Headline, June 16 2025/ ''' JOBS! STUDENTS JOBS? '''

''' JOBS!

 STUDENTS

 JOBS? '''

MECHANIZE'S APPROACH TO AUTOMATING JOBS USING A.I. is focused on a technique known as reinforcement learning - the same method that was used to train a computer to play the board game Go at a superhuman level nearly a decade ago.

TODAY, leading A.I. companies are using reinforcement learning to improve the outputs of their language models, by performing additional computation before they generate an answer.

These models, often called '' thinking '' or '' reasoning '' models have gotten impressively good at some narrow tasks, such as writing code or solving math problems.

BUT most jobs involve doing more than one task. And today's best A.I. models still aren't reliable enough to handle more complicated workloads, or navigate complex corporate systems.

To fix that, Mechanize is creating new training environments for these models - essentially, elaborate tests that can be used to teach the models what to do in a given scenario, and judge whether they've succeeded or not.

To automate software engineering, for example, Mechanize is bubuilding a training environment that resembles the computer a software engineer would use - a virtual machine outfitted with an email inbox, a Slack account, some coding tools, and a web browser.

An A.I. system is asked to accomplish a task using these tools. If it succeeds, it gets a reward.  If it fails, it gets a penalty. Then it tries again. With enough trial and error, if the simulation was well designed, the A.I. should eventually learn to do what a human engineer does.

'' It's effectively like creating a very boring video game,'' Mr. Besiroglu said.

Mechanize is starting with computer programming, an occupation where reinforcement learning for it has already shown some promise. But it hopes that the same strategy could be used to automate jobs in many other white-collar fields.

'' We'll only truly know we've succeeded once we've created A.I. systems capable of taking on nearly every responsibility a human could carry out at a computer,'' the company wrote in a recent blog post.

I have some doubts about whether Mechanize's approach will work, especially for nontechnical jobs where success and failure aren't as easily measured.

[ What would it mean, for example, for an A.I.  to '' succeed '' at being a high school teacher? 

WHAT if its students did well on standardized tests, but they were all miserable and unmotivated?

What if the A.I. teacher learned to reward-hack by feeding students the correct answers, in hopes of improving their test scores?]

Mechanize's founders aren't naive about the difficulty of automating jobs this way.

Mr. Barnett told me that his best estimate was that full automation would take 10 to 20 years.  Mr. Erdil and Mr. Besiroglu expect it to take 20 to 30 years.

These are conservative timelines, by Silicon Valley standards. And I appreciate that, unlike many A.I. companies working on labor-replacing technology behind closed doors,  Mechanize is being candid about what it's trying to do.

The Honour and Serving of the Latest Global Operational Research on Jobs, Students, A.I. and Future, continues. The World Students Society thanks Kevin Roose.

With most respectful dedication to the Global Founder Framers of The World Students Society - the exclusive and eternal ownership of every student in the world - and then Students, Professors and Teachers.

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