Written by Ieva Šataitė
AI lives, breathes, and grows on data. Companies that excel at model training are typically those that manage to collect or acquire large volumes of data. As the training becomes more ambitious and the competition intensifies, the importance of maintaining a steady stream of high-quality data flowing directly to the models increases.
Web scraping, which is the automated extraction of public data from the web, is the primary method to ensure such a flow. Collecting web data on a large scale and ensuring that it runs smoothly has its own challenges. Luckily, this is where AI can help web scraping and, by extension, help itself.
The Better Way to Solve the AI Data Problem
AI technology has great expectations. Some hope that will solve most, if not all, problems. Unsurprisingly, even when AI development has problems, our instinct is to ask whether AI can solve them.
It is often said that AI has a hallucination problem. Really, it has a data problem. AI hallucinations occur primarily due to a lack of access to accurate, high-quality data. One proposed solution to this issue is to generate more data using AI tools. Synthetic data mimics the structure and characteristics of actual datasets but does not refer to real-world events.
While some argue that synthetic data can, in some instances, be sufficient for AI training, it has its drawbacks and limitations. Training AI exclusively on synthetic data can actually increase the probability of model collapse and hallucinations and lacks the nuance and diversity of real-life data.
Thus, a better way is to unlock more publicly available real-life data with the help of AI tools. AI can play a role in acquiring public web data more efficiently and increasing its chances of succeeding. Let’s look at two major ways in which AI can help with web data collection.
Identifying Useless Results
As with any task, web scraping sometimes yields the expected and useful results, and sometimes does not work as intended. Many websites have sophisticated antibot measures primarily implemented to protect the server from being overloaded with inorganic requests.
Additionally, some explicitly wage war on AI, aiming to delay its development and increase costs by entrapping AI crawlers in an endless loop of useless pages. Finally, there are several other reasons why bad content is sometimes returned, such as website structure changes or CAPTCHAs that block scraper access.
Initial failures of scraping are neither surprising nor too worrisome. Nothing works perfectly every time. As long as AI developers can weed out the bad content and repeat the process to get what they need, model training can continue. The trick is identification itself when data collection is done on a large scale.
After all, obtaining sufficient data for AI training requires a constant stream of responses from millions of websites. Checking the usability of data manually is not an option. At the same time, you cannot feed just any data to the model, as bad data can hinder its capabilities instead of improving them.
However, LLMs themselves can help address this issue by automating response recognition. Scraping professionals can train a model to identify and classify content, separating good from unusable. By analyzing the HTML structure, it can find signs that the desired content was not returned, such as errors and automatically trigger a retry. By repeating the process, it continuously learns and improves.
Structuring the Data
The data received from the website is unstructured and not AI-ready as is. Extracting and structuring the data from HTML is known as data parsing. It is done by developers first programming a software component called a data parser that can do the parsing at hand.
The problem is that domains usually have unique website structures. In other words, developers being able to choose how they want to present the information on the webpage naturally leads to a variety of different layouts. Thus, parsing each unique layout requires manual work by the developer. When you need data from many websites with different layouts, it becomes an extremely time-consuming task. Furthermore, when layouts are updated, parsers must also be updated, or they will stop working.
All this comes down to a lot of time-consuming work for the developers. It is as if every screw had a different and constantly changing head, so technicians needed to make new screwdrivers when repairing something.
Luckily, AI can also automate and streamline parser building. This is achieved by training a model that can identify semantic changes in the layout and adjust the parser accordingly. Known as adaptive parsing, this feature of web scraping saves developers’ time and makes data intake more efficient.
For AI companies, this means fewer delays and increased confidence in obtaining the necessary training data. Together, response recognition and AI-powered parsing can go a long way in solving AI data challenges.
Summing Up
AI development requires a substantial amount of data, and the open web is its best chance of obtaining it. While there are many challenges to efficient web scraping, and many new ones are likely lurking beyond the horizon, AI itself can help solve them. By recognizing bad content, structuring usable data, and assisting with other major tasks of web data collection, AI tools feed and fuel themselves. Thus, technology keeps developing through a circle of artificial life, where web scraping technology keeps providing the data for AI to upgrade, and upgraded AI keeps enhancing web scraping capabilities.
