In today’s big data explosion, mapping data could be a hassle. This is where AI comes into play, allowing data scientists and engineers ‘make the machine work for them’. In a recent webinar, <itc> lecturer Yasha Neiman shared insights on how it’s being done.
Mapping, sorting and obtaining large databases is part of the job for many data scientists and engineers. But in today’s big data environment, it could also be a hassle. This is another place where AI can be useful. Essentially, employing AI to map big data ‘makes the machine work for you’, and using AI allows for a more tailored and effective approach.
But, how can we do it effectively?
Here at Israel Tech Challenge Hi-Tech Academy <itc>, AI is an integral key part of our curriculum in the Data Engineering and Data Science programs. On June 8, 2020, we held a webinar under the name “Mapping big data with powerful AI”. In this webinar, our lecturer Yasha Neiman, former CTO and Head of Data at InLoop.inc, shared insights and practical tips for how exactly AI is used to generate relevant data.
For example, suppose you work as a data engineer in a company, and you are assigned to help the content marketing manager in your company, who wants to stay updated with current news and trends in your business’s field. Instead of wasting time manually going over an abundance of content and deciding what’s relevant, the content marketing manager seeks your help. You choose together one (or a few) article(s) of your choice, and use AI to extract concepts automatically, by defining a few characteristics and concepts. This way, your fellow content marketing manager receives a newsfeed with relevant content which the AI tool finds, given the initial conditions you set: the chosen article(s) and the concepts for web-crawling, mapping and analysis.
So how does this magic work? As Yasha Neiman detailed in the webinar, AI is capable of understanding main concepts in unbounded sources (rather than pre-defined characteristics). Thus, by granting an algorithm with an initial reference item (which is used as an example for what we want our AI to find), we can expand our search criteria by the concepts extracted from the item. This way, our AI could find relevant sources without being constrained by set-in-stone search characteristics. Another method could be using an algorithm to transform sources into data structure graphs of our reference item. By understanding relationships between various characteristics in our reference item, our AI can assume and assert what is the importance of those characteristics, and decide if that source is relevant for us or not. These are just some of the ways we can reap and scrap more relevant data from online sources, and a leaf and effective way.
Does this require data scientists and engineers to become AI experts? Not necessarily. This is a classic example for how in today’s world, it is sometimes more effective to learn how to use certain tools, rather than learning exactly how they work. Like a professional race car driver who operates the car rather than building the engine, AI allows data engineers and scientists to focus on honing their skills and expertise on the analytical side, instead of spending time and effort gathering the data to be analyzed. In essence, using AI to map big data enables professionals to express innovative creativity and analytical thinking.
At <itc>, this is exactly the approach we take. In today’s fast-paced tech world, where speed matters and innovation is not waiting for anyone, learning how to make tools work for us without necessarily becoming experts on developing them is critical for the success of any professional, let alone data scientists and engineers.
So, what other practical tips for using AI to map big data were shared at the webinar?
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