> To be fair I think its far easier to automate leadership jobs despite how they believe in greater self-worth.
Speaking as someone who has worked at multiple companies all the way from IC to team lead and even up to senior manager with a global team:
Almost every job at every level has some version of "ok, what's the flowchart for this". People assume this is for tech jobs but they would be surprised that it's true for managerial roles as well.
e.g. you have a poor performer then you run through this checklist:
1. is it an issue with the work being given?
2. is it an issue with another co-worker?
3. are they having a family issue?
4. it's a family issue, see if you can get details
5. oh, it's a sick parent? then implement the sick parent policy e.g. more flexible work hours, reduced work load, check in with them weekly till things improve etc
Where the human part comes in is where you have some combination of the below:
- event no one at the company has seen before
- the above requires discussions amongst humans to figure out
- those humans have to talk to other humans outside the company
examples at two levels might be (both for hurricane Sandy):
a. SREs have to deal with datacenters that don't have diesel
b. HR has to deal with critical employees that have houses that were swept away
I guess an AI could propose solutions to the above but it's a human that would actually make them happen (plus humans are more resilient to power outages).
The way you have expressed it there are optimized workflows not only the managerial/executive, administrative, content producing set of workers, but in almost every profession, encoded in beaurocracy, checklist, best practices and culture: doctors, care workers, lawyers, policing ...
The question is how well language models have internalized the optimal decision pathway and mitigating strategies to deviating circumstances. What data do they have to be trained on? If an AI defeats the best Go players in the world, surely it is a good question whether they can exceed in games with large unknowns such as business administration.
As mentioned somewhere else in this thread, the moral and ethical layers is where some of the challenges of these system can be found.
That ends up going in the DeepMind philopsophy of making AI, rather than the OpenAI philosophy. DeepMind makes AI that can defeat the best human Go players, OpenAI makes LLMs that are much more versatile but struggle with finishing a chess match without making illegal moves, never mind winning.
The other question on my mind: Cases where we have known decision pathways is exactly what the expert systems of the 70s and 80s were about. And from my understanding there were some successes, but we mostly found that it doesn't scale. Both encoding optimal decisions and encoding the decision space of things that can happen are huge challenges. LLMs might be genuinely better here, because they can just make things up as they go along, but they are also difficult to control
Makes sense re Deepmind. I also guess it is easier to train an AI using Deepmind approach on the Starcraft 2, Go, or other more closed systems than in the rather open business world with all those rules that need to be interpreted by a human judge in case of problems.
Good point re expert systems. I need to look up what actually made them fail, or how they are not scaling.
Speaking as someone who has worked at multiple companies all the way from IC to team lead and even up to senior manager with a global team:
Almost every job at every level has some version of "ok, what's the flowchart for this". People assume this is for tech jobs but they would be surprised that it's true for managerial roles as well.
e.g. you have a poor performer then you run through this checklist:
1. is it an issue with the work being given?
2. is it an issue with another co-worker?
3. are they having a family issue?
4. it's a family issue, see if you can get details
5. oh, it's a sick parent? then implement the sick parent policy e.g. more flexible work hours, reduced work load, check in with them weekly till things improve etc
Where the human part comes in is where you have some combination of the below:
- event no one at the company has seen before
- the above requires discussions amongst humans to figure out
- those humans have to talk to other humans outside the company
examples at two levels might be (both for hurricane Sandy):
a. SREs have to deal with datacenters that don't have diesel
b. HR has to deal with critical employees that have houses that were swept away
I guess an AI could propose solutions to the above but it's a human that would actually make them happen (plus humans are more resilient to power outages).