In theory, an agent could use git-bisect to identify when a bug was introduced. But the operator would have to be more interested in understanding the root cause than in fixing the current form of the bug...
For me, Claude regularly looks at git blame after identifying a bug, then reads the commit message and other changes in the commit to understand the context of why that behavior was introduced like that. I wouldn't be surprised if it pulled out a git bisect
Then again, my standard instruction is something like "{bug description}. Find the root cause and propose possible solutions" so I guess I fit your profile
not really an option when you're working on a large codebase where a bisect means that a lot of steps could be multiple-hours of compiles. E.g if on a simple 12-step bisect you get 6/8 with a full rebuild you've already spent an entire work day on this
I guess DDOSs would be the typical past example. If a piece of software was not designed to DDOS, but caused one anyway, should the maker be charged? The user? If open vs closed software?
LLM chatbots may not have a sense of self preservation or a meaningful concept of legal consequences, but they have an excellent model of user engagement. Getting a user to think they are invincible is very good for engagement.
Every prediction market is an assassination market. They claim to trade on "information" but it's more like "influence", and easiest way to influence an event is usually by sabotage.
Sounds like the opposite of the social stigma in tech companies, where most people are under pressure to self-report as AI users when they are actually just using normal methods to get the same result (and spending the time saved however they like) (nothing against genAI for making work take more time, it's still good for business as long as the extra work can be moved around)
It seems like this entire category of program is designed to make the same work take more time and more human attention.
I can understand the appeal in the tech industry, where the increased costs can be deferred until the financial situation changes. But healthcare does not work like that.
I think you are right, These scribe programs can be used to help save time up front, at the cost that you have to spend the time review at the end to review. As others have said, it’s easy to skip the review part when you are distracted, and if most notes are fine with no edits needed you start to think you can get away without reviewing closely.
Why would anyone want a raw trascription in a medical record. There should be a summary and any suggestions, but the physician would still have to write the note, even if they are simply editing the AI summary. The physician would still be doing all the other charting where they enter conditions and problems.
You would get a lot of differences, but the errors would cancel each other out with enough depth of coverage.
This technology's baseline accuracy is around 95% per base, so 10x reads of every segment in the sample would give >99% accuracy for each base after aligning the reads with each other.
This is not why she was fired and wouldn't have been a plausible reason in 2020 when it happened.
>Timnit responded with an email requiring that a number of conditions be met in order for her to continue working at Google, including revealing the identities of every person who Megan and I had spoken to and consulted as part of the review of the paper and the exact feedback. Timnit wrote that if we didn’t meet these demands, she would leave Google and work on an end date. We accept and respect her decision to resign from Google.
This is Google's side of it; I think the following is a fair piece of primary-source journalism if you want to go deeper:
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