TECH-EXTRA: There Is No Finish Line.
Dr. Seth Dobrin
AI Consultant ?? Globally Recognized Leader | VC | Speaker | Entrepreneur | Formerly IBM’s First Ever Global Chief AI Officer | ?? Geneticist | ???? Golden Visa Holder
Measuring Artificial General Intelligence (AGI). The Abstraction and Reasoning Corpus (ARC) is insufficient.
This is the first Tech Extra - Silicon Sands News, a in depth explanation of the challenges facing innovation and investments in the area of Artificial intelligence written for leaders across all industries. Silicon Sands News, read across all 50 states in the US and 96 countries. Join us as we chart the course towards a future where AI is not just a tool but a partner in creating a better world for all. We want to hear from you.
TL;DR
The article critiques current AI benchmarking practices, arguing that they focus too narrowly on technical metrics like model size, training data volume, and computational resources rather than genuinely measuring intelligence or progress toward Artificial General Intelligence (AGI). Using OpenAI's GPT o1 as an example, the author expresses disappointment that AI models often excel on contrived tests that may be part of their training data rather than demonstrating true reasoning or generalization capabilities.
The article underscores the urgent need for more robust and comprehensive benchmarks that align with human measures of intelligence. It discusses various aspects of human cognition—such as communication, reasoning, learning efficiency, perception, emotional intelligence, ethical reasoning, and collaboration—that should be incorporated into AI evaluation metrics. While current benchmarks like the Abstraction and Reasoning Corpus (ARC) are seen as steps in the right direction, they are deemed insufficient. The article advocates for developing new, community-driven benchmarks that better capture the complexities of human intelligence as a means to guide responsible advancement toward true AGI.
Introduction
I started writing this article before OpenAI released the GPT o1 preview. The intent was to discuss all the benchmarks being used, what they measure and do not measure, and how we should measure our journey to artificial general intelligence (AGI). As I started to write the article, I realized a broad gap in the industry's consistency of the various metrics: what they do measure, what they don’t measure, what the objective metrics for AGI are, and what measures of human intelligence are. And finally, how they all line up.
Then, the release of GPT o1—for this exercise—could not have been more perfect! I immediately began to use it and was very impressed with some aspects but disappointed with others. Perhaps the most disappointing aspect was that they ran away from the gold-standard measures of AGI. Instead, they made up tests. Yes, they used some published tests, but chances are those tests were in the corpus of data used to train GPT o1.
When OpenAI released this preview of its newest generative AI system, GPT o1. They have claimed this is a milestone on the path to AGI. This release has sparked us to explore how the industry is measuring the race to AGI and look at some of the claims around GPT o1. The preview of GPT o1 was released with a technical paper titled “Learning to Reason with LLMs," detailing the testing behind some of the claims. Claiming a transformer-based language model can reason is bold and creates headlines, allowing for assumptions about the breadth of reasoning.
Because of attention-grabbing headlines like these, the need for robust, comprehensive benchmarks has only increased. This edition of Silicon Sands News TECH-EXTRA goes deep into the benchmarks and metrics used to evaluate generative AI models, their significance, and the ongoing debates surrounding their use. We will explore performance metrics, model architecture benchmarks, training data considerations, and emerging trends in AI evaluation.
Level-setting on AI System Benchmarks
AI system benchmarks— are standardized evaluation frameworks designed to assess performance, capabilities, and limitations. They are valuable in developing, comparing, and refining these systems as we move towards AGI. These benchmarks attempt to provide a quantifiable understanding of how well these systems can perform specific tasks or generate certain content or tasks.
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1 个月Dr. Seth Dobrin, thank you very much for sharing!