AI is transforming industries across the board, but here’s the reality: off-the-shelf solutions like ChatGPT aren’t enough for businesses that demand real precision.
I agree, it would be nice to have task-specific models. And I'd use them with great pleasure- I don't need to ask for cooking recipes while development- it would be much more profitable if some small model helps me on my local machine without sending requests to the third-party providers
I think there are a few factors that are slowing down the development of bespoke AI solutions.
- Training and deployment complexity: Task-specific models will require separate training pipelines, datasets, and updates for each individual task. This can become expensive and complex compared to maintaining a single, larger model that can generalize across multiple domains.
- Data availability: For certain niche tasks, collecting enough high-quality data to effectively train small models can be challenging. However, general-purpose models benefit from large, diverse datasets, allowing them to apply contextual knowledge to many tasks.
- Performance tradeoffs: While small models may be more efficient in terms of inference speed, they often lack the depth of understanding and flexibility of larger models.
- Cost of multi-featured models: As long as these companies with frontier models raise enough money from investors and continue to operate at a loss, the era of omni-models will continue.
I think what we see currently is a search for some glass ceil. As a big research phase to find as many possibilities of LLMs as possible.
I agree, it would be nice to have task-specific models. And I'd use them with great pleasure- I don't need to ask for cooking recipes while development- it would be much more profitable if some small model helps me on my local machine without sending requests to the third-party providers
I think there are a few factors that are slowing down the development of bespoke AI solutions.
- Training and deployment complexity: Task-specific models will require separate training pipelines, datasets, and updates for each individual task. This can become expensive and complex compared to maintaining a single, larger model that can generalize across multiple domains.
- Data availability: For certain niche tasks, collecting enough high-quality data to effectively train small models can be challenging. However, general-purpose models benefit from large, diverse datasets, allowing them to apply contextual knowledge to many tasks.
- Performance tradeoffs: While small models may be more efficient in terms of inference speed, they often lack the depth of understanding and flexibility of larger models.
- Cost of multi-featured models: As long as these companies with frontier models raise enough money from investors and continue to operate at a loss, the era of omni-models will continue.
I think what we see currently is a search for some glass ceil. As a big research phase to find as many possibilities of LLMs as possible.