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OpenAI's newly released GPT-4.5 claims to be its most powerful model yet, but user feedback indicates it falls short of expectations. Despite improvements in everyday tasks and understanding, it's scrutinized for high costs and performance limitations compared to existing models. Benchmarks show marginal gains, leading to questions about the viability of such investments in pretraining. Ultimately, concerns arise around model accessibility, effectiveness across languages, and market competition, highlighting the need for more transparent, efficient AI development.
Base models are expensive and trained on trillions of tokens.
Neural Scaling Laws relate compute budget to model performance.
GPT-4.5 outperforms 4o in everyday and professional tasks.
Users express disappointment with 4.5's reasoning capabilities.
Comparative user experience highlighted issues with model selection.
OpenAI's release of GPT-4.5 illustrates the ongoing tension between advancing capabilities and ethical governance in AI. The model's high operational costs raise questions about accessibility and inclusivity in AI advancements. Ensuring that such substantial resources are invested wisely is crucial, especially given the diminishing returns observed in scaling laws. OpenAI must prioritize transparent methodologies and rigorous evaluations to maintain public trust as AI impacts various sectors.
The launch of GPT-4.5 indicates a shift in market dynamics as OpenAI introduces significant cost differentials in model pricing, impacting consumer choice. While claiming substantial improvements, the market reaction highlights user skepticism due to cost-to-performance ratios. Businesses may favor existing models, particularly for everyday tasks, suggesting a potential stagnation in market adoption of newer, expensive models unless they can demonstrate clear, economical advantages.
Models trained on large datasets for extensive pretraining, essential for performance.
A principle that outlines the correlation between compute power and model performance.
A machine learning technique to improve model reasoning abilities through feedback.
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Alex Kantrowitz 3month
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