The artificial intelligence tools we use today might have seemed impossible way back in the year 1995, when AI was in its infancy. Forbes recently consolidated some impressive statistics taken from Stanford University’s inaugural AI Index and the Stanford One Hundred Year Study on Artificial Intelligence. Among other things, these sources provide perspective on tracking AI activity. Some fascinating takeaways: Things like visual question answering, speech recognition, and natural language understanding have improved exponentially since Stanford first began tracking AI system performance. These tasks and thousands of others have transformed industries and will continue to do so – but there’s a catch: artificial intelligence requires massive amounts of data to perform essential tasks and continue to improve.
Why So Much Data?
Imagine asking Siri, Google, or Alexa a simple question, receiving the answer, and then moving on with your day. While your curiosity has been satisfied, these common forms of artificial intelligence continue working with the data that they exchanged with you. Behind the scenes, your recorded voice, the content and context of your inquiry, and any clarifying information you provided goes into larger AI search engines for further analysis. With millions of other consumers asking for help from AI systems 24 hours per day, 7 days per week, the amount of data is unimaginably enormous – and it keeps getting bigger as systems expand and grow their knowledge base. Add in other forms of AI and the numbers continue to grow. For example, self-driving cars must work with complex scenarios, and they must learn from their own experiences. A single Google self-driving car generates approximately 1 Gigabyte of data per second, which translates to about 2 Petabytes of data per year, per self-driving car.
Your Data Usage and AI
The more you use AI-powered cloud services like Microsoft, Amazon, and Box, the more data you’ll burn through. It’s not a big deal if your plan offers unlimited data, but you may find your service and devices slowing down if they aren’t able to keep up with the flow of traffic – and that’s before service providers throttle devices that they deem to be using too much data. For companies with employees who rely on API-enabled enterprise apps alongside legacy software, the data crunch can be overwhelming. Developers are hard at work bridging the gap between older technology and new, data-hungry artificial intelligence. For example, Sapho offers a platform that automates routine tasks and allows businesses to build micro apps that provide rapid, unified access to various employee endpoints for enhanced productivity. The name of the game here? Smart tools, and smart data usage, where only essential information is delivered, so there’s no need to cover costs associated with excess.
While data usage is a concern, artificial intelligence is here to stay – and it’s becoming more popular than ever. According to Statista, 84% of all enterprises believe that AI provides a competitive edge to the marketplace. Even when it’s working in the background – and consuming data along the way – artificial intelligence impacts many aspects of modern life on a daily basis. By adopting smart strategies, it is possible to reap the benefits of AI without drastically increasing the cost of greater data usage.
