ChatGPT has appreared in November in 2022 and almost a year elapsed. Till then machine learning was used for a specific task, say protection from frauds or approval of loans. Then came the LLMs, and the old approach took a back seat. The LLMs are generalized models capable of performing many tasks. Still the task-based models have not gone away and are still alive. Amazon’s CTO calls them the ‘good old-fashioned AI’, but still it solves many problems on hand.

Before the advent of the LLMs, we had a task specific world. These days, in enterprises, people plugin LLMs via APIs. LLMs will become better and more robust, and will develop power of reasoning and other emerging abilities.

Still, there is a role for task models, since they are smaller, faster, cheaper, and are performers suited to a particular task.

However, it makes no sense to train different specific task models when all purpose models are available for the enterprise.

Machine learning platforms are still a preserve of data scientists. It is not a preserve of developers. It is not necessary to give up these large number of machine learning models. The appearance of new technology still keeps the old one relevant for some time.

Before LLMs, the task models were the flavour of the day. The enterprises used the services of data scientists to build them. Data scientists will focus on data critically. They will help people understand the relationship between AI and data within the organisations.

Both AI and data has pros and cons. Both have relevance whether an LLM or a task model is being developed. The two will co-exist for some time to come, since bigger does not always mean better.