As we move into 2019, it’s getting difficult to find an industry that’s not feeling the effects of recent advances in artificial intelligence (AI) technology. We’re witnessing the beginnings of a revolution that’s bound to change almost everything about the way companies do business and approach tasks at every level.
All of the newest AI, however, can only be put into wide use if the companies that seek to deploy it have access to high-quality data sources to feed it. Fortunately, AI is already moving into the realm of data collection as well, creating the possibility of building self-feeding AI systems in the near future. For some insight, here’s a look at the ways that AI is impacting data collection right now.
Data solicitation and classification
Generally speaking, today’s AI-powered solutions rely on existing data stores or active input from human sources to form the foundation of their operations. However, the latest generation of chatbots and related systems has started to become far more adept at soliciting information from the people they interact with and can even proactively request needed data without any human intervention. The same process can be found at work in AI-driven online surveys, which can adapt and react to factors like sentiment and context to determine how to respond and when to ask further questions. After gathering the data, another class of AI systems can sort through it (as well as any other unstructured data sources that may be available) to classify the available information so it may be used further up the chain.
Automated data extraction
The latest AI systems are also proving quite adept at scanning troves of documents for identifying information such as document numbers and other contextual clues without being preprogrammed to do so. That’s a crucial development since it enables the AI to look at digitized versions of paper records with more of a human eye, which was one of the last major hurdles for businesses trying to leverage historical data that previously would have required a large, dedicated staff to prepare and clean. Today, AI can recognize patterns in document formats to identify information that previous generations of the technology would have misinterpreted or overlooked completely.
Data validation and cross-referencing
Another way that AI is impacting data collection is the fact that the technology is now sufficiently advanced and can autonomously verify and cross-reference data inputs to maintain high data quality. It’s the latest evolution in data anomaly detection, where the systems can not only spot an outlier within collected data sets but can also cross-reference new data with existing data to look for conflicts or concurrence. That process helps to prevent the collection of duplicate data and works to keep the overall flow of information both accurate and valid. It’s also worth noting that data validation tasks had been one of the most labor-intensive parts of any data collection and storage operation, and AI now reduces it to a real-time process that requires almost no direct human intervention.
A self-contained ecosystem
As things stand today, businesses are getting closer to a time when their AI systems will become far more self-contained than they are at present. As AI gets better and better at seeking out, classifying, and validating needed data on its own without being prompted to do so, we may soon see AI systems that can evolve on their own, free from the shackles of limited or inadequate data input. Once that happens, business AI systems will be able to grow alongside the companies they serve, offering both insight and innovation, and delivering value on a scale that even the most optimistic technologist may have dismissed as fantasy just a few short years ago.
To learn more about how data controls the future of AI, read “Underfit Vs. Overfit: Why Your Machine Learning Model May Be Wrong.”