4 ms·
>Being anti AI/anti LLM is solidly in the Luddite camp; there’s really no more debates to be had. Every serious inquiry shows productivity gains by using ai. T
by snickerbockers 1y ago
>Being anti AI/anti LLM is solidly in the Luddite camp; there’s really no more debates to be had. Every serious inquiry shows productivity gains by using ai.
These two sentences appear to be at odds with one another.
- Our_Benefactors 1y agoThe data showed llms are better. This put debate to rest. Now we are post-debate.
- JohnFen 1y agoWhat data are you talking about? Why do you value it above the data showing the opposite?
- snickerbockers 1y agoIt's superior data because it supports his expectations. His expectations are right because they are based on superior data. Checkmate Luddites.
- Our_Benefactors 1y agoMeanwhile, you have furnished zero data that supports your claims. Ho hum.
- snickerbockers 1y agoYour initial statement is that you are not open to debate so i don't see what the point would be. Furthermore you defined "serious inquiries" as synonymous with your own preconceived ideas so by definition I cannot refute anything you say using a "serious inquiry". Do not interpret this as some sort of complement or concession but it is not possible to argue against you. Even putting the sophistry aside your argument is incomplete because you never defined what "productivity" means in this context or how it can be quantified. I would never dispute that a pseudo-random bullshit generator can shit out javascript faster than any human, but that's not necessarily productive.
- snickerbockers 1y ago"the data"
- lmf4lol 1y agogive me one seriously peer reviewed study please with proper controls i wait
- Our_Benefactors 1y agoGo ahead and move the goalposts now... This took about 2 minutes of research to support the conclusions I know to be true. You can waste time as long as you choose in academia attempting to prove any point, while normal people make real contributions using LLMs. ### An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation We evaluate TESTPILOT using OpenAI’s gpt3.5-turbo LLM on 25 npm packages with a total of 1,684 API functions. The generated tests achieve a median statement coverage of 70.2% and branch coverage of 52.8%. In contrast, the state-of-the feedback-directed JavaScript test generation technique, Nessie, achieves only 51.3% statement coverage and 25.6% branch coverage. - *Link:* [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation (arXiv)](https://arxiv.org/abs/2302.06527 https://arxiv.org/abs/2302.06527) --- ### Field Experiment – CodeFuse (12-week deployment) - Productivity (measured by the number of lines of code produced) increased by 55% for the group using the LLM. Approximately one third of this increase was directly attributable to code generated by the LLM. - *Link:* [CodeFuse: Generative AI for Code Productivity in the Workplace (BIS Working Paper 1208)](https://www.bis.org/publ/work1208.htm https://www.bis.org/publ/work1208.htm)
- psunavy03 1y agoIf you are seriously linking "productivity" to "lines of code produced," that says all about your credibility that I need to know.
- Our_Benefactors 1y agoDo you think LOC and program complexity are not correlated? You are arguing in bad faith.
- psunavy03 1y ago