LLMs have causal power; they can be used to make decisions and control systems in the real world. LLMs can emulate agents, they can follow goals and generate goals to follow. LLMs can create a chain of thought reflect on it. LLMs generate and use abstractions. Perception is not
AGI
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Discriminator-Generator Gap: Key to AI Scientific Innovation
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Discriminator-generator gap seems to be the most important idea in AI for scientific innovation. With compute + clever search, anything that we can measure will be optimized. First up will be environments that can be verified quickly, with continuous reward, and at scale.
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Scaling AI Systems to AGI: Uncertainties and Empirical Research
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It is unclear whether today's AI systems can be scaled to AGI/ASI, but in most cases, it seems unscientific to make strong claims to the contrary. The practical and theoretical limitations of today's most successful approaches are unknown and subject to empirical research.
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Capability Denialists and Turing Machines: Cognitive Function Debate
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Capability denialists tend to claim that a class of Turing Machines or universal learning algorithms cannot perform brain-like functions because of properties they don't like (not symbolic enough, too symbolic, too digital, not recurrent enough, not embodied, not social).
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Denied AI Capabilities: Understanding Limitations and Constraints
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The denied capabilities tend to be informally specified (understanding, intentionality, reasoning, agency, creativity, selfhood, sentience, sapience, abstraction, common sense) but can also apply to specific tasks (categorizing an object as the Charles river, winning at Go).
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Epistemological Challenges When Denying AI Capabilities
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When capability of a class of Turing complete systems is denied ("a computer/perceptron/transformer can never do X") it poses interesting epistemological and metaphysical challenges, which are unfortunately rarely discussed ("how can anything do X without breaking physics?").
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Capability Denialism in AI: Key Thinkers and Skepticism
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Some call capability denialism 'garymarcusing', but Gary Marcus is by no means the inventor of the genre. Prominent denialists include Hubert Dreyfus, James Lighthill, John Searle, Stevan Harnad, Ned Block, Colin McGinn, Rolf Pfeiffer, Noam Chomsky, Roger Penrose, Emily Bender.
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Capability Denialism: Dismissing AI Performance Potential
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Capability denialism describes the claim that a given approach in Artificial Intelligence can never achieve human performance, often despite some evidence to the contrary. Capability denialism can take many forms, but it usually does not attempt any formal proof.
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Capability Denialists Reject AI Approach Fundamentally
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Capability denialists generally don't claim that the limitations of the AI system they criticize are due to capacity constraints that could be overcome (eg. with more memory, more compute, more data, algorithmic improvements), but that the entire approach is doomed.
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AGI Alpha: Unprecedented $15 Quadrillion Market Opportunity
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"AGI Alpha" : The Greatest Alpha Opportunity Ever Predicting the sectors AGI will revolutionize first is the key to unlocking a historic, unprecedented opportunity—capturing a share of a projected $15 Quadrillion shift. Join the frontier: http://
github.com/MontrealAI/AGI
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