Artificial intelligence is becoming a great tool for many industries. This guide will teach you how to train your AI model with web data for web scraping.
By Victor Bolu @June, 23 2023
AI models need a massive glut of data to learn, iterate, and improve. ChatGPT is among the latest instances of artificial intelligence to wow the crowds. And unsurprisingly, it took about 570 GB worth of datasets to train this revolutionary chatbot.
The question then becomes how to source this training data. The Internet is replete with stories of chatbots gone rogue thanks to troublesome information bases. These projects came to an early close when engineers "lost control" of their artificial intelligence.
The key to ChatGPT's success lies in curating good data. And there's no better place to source it than from web scraping.
Follow along as we explain how you can train your AI model on web data.
Every data engineer knows well that high-quality data is better than large quantities of data. Garbage in, garbage out.
It would be easy enough to unleash an AI onto the Internet and allow it to consume endless quantities of data. But the end result would likely be an unrecognizable monster.
Perhaps one day AI can vet its own datasets. Until then, those in data engineering are responsible for spoon-feeding handpicked data to their AI.
To get half a terabyte of useful information, data engineers fed ChatGPT web text databases. This included everything from scientific articles to Wikipedia pages. And they got all of this information through one method: web scraping.
Web scraping is the key to the success of the most powerful chatbots of our age. Advantages include the following:
In other words, web scraping is your keystone when it comes to curating datasets. Large companies in every industry from consumer finance to market research are using web scraping.
You could purchase a premade dataset, but these are very expensive and not custom. They don't always update at the speed you require. Overall, web scraping is the superior information-gathering method for just about every reason.
With all that said, let's discuss how you can put this data to your own use.
Web scraping can be as complex or as limited as you like. Here are some potential use cases that industry professionals turn to all the time:
None of this would be possible without the right web scraping tool. A cursory Google search will reveal thousands of competing tools for you to use. You can even build your own with the help of ChatGPT.
However, there are already many ready-made web scrapers so you can get started. They often include an intuitive GUI to customize the web scraper to your needs. Then you can activate it with a single press of a button.
Keep a few things in mind as you search for the right tool.
This is perhaps the trickiest part of the entire process. As we've mentioned earlier, it's very easy for an AI model to produce unfavourable results. In almost every case, this is the result of a low-quality dataset.
The Internet is a vast ocean with seemingly infinite data to choose from. It's up to you which data you gather. But regardless of your industry, there are a few key things to keep in mind.
Social media and forums are both a blessing and a curse. They allow for vigorous discussion and learning moments for all involved. But they also tend to be a cesspool of conspiracy theories, political nonsense, and the vilest bigotry and depravity known to man.
Generally speaking, you should avoid scraping comments, user posts, and other user-made content. This is the "garbage" when we speak of. There are several recent examples of chatbots that went off the deep end into racism and worse.
This isn't to say that you shouldn't use user-generated information. The only way for a chatbot to develop conversational tendencies is to learn from real human conversations. Rather, you should be especially cautious with this sort of information.
Ideally, you want content that has had a few eyeballs on it. Though Wikipedia does have a history of occasional misinformation, it's a much better source than social media because editors have reviewed it. It's far less likely for bad information to make it through.
In general, you want sources that have undergone this review process. Otherwise, you would have to examine the information yourself word by word.
Choose the sort of content that you want your web scraping tool to collect. Web scrapers can collate data of practically any kind, as long as the website makes it visible. This can be buttons, headers, and even backend code for website functions.
You will need some familiarity with how websites organise their data into CSS and HTML. From there, you can select only that which you wish to gather.
Once you have selected your preferred data type, you can organize it into data sets for an AI model to parse and perform sentiment analysis.
If you require data sets with evergreen information, then you will need to automate web scraping. You can set this to happen on a daily, weekly, or even monthly basis. The tool will run on a schedule to gather only the freshest information and add it to your dataset.
You may need to update parameters as time goes on. New website updates and changes to the site map could bungle your data set.
AI is improving at a blistering pace and leading to drastic shifts in every industry. But at the heart of every AI worth its salt is robust training data. Web scraping is the most effective method for gathering this data, and is much easier to do than you might think.
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