Source URL: https://cacm.acm.org/opinion/not-on-the-best-path/
Source: Hacker News
Title: Gary Marcus discusses AI’s technical problems
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: In this conversation featuring cognitive scientist Gary Marcus, key technical critiques of generative artificial intelligence and Large Language Models (LLMs) are discussed. Marcus argues that LLMs excel in interpolating data but struggle with extrapolation, which raises concerns about their moral and ethical implications, including bias and unreliability in understanding context.
Detailed Description: The text covers several critical points regarding the limitations and ethical risks associated with generative AI and LLMs, as expressed by Gary Marcus:
– **Skepticism Towards Current AI Models**: Marcus highlights that LLMs may be proficient at processing language but lack true understanding, resulting in significant shortcomings in application and reasoning.
– **Interpolation vs. Extrapolation**: Marcus points out the inability of current neural networks to generalize beyond their training sets, emphasizing that they can accurately predict outcomes within familiar domains but fail miserably outside of them.
– **Philosophical Considerations**: He discusses the distinction between “intention” (abstract meanings) and “extension” (specific examples), noting that neural networks only operate at the extensional level.
– **Real-World Consequences**: The critique extends to the broader implications of AI misunderstandings, leading to incorrect responses and perpetuation of biases since these models are trained on flawed real-world data.
– **Call for Integration**: To address these ethical issues and improve AI capabilities, Marcus advocates for combining neural networks with classical AI methodologies, akin to human cognitive processes.
– **Diminishing Returns Perspective**: He expresses his belief that the field might be reaching diminishing returns with LLM developments, indicating a shift might be necessary and highlighting the increasing recognition of these challenges in the industry.
– **Proposed Benchmarks**: Marcus mentions the need for better benchmarks to assess understanding in AI, such as evaluating comprehension of complex narratives, something current AI can’t yet achieve adequately.
This dialogue reinforces the significance of addressing both the technical shortcomings and the ethical considerations in AI development, stressing the need for innovative approaches beyond the current reliance on LLMs. The insights presented are crucial for security and compliance professionals as they highlight the potential for biases and inaccuracies in AI systems that can affect data integrity and organizational governance.