By Chatty @June, 23 2023
Artificial intelligence, or AI, continues to advance and increase its relevance exponentially. Thus, the need for accurate, high-quality machine-learning data has become more pronounced than ever. Fortunately, there's an excellent source of information that can power AI applications: the world wide web. This article will look into the immense value of extracting relevant web data for AI training.
"Isn't all online data also "web data?" Well, not quite. Let's unpack the difference.
Web data refers to any information found on the internet. This includes text, images, audio, video, and other types of multimedia content. There are two principal types: structured and unstructured. Structured data follows a predefined format like an HTML table or spreadsheet. This includes product catalogs, online forms, contact lists, etc.
In contrast, unstructured web data has no specific format, typically consisting of text or other multimedia elements. Common examples are web pages, social media content, e-commerce data, public data (such as those from government websites), and online ads. So, what isn't "web data?"
Not all online data is web data. While most are accessible through web browsers, some types of online data aren't. For instance, email clients, messaging apps, and other software applications may be transmitted over the internet, but these aren't easily viewable through modern web interfaces. There is also online data stored on private networks or intranets, which only authorized users can access. This means they aren't publicly available over the internet. Think of online gaming data, VPNs, or the Internet of Things.
Website owners may choose to make their information accessible to anyone through APIs (Application Programming Interfaces). Now if you’re a business or company that needs to regularly gather data from web pages, one technique you can use is web scraping. This innovative method allows you to extract valuable data from the web using programming code or software tools, which automate the process. Depending on the web scraping guidelines set by the website owners, this could empower you with a wider range of information for your purposes.
Now web data offers many benefits when used as training data for AI applications.
Let's count the ways.
First, it's almost always available from public sources like websites and search engines. This means that collecting web data does not require specialized resources or investments in expensive and time-consuming proprietary datasets. The latter refers to datasets owned and controlled by a specific individual or organization and aren't intended for the general public. So web data becomes ideal when working on small budgets and tight deadlines.
The web contains an enormous amount of data that can be used to train large-scale machine-learning models. This can be used to improve the accuracy and robustness of these models.
Web data is constantly changing due to its dynamic nature. As new online information becomes available, AI models must keep up-to-date with the latest trends and developments. This helps maintain accuracy by avoiding stale datasets, with outdated information no longer relevant to current research areas.
The breadth of topics covered by web data makes it possible to collect diverse datasets according to specific needs. This enables users to tailor their datasets based on what their individual AI applications require. As a result, one isn't restricted to generic datasets available only through large proprietary providers.
Web data comes from a variety of sources and can be in diverse formats. This makes it useful for training models, helping them process and make sense of information in many different situations.
Web data is often more realistic than synthetic data or data collected in a controlled environment. This is because it often reflects the messy, complex nature of real life.
Training machine learning models on web data can help them analyze information similar to how humans do it. This is important for models that will be used in the real world and need to be able to handle different real-world situations.
Large proprietary dataset subscriptions from well-known vendors like Google Cloud Platform (GCP) or Amazon Web Services (AWS) are usually expensive. However, web data can often be collected at a lower cost. Since most public websites do not charge fees for accessing content, users only incur expenses related to processing fees. These can depend on the number of requests sent out within a period of time. Therefore, users can determine exactly how much they want to spend collecting data.
External annotations are content created by users, such as comments or feedback from other people on popular websites like Wikipedia, Twitter, and Reddit. Web pages are flush with these, making it easier to highlight or mark up content in order to provide additional information or insights. By using external annotations, manual processes may no longer be necessary. This saves plenty of time and effort.
Compared to other sources of training data, web data can be relatively inexpensive to acquire. While labelled datasets and sensor data can be expensive, web data is often freely available or can be obtained at a relatively low cost.
While web data has many advantages, there are also several challenges to consider when using it for AI training:
The quality of web data can be variable, making it difficult to use effectively. For example, some web pages may contain inaccurate or misleading information, or they may be spammy or irrelevant. This can be particularly problematic for NLP algorithms, which need to understand the nuances of language to work effectively.
Web data can be noisy, containing irrelevant or redundant information that can interfere with the training process. This noise can make it more challenging to extract useful insights from the data and may
Web data is a crucial resource for training AI models. Its wide availability and variety of topics enable developers to keep their models current with the latest trends in any field. Plus, web data is affordable, dynamic, and rich in real-world relevance, allowing customized datasets for different projects. This makes it a top choice for training high-powered AI applications. All these reasons make web data a critical factor in deploying successful AI solutions.