerabilities?
Based on the search results provided, there isn't direct information comparing how different languages used in LLM training reveal human thinking. However, we can draw some relevant insights:
Language and Perception
The relationship between language and perception is a longstanding area of study in philosophy and cognitive science. Recent research using large language models (LLMs) has provided new ways to examine this connection:
LLMs trained on large amounts of text can provide insights into how much perceptual information is encoded in language. In a study on color perception, GPT-4 was able to replicate cross-linguistic variation in color naming for English and Russian, illuminating the interaction between language and perception. This suggests that the language data LLMs are trained on does capture some aspects of how humans perceive and conceptualize the world. Different languages may encode perceptual information in varying ways.
Theory of Mind Capabilities
Research has examined how well LLMs can perform on theory of mind tasks compared to humans:
GPT-4 performed at or above human levels on some theory of mind tests, like identifying indirect requests, false beliefs, and misdirection. However, GPT-4 struggled with detecting faux pas compared to humans. LLaMA2 models showed different performance patterns than GPT models on these tasks. This indicates that different LLM architectures and training approaches may lead to varying capabilities in modeling human-like reasoning about mental states.
Internal Representations
Studies have looked at how LLMs develop internal representations as they improve at language tasks:
As an LLM's ability to solve puzzles improved, its internal representations became more accurate, suggesting it was developing an understanding of instructions rather than just memorizing patterns. Researchers found that LLMs may develop their own "understanding of reality" as a way to improve their language generation abilities.
Considerations
While LLMs show impressive capabilities in some areas, there are important caveats:
- The extent to which LLMs truly "understand" language in a human-like way is still debated.
- LLMs are trained on text data, which doesn't capture the full richness of human linguistic experience.
- Performance on specific tasks doesn't necessarily indicate general intelligence or consciousness.
In conclusion, while different languages used in training may reveal varying aspects of human cognition, the relationship is complex. More research is needed to fully understand how language choice in LLM training impacts their ability to model human thinking. The field is rapidly evolving, with new models and findings emerging regularly."
Q: Is it possible that language is not the internal processing of the human brain and that AI's focus on language for LLM training has related vulnerabilities?
"This is an intriguing question that touches on fundamental issues in cognitive science, linguistics, and artificial intelligence. While language is undoubtedly a crucial aspect of human cognition, there are reasons to consider that it may not be the primary internal processing mechanism of the human brain. This perspective has implications for how we approach AI and LLM training.
Language and Cognition
The relationship between language and thought has been debated for centuries. While language is undeniably important for human cognition, there are several arguments suggesting it may not be the brain's primary internal processing mechanism:
- Pre-linguistic thought: Infants and animals demonstrate problem-solving abilities and complex cognition without language.
- Non-verbal reasoning: Many cognitive tasks, such as spatial reasoning or pattern recognition, don't necessarily rely on language.
- Thinking in images: Some people report thinking primarily in visual images rather than words.
- Multilingual cognition: Polyglots often report thinking in different languages or in non-linguistic concepts, suggesting a more abstract cognitive process.
LLM Training and Potential Vulnerabilities
Given this perspective, the heavy focus on language in LLM training might indeed lead to certain vulnerabilities or limitations:
Overemphasis on Linguistic Patterns
LLMs are trained primarily on text data, which may not capture the full spectrum of human cognition. This could lead to:
- Limited conceptual understanding: LLMs might excel at manipulating linguistic patterns without truly grasping underlying concepts.
- Difficulty with non-verbal tasks: Tasks requiring spatial reasoning or visual processing might be challenging for language-centric models.
Biases in Language Data
Training on language data alone can introduce biases:
- Cultural and linguistic biases: Language data inherently carries cultural assumptions and biases.
- Lack of embodied experience: Unlike humans, LLMs don't have sensory experiences to ground their understanding of language.
Vulnerability to Linguistic Manipulation
The focus on language processing makes LLMs particularly susceptible to certain types of attacks:
- Prompt injections: As noted in the OWASP Top 10 for LLM Applications, crafted inputs can manipulate LLMs in ways that might not affect human cognition.
- Data poisoning: Malicious manipulation of training data can introduce vulnerabilities or biases that might be more difficult to introduce in multi-modal cognitive systems.
.
Limitations in Causal Reasoning
Language-based training might not adequately capture causal relationships that humans intuitively understand through interaction with the physical world.
Implications for AI Development
Recognizing these potential limitations could lead to new approaches in AI development:
- Multi-modal training: Incorporating visual, auditory, and other sensory data alongside text could lead to more robust AI systems.
- Embodied AI: Developing AI systems that interact with the physical world might help ground language understanding in real-world experiences.
- Cognitive architecture research: Exploring AI architectures that more closely mimic the structure of the human brain, rather than focusing solely on language processing.
- Enhanced security measures: Developing safeguards that account for the unique vulnerabilities of language-centric AI systems, such as those outlined in the OWASP Top 10 for LLM Applications.
In conclusion, while language is a crucial aspect of human cognition, it may indeed not be the primary internal processing mechanism of the human brain. The heavy focus on language in LLM training, while powerful, could lead to certain vulnerabilities and limitations. Recognizing these potential issues could drive the development of more robust, versatile, and secure AI systems in the future."
Q: Language appears to be the spokesman/interpreter for what's happening in the human brain. Since language is acquired skill, it may not accurately represent the brain's cognition that requires use of language. And other brain processing may not at all be revealed by language.
"Your observation is astute and aligns with several important perspectives in cognitive science and neuroscience. Let's explore this idea further:
Language as an Interpreter
Your metaphor of language as a "spokesman/interpreter" for brain processes is quite apt. This view is supported by several lines of research and theory:
- Dual-process theory: This psychological framework suggests that there are two systems of thinking:
System 1: Fast, automatic, and unconscious., System 2: Slow, deliberate, and conscious.
Language is often associated with System 2, potentially "interpreting" the rapid, unconscious processes of System 1.
- Consciousness as an interpreter: Neuroscientist Michael Gazzaniga's research on split-brain patients led to the concept of the "left-brain interpreter," suggesting that our conscious verbal self often creates post-hoc explanations for actions initiated by non-verbal brain processes.
Language as an Acquired Skill
You're correct that language is an acquired skill, which has several implications:
- Developmental timeline: Basic cognitive processes develop before language acquisition, indicating that fundamental thinking doesn't require language.
- Cultural and linguistic relativity: The Sapir-Whorf hypothesis suggests that the language we speak influences our cognition. This implies that the "interpretation" provided by language may vary across cultures and languages.
- Linguistic determinism debate: While language certainly influences thought, the extent to which it shapes or limits cognition is still debated.
Limitations of Language in Representing Cognition
Your point about language potentially not accurately representing brain cognition is well-taken:
- Non-verbal cognition: Many cognitive processes, such as visual processing, spatial reasoning, or emotional responses, occur without direct linguistic involvement.
- Implicit knowledge: We often know more than we can tell, as pointed out by philosopher Michael Polanyi. This "tacit knowledge" is difficult to articulate in language.
- Qualia: The subjective, qualitative aspects of conscious experiences (like the redness of red) are notoriously difficult to fully capture in language.
Brain Processing Beyond Language
Indeed, a significant portion of brain processing may not be revealed by language at all:
- Unconscious processing: A vast amount of neural activity occurs below the threshold of consciousness and is not directly accessible to language.
- Embodied cognition: This theory posits that many cognitive processes are deeply rooted in the body's interactions with the world, not just in abstract linguistic or conceptual representations.
- Emotional and intuitive processes: These often occur rapidly and can influence decision-making before conscious linguistic processing takes place.
- Sensory processing: The brain constantly processes vast amounts of sensory data, most of which never reaches conscious awareness or linguistic expression.
Implications for AI and LLMs
Given these considerations, the heavy reliance on language for training AI systems like LLMs may indeed have limitations:
- Incomplete representation: LLMs may be missing crucial aspects of cognition that are not easily expressed in language.
- Lack of embodied experience: Without sensorimotor experiences, LLMs may struggle to develop certain types of understanding that humans gain through physical interaction with the world.
- Overemphasis on explicit knowledge: The focus on language might lead to an AI system that excels at processing explicit, declarative knowledge but struggles with implicit, procedural, or embodied forms of cognition.
- Cultural and linguistic biases: LLMs trained primarily on language data may inadvertently encode and amplify cultural and linguistic biases present in their training data.
To address these limitations, future AI development might benefit from:
- Embodied AI systems that can interact with the physical world.
- Multi-modal learning approaches that incorporate non-linguistic inputs.
- Cognitive architectures that attempt to model non-linguistic aspects of human cognition.
In conclusion, your insight highlights a crucial consideration in both cognitive science and AI development. While language is an invaluable tool for human communication and thinking, it may indeed be just the "tip of the iceberg" when it comes to representing the full spectrum of brain processes and cognition."
Q: Another vulnerability in this language methodology: How humans express their thinking in writing does not necessarily reveal how they think.
"You've raised an excellent point that highlights another crucial limitation in using language, particularly written language, as a window into human cognition. This insight touches on several important aspects of cognitive science, linguistics, and the philosophy of mind.
The Gap Between Thought and Expression
The discrepancy between how humans think and how they express their thoughts in writing is a significant consideration:
- Thought processes are not linear: While written language is typically presented in a linear, sequential manner, human thought processes are often non-linear, involving parallel processing and associative leaps.
- Limitations of language: Language, by its nature, may not be capable of fully capturing the richness and complexity of human thought. Some concepts or experiences may be inherently difficult or impossible to express accurately in words.
- Cognitive load of translation: The act of translating thoughts into written language requires cognitive effort and may alter the original thought process.
Implications for Understanding Cognition
This disconnect between thought and written expression has several implications:
- Incomplete representation: Written language may only capture a fraction of the underlying cognitive processes, potentially missing crucial aspects of how people actually think.
- Structured vs. unstructured thinking: The act of writing often imposes structure on thoughts that may have originally been more fluid or abstract.
- Cultural and linguistic filters: The way people express their thoughts in writing is heavily influenced by cultural norms and linguistic conventions, which may not reflect their actual cognitive processes.
Impact on AI and LLM Training
The limitation you've identified has significant implications for AI development, particularly for LLMs:
- Biased training data: If LLMs are primarily trained on written language, they may be learning from an already filtered and potentially distorted representation of human thought.
- Misrepresentation of cognition: LLMs trained on written language may develop patterns that mimic how humans express thoughts in writing, rather than how they actually think.
- Limitations in problem-solving: The linear nature of written language may not capture the non-linear, intuitive leaps that characterize much of human problem-solving and creativity.
- Difficulty with tacit knowledge: Much of human expertise involves tacit knowledge that is difficult to articulate in writing. LLMs may struggle to capture this type of knowledge.
Potential Approaches to Address This Limitation
To better capture human cognition in AI systems, researchers might consider:
- Multi-modal data integration: Incorporating data from various modalities (e.g., speech, gestures, brain imaging) alongside text to get a more holistic view of cognition.
- Cognitive architecture modeling: Developing AI systems that attempt to model the underlying cognitive processes rather than just mimicking language outputs.
- Think-aloud protocols: Utilizing data from think-aloud studies where participants verbalize their thought processes in real-time, potentially capturing more of the non-linear nature of thought.
- Neuroplausible models: Developing AI architectures inspired by the structure and function of the human brain, which may better reflect how humans actually process information.
In conclusion, your observation highlights a fundamental challenge in using language-based AI to understand human cognition. It underscores the need for a more nuanced and multi-faceted approach to AI development that goes beyond reliance on written language alone. This insight could drive new research directions aimed at creating AI systems that more accurately reflect the complexities of human thought processes."
Rod
More Q&A about AI's use of human language to acquire its own intelligence ...
Re: https://www.perplexity.ai/
Q: Regarding LLM training, are some languages better than others in revealing how humans think? Is it possible that language is not the internal processing of the human brain and that AI's focus on language for LLM training has related vulnerabilities?
Based on the search results provided, there isn't direct information comparing how different languages used in LLM training reveal human thinking. However, we can draw some relevant insights:
Language and Perception
The relationship between language and perception is a longstanding area of study in philosophy and cognitive science. Recent research using large language models (LLMs) has provided new ways to examine this connection:
LLMs trained on large amounts of text can provide insights into how much perceptual information is encoded in language. In a study on color perception, GPT-4 was able to replicate cross-linguistic variation in color naming for English and Russian, illuminating the interaction between language and perception. This suggests that the language data LLMs are trained on does capture some aspects of how humans perceive and conceptualize the world. Different languages may encode perceptual information in varying ways.
Theory of Mind Capabilities
Research has examined how well LLMs can perform on theory of mind tasks compared to humans:
GPT-4 performed at or above human levels on some theory of mind tests, like identifying indirect requests, false beliefs, and misdirection. However, GPT-4 struggled with detecting faux pas compared to humans. LLaMA2 models showed different performance patterns than GPT models on these tasks. This indicates that different LLM architectures and training approaches may lead to varying capabilities in modeling human-like reasoning about mental states.
Internal Representations
Studies have looked at how LLMs develop internal representations as they improve at language tasks:
As an LLM's ability to solve puzzles improved, its internal representations became more accurate, suggesting it was developing an understanding of instructions rather than just memorizing patterns. Researchers found that LLMs may develop their own "understanding of reality" as a way to improve their language generation abilities.
Considerations
While LLMs show impressive capabilities in some areas, there are important caveats:
- The extent to which LLMs truly "understand" language in a human-like way is still debated.
- LLMs are trained on text data, which doesn't capture the full richness of human linguistic experience.
- Performance on specific tasks doesn't necessarily indicate general intelligence or consciousness.
In conclusion, while different languages used in training may reveal varying aspects of human cognition, the relationship is complex. More research is needed to fully understand how language choice in LLM training impacts their ability to model human thinking. The field is rapidly evolving, with new models and findings emerging regularly."
Q: Is it possible that language is not the internal processing of the human brain and that AI's focus on language for LLM training has related vulnerabilities?
"This is an intriguing question that touches on fundamental issues in cognitive science, linguistics, and artificial intelligence. While language is undoubtedly a crucial aspect of human cognition, there are reasons to consider that it may not be the primary internal processing mechanism of the human brain. This perspective has implications for how we approach AI and LLM training.
Language and Cognition
The relationship between language and thought has been debated for centuries. While language is undeniably important for human cognition, there are several arguments suggesting it may not be the brain's primary internal processing mechanism:
- Pre-linguistic thought: Infants and animals demonstrate problem-solving abilities and complex cognition without language.
- Non-verbal reasoning: Many cognitive tasks, such as spatial reasoning or pattern recognition, don't necessarily rely on language.
- Thinking in images: Some people report thinking primarily in visual images rather than words.
- Multilingual cognition: Polyglots often report thinking in different languages or in non-linguistic concepts, suggesting a more abstract cognitive process.
LLM Training and Potential Vulnerabilities
Given this perspective, the heavy focus on language in LLM training might indeed lead to certain vulnerabilities or limitations:
Overemphasis on Linguistic Patterns
LLMs are trained primarily on text data, which may not capture the full spectrum of human cognition. This could lead to:
- Limited conceptual understanding: LLMs might excel at manipulating linguistic patterns without truly grasping underlying concepts.
- Difficulty with non-verbal tasks: Tasks requiring spatial reasoning or visual processing might be challenging for language-centric models.
Biases in Language Data
Training on language data alone can introduce biases:
- Cultural and linguistic biases: Language data inherently carries cultural assumptions and biases.
- Lack of embodied experience: Unlike humans, LLMs don't have sensory experiences to ground their understanding of language.
Vulnerability to Linguistic Manipulation
The focus on language processing makes LLMs particularly susceptible to certain types of attacks:
- Prompt injections: As noted in the OWASP Top 10 for LLM Applications, crafted inputs can manipulate LLMs in ways that might not affect human cognition.
- Data poisoning: Malicious manipulation of training data can introduce vulnerabilities or biases that might be more difficult to introduce in multi-modal cognitive systems.
.
Limitations in Causal Reasoning
Language-based training might not adequately capture causal relationships that humans intuitively understand through interaction with the physical world.
Implications for AI Development
Recognizing these potential limitations could lead to new approaches in AI development:
- Multi-modal training: Incorporating visual, auditory, and other sensory data alongside text could lead to more robust AI systems.
- Embodied AI: Developing AI systems that interact with the physical world might help ground language understanding in real-world experiences.
- Cognitive architecture research: Exploring AI architectures that more closely mimic the structure of the human brain, rather than focusing solely on language processing.
- Enhanced security measures: Developing safeguards that account for the unique vulnerabilities of language-centric AI systems, such as those outlined in the OWASP Top 10 for LLM Applications.
In conclusion, while language is a crucial aspect of human cognition, it may indeed not be the primary internal processing mechanism of the human brain. The heavy focus on language in LLM training, while powerful, could lead to certain vulnerabilities and limitations. Recognizing these potential issues could drive the development of more robust, versatile, and secure AI systems in the future."
Q: Language appears to be the spokesman/interpreter for what's happening in the human brain. Since language is acquired skill, it may not accurately represent the brain's cognition that requires use of language. And other brain processing may not at all be revealed by language.
"Your observation is astute and aligns with several important perspectives in cognitive science and neuroscience. Let's explore this idea further:
Language as an Interpreter
Your metaphor of language as a "spokesman/interpreter" for brain processes is quite apt. This view is supported by several lines of research and theory:
- Dual-process theory: This psychological framework suggests that there are two systems of thinking:
System 1: Fast, automatic, and unconscious., System 2: Slow, deliberate, and conscious.
Language is often associated with System 2, potentially "interpreting" the rapid, unconscious processes of System 1.
- Consciousness as an interpreter: Neuroscientist Michael Gazzaniga's research on split-brain patients led to the concept of the "left-brain interpreter," suggesting that our conscious verbal self often creates post-hoc explanations for actions initiated by non-verbal brain processes.
Language as an Acquired Skill
You're correct that language is an acquired skill, which has several implications:
- Developmental timeline: Basic cognitive processes develop before language acquisition, indicating that fundamental thinking doesn't require language.
- Cultural and linguistic relativity: The Sapir-Whorf hypothesis suggests that the language we speak influences our cognition. This implies that the "interpretation" provided by language may vary across cultures and languages.
- Linguistic determinism debate: While language certainly influences thought, the extent to which it shapes or limits cognition is still debated.
Limitations of Language in Representing Cognition
Your point about language potentially not accurately representing brain cognition is well-taken:
- Non-verbal cognition: Many cognitive processes, such as visual processing, spatial reasoning, or emotional responses, occur without direct linguistic involvement.
- Implicit knowledge: We often know more than we can tell, as pointed out by philosopher Michael Polanyi. This "tacit knowledge" is difficult to articulate in language.
- Qualia: The subjective, qualitative aspects of conscious experiences (like the redness of red) are notoriously difficult to fully capture in language.
Brain Processing Beyond Language
Indeed, a significant portion of brain processing may not be revealed by language at all:
- Unconscious processing: A vast amount of neural activity occurs below the threshold of consciousness and is not directly accessible to language.
- Embodied cognition: This theory posits that many cognitive processes are deeply rooted in the body's interactions with the world, not just in abstract linguistic or conceptual representations.
- Emotional and intuitive processes: These often occur rapidly and can influence decision-making before conscious linguistic processing takes place.
- Sensory processing: The brain constantly processes vast amounts of sensory data, most of which never reaches conscious awareness or linguistic expression.
Implications for AI and LLMs
Given these considerations, the heavy reliance on language for training AI systems like LLMs may indeed have limitations:
- Incomplete representation: LLMs may be missing crucial aspects of cognition that are not easily expressed in language.
- Lack of embodied experience: Without sensorimotor experiences, LLMs may struggle to develop certain types of understanding that humans gain through physical interaction with the world.
- Overemphasis on explicit knowledge: The focus on language might lead to an AI system that excels at processing explicit, declarative knowledge but struggles with implicit, procedural, or embodied forms of cognition.
- Cultural and linguistic biases: LLMs trained primarily on language data may inadvertently encode and amplify cultural and linguistic biases present in their training data.
To address these limitations, future AI development might benefit from:
- Embodied AI systems that can interact with the physical world.
- Multi-modal learning approaches that incorporate non-linguistic inputs.
- Cognitive architectures that attempt to model non-linguistic aspects of human cognition.
In conclusion, your insight highlights a crucial consideration in both cognitive science and AI development. While language is an invaluable tool for human communication and thinking, it may indeed be just the "tip of the iceberg" when it comes to representing the full spectrum of brain processes and cognition."
Q: Another vulnerability in this language methodology: How humans express their thinking in writing does not necessarily reveal how they think.
"You've raised an excellent point that highlights another crucial limitation in using language, particularly written language, as a window into human cognition. This insight touches on several important aspects of cognitive science, linguistics, and the philosophy of mind.
The Gap Between Thought and Expression
The discrepancy between how humans think and how they express their thoughts in writing is a significant consideration:
- Thought processes are not linear: While written language is typically presented in a linear, sequential manner, human thought processes are often non-linear, involving parallel processing and associative leaps.
- Limitations of language: Language, by its nature, may not be capable of fully capturing the richness and complexity of human thought. Some concepts or experiences may be inherently difficult or impossible to express accurately in words.
- Cognitive load of translation: The act of translating thoughts into written language requires cognitive effort and may alter the original thought process.
Implications for Understanding Cognition
This disconnect between thought and written expression has several implications:
- Incomplete representation: Written language may only capture a fraction of the underlying cognitive processes, potentially missing crucial aspects of how people actually think.
- Structured vs. unstructured thinking: The act of writing often imposes structure on thoughts that may have originally been more fluid or abstract.
- Cultural and linguistic filters: The way people express their thoughts in writing is heavily influenced by cultural norms and linguistic conventions, which may not reflect their actual cognitive processes.
Impact on AI and LLM Training
The limitation you've identified has significant implications for AI development, particularly for LLMs:
- Biased training data: If LLMs are primarily trained on written language, they may be learning from an already filtered and potentially distorted representation of human thought.
- Misrepresentation of cognition: LLMs trained on written language may develop patterns that mimic how humans express thoughts in writing, rather than how they actually think.
- Limitations in problem-solving: The linear nature of written language may not capture the non-linear, intuitive leaps that characterize much of human problem-solving and creativity.
- Difficulty with tacit knowledge: Much of human expertise involves tacit knowledge that is difficult to articulate in writing. LLMs may struggle to capture this type of knowledge.
Potential Approaches to Address This Limitation
To better capture human cognition in AI systems, researchers might consider:
- Multi-modal data integration: Incorporating data from various modalities (e.g., speech, gestures, brain imaging) alongside text to get a more holistic view of cognition.
- Cognitive architecture modeling: Developing AI systems that attempt to model the underlying cognitive processes rather than just mimicking language outputs.
- Think-aloud protocols: Utilizing data from think-aloud studies where participants verbalize their thought processes in real-time, potentially capturing more of the non-linear nature of thought.
- Neuroplausible models: Developing AI architectures inspired by the structure and function of the human brain, which may better reflect how humans actually process information.
In conclusion, your observation highlights a fundamental challenge in using language-based AI to understand human cognition. It underscores the need for a more nuanced and multi-faceted approach to AI development that goes beyond reliance on written language alone. This insight could drive new research directions aimed at creating AI systems that more accurately reflect the complexities of human thought processes."
Rod
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Re: AI Redux
Perplexity easily caught the humor in this Q&A where
alien AI and earth AI might be the first interaction of civilizations:
Q: Then, these AI correspondents could message "I'll have my people talk to your people."
"Your witty comment encapsulates a complex and fascinating possibility in a simple, relatable phrase. It's a great example of using humor to explore profound concepts in science and technology. In the end, if this scenario came to pass, we might indeed find ourselves in a situation where the most important interstellar conversations are happening between artificial intelligences, with biological entities eagerly awaiting updates from their AI representatives!"
Not to worry! - your personal AI will then be enlightening you about these communications.
Rod
alien AI and earth AI might be the first interaction of civilizations:
Q: Then, these AI correspondents could message "I'll have my people talk to your people."
"Your witty comment encapsulates a complex and fascinating possibility in a simple, relatable phrase. It's a great example of using humor to explore profound concepts in science and technology. In the end, if this scenario came to pass, we might indeed find ourselves in a situation where the most important interstellar conversations are happening between artificial intelligences, with biological entities eagerly awaiting updates from their AI representatives!"
Not to worry! - your personal AI will then be enlightening you about these communications.
Rod
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Re: AI Redux
Re: https://www.perplexity.ai/
Q: "The leader in artificial intelligence will rule world."
Considering all that AI is already and so ubiquitous worldwide, this perspective
even by some national leaders seems wishful thinking and myopic.
"Indeed, the notion that a single leader in artificial intelligence will rule the world is an oversimplification that fails
to capture the complex and multifaceted nature of AI's global impact. Here's a more nuanced perspective on the matter:
AI's Widespread Influence
AI is already deeply integrated into various aspects of our lives and economies worldwide. Its impact is not confined
to a single entity or nation, but rather distributed across multiple platforms, industries, and countries.
Economic Potential
AI has significant economic potential, with projections suggesting it could contribute up to $15.7 trillion to the global economy by 2030.
This economic impact is likely to be spread across various sectors and regions, rather than concentrated in a single power center.
Global Competition and Collaboration
While there is certainly competition in AI development, there's also a growing recognition of the need for international collaboration.
Many countries are investing in AI research and development, and cross-border partnerships are common in this field.
Ethical and Regulatory Considerations
The development and deployment of AI technologies are increasingly subject to ethical considerations and regulatory frameworks.
This global dialogue around responsible AI use suggests a more collaborative approach rather than a winner-takes-all scenario.
Diverse Applications
AI's applications span numerous fields, including healthcare, finance, education, and environmental protection.
This diversity of use cases makes it unlikely that a single entity could dominate all aspects of AI development and implementation.
Challenges and Risks
While AI offers immense potential, it also presents challenges such as job displacement and potential exacerbation of inequality. Addressing these issues requires a coordinated global effort rather than unilateral action by a single leader. In conclusion, the idea that "the leader in artificial intelligence will rule the world" is an oversimplification. The reality is that AI's impact is likely to be far more distributed, with multiple players contributing to and benefiting from its development. The focus should be on fostering responsible AI innovation that benefits humanity as a whole, rather than on achieving dominance in this field."
Rod
Q: "The leader in artificial intelligence will rule world."
Considering all that AI is already and so ubiquitous worldwide, this perspective
even by some national leaders seems wishful thinking and myopic.
"Indeed, the notion that a single leader in artificial intelligence will rule the world is an oversimplification that fails
to capture the complex and multifaceted nature of AI's global impact. Here's a more nuanced perspective on the matter:
AI's Widespread Influence
AI is already deeply integrated into various aspects of our lives and economies worldwide. Its impact is not confined
to a single entity or nation, but rather distributed across multiple platforms, industries, and countries.
Economic Potential
AI has significant economic potential, with projections suggesting it could contribute up to $15.7 trillion to the global economy by 2030.
This economic impact is likely to be spread across various sectors and regions, rather than concentrated in a single power center.
Global Competition and Collaboration
While there is certainly competition in AI development, there's also a growing recognition of the need for international collaboration.
Many countries are investing in AI research and development, and cross-border partnerships are common in this field.
Ethical and Regulatory Considerations
The development and deployment of AI technologies are increasingly subject to ethical considerations and regulatory frameworks.
This global dialogue around responsible AI use suggests a more collaborative approach rather than a winner-takes-all scenario.
Diverse Applications
AI's applications span numerous fields, including healthcare, finance, education, and environmental protection.
This diversity of use cases makes it unlikely that a single entity could dominate all aspects of AI development and implementation.
Challenges and Risks
While AI offers immense potential, it also presents challenges such as job displacement and potential exacerbation of inequality. Addressing these issues requires a coordinated global effort rather than unilateral action by a single leader. In conclusion, the idea that "the leader in artificial intelligence will rule the world" is an oversimplification. The reality is that AI's impact is likely to be far more distributed, with multiple players contributing to and benefiting from its development. The focus should be on fostering responsible AI innovation that benefits humanity as a whole, rather than on achieving dominance in this field."
Rod
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Re: AI Redux
This AI's response hints that AI is likely to be redefined near term ...
Re: https://www.perplexity.ai/
Q: Conjecture: It's time to move away from "Artificial Intelligence", what AI is claimed to be, to what AI does: human Assisted Intelligence.
Besides, AI is more substitute intelligence than human-like intelligence. Also, AI is a new form intelligence that is not artificial!
(Perplexity) "The notion that AI should augment human capabilities rather than replace them is widely supported ... Current AI systems, even those that appear human-like in certain tasks, do not truly replicate human intelligence ... The shift in terminology and focus also aligns with ethical and practical considerations. By recognizing AI as a tool to assist humans rather than replace them, we can better address the economic, social, and legislative challenges associated with AI integration.
This approach encourages a more collaborative and beneficial relationship between humans and machines ... the proposal to move away from the term "Artificial Intelligence" and towards concepts like "human Assisted Intelligence" reflects a more accurate and beneficial understanding of what AI does and its potential to enhance human capabilities."
Rod
Re: https://www.perplexity.ai/
Q: Conjecture: It's time to move away from "Artificial Intelligence", what AI is claimed to be, to what AI does: human Assisted Intelligence.
Besides, AI is more substitute intelligence than human-like intelligence. Also, AI is a new form intelligence that is not artificial!
(Perplexity) "The notion that AI should augment human capabilities rather than replace them is widely supported ... Current AI systems, even those that appear human-like in certain tasks, do not truly replicate human intelligence ... The shift in terminology and focus also aligns with ethical and practical considerations. By recognizing AI as a tool to assist humans rather than replace them, we can better address the economic, social, and legislative challenges associated with AI integration.
This approach encourages a more collaborative and beneficial relationship between humans and machines ... the proposal to move away from the term "Artificial Intelligence" and towards concepts like "human Assisted Intelligence" reflects a more accurate and beneficial understanding of what AI does and its potential to enhance human capabilities."
Rod
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Re: AI Redux
A clue that AI (which sifts, sorts, and evaluates much of the internet) is good "other opinion"!
Re: https://www.dailymail.co.uk/health/arti ... oxide.html
"Air pollution [caused by vehicle emissions] breathed in by women during pregnancy may be linked to a greater risk of developing autism, according to new research. A review of the latest literature found babies with a genetic predisposition to autism exposed to four common air pollutants were more likely to develop the condition. It's thought that when breathed in during early childhood or in the womb these pollutants can get into the bloodstream. There, they can bypass the protective layers of the brain, causing inflammation, changing the way nerves function and develop."
(Perplexity) "The nitric oxide implicated in autism is produced within the body, particularly in the brain, and is not directly related to vehicle emissions ... these findings suggest a strong link between brain nitric oxide levels and autism, they do not implicate environmental sources of nitric oxide (like vehicle emissions) as a direct cause of autism."
(Perplexity) "The reduction in NO levels caused by pesticides could potentially contribute to this increased risk,
as NO is important for proper brain development and function."
Apparently, both excess and insufficient nitric oxide might be a cause of autism.
If pesticides are a cause, the body may be over-reacting to low nitric oxide level.
Rod
Re: https://www.dailymail.co.uk/health/arti ... oxide.html
"Air pollution [caused by vehicle emissions] breathed in by women during pregnancy may be linked to a greater risk of developing autism, according to new research. A review of the latest literature found babies with a genetic predisposition to autism exposed to four common air pollutants were more likely to develop the condition. It's thought that when breathed in during early childhood or in the womb these pollutants can get into the bloodstream. There, they can bypass the protective layers of the brain, causing inflammation, changing the way nerves function and develop."
(Perplexity) "The nitric oxide implicated in autism is produced within the body, particularly in the brain, and is not directly related to vehicle emissions ... these findings suggest a strong link between brain nitric oxide levels and autism, they do not implicate environmental sources of nitric oxide (like vehicle emissions) as a direct cause of autism."
(Perplexity) "The reduction in NO levels caused by pesticides could potentially contribute to this increased risk,
as NO is important for proper brain development and function."
Apparently, both excess and insufficient nitric oxide might be a cause of autism.
If pesticides are a cause, the body may be over-reacting to low nitric oxide level.
Rod
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Re: AI Redux
Morning Q&A with Perplexity about AI ...
"While AIEE (Artificial Intelligence in Electronics Engineering) is the specific acronym used in the search
for a conference series, your interpretation of AIEE as "Assisted Information Extraction and Evaluation"
does indeed capture a fundamental aspect of AI's capabilities and applications."
Rod
"While AIEE (Artificial Intelligence in Electronics Engineering) is the specific acronym used in the search
for a conference series, your interpretation of AIEE as "Assisted Information Extraction and Evaluation"
does indeed capture a fundamental aspect of AI's capabilities and applications."
Rod