Submitted by [deleted] t3_yyq411 in singularity
ProShortKingAction t1_iwvkss0 wrote
Automating software tasks is an entirely different skillset than building a Machine Learning model for automating something like driving. Automating software tasks involves slowly documenting and analyzing each best practice, finding what is repetitive and creating scripts/pipelines for those repetitive tasks. It's not AI based it's more a bunch of if else statements that you slowly build over years. Even if the skillset was the same and Elon brought on one engineer for every tech employee at Twitter it would still take months if not years of documentation and slow replacement to make up for the kind of skill drain that has happened at Twitter over the past two weeks. Nine women can't make a baby in a month and all that
TJ137 t1_iwz6x6c wrote
I think your partly right... but keep this in mind... infrastructure projects can take years sometimes. But when cities/companies have budgets that are unused and must be spent by a certain time we watch amazing things happen lol. In my city we watched a 2 year Bridge project be finished in roughly 90 days because they had to spend the money by a certain time lol. I know my analogy is low level compared to working on the things you are talking about. But I can't help but think that Twitter was on manual autopilot... you would be amazed what motivated humans can do... I truly think the employee model of the future is not a set time of work hours. Example 9-5... 70% of the employees time is wasted passing time.. most people could finish a day's works in 2-4 hours if there was enough incentive, ie being able to go home when they are done their work. I totally think it will still take a long time to automate Twitter but might be alot faster than we think.
rnimmer t1_iwvlk7a wrote
Well, I myself am a developer and familiar with CICD and devops etc. If you approach the problem from automating those things, I'd have the same take as you. However, if you approach the problem as automating the actions of the engineers themselves on their machines, it becomes a totally different problem with some machine learning solutions available that closely resemble the ones you can use for self driving. For instance, large neural network architectures.
Karpathy has said that while at Tesla they had been working on AI that would autopilot computers at the mouse and keyboard level. He talks about it in the Lex Fridman podcast he did
ProShortKingAction t1_iwvoojk wrote
Even with the farthest stretch of the imagination as to what the ML researchers/developers at Tesla are capable of you would still need a significant amount of data on the tasks that need to be automated. If entire teams are laid off how will their tasks even be explained to the model let alone demonstrated enough times for the model to understand?
rnimmer t1_iwwdvna wrote
the only way it could work is through natural language description of the jobs, maybe training against internal documents. it's not outside the realm of possibility at all, although admittedly doing something this large in such a narrow time window seems very implausible
pandasashu t1_iwx7h7d wrote
They weren’t there for long enough to do this. Are you familiar with how long it takes to even train large models?
Its an interesting idea for the future, but not realistic in the slightest for the present.
The reality was that tesla engineers use a similar tech stack to twitter. So elon pulled them over to help him gauge how best to reduce the twitter employee count and also get a general idea of what the twitter code base looked like.
rnimmer t1_iwyfeaf wrote
they wouldn't need to train an entirely new model, necessarily. they have been working on a model which controls interfaces through mouse and keyboard based on natural language instructions, according to Karpathy. Such a model may be designed to work on fine tuning, or few shot learning
SoylentRox t1_iwwq5w1 wrote
This is vaguely possible eventually but as you know DevOps is incredibly fragile. You can't just get close you need the exact correct commands to merge repos, build the codebase, run unit tests etc.
This isn't really automatable with current or plausible future AI ML tech. Note I do work in AI, have also done some DevOps, and am very enthusiastic about AI/ML for task spaces where the goal is clearly defined, the task environment can be adequately simulated, and performance is differentiable.
For example a robot moving boxes in a warehouse fits that criteria. It's relatively simple what the goal is and what not to do, and expressible in a robust sim. DevOps has many many subtle consequences and you need the solution to get every relevant detail right.
Software that code translates from one language to a faster language without loss of correctness is also possible a similar way.
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