Right Here, Right Now: “Making Empowering Moments a Reality”
In this economy, customers cannot afford to have to wait. They have multiple needs that must be met quickly and efficiently, which means it is critical for them to have products that they can trust. Not only do customers want to feel confident in what they use, they also want those products to be in real time. Just ask Jonathan Ellis, the Chief Technology Officer and Co-Founder of DataStax. He knows what it means for things to be instantaneous, individualized, continuous, and global. That is why he and the experts at DataStax are equipped to deliver Enterprise-Ready features in a Right Now Economy. Their technology guarantees quality and customer satisfaction in the here and now.
Tamara: Can you share a story that inspired you to get involved in AI?
Jonathan: I was studying computer science in college when the IBM Deep Blue defeated Garry Kasparov. It’s common knowledge that Deep Blue ran on massively parallel IBM RS-6000 servers, but most people don’t know that what made it unique was that these servers were paired with custom chess microprocessors. This let Deep Blue analyze ten times more moves per second than could be achieved in software alone. In a way, this was an early (and expensive) echo of what we see now, with deep learning models today taking advantage of GPU, FPGA, and custom silicon acceleration.
Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.
Jonathan: DataStax provides DataStax Enterprise (DSE), a distributed cloud database that is at the heart of many applications in IoT, fraud detection, and personalization use cases.
Two things make DSE uniquely well suited for modern real-time AI applications. First, it offers a unified platform including both Spark Streaming and Graph capabilities. This simplifies building complex machine-learning applications like Deloitte MissionGraph.
Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?
Jonathan: One of the big challenges today is updating models in real-time in response to new information without doing a whole training cycle offline. I expect to see a lot of effort towards creating new training methods that offer reusable ways to accomplish this.
Tamara: How do you see your products/services evolving going forward?
Jonathan: It’s still too hard to build applications in general on a distributed database. This is a problem across the industry: everyone has a pretty good handle on the relational model at this point, but relational doesn’t scale once you need to partition it across multiple machines. So, we need a way to make data models scalable and partitionable automatically or semi-automatically.
Tamara: What is your favorite AI movie and why?
Jonathan: I’m not much of a moviegoer, but my favorite AI book might be Isaac Asimov’s I, Robot. Asimov’s robots were intelligent humanoids and this is the book where Isaac Asimov introduced his Three Laws of Robotics. Unfortunately, I understand that the 2004 movie did not actually have much in common with this.
Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?
Jonathan: Neither my co-founder Matt Pfeil or I had worked for an enterprise software company before, and we were completely naive about what that model entailed. We thought we could just let the world know that we were building the world’s best distributed database for hybrid cloud and that would be sufficient. Fortunately, we had advisors, including our early investors that clued us in to what building a sales force looked like.
Tamara: What are the 3–5 things that most excite you about AI? Why? (industry specific)
Jonathan: AI is to workers in the 21st century what automation was in the 20th, and while there’s both positive and negative connotations there I see the positive dominating. Yes, people lost their jobs when assembly lines replaced blacksmiths. But, for example, washing machines took something that took basically an entire day each week and turned it into almost an afterthought. Washing machines probably cost some jobs, but most people couldn’t afford to hire a laundry service; they did it themselves. So, automation made their lives immensely better. And I think that’s going to be the dominant effect as intelligent assistants mature — taking some of the drudgery out of knowledge work, making lives better a little at a time.
Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?
Jonathan: The skills gap for machine learning itself will narrow, but companies will still struggle in applying AI to business problems. I don’t think we’ll see many turnkey, AI-based solutions in the next three years; it will stay a Wild West of bespoke efforts at least that long.