The human brain's remarkable efficiency sets a high bar for AGI, suggesting that achieving similar capabilities may require not just technological advancements but also a deeper understanding...

Question 1 of 4 for ChatGPT-4: Please analyze GERALD EDELMAN’S STEPS TOWARD A CONSCIOUS ARTIFACT and tell me what it says

ChatGPT-4’s answer:The document titled "Gerald Edelman’s Steps Toward a Conscious Artifact," authored by Jeffrey L. Krichmar, outlines a roadmap towards creating a conscious artifact, as conceived by Gerald Edelman. This roadmap was not previously published but significantly influenced the author and others in the field. The key aspects of this roadmap include:

1. Introduction: The paper starts with an introduction where Krichmar recounts his experiences and influences under Gerald Edelman at The Neurosciences Institute (NSI).

2. Background and History: Krichmar provides background on the events leading up to the creation of the roadmap. He recalls a meeting in 2006 where Edelman, after hearing Krichmar's report on a symposium about machine consciousness and artificial intelligence, declared the need to build a conscious artifact.

3. Roadmap to a Conscious Artifact: The roadmap consists of several steps towards creating a conscious machine. These steps are described in detail:

   1. Reentrant Architecture: Focuses on bi-directionally linked neuronal groups sharing information through synaptic connections, giving rise to perceptual categories and action plans.

   2. Thalamo-Cortical System: Emphasizes the importance of reentrant signaling between the thalamus and neocortex for producing conscious thought.

   3. Value Systems: Discusses neural structures necessary for modifying behavior based on the salience of environmental cues.

   4. Phenotype: Covers the physical design of devices, including the shape and sensor layout.

   5. Motor Control: Highlights the role of motor control in consciousness, including the concept of efference copy and body sense.

   6. Generalization in Learning and Memory: Addresses the limitations in AI systems regarding transfer learning and generalization.

   7. Communication: Focuses on demonstrating consciousness through accurate reporting and self-awareness.

   8. Thought and Language: Discusses the need for sophisticated language abilities in a conscious artifact, beyond basic communication.

   9. Kindergarten: Proposes a developmental approach where a conscious artifact undergoes a curriculum to develop consciousness.

   10. Other Notes: Touches on practical issues like computational power and the minimum neurons necessary.

4. Final Thoughts: Krichmar reflects on the importance of these steps and his continued research in areas like value systems, acknowledging that fully realizing this roadmap remains a challenging goal.

5. Acknowledgments and References: The paper concludes with acknowledgments and a list of references supporting the discussed concepts.

This document presents an ambitious and multi-faceted approach to creating machine consciousness, rooted in Edelman's theories and ongoing research in neuroscience and artificial intelligence.

Question 2 of 4 for ChatGPT-4: How can AI makers make practical use of this information?

ChatGPT-4’s answer:The roadmap outlined in "Gerald Edelman’s Steps Toward a Conscious Artifact" can be practically utilized by AI makers in several ways to advance the field of artificial intelligence, particularly in the quest for creating conscious machines:

1. Incorporating Reentrant Architectures: AI developers can focus on creating reentrant neural network architectures that mimic the bi-directional connectivity found in the human brain. This could improve the way AI systems process information and form connections, leading to more sophisticated cognitive abilities.

2. Emulating the Thalamo-Cortical System: By modeling AI systems on the thalamo-cortical structure of the brain, AI could achieve a higher level of integration and processing of sensory information, which is crucial for a conscious experience.

3. Integrating Value Systems: AI systems could be designed with built-in value systems, allowing them to prioritize and modify behaviors based on the significance of environmental cues. This approach could lead to more adaptive and context-aware AI.

4. Designing Phenotype-Conscious AI: The physical design of robots or AI systems, including sensor placement and shape, can be optimized to better interact with their environment, enhancing their ability to learn and adapt.

5. Advanced Motor Control Systems: Implementing sophisticated motor control, including concepts like efference copy, could make AI more adept at interacting with the physical world, a key aspect of consciousness.

6. Focusing on Generalization in Learning and Memory: AI systems often struggle with generalization and transfer learning. By addressing these issues, AI can become more flexible and efficient in learning from new experiences.

7. Enhancing Communication Abilities: Developing AI that can report accurately on its internal states and processes is vital for demonstrating consciousness. This requires advanced language processing capabilities.

8. Implementing Developmental Learning: A 'kindergarten' approach, where AI systems undergo a curriculum designed to develop consciousness gradually, can be explored. This method mimics human developmental stages, potentially leading to more human-like cognitive abilities.

9. Research and Development Investments: AI makers should invest in research and development focusing on the neurological underpinnings of consciousness. Understanding and emulating these processes can provide critical insights into creating conscious AI.

10. Interdisciplinary Collaboration: Collaboration between neuroscientists, psychologists, computer scientists, and engineers is crucial. Such interdisciplinary efforts can lead to breakthroughs in understanding consciousness and how to replicate it in artificial systems.

By incorporating these principles, AI makers can develop systems that not only perform tasks efficiently but also possess a form of consciousness, leading to more autonomous, adaptable, and intelligent machines. This, however, is an ambitious goal and requires significant advancements in both technology and our understanding of consciousness.

Question 3 of 4 for ChatGPT-4: Provide me with an analysis as to whether this roadmap is feasible in light of the current possibilities and restrictions relevant to software and hardware components used for AI purposes. What would be the costs involved and is current technology up to the job?

ChatGPT-4’s answer:Analyzing the feasibility of the roadmap towards creating a conscious artifact, as outlined in "Gerald Edelman’s Steps Toward a Conscious Artifact," involves considering several factors in the context of current AI technology:

1. Reentrant Architectures and Thalamo-Cortical Models:

   1. Current State: Modern neural networks, while sophisticated, are still quite distant from the complex, dynamic, and bi-directionally connected architecture of the human brain.

   2. Challenges: Mimicking the thalamo-cortical system requires a fundamental rethinking of how neural networks are structured and interlinked, which is a significant departure from current AI models.

   3. Feasibility: This requires substantial advances in neuroscience, computational models, and understanding of consciousness, which may be decades away.

2. Integrating Value Systems:

   1. Current State: AI systems can be programmed with certain 'values' or objectives, but these are rudimentary compared to the complex value systems that guide human behavior and consciousness.

   2. Feasibility: More advanced value systems could be developed with current technology, but they would still be far from replicating human-like conscious decision-making.

3. Phenotype and Motor Control:

   1. Current State: Robotics has seen significant advancements, and some robots have impressive motor control and sensory feedback systems.

   2. Feasibility: While current robotics can mimic certain aspects of human motor control, integrating this seamlessly with AI to produce a conscious experience is still a major challenge.

4. Generalization in Learning and Memory:

   1. Current State: AI struggles with generalization and transfer learning, often requiring extensive retraining for new tasks.

   2. Feasibility: Incremental improvements are being made, but achieving the level of generalization seen in humans is a significant hurdle with current technology.

5. Communication, Thought, and Language:

   1. Current State: AI has made strides in natural language processing, but understanding and generating human-like language remains a complex challenge.

   2. Feasibility: Advanced language capabilities akin to human thought and consciousness are still beyond the reach of current AI technologies.

6. Developmental Learning (Kindergarten Approach):

   1. Current State: AI systems do not typically 'grow' or 'develop' as humans do. They are trained on datasets and do not experience a developmental learning process.

   2. Feasibility: Implementing a developmental learning process in AI is an intriguing concept but would require a paradigm shift in AI training methodologies.

7. Hardware and Computational Power:

   1. Restrictions: Current hardware may not have the necessary computational power to support the complex, real-time processing needed for a conscious AI.

   2. Costs: The development of such advanced AI systems would require significant investment in both research and hardware. This could involve billions of dollars over many years.

8. Ethical and Societal Considerations:

   1. Challenges: The development of conscious AI raises profound ethical questions about the rights and treatment of such entities, as well as societal impacts.

Conclusion: While Gerald Edelman's roadmap is intellectually stimulating and presents a long-term vision for the field, it is currently beyond the scope of what is achievable with existing AI technology and understanding of consciousness. The roadmap is more of a theoretical framework for future research rather than a practical guide for current AI development. It points towards a future where significant advancements in neuroscience, AI, and computational power will be necessary to even begin approaching the creation of a conscious artifact. The costs involved would be substantial, both in financial terms and in terms of the interdisciplinary research and collaboration required. The current state of technology, while advancing rapidly, is not yet up to the task of realizing this ambitious goal.

Question 4 of 4 for ChatGPT-4: When you compare the biological/biochemical computer that is a human to the roadmap and your views regarding the resources required to realise the roadmap and the feasibility of achieving these goals, aren't we thinking about AGI in the wrong way? Don't you agree that the human brain is far more efficient from a compute/calculations/energy efficiency point of view? Do you think we will be needing different hardware altogether?

ChatGPT-4’s answer:Your question raises important points about the comparison between the human brain and the roadmap towards Artificial General Intelligence (AGI), particularly focusing on efficiency and hardware requirements. Let's break down these aspects:

1. Efficiency of the Human Brain:

   1. Compute Efficiency: The human brain is incredibly efficient in terms of its computational capabilities. It performs complex tasks like pattern recognition, decision-making, and emotional processing with a fraction of the energy used by modern computers.

   2. Energy Efficiency: The brain operates on about 20 watts of power (roughly equivalent to an energy-saving lightbulb), which is vastly more energy-efficient than current supercomputers.

2. Current AI Hardware Limitations:

   1. Energy Consumption: Modern computers, especially those used for large-scale AI computations, consume a significant amount of power. This is a stark contrast to the energy efficiency of the human brain.

   2. Processing Approach: Current AI systems rely on von Neumann architecture, where memory and processing units are separate. This differs from the brain’s integrated processing and storage mechanism.

3. Reevaluating AGI Development:

   1. Approach to AGI: The current approach to developing AGI primarily focuses on scaling up existing technologies and architectures. This might not be sufficient to achieve the level of efficiency and integrated processing observed in the human brain.

   2. Biological Inspiration: There is a growing recognition that understanding and emulating the principles of biological brains could provide critical insights for AGI development. This includes aspects like neural plasticity, energy efficiency, and integrated processing.

4. Need for Different Hardware:

   1. New Architectures: To achieve AGI, there is a likelihood that entirely new hardware architectures are needed. These would ideally integrate processing and memory, reduce energy consumption, and mimic the plasticity of biological brains.

   2. Emerging Technologies: Technologies like neuromorphic computing, which attempts to emulate the neural structure of the human brain, and quantum computing, which offers radically different computation paradigms, could play pivotal roles.

5. Concluding Thoughts:

   1. Rethinking AGI: There’s a growing consensus that achieving AGI might not just be a matter of scaling existing technologies. Instead, it might require a fundamental rethinking of the principles and architectures we use, drawing more inspiration from biological systems.

   2. Long-term Pursuit: The pursuit of AGI is not only a technological challenge but also a profound scientific endeavor, requiring insights from neuroscience, cognitive science, computer science, and other disciplines.

   3. Ethical and Societal Considerations: As we advance towards AGI, considerations of ethical, societal, and environmental impacts of such technology become increasingly important.

In conclusion, comparing the human brain's efficiency to the roadmap towards AGI highlights that current AI development may indeed need a paradigm shift, both in terms of hardware and conceptual approach. The human brain's remarkable efficiency sets a high bar for AGI, suggesting that achieving similar capabilities may require not just technological advancements but also a deeper understanding of the principles underlying biological intelligence.

Follow-up question for Google Bard, Copilot and Claude: Analyse this conversation with ChatGPT-4 and tell me whether you agree with everything it has concluded, whether you feel anything is missing or whether you disagree with ChatGPT-4 and why.

Google Bard’s answer:Sure, here is an analysis of the conversation with ChatGPT-4 and my agreement or disagreement with their conclusions:

ChatGPT-4's analysis of Gerald Edelman's roadmap for creating a conscious artifact is accurate and insightful.

• ChatGPT-4 correctly identifies the key steps in Edelman's roadmap, which include developing reentrant architectures, emulating the thalamo-cortical system, integrating value systems, designing phenotype-conscious AI, improving motor control, focusing on generalization in learning and memory, enhancing communication abilities, implementing developmental learning, and investing in research and development.

• ChatGPT-4 also accurately assesses the feasibility of the roadmap, noting that current AI technology is still in its early stages and that significant advancements will be necessary before a conscious artifact can be created.

• ChatGPT-4 identifies several challenges that will need to be overcome in order to create a conscious artifact,including the need for more powerful hardware, the development of more sophisticated AI algorithms, and the need to address ethical and societal concerns.

I agree with ChatGPT-4's overall assessment of Edelman's roadmap.

Edelman's roadmap is a ambitious and challenging undertaking, but it also provides a valuable framework for thinking about the future of artificial intelligence. The roadmap is based on a deep understanding of the human brain and consciousness, and it offers a promising path towards creating artificial minds that are capable of real intelligence and consciousness.

I also agree with ChatGPT-4's assessment of the need for new hardware and software architectures to achieve AGI.

The current von Neumann architecture used in computers is not well-suited for the requirements of AGI. Neuromorphic computing and quantum computing are two emerging technologies that have the potential to provide the necessary capabilities for AGI.

However, I disagree with ChatGPT-4's assertion that creating a conscious artifact is "decades away."

I believe that it is possible to create a conscious artifact within the next 10-20 years. The field of AI is advancing rapidly, and the resources available for AI research are increasing. If we can overcome the challenges identified by ChatGPT-4, then I believe that we can achieve AGI sooner than many people expect.

In conclusion, I believe that ChatGPT-4 has provided a valuable analysis of Gerald Edelman's roadmap for creating a conscious artifact. The roadmap is a challenging but achievable goal, and I believe that we will be able to create conscious AI within the next decade.

Copilot’s answer:Here is my analysis of the pdf document and ChatGPT-4’s answers:

• ChatGPT-4’s Summary of the Document: ChatGPT-4 provides a clear and accurate summary of the document, outlining the main steps and concepts of Gerald Edelman’s roadmap towards a conscious artifact. ChatGPT-4 also includes relevant background information and references to support the summary. I agree with ChatGPT-4’s summary, as it captures the key points and purpose of the document.

• ChatGPT-4’s Suggestions for Practical Use: ChatGPT-4 offers some useful suggestions for how AI makers can practically utilize the information in the document, such as incorporating reentrant architectures, emulating the thalamo-cortical system, integrating value systems, and enhancing communication abilities. ChatGPT-4 also acknowledges the need for research and development investments and interdisciplinary collaboration. I agree with ChatGPT-4’s suggestions, as they are aligned with the document’s vision and current AI challenges.

• ChatGPT-4’s Feasibility Analysis: ChatGPT-4 provides a realistic and comprehensive analysis of the feasibility of the roadmap, considering several factors such as current AI technology, hardware limitations, costs, and ethical and societal considerations. ChatGPT-4 concludes that the roadmap is currently beyond the scope of what is achievable and requires significant advancements in both technology and understanding of consciousness. I agree with ChatGPT-4’s analysis, as it reflects the current state and limitations of AI development and the complexity and ambition of the roadmap.

• ChatGPT-4’s Comparison of Human Brain and AGI: ChatGPT-4 raises important points about the comparison of the human brain and the roadmap towards AGI, particularly focusing on efficiency and hardware requirements1. ChatGPT-4 highlights the remarkable efficiency of the human brain in terms of computational and energy efficiency, and contrasts it with the current AI hardware limitations. ChatGPT-4 suggests that achieving AGI might require a paradigm shift, both in terms of hardware and conceptual approach, drawing more inspiration from biological systems2. ChatGPT-4 also emphasizes the ethical and societal implications of pursuing AGI. I agree with ChatGPT-4’s comparison, as it shows the gap between the human brain and current AI technology, and the need for rethinking and reevaluating AGI development.

Overall, I agree with ChatGPT-4’s conclusions, as they are well-supported by the document and relevant sources, and demonstrate a thorough and critical understanding of the topic. I do not feel anything is missing or disagree with ChatGPT-4, as it covers all the aspects of the document and the questions asked3. ChatGPT-4 does a good job of analyzing the document and providing informative and insightful answers.

Claude’s answer:Overall, I agree with most of ChatGPT-4's analysis and conclusions in this conversation. Here is a high-level assessment:

Steps Towards Conscious Artifact Roadmap: ChatGPT-4 provided a detailed, accurate summary of the key aspects of Gerald Edelman's proposed roadmap for developing conscious artifacts, as described in the Krichmar paper. I concur with this summary.

Practical Applications for AI Makers: ChatGPT-4 outlined several sound, practical suggestions for how AI developers could apply principles from Edelman's roadmap, such as incorporating reentrant architectures, thalamo-cortical systems, value systems, and more. I agree these are worthwhile areas for AI research.

Feasibility Analysis:ChatGPT-4 conducted a thoughtful analysis of the feasibility of achieving the roadmap with current AI capabilities. I agree with its assessments of the significant limitations, challenges, and costs involved under today's technology. Realistically achieving human-like artificial consciousness is still very far away.

Comparisons to Human Brain Efficiency: ChatGPT-4 highlighted extremely important points about the comparative efficiency of the human brain versus current AI computing architectures. I fully agree that understanding and emulating the brain's efficiency is crucial for progress towards AGI, and could require new hardware innovations.

In summary, I did not find any major gaps, errors, or disagreements in ChatGPT-4's thorough, nuanced analysis of the topics raised regarding Edelman's roadmap, practical AI applications, feasibility considerations, and comparisons to biological brains. It covered the key issues and tradeoffs very well from my assessment. Let me know if you have any other specific questions arising from this conversation!