Summary Getting from Generative AI to Trustworthy AI arxiv.org
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Generative AI, the prevailing method in AI, is deficient in reasoning and trustworthiness, while trustworthy AI should possess 16 capabilities, including the ability to provide explanations.
Slides
Slide Presentation (12 slides)
Key Points
- Generative AI, relying on large language models (LLMs), is the dominant approach in AI, but has limitations in reasoning and trustworthiness.
- A trustworthy AI should possess 16 capabilities, including providing explanations for its reasoning and performing deductions.
- AI should be able to communicate effectively and adapt to the level of ambiguity or vagueness in a conversation.
- Knowledge contextualization and reasoning within and across contexts are crucial for trustworthy AI.
- Cyc, a rule-based AI system, uses general rules to handle complex knowledge and prioritizes arguments using meta-level rules.
Summaries
28 word summary
Generative AI, powered by large language models (LLMs), is the dominant approach in AI, but lacks reasoning and trustworthiness. Trustworthy AI should have 16 capabilities, including providing explanations.
36 word summary
Generative AI, powered by large language models (LLMs), is currently the dominant approach in AI. However, LLMs have limitations in reasoning and trustworthiness. To create trustworthy AI, it should possess 16 capabilities including providing explanations for
495 word summary
Generative AI, which relies on large language models (LLMs), has become the dominant approach in artificial intelligence (AI). However, LLMs have limitations when it comes to reasoning and trustworthiness. They produce outputs that are plausible but not
A trustworthy general AI should possess 16 capabilities. Firstly, it should be able to provide explanations for its reasoning, including the sources of its evidence and knowledge. Secondly, it should be able to perform deductions, such as making inferences based on known
AI should be able to communicate in a way that is neither overly verbose nor too terse. It should be able to adapt to the level of ambiguity or vagueness in a conversation and adjust its responses accordingly. The AI should continuously update its model of
Knowledge contextualization and reasoning within and across contexts is important for trustworthy AI. Implicit elements of context in human communication can lead to conflation when training AI models. Understanding the use context is crucial, including inferring the purpose of a question and considering resource
A trustworthy AI should possess a broad and deep knowledge of the world, including common sense, models of various subjects, and the ability to access relevant information quickly. It should be able to explain and reason about its knowledge, deduce logical conclusions, analog
CycL, a language developed in the late 1980s, allows for the expression of Cyc assertions and rules using full first-order logic. It enables statements about other statements, functions, and the inference engine's actions. Cyc's reasoning mechanism
Logic-based AI systems, such as first-order logic engines, provide a different source of power compared to statistically-driven operations in large language models (LLMs). However, even logic-based AI can lead to errors, especially if the reasoning chain is long.
Cyc, an AI system, uses a general rule-based approach to handle complex knowledge. Rather than relying on a large number of specific facts, Cyc applies general rules to make inferences. In order to build its knowledge base efficiently, Cyc's developers
Cyc, an AI system, is often slow in returning answers, leading to the development of special-purpose reasoners for faster results. The Cyc team realized that there isn't just one representation for knowledge and built a large arsenal of redundant representations and reason
Cyc, an AI system, gathers pro- and con- arguments for different answers to a question and uses meta-level rules to determine which arguments to prioritize. Cyc's natural language understanding and generation capabilities are not as good as other AI models like Chat
The excerpt discusses the potential for combining generative AI systems with symbolic systems like Cyc to create trustworthy AI. The author suggests several ideas for integrating these systems. One idea is to use Cyc to translate sentences generated by a language model (LLM) into
The summary provides a list of references cited in the document "Getting from Generative AI to Trustworthy AI." The references include articles, books, and online sources that discuss various aspects of artificial intelligence (AI) and its challenges. Some key points mentioned