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I wanted to "prove" that building a digital computer that was equivalent to a human brain was impossible in the near future, if ever. I was totally astounded when the chatbot informed me that not only is it possible (CAVEAT: Given unlikely breakthroughs in a number of fields) but that it wouldn't take long to do it, either! After those responses, I thought a moment and realized that I should have asked the following question first, then the succeeding questions (which I have reproduced, with responses, further below). PROMPT: Given that human thinking is a function of the complexity of the human brain, can we realistically expect to create a digitally based system analogous and with the same potential for thoughts and thinking processes as the human brain? RESPONSE The question of whether we can create a digitally based system capable of matching the human brain's complexity and its capacity for thoughts and thinking processes is a profound and multifaceted one. It involves considerations from neuroscience, computer science, philosophy, and ethics. Here are some key points to consider: 1. Complexity of the Human Brain: The human brain contains approximately 86 billion neurons interconnected by hundreds of trillions of synapses. This intricate network supports not only raw computational power but also consciousness, subjective experience, emotions, and self-awareness. Replicating this level of complexity in a digital system is an enormous challenge. 2. Current Technological Capabilities: While advances in artificial intelligence (AI), machine learning, and neuromorphic computing have led to systems that can perform specific tasks—such as image recognition, language processing, and strategic games—they do not yet exhibit general intelligence comparable to humans. These systems lack true understanding, consciousness, and the depth of human thought. 3. Modeling the Brain: Efforts like brain simulation projects aim to create detailed models of brain activity. However, simulating every neuron and synapse at the level of biological detail requires immense computational resources and a complete understanding of how the brain's structure relates to its function—knowledge we do not yet possess. 4. Potential for Analogous Systems: Theoretically, a sufficiently advanced digital or neuromorphic system could approximate certain aspects of human cognition. This might include pattern recognition, learning, problem-solving, and even some forms of reasoning. However, whether such a system could truly replicate conscious experience or subjective thought remains an open question. 5. Philosophical and Ethical Considerations: Even if technologically feasible, creating a system with human-like thoughts raises questions about consciousness, personhood, and moral status. Would such a system possess rights? What does it mean to "think" or "be conscious" in a digital medium? 6. Realistic Outlook: Given current understanding and technology, achieving a system that fully emulates human thought and consciousness is not imminent. It might be possible in the distant future, but significant scientific breakthroughs are necessary. Moreover, some experts argue that certain aspects of human cognition—such as subjective experience—may be inherently biological and difficult or impossible to replicate digitally. In summary, while creating a digital system with some functional parallels to human cognition is within the realm of possibility in the future, replicating the full depth, nuance, and consciousness of human thought remains an open and highly complex challenge. Progress will depend not only on technological advancements but also on deeper scientific understanding and philosophical considerations. PROMPT: Given that the typical human brain contains approximately 86 billion neurons, and that the total number of synapses in the human brain is estimated to be on the order of 100 to 500 trillion, what would be required to build a computer with similar attributes? RESPONSE To approximate a computer with similar neural complexity to the human brain, we need to consider both the number of processing units (analogous to neurons) and the connections between them (analogous to synapses). Here's a breakdown: 1. Neurons (Processing Units): - Human brain: ~86 billion neurons - To match this, a computer would need approximately 86 billion processing elements (PEs). 2. Synapses (Connections): - Human brain: ~100 to 500 trillion synapses - Each synapse can be thought of as a connection weight, enabling communication between neurons. --- What Would Be Required?a. Hardware Architecture:- Massive Parallelism: The computer must support an extremely large-scale parallel architecture, with on the order of 86 billion interconnected PEs. - Memory and Storage: Each connection (synapse) has a weight (strength). Storing ~100-500 trillion weights would require: - Memory Capacity: - For 1 trillion synapses, storing a single 16-bit weight requires about 2 terabytes. - For 100 trillion synapses, it would require roughly 200,000 terabytes (~200 petabytes) of memory, which is enormous by current standards. b. Connectivity: - Connectivity Density: - The brain’s neurons are highly interconnected, with each neuron connecting to thousands of others. - Achieving similar density in hardware would mean designing an interconnection network capable of supporting trillions of links efficiently. c. Power and Fabrication: - Energy Efficiency: - The human brain operates at roughly 20 watts. - Building a device of this scale would require innovations in low-power electronics. - Manufacturing Challenges: - Fabricating billions of interconnected processing units with trillions of connections exceeds current chip fabrication capabilities. - Novel 3D integration and neuromorphic hardware technologies would be necessary. d. Software and Algorithms: - Neuroscientific Models: - To effectively utilize such hardware, advanced algorithms that mimic synaptic plasticity, learning, and adaptation would be essential. --- SummaryTo build a computer with attributes similar to the human brain, you would need:- ~86 billion processing units with high interconnectivity - ~100 to 500 trillion synaptic connections with adjustable weights - Advanced interconnection networks capable of supporting dense, scalable connectivity - Massive, energy-efficient memory storage for synaptic weights - Innovative fabrication techniques to produce such complex hardware at scale - Sophisticated algorithms to manage learning and information processing In essence, achieving a human-brain-level computational architecture remains a formidable challenge, requiring breakthroughs across hardware, materials science, and computational neuroscience. PROMPT: Given that to build a computer with attributes similar to the human brain, you would need 86 billion processing units with high interconnectivity, ~100 to 500 trillion synaptic connections with adjustable weights, advanced interconnection networks capable of supporting dense, scalable connectivity, massive, energy-efficient memory storage for synaptic weights, Innovative fabrication techniques to produce such complex hardware at scale, and sophisticated algorithms to manage learning and information processing, how long do you estimate it would take to achieve these goals? RESPONSE Estimating the timeline to build a computer with attributes comparable to the human brain involves numerous uncertainties and depends on technological, scientific, and engineering advancements. Based on current trends and projected developments, here's a comprehensive assessment: 1. Scaling Processing Units to 86 Billion Neurons: - *Current State:* Neuromorphic hardware prototypes have demonstrated tens of thousands to millions of neurons, but scaling to tens of billions remains a significant challenge. - *Projection:* With continued progress in fabrication, modular design, and integration techniques, achieving this scale could take 10–20 years. 2. Implementing 100–500 Trillion Synaptic Connections with Adjustable Weights: - *Current State:* Existing neuromorphic systems and large-scale neural network accelerators have billions to trillions of connections, but dense, energy-efficient, and fully programmable synapses at this scale are still in development. - *Projection:* Advances in memristor and other non-volatile memory technologies may accelerate this, but reaching full-scale dense connectivity could take 15–30 years. 3. Advanced Interconnection Networks for Dense Connectivity: - *Current State:* Scalable, low-latency interconnects are under active research, but integrating them at this scale remains complex. - *Projection:* Significant breakthroughs in network-on-chip architectures and 3D integration could reduce this timeframe to 10–20 years. 4. Massive, Energy-efficient Memory Storage for Synaptic Weights: - *Current State:* Emerging memory technologies like resistive RAM, phase-change memory, and spintronic devices are promising but not yet at the required scale and reliability. - *Projection:* Widespread adoption and manufacturing maturity might be achieved in 10–20 years. 5. Innovative Fabrication Techniques for Complex Hardware at Scale: - *Current State:* Fabrication of complex neuromorphic systems is still in early stages; techniques like monolithic 3D integration are promising. - *Projection:* Breakthroughs could happen over the next 10–25 years. 6. Sophisticated Algorithms for Learning and Processing: - *Current State:* Machine learning algorithms continue to evolve rapidly, with ongoing research into biologically inspired learning rules and scalable training methods. - *Projection:* Such algorithms could be mature and efficient within 10–15 years. --- Overall EstimateConsidering these overlapping timelines and the need for integration of all components:- Optimistic Scenario: ~10–15 years with accelerated research and breakthroughs. - Moderate Scenario: ~20–30 years, aligning with typical technological development cycles. - Conservative Scenario: 30+ years, accounting for unforeseen scientific or engineering challenges. Final ThoughtsWhile rapid progress is possible, achieving a system truly comparable to the human brain in processing capacity, connectivity, and energy efficiency will likely be a multi-decade endeavor, spanning 20 to 30 years or more, depending on technological breakthroughs, funding, and interdisciplinary collaboration.--- Disclaimer: These estimates are speculative and subject to change based on future scientific discoveries, engineering innovations, and unforeseen challenges. PROMPT: What in the human brain is analogous to software in a computer that allows the brain to process the information it receives within the network of neurons extent? RESPONSE In the human brain, the structure most analogous to software in a computer is the brain's neural circuits and the underlying neural coding mechanisms—specifically, the patterns of electrical and chemical activity that encode, process, and transmit information within the network of neurons. Here's a more detailed explanation: - Neural Circuits and Networks: These are the interconnected groups of neurons that process specific types of information, similar to how software modules handle particular tasks in a computer. - Neural Coding: The way neurons represent information through patterns of action potentials (spikes), frequency, timing, and synchronization. This is akin to the data and instructions that software uses to operate. - Synaptic Plasticity: The ability of synapses to strengthen or weaken over time (learning and memory) is comparable to software updates or algorithms that adapt and improve performance. - Neural Algorithms: The brain employs various processing "rules" and "programs"—like pattern recognition, prediction, and decision-making algorithms—that are encoded in the way neural circuits are organized and how they change. In essence, while the brain doesn't have "software" in the digital sense, the dynamic patterns of neural activity, their organization, and their plasticity serve a similar role: they govern how the brain interprets incoming sensory information, makes decisions, and produces behavior. These processes form the "software" layer that runs on the biological hardware of the nervous system. PROMPT: (I repeated what the chatbot had said, in essence I said, "Given the above, then how many man-years would you estimate necessary to program a digital computer with the complexity of the human brain? Here is what I actually wrote. Skip it if you like: Given that neural circuits and networks are the interconnected groups of neurons that process specific types of information, similar to how software modules handle particular tasks in a computer, and given that neural coding is the way neurons represent information through patterns of action potentials (spikes), frequency, timing, and synchronization, and given that synaptic plasticity is the ability of synapses to strengthen or weaken over time (learning and memory) and given that neural algorithms are what the brain employs as various processing "rules" and "programs"—like pattern recognition, prediction, and decision-making algorithms—that are encoded in the way neural circuits are organized and how they change, and given that the brain doesn't have "software" in the digital sense, but instead the dynamic patterns of neural activity, their organization, and their plasticity serve a similar role as they govern how the brain interprets incoming sensory information, makes decisions, and produces behavior, then how many man-years would you estimate necessary to program a digital computer with the complexity of the human brain? RESPONSE Estimating the number of man-years required to program a digital computer to emulate the full complexity of the human brain involves considering several factors: 1. Complexity of the Human Brain: - Approximate number of neurons: 86 billion - Synapses: around 100 trillion - Dynamic, plastic, and context-dependent neural activity - Rich neural coding schemes involving timing, synchronization, and modulation 2. Current State of Brain Modeling and AI: - Projects like the Human Brain Project or Blue Brain aim to simulate brain regions but are limited in scope and scale - AI systems (e.g., deep learning) are inspired by neural principles but are far simpler and less dynamic 3. Challenges of Emulating Neural Functionality: - Capturing the organization of neural circuits and their plasticity - Reproducing neural coding schemes (spike timing, frequency, synchronization) - Implementing adaptive learning and decision-making algorithms at the scale and fidelity of biological systems 4. Estimating Effort Based on Analogies: - Developing a detailed, functional, and scalable model of the human brain would involve interdisciplinary expertise—neuroscience, computer science, engineering, cognitive science, etc. - Given individual projects (like BrainScaleS, or large-scale neural simulations) take many person-years, scaling up to a full human brain model would multiply that effort substantially A rough, heuristic estimate: - Small-scale neural simulations (e.g., simulating a single cortical column or a small neural circuit) may take a few person-years - Mid-scale projects (simulating entire brain regions or simplified models) have historically taken decades of combined effort - Full-scale, detailed brain emulation—including all neural circuits, plasticity, and coding—could require on the order of hundreds to a few thousand man-years of dedicated, interdisciplinary effort Final estimate: > Approximately 1,000 to 3,000 man-years of focused, collaborative research and development effort might be needed to program a digital computer to emulate the complexity of the human brain at a comparable level of detail and functionality. Note: This is a very rough estimate based on current technological and scientific capabilities. Advances in neuroscience, computing hardware, and AI algorithms could significantly reduce or increase this estimate in the future. Well, I still believe that it is extremely unlikely that we will ever construct a "digital brain" that can perform all the tasks that a human brain does, and even less likely one that could outperform a human brain. But who knows what will happen in 100, 200, or 1,000 years? I don't, and neither does any other human alive today. TL;DR Don't worry about AI taking over. Worry instead about rogue states/rogue programmers creating AI systems that could allow them to take virtually (pun intended) total control of essential systems we depend on and then, possibly, holding the human race to ransom. Or else we give control to AI for managing some complex system or other, but we miss something important, and the whole system comes crashing down around us. And it could take a LONG time to recover from that... Anybody with me? What do you think? M. "What do you think?" he asks, knowing full well that most of the above is totally unintelligible to the average poster here. Why, why do I bother?... |