Big Data in Artificial Intelligence - Part 2
In the Part 1 of Big Data in Artificial Intelligence blog, we saw how organizations are using Big Data Analytics to formulate corporate strategies and achieve bigger goals. Today, we will look at how to better use Big Data to make analytics derive the right results from the huge data available with the corporations.
- These are the types of data that humans find it very easy to interpret but the systems need to be trained really hard in order to comprehend this type of data. For example, images, audio, videos and text. There's a certain types of AI techniques that could work with images to recognize cats or audios to recognize speech or texts or understand that email is spam.
- These are types of data that is uniformly formatted and easy to interpret by the systems. Examples could be a feed from radar or the data that lives in a giant spreadsheet. The techniques for dealing with unstructured data are little bit different than the techniques for dealing with structured data. But AI techniques can work very well for both types of data.
The question that comes to the mind is when can we start doing analytics based on the data? Should we start when we have a large dataset collected over several years for example? It turns out that it is a really bad strategy. Instead, what experts recommend to every company, is once you've started collecting some data, go ahead and start showing it or feeding it to an AI team. Because often, the AI team can give feedback to your IT team on what types of data to collect and what types of IT infrastructure to keep on building.
One more misconception is if we have huge amount of data, AI team can build a large AI system and make use of this data. Unfortunately, this doesn't always work out. More data is usually better than less data, but I wouldn't take it for granted that just because you have many terabytes or gigabytes of data, that an AI team can make that valuable. So, the advice here is don't throw data to the AI team and assume it will be valuable. You may have heard the phrase garbage in garbage out, and if you have bad data, then the AI will learn inaccurate things. You must ensure the data is cleansed and all ambiguous or inaccurate data is flushed out before the AI team can work with that data.
Data is the fuel that powers AI, and large data sets make it possible for machine learning applications to learn independently and rapidly. The abundance of data we collect supplies our AIs with the examples they need to identify differences, increase their pattern recognition capabilities, and see the fine details within the patterns.
AI enables us to make sense of massive data sets, as well as unstructured data that doesn’t fit neatly into database rows and columns. AI is helping organizations create new insights from data that was formerly locked away in emails, presentations, videos, and images. Within that data, if we know how to unlock it, lies the potential to build amazing new businesses and solve some of the world’s greatest challenges.
In the next article, we will deep dive into Data Science and how it differs from Machine Learning. Stay tuned.