Nowadays, speed is the key to artificial intelligence research.
Haebom
I was taught in graduate school that research is done with the power of the buttocks, but these days, when I look at artificial intelligence research, it doesn't seem to be done with the buttocks. The speed of artificial intelligence research has become important in the past two years. Here, speed is not simply about acting quickly, but the goal is to complete efficient and qualitative research within a timely manner. This principle appears especially important in the context of rapid developments in the field of AI research and in a global competitive environment.
Wasn’t it originally fast?
AI research centers on data-based experiments and analysis. By obtaining rapid feedback through rapid experimentation and analysis, you can increase the accuracy of models and algorithms.
New discoveries and advancements in AI technology are being made every day. Rapid research progress is essential for researchers to keep up with these developments. Many researchers around the world are trying to develop new methods and systems in the field of AI. To stay ahead in this race, speed becomes the deciding factor.
Because of these efforts, the artificial intelligence industry needs a better research environment, or infrastructure. (At heart, everyone would like to have an H100 cluster...) However, since not everyone can have a good and optimized infrastructure, this is where the business or role of MLOps and LLMOps comes into play.
I can't give up on this
Do not sacrifice quality: Speed of research does not mean pursuing speed at the expense of quality. Rather, it is about producing better quality research results through continuous practice and rapid iteration.
This doesn't mean you're constantly rushing: It's not about working on your toes all the time, but about minimizing delays through efficient processes.
Not speed with a short-term focus: Speed takes into account long-term goals and sustainability. You must focus on your long-term vision while accelerating the achievement of short-term goals.
I know it's important, but what should I do?
Iteration: After setting a goal, it is important to continuously iterate and get feedback.
The big model is not the answer, and we cannot use a prayer meta at each checkpoint.
Ultimately, we need to create an environment where we can quickly see results and provide feedback even on small or existing models.
Adaptation: The ability to quickly adapt to changes in the AI field and change direction when necessary is important.
In fact, this is very important... You must also know how to quickly give up what you were doing.
There are many cases where a method that I have been working on for several months is actually solved in a different model in one go. If you hold on to this because it is a sunk cost, you will fall behind alone.
In a period of technological transition like the present, the best strategy may be to hone basic knowledge and try to reproduce new models or technologies.
Prioritization: Identify which tasks are most important and establish an efficient sequence of tasks based on these.
Determination: You need to be persistent in your efforts to achieve your goals even when unexpected problems arise.
Honestly, with so many papers coming out, it's not easy to focus on just one field. This is somewhere in the singularity...
Recent research methods that seek to reach a peak in a specific field seem to have a slightly faster tempo. In particular, a speed-focused approach can lead to greater performance in AI research, which will have a positive impact on individuals, teams, and the entire research field. The nature of AI research is rapidly changing, and conducting research quickly and efficiently to keep pace with this appears to be the key to current and future research success.
