Data Collection and AI

By Matt Brennan

Data collection and AI are increasingly important tools for a variety of human tasks. Our use of artificial intelligence is only limited by our imagination, and we’re able to do more with it seemingly every day. Programs are being built that can beat the smartest humans at our favorite games, such as chess and go. Soon it will be driving our cars, cooking our meals, diagnosing our medical conditions, working in law enforcement, and more.

These AIs work on mass data collection, at a scale beyond what most humans comprehend. The definition of data collection is simply gathering and evaluating information from countless sources. But when AI is involved, the scale of the operation accelerates. That AI needs to be able to account for nearly any variable that might be thrown in its direction. When we’re talking about a task as monumental as driving a car, that’s a significant number of variables.

That data collection allows AI to find recurring patterns. From those patterns it can use machine learning algorithms to form its own predictive models in order to establish trends. This is how AI can be used to reduce the number of traffic accidents, or more accurately diagnose our medical conditions. It’s why there is potential to use AI in the law enforcement world to help get criminals off the streets.

But there are massive amounts of data that need to be collected in order to safely incorporate AI to complete these functions.

How Much Data is Needed for AI?

The reality is that there is no perfect answer to that question, and it is heavily reliant on the function that you are looking for the AI to complete. Many will build their early models with as little data as possible to keep it working, for simplicity’s sake. There are complex mathematical formulas designed to let data scientists and engineers know when it is time to stop and when you need to collect more data to make it work.

More data is not always the answer. Sometimes there’s no more data to be had, and the options are to either generate new data points based on what you currently have (data augmentation), or to create new data points using complex sampling techniques (data synthesis).

Data collection is almost always the best option for AI, especially if there’s more data to consider. It will give the model more accurate data points to rely on.

Data Collection and AI: Protecting What’s Collected

Data collection and AI are forever intertwined, and that comes with a new set of cybersecurity risks. Companies using data to build complex AI systems will need to be ever-vigilant at protecting that data from hackers and general data loss. This is especially true in cases where the data collected may include sensitive information.

Practical steps such as using a secure network, backing up data, and making sure your data is password protected will all work to safeguard against data loss and potential cybersecurity issues.

And in the event of a data loss, companies may be tempted to try free software and other popular means to attempt recovery. But that route can often result in more damage. An experienced data recovery company may be their best chance at regaining access to that critical information.