From: jimruttshow8596
The field of Artificial General Intelligence (AGI), a term coined by Ben Goertzel [00:00:48], refers to computer systems capable of intelligent behavior beyond their specific programming or training, demonstrating a capacity for generalization [02:01:59]. This contrasts sharply with narrow AI, which excels at highly particular tasks based on programming or data-driven training [02:37:05]. While narrow AI has seen significant practical success, it often fails to generalize effectively to new or slightly different situations [03:38:23]. The pursuit of AGI is increasingly accepted as feasible within our lifetimes, though a consensus on the most viable route to achieve it has not been reached [06:01:46].
Many in the field, including Ben Goertzel, believe that current Deep Neural Networks (DNNs) and other mainstream Machine Learning (ML) algorithms are fundamentally unsuited for creating human-level AGI [00:13:04]. These systems often operate as “very large lookup tables” that recognize shallow, surface-level patterns in data, rather than building deep, abstract models of reality [01:46:27]. The ability to find concise abstractions of experience is crucial for generalization, and this is largely missing in current DNNs [02:20:00].
Instead, Ben Goertzel proposes three viable paths to true AGI that emphasize interdisciplinary methods [00:17:11]:
Cognitive Level Approach: Hybrid Neural-Symbolic, Evolutionary, Metagraph-Based AGI
This approach, exemplified by OpenCog, is inspired by cognitive science and optimized by advanced mathematics [00:32:36]. It aims to emulate high-level human cognitive functions using various computer science algorithms, rather than strictly adhering to biological emulation [00:33:30].
Key aspects include:
- Diverse Function Emulation: Breaking down human cognitive functions (perception, action, planning, working memory, long-term memory, social reasoning) and developing specific computer science algorithms for each [00:35:06].
- Cognitive Synergy: The critical challenge is to connect these diverse components so they can assist each other at a deep level, with transparency into each other’s internal processing [00:36:32].
- Knowledge Representation: A large, distributed hypergraph (or metagraph) serves as a common knowledge base, where different AI algorithms interact [00:38:01].
- Modern Logic and Learning: Unlike “good old-fashioned AI” which used crisp logic and lacked learning, this approach incorporates fuzzy, probabilistic, paraconsistent, and intuitionist logics to handle uncertainty, and does not rely on hand-coding common sense knowledge [00:43:08].
- Implicit and Explicit Evolution:
- Implicit: The system’s attention allocation and logical reasoning processes, which select and combine knowledge chunks based on “fitness” (importance), mathematically resemble population genetics [00:47:48]. This means a distributed system with parallel operations and selection of best results inherently has an evolutionary aspect [00:49:17].
- Explicit: Genetic algorithms are used for tasks like procedure learning and creativity, such as evolving new logical predicates [00:49:33].
This approach demonstrates interdisciplinary integration by combining symbolic logic, neural network-like pattern recognition (via attention mechanisms), and evolutionary principles within a unified framework.
Brain Level Approach: Large-Scale Non-Linear Dynamical Brain Simulation
This path involves simulating the human brain at a more detailed, non-linear dynamical level, moving beyond the simplified models of deep neural networks [00:51:25].
Challenges and Interdisciplinary Needs:
- Measurement Limitations: Current brain imaging instruments are insufficient to understand deep neural processes like abstraction formation or how chaotic attractors are driven in the cortex [00:52:48]. A revolution in brain imaging or direct neural activity measurement is needed [00:53:50].
- Biological Realism: Simulating beyond just neurons to include glia, astrocytes, cellular diffusion, and even potential “wet quantum biology” is crucial [00:54:11].
- Neuromorphic Computing: While current neuromorphic chips focus on spiking neural networks, specialized hardware is needed for more complex chaotic neuron models [01:04:37]. This also ties into efforts in creating a scalable infrastructure for AGI.
- Learning Algorithms: Newer learning mechanisms, such as Alex Aurobia’s predictive coding-based learning, which works with more biologically realistic neurons (e.g., integrate and fire with sub-threshold spreading of activation), are essential for better generalization and more compact neural networks [00:57:41]. These can potentially bridge the gap between biologically inspired neural nets and symbolic OpenCog systems [01:00:00].
- Specialized Hardware: The emergence of specialized chip architectures beyond GPUs, designed for specific AI algorithms like OpenCog’s pattern matching or chaotic neurons, will be crucial for scaling up brain simulations [01:03:59]. This includes MIMD parallel processors and RAM-embedded architectures [01:05:29].
This approach highlights the critical interplay between computer science, neuroscience, and hardware engineering.
Chemistry Level Approach: Massively Distributed AI Optimized Artificial Chemistry Simulation
This highly speculative but intriguing approach draws inspiration from artificial life and biochemistry, simulating the emergence of complex systems from a chemical “soup” [01:15:51].
Key concepts include:
- Algorithmic Chemistry: Abstracting the principles of chemistry, where “little programs” (codelets) interact to produce new programs in complex reaction chains [01:19:00]. Early work by Walter Fontana explored this [01:18:43].
- Dual Evolution (Evo-Devo): The idea of co-evolving the underlying “chemistry” or gene expression machinery along with the resulting “phenotype” (intelligence) [01:20:21]. This acknowledges that the physical substrate and its emergent properties are intertwined.
- Computational Challenges: Simulating detailed, realistic chemistry or even highly abstracted algorithmic chemistry requires massive compute resources due to the inherent parallelism of chemical processes [01:26:21].
- AI-Guided Evolution: Incorporating an AI “observer” or “pattern miner” to study the evolving chemical soup, identify promising areas, and direct the evolution through techniques like mutation, crossover, or probabilistic analysis [01:29:08]. This creates a hybrid architecture where AGI aids in its own creation [01:31:17].
- Decentralized Platforms: Leveraging decentralized computing platforms like SingularityNET’s NuNet could allow millions of individuals to contribute processing power to run virtual algorithmic chemistry simulations, fostering a grassroots research effort [01:31:53].
This approach combines computer science, biochemistry, evolutionary computing, and distributed systems design.
Significance of Interdisciplinary Approaches
The journey towards AGI is inherently interdisciplinary because human-level intelligence itself is multifaceted, involving perception, reasoning, learning, creativity, and social interaction, all grounded in a complex biological substrate [00:35:06]. Relying solely on one narrow paradigm, such as the current focus on deep neural networks, overlooks critical aspects of intelligence [00:13:04].
For instance, the cognitive synergy sought in the cognitive level approach necessitates integrating different computational paradigms (neural, symbolic, evolutionary) that traditionally exist in separate research silos [00:36:32]. Similarly, the brain and chemistry level approaches require deep collaboration between computer scientists, neuroscientists, biologists, physicists, and hardware engineers to bridge the gap between theoretical models and practical implementation [00:52:48].
Funding and Future Directions
Despite the promise of these diverse approaches, AGI research remains on the “margins” of the AI field, largely due to commercial interests prioritizing short-term financial returns from narrow AI applications [03:00:59]. There is a need for greater societal investment in a diverse “portfolio of bets” on less mainstream AGI approaches [01:39:42].
Such funding could come from:
- Government Initiatives: Like the NIH in biology or DARPA in defense, governments could establish large-scale, integrated projects for holistic brain modeling or other AGI pursuits [01:45:31].
- Cultural Shift: A grassroots movement, similar to open-source software development, could emerge if more people recognize the viability and importance of AGI research [01:47:01].
Ultimately, the first path to AGI may incorporate ideas inspired by all three approaches, leading to a highly integrated, hybrid system [01:38:10]. This underscores the profound cognitive synergy that comes from interdisciplinary thinking and experimentation.