21–30 of 62 posts
Not for RAG and Automation applications But for Fixing Hallucinated code that written by Non Software Engineers.
That is completely contingent on if present the day SWEs stop working on trying to eliminate the hallucinations.
All the solutions I've heard up until now is "we just need more data" which isn't particularly sustainable or something a SWE would be in charge of doing
Not for RAG and Automation applications But for Fixing Hallucinated code that written by Non Software Engineers.
That is completely contingent on if present the day SWEs stop working on trying to eliminate the hallucinations.
This article feels like stating an opinion. I suppose the autopilot analogy is at the heart of their argument but the argument as of now feels a bit too flat. There are definitely some jobs that technology completely displaced, and there are some that merely made something more accessible. For example, grinding grain with the advent of modern milling equipment vs microwaves. Milling machines make hand driven milling…
I would argue that generative AI in the hands of an already skilled developer is equivalent to the mill scenario. One person's expertise leveraged by way of this tool can allow for output that is orders of magnitude greater. The mere inertia of the "machine" encourages more productivity than otherwise. It is almost as if it is pulling you forward when you use it properly. Knowing when not to use it is also very important.
The concerns over hallucinatory output do not seem as relevant to me in this scenario of highly-skilled milling machine operator. You expose yourself to something for 10k+ hours and you will immediately recognize when it does something funny. Novices will cause trouble in any domain regardless of quirks in their tools. I don't think lack of experience should be conflated with hallucinations when evaluating the impact these tools might have at large.
There's still massive hype and group think about LLMs and what people project they will soon be able to do (or even what they can currently do - Dwarkesh's insider friends surprised it fails on Chollet's simple ARC tests!). A large part of this seems to be based on how new the tech is, and how capable it is, but people seem to be forgetting that the capability is based on brute force training time gradient descent. There is no runtime gradient descent or any alternative runtime learning method ... just limited in-context learning by example.
Not for RAG and Automation applications But for Fixing Hallucinated code that written by Non Software Engineers.
Not for RAG and Automation applications But for Fixing Hallucinated code that written by Non Software Engineers.
It's frustrating just how shit AI is at coding. The new gpt model seems even worse, splurging out a load of guff that makes it even more difficult to see the bullshit it's produced. AI a threat to humanity, my ass.
It’s weird that there’s people who seem to be “against” AI assisted programming. That’s like being against IDEs or git. It’s like writers who were brought up on typewriters being against word processors.
Some people are against a technology which isn't mature enough. I don't know about word processors, but take digital photography for example. Some people stuck with film photography even after digital became cool because it was superior in some regards (such as dynamic range). Once digital improved in every way over film, those people moved too.
Another reason is job security. Why would someone support something that will leave him homeless and destitute just because the software will be superior?
Personally, I think that software engineering jobs will go up while AI assisted programming is perfected (10-20 years), and afterwards there will be a huge decline (10x shrinkage) once the tools won't need people anymore.
Not for RAG and Automation applications But for Fixing Hallucinated code that written by Non Software Engineers.
That is completely contingent on if present the day SWEs stop working on trying to eliminate the hallucinations.
I am not one to talk in absolutes but the basic circumstances of current LLMs have hallucination at their core and can likely not be guaranteed fact based. It's just an expression of likelihood where you rely on your training data to contain truths.
This is why OpenAI gives more weight to Wikipedia than Reddit comments - the model contains no reasoning mechanism.
It ought to be treated more as a fallible human rather than a superintelligent lookup resource based on fact checked data.