Article from The Atlantic, archive link: https://archive.ph/Vqjpr
Some important quotes:
The tensions boiled over at the top. As Altman and OpenAI President Greg Brockman encouraged more commercialization, the company’s chief scientist, Ilya Sutskever, grew more concerned about whether OpenAI was upholding the governing nonprofit’s mission to create beneficial AGI.
The release of GPT-4 also frustrated the alignment team, which was focused on further-upstream AI-safety challenges, such as developing various techniques to get the model to follow user instructions and prevent it from spewing toxic speech or “hallucinating”—confidently presenting misinformation as fact. Many members of the team, including a growing contingent fearful of the existential risk of more-advanced AI models, felt uncomfortable with how quickly GPT-4 had been launched and integrated widely into other products. They believed that the AI safety work they had done was insufficient.
Employees from an already small trust-and-safety staff were reassigned from other abuse areas to focus on this issue. Under the increasing strain, some employees struggled with mental-health issues. Communication was poor. Co-workers would find out that colleagues had been fired only after noticing them disappear on Slack.
Summary: Tech bros want money, tech bros want speed, tech bros want products.
Scientists want safety, researchers want to research…
Scripts and automation do what thier programmed to. There are bugs and mistakes, but you can theoretically get something programmed right. LLM’s generate text that looks like a human language. If they were just getting used to make up random bullshit it wouldn’t be a problem, but there are few applications where random bullshit is actually beneficial.
Just like the executives assist that was tasked with scanning documents. And LLM can likely safely and quickly do many people tasks:
There are a lot of human language job tasks that have zero imagination required just the ability to read summarize and write some proper English.
Thouse all sound like things where it might be really bad if it injects untrue information, and with an LLM, by definition it has no understanding of what it’s summarizing. It could be especially bad if the people useing it actually trust what it outputs as facts about what was fed into it, but if they don’t and still check the source than what’s the point.
“That’s not writing, that’s just typing!”
If I hand someone a set of bullet notes and ask them to send out a notice in writing to the company. They are going to convert those notes into paragraphs and sentences… Not just send out the notes.
Also MS already has a module for teams that will take the conversation transcript, and output action items based on the conversation… It is like having a note taker during the meeting. https://www.youtube.com/watch?v=N1gpkk-MwpY
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