Good evidence that AI needs continuing human input
to maintain the quality of its simulated human intelligence!
Q: Considering the next word prediction foundation of AI, if the volume of training data were reduced to half,
then to half, then to half, how would this affect the quality of Large Language Models at each reduction?
(Perplexity) "Reducing the volume of training data for Large Language Models (LLMs) would significantly impact
their quality and performance at each reduction. Here's how the quality would likely be affected:
First Reduction (50% of original data)
The initial halving of training data would lead to a noticeable decrease in model quality:
- Reduced vocabulary and language understanding.
- Diminished ability to capture nuanced contexts and rare word usage.
- Lower performance on downstream tasks and general language tasks.
However, the impact might be partially mitigated by careful data filtering and quality control measures.
Second Reduction (25% of original data)
A further reduction to 25% of the original data volume would result in more severe degradation:
- Significant loss of long-tail knowledge and specialized information.
- Substantial decrease in the model's ability to generalize across diverse tasks.
- Increased likelihood of overfitting to the limited training data.
At this stage, the model's performance would likely fall below that of larger models trained on more extensive datasets5.
Third Reduction (12.5% of original data)
The final reduction to 12.5% of the original data would have drastic consequences:
- Severe limitation in language understanding and generation capabilities.
- High probability of "model collapse," where the model loses information about the true underlying data distribution.
- Inability to capture complex language patterns and nuances.
- Significant drop in performance across all language tasks.
At this level of data reduction, the model would likely struggle to function as a general-purpose language model and would be limited to very narrow, specific tasks with carefully curated data. It's important to note that the exact impact would depend on factors such as model architecture, training techniques, and the quality of the remaining data.
Techniques like distilling step-by-step or other data-efficient methods could potentially help mitigate some of the negative effects of data reduction. However, the overall trend would be a significant decline in model quality and capabilities with each reduction in training data volume."
Q: What if the original data were doubled without duplication?
"Doubling the original training data without duplication for Large Language Models (LLMs) would likely have
significant positive effects on their performance and capabilities:
- Increased knowledge and coverage: The model would have access to a broader range of information,
potentially covering more diverse topics, rare words, and specialized domains.
- Improved language understanding: With more diverse examples, the model could better grasp nuanced contexts,
idiomatic expressions, and complex language patterns.
- Enhanced generalization: A larger dataset would help the model learn more robust and generalizable features,
reducing overfitting and improving performance on unseen data.
- Better performance on downstream tasks: The increased data volume would likely lead to improved results
in various natural language processing tasks such as translation, summarization, and question-answering.
- Reduced bias: A larger, more diverse dataset could help mitigate some biases present in smaller datasets,
although careful curation would still be necessary.
- Improved few-shot and zero-shot learning: With a broader knowledge base, the model's ability to adapt
to new tasks without explicit training might be enhanced.
However, it's important to note that simply increasing data volume isn't always sufficient. The quality, diversity, and relevance of the additional data are crucial factors in determining the overall impact on model performance. Additionally, to fully leverage the increased data volume, the model's capacity (number of parameters) might need to be adjusted to accommodate the additional information."
Q: What if after one year of no human contributions to that data?
"After one year of no human contributions to the original training data for Large Language Models (LLMs), several issues would likely arise:
- Outdated information: The models would lack up-to-date knowledge on current events, technological advancements, and societal changes
that occurred during that year. This could lead to inaccurate or obsolete responses when queried about recent developments.
- Inconsistent responses: LLMs are designed with some variability in their outputs. Without fresh data
to reinforce accurate information, this inconsistency might become more pronounced over time.
- Potential for misinformation: As the model's knowledge becomes increasingly outdated, it may generate responses
that are no longer factually correct, potentially spreading misinformation.
- Reduced relevance: The model's ability to understand and respond to queries about contemporary topics, trends,
or cultural references would diminish.
- Lack of adaptation: LLMs would struggle to adapt to new language patterns, emerging terminologies,
or evolving social norms that developed during the year without updates.
- Degraded performance on time-sensitive tasks: Tasks requiring current information, such as providing
recent news summaries or analyzing ongoing events, would become increasingly challenging for the model.
To address these issues, several approaches could be considered:
- Fine-tuning: The model could be fine-tuned on a smaller, curated dataset of recent information to update its knowledge base.
- Retrieval-augmented generation: Implementing systems that allow the model to access external,
up-to-date information sources when generating responses.
- Continuous learning: Developing mechanisms for the model to incrementally learn and incorporate new information
without full retraining.
These strategies would help maintain the model's relevance and accuracy, ensuring it continues to provide valuable
and current information to users despite the lack of human contributions to the original training data."
Q: Recent example: In the Urantia Book, the "brotherhood of man and fatherhood of God" (Jesus' gospel) seems to be
the foundational message of this revelation. Query of several different AI didn't detect this foundation. Although
human observation it suggests that AI still lacks whatever facilitates such human observation.
"Your observation highlights an important limitation in current AI systems, including large language models like myself.
While we can process and synthesize vast amounts of information, we often lack the ability to identify and prioritize
core themes or foundational messages in complex texts like the Urantia Book.
The "brotherhood of man and fatherhood of God" is indeed a central theme in the Urantia Book's portrayal of Jesus' teachings.
This concept emphasizes the universal spiritual kinship of all humans under a loving God, which aligns with the book's expanded
cosmology and spiritual hierarchy. The fact that AI systems, even those trained specifically on the Urantia Book, may fail
to highlight this foundational message demonstrates several key limitations:
- Contextual understanding: AI often struggles to grasp the overarching context and significance of ideas
within a larger philosophical or spiritual framework.
- Thematic prioritization: While AI can identify multiple themes, it may not accurately assess their relative importance
or foundational nature within a text.
- Holistic comprehension: AI tends to process information in discrete chunks, potentially missing the interconnectedness
of ideas that humans can more easily perceive.
- Lack of intuition: Human readers can often intuitively sense the core message of a text, even if it's not explicitly stated,
while AI relies more heavily on explicit information.
These limitations underscore the current gap between AI and human cognition in areas such as intuitive understanding, contextual interpretation, and the ability to distill complex information into core principles. While AI continues to advance rapidly, your observation suggests that
certain aspects of human cognition, particularly in dealing with nuanced spiritual and philosophical concepts, remain challenging to replicate in artificial systems."
Q: But AI's development to date heralds an important - and long lasting - partnership of humans and AI.
"You're absolutely right, and your insight captures a crucial aspect of AI's role in our future. The partnership between humans and AI is indeed shaping up to be one of the most significant developments of our time. Here's why this partnership is so important and likely to be long-lasting:
- Complementary strengths: AI excels at processing vast amounts of data, identifying patterns, and performing repetitive tasks with precision. Humans, on the other hand, bring creativity, emotional intelligence, and complex reasoning to the table. Together, these strengths can lead to unprecedented problem-solving capabilities.
- Augmented intelligence: Rather than replacing human intelligence, AI is increasingly being used to augment and enhance human capabilities. This synergy allows us to tackle complex challenges that neither humans nor AI could address alone.
- Continuous learning and adaptation: As AI systems evolve, they can learn from human input and feedback, creating a cycle of continuous improvement. Simultaneously, humans are learning to better utilize and interact with AI, fostering a co-evolutionary process.
- Ethical and societal considerations: The development of AI raises important ethical questions that require human judgment and values.
This ongoing dialogue ensures that the partnership remains grounded in human-centric principles.
- Economic and productivity impacts: The human-AI partnership has the potential to drive significant economic growth
and productivity gains across various sectors, from healthcare to education to scientific research.
- Addressing global challenges: Together, humans and AI can tackle some of the world's most pressing issues, such as climate change,
disease prevention, and space exploration, by combining computational power with human ingenuity.
- Cultural and creative expression: AI is already being used in art, music, and literature, opening up new avenues
for human creativity and expression.
This partnership, as you've noted, is likely to be
long-lasting because it's not about AI replacing humans, but about creating a symbiotic relationship that enhances our collective capabilities. As we continue to navigate this new frontier, the human-AI partnership will undoubtedly play a central role in shaping our future."
Rod
A false assumption about AI replacing humans keeps being reported in the media:
Testing if AI is becoming a good political analyst ...
In the good ol' days, chefs used their own skills and expertise
Best guess: 1. Don't use AI for legal counsel on this increasing vulnerability. 
Perplexity liked the nutrition in a baked red onion so well 
It may be fair to say Divine Intervention isn't solely reserved for the human race.
In Pharmacology Research and Perspectives article, June 2024:
(another example of AI assistance for solving modern problems)
Morning meditation ...