Web Scraping vs. AI: Who Wins? Nobody—It’s Not The Same Game

Nowadays, you can’t log into LinkedIn without seeing a post about AI.?

Surprise, here’s another one—to give you more PTSD.?

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The AI obsession comes in waves. Especially while it’s free.

More recently, ChatGPT (OpenAI) took the tech world by storm. The infamous ChatGPT 3.5 is trained on a larger dataset (compared to other technologies), and can “understand” linguistic structures and patterns of human language. As a result, people are prompting it with all types of tasks ... including web scraping.

While the results are not always accurate to the trained eye—(depending on the complexity of the framework you use, the response will differ)—the fact that it’s using Transformer network architecture means it can spit out text data at lightning speed. That in itself will take your web scraping efforts to new heights.

So, what does this mean for web scrapers?

Well, there are enough tried and tested examples to prove that AI can write code, but it cannot analyze or parse HTML. ChatGPT, specifically, is trained on old data, which puts you at a disadvantage with current happenings. If you use a scraping API, for example, it is non-deterministic compared to deterministic algorithms. This means that it “can exhibit different behaviors on different runs, as opposed to a deterministic algorithm which will always produce the same output for a given input going through the same outputs.”

Next, there’s the hiccups of getting blocked by anti-bot techniques and the general moralities of data collection. AI does not take this into account.

So, as you can see, you cannot rely on AI for scraping without the human element. But for what it's worth, it can help you save you time and resources.

You can use AI in web scraping to (amongst other things):

?? Improve your datasets so that scraping is faster with higher success rates?

?? Identify data patterns from your scrape and build predictive analytics models

?? Classify active URLs in bulk to avoid time-outs and resubmissions

?? Analyze used datasets to remove unnecessary information for future scrapes

?? Suggest improvements for the web scraping service or API you’re using

As new artificial models arise, it will simply give web scrapers a chance to explore new public avenues. For example, “Microsoft is on the top of investing billions more in OpenAI, for Bing. This means you’ll not only have to rely on the accuracy of Google Search data, but also other, evolving search engines.

The opportunities are endless.?

Final Thoughts

These tools complement each other and should not be used against each other. Neither can outshine or skillfully complete the tasks of the other. (Not to mention, artificial intelligence tools are still a long way from being perfect, trusted solutions.) Until then, combine both and take advantage of increased efficiency.?

If you want to try web scraping with your preferred AI solution, sign up and get 5,000 free credits. Any questions or need a hand? Get in touch with us!

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Your ScraperAPI Team! ??