Notes on OpenAI's new o1 chain-of-thought models

Re:Notes on OpenAI's new o1 chain-of-thought models by ryoshu on Friday September 13, 2024 @08:53AM (#41530784)

Agreed, but the problem is if these things replace practitioners (what every MBA wants them to do), it's going to wreck the industry. Or maybe we'll get paid $$$$ to fix the problems they cause. GPT-4 introduced me to window functions in SQL (haven't written raw SQL in over a decade). But I'm experienced enough to look at window functions and compare them to subqueries and run some tests through the query planner to see what happens. That's knowledge that needs to be shared with the next generation of developers. And LLMs can't do that accurately.

[ Parent | on HN ]

Re:Notes on OpenAI's new o1 chain-of-thought models by lupire on Friday September 13, 2024 @03:26PM (#41534310)

Optimizing a query is certainly something the machine (not necessarily the LLM part) can do better than the human, for 99.9% of situations and people.

PostgreSQL developers are oposed to query execution hints, because if a human knows a better way to execute a query, the devs want to put that knowledge into the planner.

[ Parent | on HN ]

Re:Notes on OpenAI's new o1 chain-of-thought models by RaftPeople on Saturday September 14, 2024 @12:18PM (#41540751)

Tangent:

> PostgreSQL developers are oposed to query execution hints, because if a human knows a better way to execute a query, the devs want to put that knowledge into the planner.

This thinking represents a fundamental misunderstanding of the nature of the problem (query plan optimization).

Query plan optimization is a combinatorial problem combined with partial information (e.g. about things like cardinality) that tends to produce worse results as complexity (and search space) increases due to limited search time.

Avoiding hints won't solve this problem because it's not a solvable problem any more than the traveling salesperson is a solvable problem.

[ Parent | on HN ]