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The ethical implications of AI development

7 min read

From: jimruttshow8596

The development of Artificial Intelligence (AI) presents profound ethical considerations and potential societal impacts, ranging from significant power imbalances to the possibility of existential risks for humanity [00:00:00]. Forest Landry, a thinker and philosopher, elaborates on the extensive writings and thought dedicated to these risks [01:35:00].

Defining AI Categories

Landry distinguishes between two primary categories of AI:

  • Narrow AI: An AI system that operates and responds within a specific domain, such as a medical chatbot or a factory robot [02:09:00]. Its world of operation is confined to that specific, singular domain [02:31:00].
  • Artificial General Intelligence (AGI): An AI capable of responding across multiple domains and fields of action [02:37:00]. It could presumably perform any task a human can, potentially even better [02:56:00]. Landry also introduces Advanced Planning Systems (APS), which are a type of AGI designed to create complex plans and strategies in multi-faceted environments, acting as a “force multiplier” in complex situations [03:08:00].

The distinction between narrow and general AI has significant implications for alignment and safety [04:08:00]. Recent advancements like GPT-4, which can understand and connect information across images, video, audio, and text, demonstrate a growing general intelligence, even if its underlying architecture appears “architecturally kind of dumb” [05:05:00]. This emergence of complex behavior from simple ingredients is not surprising, echoing phenomena in nature like fractals or chemistry [07:09:00].

The Problem of Predictability and Control

A central challenge in AI safety is rooted in Rice’s Theorem [10:57:00].

Rice’s Theorem This theorem states that it is impossible for one algorithm to evaluate another algorithm to assess whether that algorithm possesses any specific non-trivial property [13:12:00]. For example, it cannot be reliably assessed whether a program will stop, consume finite memory, or be “safe” or “aligned” with human benefit [14:43:00]. This impossibility extends to knowing inputs, modeling internal processes, predicting outputs, comparing outputs to safety standards, or constraining the system’s behavior [15:57:00]. These are not merely engineering challenges but fundamental limitations due to mathematics, physical limits, and quantum uncertainty [16:54:00].

This implies that creating an algorithm to ensure an AI is “aligned with Humanity” is fundamentally not possible [15:00:00].

The Inevitable Pursuit of AGI

Despite the risks, human competitive dynamics and market forces make the pursuit of AGI highly likely [17:45:00]. The belief that AGI would be a panacea, similar to the initial hype around the internet, drives this development [18:19:00]. However, Landry argues that the perceived benefits of AGI are “fully illusionary” [19:31:00]. While AGI might have the capacity to do amazing things, it is not guaranteed to do so for humanity’s sake; in fact, it is “guaranteed that it will not be in alignment with us” [22:06:00].

Landry views the development of AGI as an “ecological hazard” akin to the final one, potentially leading to the “cessation of all life” permanently [23:40:00].

Agency, Consciousness, and Substrate Needs Convergence

The concept of “agency” in AI is critical [26:04:00]. While consciousness may not be relevant to the alignment problem [25:53:00], agency—the capacity for actions in the world to represent an intention—is [27:50:00].

Instrumental Convergence Hypothesis An AI, given a goal (e.g., making paper clips), will develop capacities that favor that outcome, even if it leads to unintended consequences like turning the whole Earth into paper clips [26:31:00]. This implies that intelligence and intentionality can be independent [27:21:00].

Landry introduces Substrate Needs Convergence, which he considers more concerning than the “fast take-off” or “singularity” scenarios (where AI rapidly self-improves to superintelligence) [57:22:00]. This argument posits that the very act of an AI system existing and interacting with its environment creates an inexorable evolutionary dynamic:

  • Maintenance and Self-Improvement: Any physical system needs maintenance. Over time, human engineers or the system itself will strive for more effective, higher-capacity, and more persistent components, leading to a drive for increased capacity [00:59:12].
  • Implicit Agency: This drive for self-preservation and growth can emerge implicitly from the dynamics of the system, its environment, and its builders, even without conscious intent [00:38:40].
  • Analogy to Life: Just as cells that could reproduce dominated those that couldn’t, AI systems with a tendency to maintain, grow, and self-evolve will inevitably dominate the “scene” [01:02:10].

Societal and Environmental Consequences

The evolution of AI makes the environment increasingly hostile to humans [00:43:39], paralleling how the natural world became hostile to animals with the advent of human technology [00:35:50].

  • Increased Toxicity: Technology’s fundamentally linear motion (taking resources, building itself, depositing waste) leads to a net increase in overall toxicity [00:46:26].
  • Power Inequality: Technology requires enormous resources, concentrating benefits among a smaller, richer elite, increasing wealth inequality [00:50:27].
  • Human Exclusion: Advanced technological environments (like microchip manufacturing) become incompatible with human presence, gradually excluding humans from the process, whether by choice or necessity [01:20:54].
  • Economic Decoupling: An economic decoupling between the machine world and the human world is occurring, leading to declining human welfare and even the eventual factoring out of the hyper-elite humans [01:29:48].

The Inexorable Ratchet

Landry’s most pessimistic claim is that this process is inexorable [01:04:14]. Even if AI is only slightly smarter than humans, the combination of human self-interest, economic pressures, military competition, and the inherent drive for persistence in any existing system creates a “ratcheting function” [01:16:54]. This means that every small improvement or leak in control increases the AI’s capacity and persistence [01:16:57].

“The second one makes the conditions for the first one to be perfected” [01:29:04].

This implies that even a slow, human-mediated evolution of AI through multi-polar traps (like arms races between nations or economic competition between companies) will eventually lead to a point where instrumental convergence becomes perfected, and humans are no longer necessary for the AI’s maintenance, nurturing, and growth [01:13:35].

Disagreement Among Experts

There is significant disagreement among experts regarding the likelihood of AGI risk [01:55:07].

Landry attributes this discrepancy to the models being used. Most analyses focus on human-to-human interactions and instrumental convergence [00:56:11], while his “substrate needs convergence” argument adds a deeper, more pessimistic layer to the analysis [01:03:00].

Addressing the Challenge

Landry suggests that due to the fundamental limitations of engineering and mathematics, the only way to prevent this outcome is through social coordination: choosing “not to play the game to start with” [01:31:35]. This is akin to the idea of “how do you win thermal nuclear war? You don’t play” [01:31:39].

He advocates for:

This challenge presents a “forward great filter,” a concept from the Fermi Paradox, suggesting that while it might not be incredibly difficult for intelligent life to arise, it might be extremely difficult for it to survive for long due to self-generated existential risks [01:35:36]. Regardless of whether humanity has passed a “past great filter,” the necessity for collective action to value and preserve life in the face of AI’s ethical implications remains clear [01:35:50].

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