How Public Web Data Drives Future of AI

With economic uncertainty on the horizon, Artificial Intelligence tools will continue to optimise workflows and boost productivity and efficiency. As part of that, companies will be looking to eliminate the dependency on data scientists as middlemen by adopting technologies that incorporate low-code extensibility, and intuitive user experiences, lowering the bar for people with no technical background, the Innovation News Network reported.

From AI-based chatbots to automatic tools that analyse user behaviour and maximise engagement — the 2023 business outlook establishes AI as an enterprise necessity in the current business environment.

However, AI systems are only as good as the information they are fed. DeepMind researchers concluded that in order to maximise AI models’ performance, they should be trained on larger datasets. Additionally, the quality and diversity of the dataset used to train an AI model play a critical role in the performance and accuracy of these algorithms. Moreover, AI models must have access to up-to-date, frequently updated data, otherwise, by the time the model is deployed, it might no longer be relevant.

In order to train emerging AI models on larger datasets, enterprises must have access to the world’s largest up-to-date database in the history of mankind, the internet, as public web data is vital for AI models to be trained on diverse sets of frequently updated information and examples. OpenAI’s ChatGPT’s success, for instance, derives from being fed a large public dataset of text scraped from online websites, blogs, articles and forums.

While businesses can attempt to scrape public web data independently, it is a time-consuming and tedious endeavour requiring a large amount of resources. On average, companies spend 78% of data collection budgets on data specialists who spend most of their time developing the necessary architecture. Once collected, the data still needs to be structured and then analysed, as missing or inaccurate data could affect the performance and accuracy of AI models.

In fact, 66% of companies claim poor-quality data impairs their ability to deploy and adopt AI effectively and that poor-quality data is the main hindrance for businesses to create high-quality functioning AI tools, a Refinitiv study found.

With the new advancements in web data collection technology that simplifies collecting and structuring public web data, any company big or small can get their hands on qualified data to train their machines without having a full-blown data operation in place.

The available tools vary from low-code or no-code software tools that allow companies to create automated scrapers that return custom datasets, which companies can then plug directly into the AI via an API to continuously feed their algorithms with constant streams of public web data.

Web data providers also structure, clean, and synthesise the datasets collected for immediate implementation, a resource-heavy and time-consuming process.

Alternatively, companies can purchase pre-collected datasets on demand, which hold a tremendous amount of public web data and can be ideal for training AI models. These datasets can be acquired once and be refreshed at periodic intervals as a cost-effective and speedy way for companies to get their hands on massive amounts of frequently updated public web data from multiple different sources. For example, an up-to-date dataset pulled from multiple online job boards could help employers find candidates for their most important roles and remove bias within the hiring process.

Whether AI will automate time-consuming tasks, improve the speed and accuracy of work, or predict potential problems, every business can use AI more than it does today. How well those tools will perform? That is up to the quality of data they are trained on – the more extensive and reliable the data, the better the performance rate and, consequently, the more valuable the results.

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