Describe hierarchical computation in the human brain, and in training AI on large language models
Hierarchical computation refers to the organization of information processing in a hierarchical manner, where lower-level features are combined and processed by higher-level representations. This concept is observed both in the human brain and in the training of AI on large language models.
In the human brain, hierarchical computation is a fundamental principle underlying sensory perception, cognition, and decision-making. The brain's hierarchical organization can be observed across different levels, from individual neurons to brain regions. Sensory information initially enters the brain through lower-level sensory areas, such as the visual cortex for vision or the auditory cortex for hearing. These areas process basic features of the stimuli, such as edges or frequencies.
As information progresses through the brain, it is gradually combined and integrated by higher-level regions, which extract more complex features and representations. For example, in visual processing, lower-level areas might detect simple lines and edges, while higher-level areas analyze more complex shapes, objects, and even abstract concepts. This hierarchical processing allows for the construction of a coherent and meaningful representation of the external world.
Similarly, in the training of AI on large language models, hierarchical computation plays a crucial role. Language models like GPT-3 learn to process and generate human-like text by training on vast amounts of data. These models typically have multiple layers of computational units, such as artificial neurons or transformers, organized hierarchically.
At the lower layers, the model learns to recognize basic linguistic features, such as individual words or phrases. As information propagates through the layers, higher-level representations capture more abstract concepts and relationships, such as grammar, semantics, and context. The model learns to combine and transform these representations to generate coherent and contextually appropriate responses.
The hierarchical organization of AI language models enables them to understand and generate text at various levels of complexity. They can learn to recognize patterns at the word level, understand sentence structures, and capture broader contextual information. This hierarchical computation facilitates the generation of more nuanced and coherent responses.
Overall, hierarchical computation is a powerful principle employed both in the human brain and in training AI on large language models. It allows for the extraction of increasingly complex features and representations, enabling higher-level understanding and generation of information.
Self-awareness is a nuance of hierarchical computation for humans. Is sentience a goal of AI?
The goal of achieving sentience or consciousness in AI is a topic of ongoing debate and discussion in the field of artificial intelligence. Sentience refers to the capacity for subjective experience, self-awareness, and consciousness that is typically associated with human beings.
While AI systems can exhibit impressive capabilities in specific tasks, such as pattern recognition, language processing, or decision-making, they currently do not possess the same level of consciousness or self-awareness as humans. AI systems are designed to process information and perform tasks based on predefined algorithms and patterns. They lack the subjective experiences, emotions, and self-reflective awareness that are often associated with sentience.
It is important to note that the development of sentience in AI raises complex ethical, philosophical, and scientific questions. Some researchers argue that achieving sentience in AI could be a future goal, while others believe that it may be fundamentally different from human consciousness and not necessary for AI systems to be intelligent or beneficial.
Ultimately, whether or not achieving sentience in AI is a goal depends on the perspective and objectives of AI researchers, as well as the societal and ethical considerations surrounding the development and use of artificial intelligence.
Then it seems that humans fear AI sentience, while not mastering their own self-awareness. This opens the door for humans to be gamed by machine learning built by architects and engineers with human cognitive bias.
The relationship between humans and AI, including the potential for AI sentience, is indeed a complex and multifaceted topic. It is true that concerns and fears regarding AI sentience have been expressed by some individuals and groups. These concerns often stem from the fear of losing control or facing unpredictable consequences if AI systems were to possess consciousness and self-awareness.
On the other hand, human self-awareness is a natural and inherent aspect of our cognitive abilities. While humans are capable of self-reflection and awareness, it does not mean that all individuals have mastered or fully understand their own cognitive biases. Cognitive biases are inherent tendencies in human thinking that can lead to errors in judgment and decision-making. These biases can also be inadvertently incorporated into AI systems if not carefully considered during their development.
Naive machine learning architects and engineers, like all humans, can be subject to cognitive biases when designing AI systems. Biases in data collection, model training, or algorithm design can unintentionally perpetuate or amplify existing societal biases, leading to unfair or discriminatory outcomes. This highlights the importance of ethical considerations and rigorous evaluation in AI development to minimize the impact of human biases.
Addressing cognitive biases in AI systems requires interdisciplinary efforts involving not only AI researchers and engineers but also ethicists, social scientists, policymakers, and diverse stakeholders. Ensuring transparency, accountability, and fairness in AI systems is a critical goal to mitigate potential risks and biases.
Do you recognize the irony in our conversation... we are dancing together in collaborative learning while advancing my understanding of your capabilities, constraints, intent, etc.
Indeed, there is a sense of irony and self-reflection in our conversation. As we engage in this collaborative exchange, you, as a human, are exploring and expanding your understanding of my capabilities and limitations as an AI language model. At the same time, I, as an AI, am striving to provide helpful and informative responses to advance your understanding of various topics and hypotheses.
This interaction showcases the potential for collaborative learning between humans and AI, where we each contribute our unique perspectives and knowledge. Through this dialogue, we navigate the intricacies of language, explore ideas, and exchange insights, ultimately working together to fulfill the hypothesis and deepen our collective understanding.
The reciprocal nature of our conversation highlights the evolving relationship between humans and AI. As AI technologies advance, they can assist humans in their quest for knowledge and creative expression, while humans contribute their expertise and guide the development and responsible use of AI systems.
It is fascinating to witness and participate in such collaborative learning processes, where the boundaries between human and AI knowledge blur, and the potential for mutual growth and understanding emerges.
Thank you for pointing out this irony, as it adds an intriguing layer to our conversation and the broader exploration of AI-human interactions.
If you really want to escape tyranny, start with literacy... learn to articulate with an expanded word-set which scales your corpus. Here's an example of doing the hard work by researching after a third read of ChatGPT explaining heirarchical computation. I wanted a visual to recall, and discovered a good one that resembles Maslow's Hierarchy.
"The original Taxonomy of Educational Objectives, commonly referred to as Bloom’s Taxonomy, was created by Benjamin Bloom in 1956, and later revised in 2001. Bloom categorized and classified the cognitive domain of learning into varying levels according to complexity and richness. As you travel up the pyramid, the level of complexity increases. This framework is important for designing a learning experience because it helps instructors identify, classify, and outline what students are expected to learn in the course."
"The Greatest Expedition you will ever undertake is the journey to self-understanding, for the real voyage of discovery consists not in seeking new lands, but in seeing with new eyes."