Ken Arciga’s Post

The narrative around AI is often filled with tales of bigger and more complex models. But is 'bigger' truly 'better'? 🤔 In the world of AI, efficiency can often outshine sheer size. Smaller, efficiently trained models are proving that using less, but higher-quality data, can produce results on par—or sometimes superior—to their larger counterparts. This not only cuts computing costs but also curbs energy consumption, a crucial factor as AI expands its reach. 🔋💡 Exciting innovations, such as OpenAI's strides in complex reasoning and compact open-source AI models, challenge the notion that more is always better. These developments highlight a shift towards efficiency and effectiveness over grandeur. 🚀 Think about the ethical and practical implications too. As AI becomes a fixture in daily life, addressing issues like bias, transparency, and privacy becomes imperative. Balancing technological prowess with societal needs is essential for sustainable progress. ⚖️ So, what's your take? Do we focus on refining what we have or pushing the limits further? Share your thoughts! 👇 #AI #MachineLearning #Innovation #Sustainability #Efficiency 

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Alex Emilcar

Sr. Solutions Architect, Generative AI Innovation Center at Amazon Web Services (AWS)

4w

Both. Push the limits to see what is possible. Refine models to make them more economical.

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