Artificial intelligence (AI) has been experiencing an incredible boom over the last few years. The quality of texts, images, and even videos that AI can generate nowadays is often mind-blowing.
The rapid development of AI technologies was made possible by one simple thing — data. Incredibly large amounts of data, to be exact. Language learning models (LLMs) and other AI learning tools need a lot of data to improve. And that’s where web scraping and proxy servers come into play.
Follow along to learn exactly how web scraping, used together with proxies, has led the way for AI development and will continue to do so in the future.
How do AI use web-data for training?
In simple terms, AI attempts to mimic human intelligence as well as possible. AI needs as much sample data as possible to provide solid answers to your questions or make informed decisions on its own.
Many AI tools use Common Crawl, a vast database of already-scraped data, to start teaching its algorithms. But while this data provides a solid base, any tool needs additional data to have a chance at standing out.
For example, Nvidia scraped thousands of hours of video gameplay from YouTube to train its AI, while some LLMs constantly crawl live data for responses that need to have the latest information.
The data used depends mostly on the type of AI you’re looking to train. Text-based AI such as ChatGPT needs information in a text to learn, image generation AI such as Mid Journey needs as many reference pictures as possible, while voice generation AI such as ElvenLabs needs endless hours of voice recordings.
How is Web Scraping used to improve AI?
Simply put — the amount of data that LLMs require is impossible to gather manually. Web scraping is the best solution when you need automated data collection from the internet. It scrapes the web, collects information, and returns it to you in a structured format, which is usually CSV or JSON.
Most modern web scrapers also include a web crawler, so you simply define the types of URLs that you want to scrape and run the tool. It’ll crawl the web, find suitable websites, and collect data from them.
It’s easy to see how that benefits AI learning models. You can continuously run a scraper and feed data to the AI. And since it learns from the data, your AI tool will improve every day as long as the collected information matches what you want it to learn.
How does web scraping benefit from proxies?
Nearly all advanced web scraping operations are run through proxy servers. By pairing the two, you can avoid regional restrictions, rate limits, and being blocked by websites. This greatly improves the amount and, perhaps more importantly, the diversity of data you can collect.
Overcome regional restrictions
The websites and the information you can access can differ depending on where you’re connecting from. By using proxies, you can rotate your IP (Internet Protocol) addresses and access the internet as if you’re connecting from a different country within seconds.
This not only allows you to scrape local content from various regions, but also to access websites that are blocked or restricted in your country. Essentially, a proxy turns web scraping from a local operation to a global one.
Avoid rate limits
Many websites limit the number of requests a single user can make within a given amount of time. With a proxy, you can change your IP and make it seem like requests are coming from different users, effectively beating rate limits and maximising the number of requests you can make.
Limit IP bans
Not all websites like it when someone scrapes their data. So if you use a web scraper without a proxy server, your IP may quickly be singled out and banned by a website. By constantly rotating your IP, a proxy service makes it look like your requests are coming from different users, which makes the scraper much harder to detect.
How do you achieve optimal proxy performance when AI training?
Not all proxies are created equal. Poor quality proxies can hurt scraping performance instead of helping it, so choosing a reliable proxy provider and monitoring your activity is crucial.
Choose a good proxy provider
Free proxies may seem tempting, but their servers are typically overcrowded and slow, not to mention the potential cybersecurity risks associated with free proxies.
On the other hand, premium proxy providers such as Ping Proxies offer dedicated IP addresses, optimal speeds, and an infrastructure capable of maintaining large web scraping operations.
Monitor proxy performance
Even though proxy use can help reduce IP bans while web scraping, proxies aren’t immune to IP bans. To ensure optimal performance, monitor your proxy IP addresses and pick out the one’s that have been blocked by the websites you want to scrape. Replace them with new ones, and keep your operation going.
Keep Web Scraping ethical
While web scraping is legal, it doesn’t mean you don’t have to adhere to the law and terms of service agreements when collecting data. Make sure to only scrape publicly available information, don’t collect personally identifiable information, and limit the number of requests you make to avoid hurting the website’s performance.
Rotate IP addresses regularly
By regularly rotating IP addresses you minimize the chances of being detected by proxy detection software or anti-scraping tools. Make sure you get a proxy service that allows IP rotation and rotate them regularly for the best results.
Conclusion
Until there’s a more efficient way to collect data from the internet, web scraping tools used together with proxies remain the best way to scale your data collection and feed AI learning models.
It allows you to scrape large amounts of data and provides it in a structured format, so you can feed it to the AI and help it improve. All that’s left for you to do is ensure that you’re using quality proxies and web scraping tools for fast and secure data collection.
