[ad_1]
Exploring the limitless potential of ChatGPT in research
Last month saw one of the most important conferences for the High Energy Physics (HEP) computing community: e. year CHEP 2023are standing Ccalculation HAha eNervousness PPhysics and nuclear physics – Yes, simple! 🙂
As a computer engineer working at CERN, this is a major event: yes An opportunity to see the latest technology trends in our field. Still, even though I was fully aware of ChatGPT’s current popularity, I didn’t expect to find any conversation on the subject. but i was totally wrong There really was a couple!
I found them very attractive, so in this article, I would like to present to you the main messages of such negotiations. ChatGPT is not only changing our daily tasks but also major research areas like HEP.
Let’s explore what’s coming!
The HEP Society refers to a global network of scientists, researchers, engineers, technicians and institutions involved in the field of high energy physics. This community is dedicated to learning Fundamental components of matter, forces which regulates their interaction and learning Fundamental laws of the universe.
CHEP is a conference series focusing on the applications of computing, software and data management in the field of HEP. – and also nuclear physics.
In fact, CHEP is quite an old conference. The first one took place in 1985 and since then it has been held every two years. Overall, CHEP conferences play a critical role in advancing advances in computing and data management.
CHEP serves as a platform for knowledge exchange, collaboration and exploration of new computing techniques. Here’s why I was actually surprised: If something shows up on CHEP, it’s likely an incoming trend! And at this recent CHEP 2023, we had two plenary sessions on ChatGPT at HEP.
is it ready
The first plenary session on ChatGPT was held at the beginning of the schedule David Dean from Jefferson Lab. under the name Evolution and Revolutions in Computing: Science at the FrontierDavid provided a broad overview of the latest computer revolutions. And fear not, ChatGPT was one of them!
He specifically asked the question around If ChatGPT can do physicsAnd the message was clear: it’s a thought-provoking tool that can pass physics exams, but There is a major flaw that could stop ChatGPT from being included as a tool in the near future: the model hallucinations.
model hallucinations
Although the model has the ability to reproduce human-like responses, there are times when it retains a tendency to make up facts, duplicate incorrect information, and perform tasks incorrectly. These incorrect answers are known as hallucinations.
In fact, giving wrong answers is not the problem itself. The main point is that ChatGPT often shows these trends in a convincing and authoritative manner. Hallucinations sometimes even appear as very detailed information, giving the reader a false sense of accuracy.Increased risk of over-reliance. And this is definitely a problem in the research community.
In order to use ChatGPT as a reliable support tool, hallucinations need to be controlled. Currently, ChatGPT will try to answer any given query, even if it doesn’t have enough information about the target topic.
There should be nothing wrong with ChatGPT admitting that it cannot provide an exact answer to a given query And this would make the tool more suitable for precise environments like HEP research.
The second plenary session was about ChatGPT Radically different futures for HEP enabled by AI/MLContributed by Kyle Kramer of the University of Wisconsin-Madison.
This second conversation was more optimistic about implementing ChatGPT as a valuable asset to the HEP toolkit.. Actually, Kyle mentioned another conversation Christian Weber from Brookhaven National Laboratory, in which He presented real use cases of ChatGPT as a coding assistant, especially for migrating and converting code to new platforms. In fact, ChatGPT already implements a Python interpreter for coding purposes.
Each experiment in the HEP community has its own coding templates, That is, even though the coding is done in Python, scientists must adhere to some class or style conventions. One of the use cases presented was specifying ChatGPT to write analysis code based on an experiment template.
Attracted by this use case, I tried to generate a template for analysis at my current experiment, the CMS experiment at CERN, Switzerland, and ChatGPT did a great job of generating the first template. And I was just using the web interface, Imagine how powerful it can be once refined with the right data.
According to the presentation, even if sometimes the analysis was not precise enough, it allowed to generate a first template or backbone for the analysis. This idea was explored for faster onboarding and faster prototyping for new experiment members, among other use cases.
We can’t deny that large language models (LLMs) like ChatGPT are changing the way we search for information, build applications, and even code.
As with any advancement in technology, I think it’s a good idea to evaluate any new tool to take advantage of it and apply it to our core areas. These two plenary sessions are just two examples of this evaluation process in a large research community like HEP.
While some assessments may temporarily exclude ChatGPT as a research aid, others may allow the inclusion of such tools in specific and dedicated domains.. In any case, I believe it is important not to be afraid of AI and to continue to develop it, analyze its advantages, know how to improve its performance for your target domain, and most importantly, know the pitfalls to maintain a critical spirit. Always alert!
[ad_2]
Source link
