타사 데이터 수집을위한 모범 사례 : AI 컨텍스트 파워

The proliferation of GenAI tools continues to compel us to critically reassess how we gauge success in the modern digital age. Like other transformative technologies before it, the rise of AI necessitates a shift in our focus. The future vitality of the internet and the broader tech ecosystem will no longer be solely defined by metrics of success outlined in the 90s or early 00s. Instead, the emphasis is increasingly on the caliber of data, the reliability of information, and the incredibly vital role of expert communities and individuals in meticulously creating, sharing and curating knowledge.

In the light of that new world, we’re kicking off this new blog series focused on the challenges we face in determining how to evaluate the quality of internal and external datasets.

Data acquisition, the process of gathering information for analysis, forms the foundation for informed decision-making across numerous fields. However, the sheer volume of data available today can be overwhelming. This post explores crucial lessons learned in the trenches of data licensing, drawing insights from Stack Overflow and the growing importance of socially responsible data practices in a changing internet landscape.

The old adage “garbage in, garbage out” is more relevant than ever when it comes to data acquisition. Collecting vast amounts of data is futile, even detrimental, if that data is irrelevant, inaccurate, or poorly structured. Storing, transferring, and processing data costs money, so if you start with a mountain of bad data, you’ll pay more to get it close to good—if that’s even possible.

As discussed in numerous posts here from our team at Stack Overflow, the focus should always be on identifying and acquiring the right data. This is particularly important in the age of AI, where the quality of the training data directly impacts the performance of AI models and opens new research opportunities. As our CEO Prashanth Chandrasekar noted during his time at HumanXprinciples of socially responsible AItold usbased on testsshifting business modelsoutlined in an earlier blog postsocially responsible AI principlesQuestion Assistantplease reach out.