Source URL: https://www.theregister.com/2024/08/30/ai_language_cognition_research/
Source: Hacker News
Title: Have we stopped to think about what LLMs model?
Feedly Summary: Comments
AI Summary and Description: Yes
**Summary:** The text discusses insights from the paper “Large Models of What?” that critiques the exaggerations surrounding large language models (LLMs) and their supposed capabilities in understanding human language. It emphasizes the fundamental differences between LLMs and human linguistic behavior, detailing the implications of misrepresenting LLMs as entities capable of human-like comprehension. This analysis is crucial for those monitoring the security, compliance, and ethical aspects of AI deployment, particularly in high-stakes sectors like healthcare and education.
**Detailed Description:**
The text dives deep into the criticisms of large language models (LLMs), highlighting essential insights from a peer-reviewed paper by Abeba Birhane and Marek McGann. Here are the major points discussed:
– **Sam Altman’s Investment Focus:** The CEO of OpenAI expressed a willingness to incur high costs (potentially billions) to achieve artificial general intelligence (AGI), showcasing the unrestrained ambition in the pursuit of advanced AI.
– **Exaggeration of AI Capabilities:** The paper argues that the industry is misusing terms associated with human linguistic abilities, which could misguide policymakers and regulators. Key concerns include:
– Misclassification of LLMs as capable of true language understanding.
– The risks of treating complex engineering achievements as equivalents to human cognitive capabilities.
– **Assumptions of LLMs:**
– **Language Completeness:** Assumes a stable, quantifiable language exists that can be fully replicated.
– **Data Completeness:** Suggests all essential aspects of language are present in training data, which is misleading.
– **Language as Behavior:** The authors emphasize a modern view of language as behavior intertwined with social interactions, highlighting that factors like tone, context, and non-verbal cues enrich communication—elements that LLMs inherently lack.
– **Social Interaction and Risks:** LLMs do not engage in social contexts or experience consequences, contrasting sharply with human language use, which is inherently precarious and rich in meaning derived from shared experiences.
– **Call for Caution in Deployment:** Birhane urges a cautious approach toward deploying LLMs in sensitive areas, stressing the absence of rigorous testing in contrast to industries like pharmaceuticals or civil engineering.
– **Critique of Corporate Assertions:** The paper suggests that claims regarding the usefulness of LLMs have been overstated, presenting examples from various domains (e.g., legal, medical) where LLM reliability is questioned.
– **Gartner’s Hype Cycle Reference:** The text mentions Gartner’s identification of Generative AI nearing the “trough of disillusionment,” suggesting a reevaluation of expectations surrounding the technology.
This critical examination serves as a cautionary note for AI practitioners, security and compliance professionals involved in AI-related projects, urging them to consider the significant implications of misinterpreting LLM capabilities, particularly regarding privacy, ethical use, and compliance standards. It highlights the necessity for a more nuanced understanding of AI technologies to prevent oversights in governance and regulatory frameworks.