Passionate teen coding phenom imagines the future with AI
I started coding at age five because I was interested in what was going on behind the scenes on the computer. Something as simple as how to change the color of the screen fascinated me. And I kept coding because my parents encouraged me. And now it’s a passion. A hobby. Something I just love to do.
AI ignites a new passion
When I was around nine, I almost lost that passion. I’d already created my own iOS app and felt like programming was very finite, very literal. But then I saw a video of IBM Watson playing and winning on Jeopardy! and it immediately reinstated my faith in programming and technology. Watson could understand the clues, and their context, and answer questions in under three seconds. We humans have a hard time doing that!
I knew that AI was the future of technology, and that I had to explore and find out more. That was at the end of 2015. I found out about and started using the Bluemix platform, which, at the time, was how IBM made Watson APIs available to developers.
IBM Watson makes AI accessible
Watson fascinates me. It takes really complex, really powerful deep learning technology and brings it into the hands of more and more developers all the time. If someone wants to use one of the 20 or so different Watson services available on the IBM Cloud, they don’t need to have any expertise in machine learning. They don’t need to know anything about the infrastructure behind it. Just plug in and go. If they want to customize something, or even create their own custom deep learning models, they can use the Watson Knowledge Studio and Watson Studio to teach Watson.
Watson makes deep learning accessible to developers around the world, so they can implement those powerful technologies into their everyday applications. I think businesses are really starting to use deep learning technology and AI to understand all the data they couldn’t tap into, before now.
AI – augmenting human ingenuity
Approximately 80 percent of the data that businesses generate and collect is unstructured. By that I mean it’s not a structured file, not a database; it has no metadata. It’s natural language used in context; for example, in customer service transactions or on social media. We humans have this innate ability to understand natural language. But the problem is that we can’t work with massive amounts of data.
If I asked someone to analyze 12,000 tweets and pull out the average sentiment of those tweets, they wouldn’t be able to do it in the next two years. A computer could easily go through those tweets, but it can’t understand the sentiment. That’s where AI and machine learning come in. With machine learning we can combine the best of both worlds. We give Watson our ability to understand sentiment, and we get Watson’s ability to crunch massive amounts of data really, really quickly.
Imagining the future with AI
What also captures my imagination is the application of AI and deep learning in healthcare. I absolutely love the idea of taking machine learning algorithms and saving or improving people’s lives. Already, I’m working on a couple of different projects in mental healthcare, including a social media-based early-warning system for depression in teens and an AI-powered chatbot to supplement crisis-center hotlines.
The bottom line is that in business or healthcare – or in any other field where we need to crunch huge amounts of data and where we need to understand sentiment or unstructured information – that’s where AI and machine learning are going to add the most value. As you can tell, I’m pretty passionate about spreading the word about AI and Watson, and about using it to improve and refine how we apply and refine technology to improve peoples’ lives.
Watch the video to see how Tanmay Bakshi is using Watson to push the boundaries of AI: