We recently sat down with Mohammad Shokoohi-Yekta, Senior Data Scientist at Apple.
Mohammad is currently a Senior Data Scientist at Apple and a Lecturer at Stanford University. Prior to joining Apple, he worked for Samsung, Bosch, General Electric Research and UCLA on predictive modeling projects. He received a PhD in Computer Science from the University of California, Riverside and B.Sc. from University of Tehran. Mohammad is also the author of the book, “Applications of Mining Massive Time Series Data.”
How have you seen Big Data changing traditional industries?
Traditional industries now understand if they can’t catch up in AI, they will sooner or later be out of the market. Some retail stores have gone bankrupt recently mainly because they couldn’t catch up with Amazon’s technology which is eating the whole retail market.
What unique data challenges do you currently face?
According to IDC, by 2020, more than 4 billion people will be connected to the web, alongside over 50 billion connected devices which will produce over 50 trillion gigabytes of data. Unstructured data generated from IoT devices makes analysis very challenging. The variety of format and modality of data will be more diverse than ever. The rate of data production will also not let current algorithms analyze correlations of streaming data vs offline data.
Does being involved in academia and private industry give you a unique insight into the future of data technology?
It absolutely does. In academia you get the chance to research and develop new methodologies in AI and in industry you get to make apply it and deal with real world challenges. To conclude, academia is fueling industry big time!
What is going to be the next big game changer in the data space?
Real-time analytics has become a huge buzzword. Predicting the next move of customers is based on real-time analytics. For example, some big corporates have already harnessed intelligence into automated phone calls to analyze customers’ behavior and temper over the phone and make the best decision in real time. For instance, if a customer sounds angry, the automated system will forward their call to a promotion specialist to in order to satisfy their needs.
Imagine a store which a customer puts an item in their cart but before leaving, decides not to buy it. Real-time analytics would suggest emailing a coupon for the same item to corresponding customers so that they are persuaded to purchase the items later on.
Real-time analytics has become a game changer in digital marketing as well. It is why Google monitors mouse pointers moving around the screen so that the logged data will suggest the most interesting parts/sections on the screen for each user. This data may also lead to predicting a users’ interest.
The development of AI is clearly accelerating with new embedded technologies, how do you see this developing in the future?
AI will be the top disruptor in technology very soon. Many giant are now out of the market because of the not embracing AI. As computational costs go down and AI methods become more advanced, the disruption of AI will accelerate.
Are there any new technologies or ideas in the machine learning space that you find particularly exciting or believe will be especially important in the next few years?
Wearable devices, IoT, VR and autonomous transportation are the hottest and leading technologies.
You can catch minds like Mohammed's at the Big Data Innovation Summit in Beijing on November 22 & 23.