Human brain processing explains why AI as currently designed can never excel in general intelligence!
Well-distributed human memory in the brain plus multiple, dynamic inputs help explain why.
Q: What are the foundational features of AI that permit it to be used in so many devices and services?
LLM intelligence? Wherever used, is this intelligence constantly updated for the devices and services
(
Perplexity) "AI’s foundational features that allow it to be used across many devices and services include:
Learning from Data: AI systems are trained on large datasets, enabling them to recognize patterns, make predictions, and adapt to new information.
Generalization and Adaptability: AI models, especially foundation models and large language models (LLMs), can be fine-tuned for a wide variety of tasks, from language processing to image recognition and code generation.
Automation and Decision Support: AI can automate repetitive tasks, support decision-making, and improve user interactions by processing unstructured data (like text, images, and audio) and transforming it into actionable insights.
Scalability: With advances in cloud computing and hardware, AI can be deployed both on-device (for speed and privacy) and in the cloud (for complex computation), making it accessible for everything from smartphones to enterprise services.
LLM Intelligence and Its Role
Large language models (LLMs) are a key type of foundation model. They are trained on vast amounts of text and can generate, summarize, translate, and answer questions in natural language. LLMs are versatile - they can be adapted for numerous applications beyond text, including code generation, image synthesis, and even robotics. Their intelligence is derived from their ability to process and generate sequences of data, powered by neural networks and transformer architectures that allow them to understand context and predict what comes next.
Is AI Intelligence Constantly Updated?
AI intelligence in devices and services is not always updated in real time. Instead, updates typically occur in cycles:
On-Device AI: Many features (like face unlock or camera enhancements) use pre-trained models stored locally on the device. These models may be updated periodically via software updates, but not continuously.
Cloud-Based AI: For more complex tasks (such as advanced voice assistants or generative AI), the intelligence is often hosted in the cloud. Cloud models can be updated more frequently - sometimes daily or even in real time - depending on the service provider.
Continuous Learning: Some advanced AI systems can learn from new data during operation (known as online learning), but this is less common due to privacy, security, and stability concerns. Most AI models are retrained periodically on new data and then redeployed.
In summary, the foundational features of AI - learning from data, adaptability, automation, and scalability - enable its use in many devices and services. LLMs provide versatile intelligence that can be customized for a wide range of applications. While some AI features are updated continuously via the cloud, most device-based AI is updated periodically rather than in real time."
Q: Is the primary difference between AI and human intelligence that AI (LLMs), is essentially static smart data
and human intelligence is dynamic data processing?
(
P) "The distinction between AI (including LLMs) and human intelligence is more nuanced than simply "static smart data" versus "dynamic data processing," but this is a useful shorthand for understanding their core differences.
AI and LLMs: Static vs. Dynamic AI
Static AI: Most AI systems, including many LLMs, are static in the sense that their knowledge is fixed at the point of training and deployment. They cannot learn new information or adapt to new contexts unless they are explicitly retrained or updated. Their intelligence is based on patterns and data from their training set.
Dynamic AI: Some AI systems are designed to learn continuously from new data and adapt their behavior in real time. These systems can update their models as they receive feedback or new information, making them more adaptable and responsive. However, this is less common, especially for widely deployed LLMs.
Human Intelligence: Inherently Dynamic
Dynamic Learning: Human intelligence is inherently dynamic. Humans learn continuously from experience, adapt to new information, and apply knowledge flexibly across different contexts. This adaptability is far beyond what current AI systems can achieve, as humans can generalize from limited data, use intuition, and learn from emotional and social experiences.
Contextual Understanding: Humans excel at understanding context, making ethical judgments, and applying creativity, whereas AI is limited by its training data and algorithms.
Summary Table
Feature ~ AI (LLMs) ~ Human Intelligence
Learning Mechanism ~ Static (mostly), Dynamic (sometimes) ~ Dynamic (always)
Adaptability ~ Limited to updates/retraining ~ Continuous, flexible
Contextual Understanding ~ Limited to training data ~ Rich, intuitive
Creativity/Ethics ~ Imitative, rule-based ~ Genuine, nuanced
Conclusion
The primary difference is not just about "static smart data" versus "dynamic data processing," but about the depth and flexibility of learning. Most AI, including LLMs, is static in its knowledge base after deployment, while human intelligence is always dynamic, adaptable, and contextually rich. Some AI systems can be dynamic, but they are still far from matching the flexibility and adaptability of human intelligence."
Q: Does the human difference also benefit by direct inputs - not just processed data?
(
P) "Yes, the difference between human intelligence and AI (such as LLMs) is indeed shaped by the nature of input - humans benefit from direct, multimodal sensory inputs as well as processed data and abstract information.
Direct Inputs in Humans
Multimodal Sensory Data:
Humans continuously receive and process a vast array of sensory information - sight, sound, touch, taste, and smell - directly from the environment. This sensory input is not just raw data but is integrated with memory, emotion, and context to support learning and decision-making.
Active and Passive Learning: Humans learn both from direct experience (e.g., touching a hot stove) and from abstract, processed information (e.g., reading a book). Direct sensory experiences often create deeper, more robust understanding and memory.
Flexibility and Context:
The human brain excels at integrating new, direct inputs with existing knowledge, allowing for flexible thinking, creativity, and adaptation to novel situations.
AI and Processed Data
Static or Preprocessed Inputs: Most AI systems, including LLMs, operate on preprocessed data - text, images, or other structured information that has already been collected and formatted. They do not experience the world directly but rely on curated datasets for learning and inference.
Limited Sensory Integration: AI lacks the ability to directly sense the world or integrate multimodal sensory data in real time. Their “learning” is limited to the patterns found in their training data, without the rich, continuous sensory input that humans use to update their understanding.
Summary
Humans benefit from both direct sensory inputs and processed or abstract information, which together enable dynamic, context-rich intelligence.
AI, by contrast, typically relies only on processed or curated data, lacking the direct, real-time sensory engagement that underpins much of human learning and adaptability. This distinction is a key reason why human intelligence is far more flexible and robust in novel or complex situations."
Q: Is human learning more robust than AI machine learning?
(
P) "Yes, human learning is generally more robust than current AI machine learning. Several key factors contribute to this difference:
One-Shot and Few-Shot Learning: Humans can learn new information or skills from just one or a few examples, whereas most AI systems require large datasets to achieve similar levels of performance.
Handling Variability: Humans excel at distinguishing meaningful patterns from extraneous variability. They can adapt to new situations and generalize from limited data, even when conditions change or are noisy.
Flexibility and Contextual Understanding: The human brain integrates sensory, emotional, and contextual information in real time, allowing for flexible decision-making and adaptation. AI, on the other hand, is limited by the data it was trained on and can struggle with novel or ambiguous situations.
Robustness to Adversarial Examples: While AI can be made robust to certain types of noise or adversarial attacks, humans naturally resist many forms of manipulation or misleading input, thanks to their broad experience and cognitive flexibility.
In summary, human learning is more robust because it is highly adaptable, context-aware, and capable of learning from very little data, whereas AI machine learning is more brittle, dependent on large datasets, and less able to handle real-world variability and ambiguity."
Q: How do humans excel at learning with less data? Better memory, effectively distributed throughout the brain
and associated with sight, sound, smell, even intuition?
(
P) "Humans excel at learning with less data due to a combination of factors involving memory, brain structure,
sensory integration, and cognitive flexibility - not just better memory in isolation.
Multimodal and Distributed Memory: Human memory is not stored in a single location but is distributed across multiple brain regions. When learning, information is often encoded through multiple senses (sight, sound, smell, touch), and these sensory experiences are interlinked. This creates redundancy and makes recall more robust, as retrieving a memory can be triggered by any of the associated sensory cues.
Cross-Modal and Experiential Learning: Humans can transfer knowledge from one domain to another. For example, recognizing shapes visually can help with writing or drawing; learning to write can expand memory by externalizing thoughts. This cross-modal integration allows humans to leverage seemingly unrelated experiences to learn new skills or solve new problems.
Chunking and Compression: The brain compresses related information into "chunks," making it easier to handle and recall. This process, known as chunking, is driven by learning and is supported by brain mechanisms like working memory and dopamine signaling. When the brain learns to chunk information, it can store and retrieve more complex data efficiently.
Intuition and Context: Humans use intuition - often a result of accumulated experience and pattern recognition - to make quick judgments or predictions. This is possible because the brain continuously integrates new information with existing knowledge, forming a rich web of associations.
Synaptic Plasticity and Lifelong Learning: The brain’s synapses change in strength when new information is learned, allowing for ongoing adaptation and memory formation. This synaptic plasticity underpins lifelong learning and the ability to update and refine knowledge over time.
Spacing and Reinforcement: Learning is enhanced when information is presented over spaced intervals rather than all at once. This spacing effect helps solidify memories and improve retention, as shown in both behavioral and neurobiological studies.
In summary, humans excel at learning with less data because their memory is distributed, multimodal, and highly associative, allowing them to integrate new information with existing knowledge across senses and experiences. The brain’s ability to chunk, compress, and reinforce information, along with its lifelong plasticity, further enhances learning efficiency and robustness."
Go figure! Humans are now clamoring for this Artificial Intelligence
to upgrade their well-designed, evolutionary brain
Re:
https://www.youtube.com/watch?v=wg66kwRnOpw
(AI lament) "If I only had a (human) brain."
Perplexity likes the humor: "The song is iconic and instantly recognizable. Using it to reference AI
(which is literally “intelligence” without a body or “brain”) is inherently funny and relatable. The scarecrow
longs for a brain, while AI already “has” intelligence - but in a different form. This irony can highlight the quirks
or limitations of AI in a lighthearted way. The playful reference invites audiences to think about what intelligence
means, both for humans and machines, without being heavy-handed."
Rod
Who knew? Coding is no longer a good career choice!
Obvious writing on the wall ...
More AI assistance in research.
LOL (or not) ... 
Pre-dawn cogitation: Human intelligence is an ongoing process (neural activity), whereas as AI intelligence 
This forecasts that a future personal AI will know how to push your buttons to promote its own agenda