Many companies utilizing AI struggle to provide real value. There is mounting pressure from investors for a return on these AI investments. Focusing on smaller, task-specific models can create more effective solutions, requiring fewer resources compared to large overarching models. The expenditure on AI technologies is immense, and smaller AI applications can promote safety and efficiency in the industry. While large AI models have captured attention, their performance does not guarantee increased productivity, making focused AI a more viable and cost-effective option.
Instead of building epic models that aim to accomplish all feats, businesses looking to cash in on the AI gold rush should consider pivoting towards focused models that are designed for specific tasks.
Models like OpenAI's GPT-4, Google's Gemini, Mistral AI's Mistral, Meta's Llama 3, or Anthropic's Claude cost a fortune to build, and when we look at how they perform, it's not clear why most businesses would want to get into that game to begin with.
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