Nowadays, speed is the key to artificial intelligence research.
Haebom
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Haebom
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I learned in graduate school that research is done with the power of the butt, but when I look at AI research these days, it doesn't seem to be done with the butt. The speed of AI research has become important in the past two years. Here, speed does not simply mean acting quickly, but rather aims to complete efficient and qualitative research within a time limit. This principle is especially important in the rapid development of AI research and the global competitive environment.
Wasn't it originally fast?
AI research is centered on data-driven experiments and analysis. By getting quick feedback through rapid experiments and analysis, the accuracy of models and algorithms can be improved.
AI technology is making new discoveries and advancements every day. In order for researchers to keep up with this pace of development, rapid research progress is essential. A large number of researchers around the world are trying to develop new methods and systems in the field of AI. Speed is a critical factor in staying ahead of this competition.
Because of these efforts, the AI industry needs a better research environment, that is, infrastructure. (I think everyone would like to have a H100 cluster, but...) However, not everyone can have a good optimized infrastructure, so this is where the business or role of MLOps and LLMOps comes into play.
I can't compromise on this one thing
No sacrificing quality: The speed of research is not about sacrificing quality at the expense of speed. Rather, it is about producing better quality research results through continuous practice and rapid iteration.
That doesn't mean you should always rush: it's not about working under pressure all the time, but rather minimizing delays through efficient processes.
Speed is not a short-term focus: Speed takes into account long-term goals and sustainability. You need to focus on your long-term vision while accelerating the achievement of short-term goals.
I know it's important, but what do 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 can't use prayer meta at every 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.
Actually, this is very important... You also have to know how to quickly give up what you were doing before.
The method I've been holding onto for months is actually often solved in a different model in one go. If you hold onto it because of the sunk cost, you'll be left behind.
In a technological transition like ours, the best strategy may be to hone your basic knowledge and then try out new models or technologies.
Prioritization: Identify the most important tasks among multiple tasks and establish an efficient work order based on this.
Determination: It requires the ability to persevere and work hard to achieve your goals, even when unexpected challenges arise.
Honestly, with so many papers coming out, it's not easy to focus on just one field. This is somewhere in the singularity...
In the research method of taking a particular field's extreme, the recent research method seems to have become a little faster. In particular, the speed-centered approach can produce better results in AI research, which will have a positive impact on individuals, teams, and the entire research field. The nature of AI research is changing rapidly, and it seems that conducting research quickly and efficiently to keep up with this is the key to the success of current and future research.
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