LR: Is deep learning being used successfully today in enterprises?
HH: It is being used commercially today. It's the basis for a good number of applications you have today that have speech recognition. People are starting to try to use a little bit of deep learning in chatbots for customer service. It's not too broadly deployed in that context. The social media providers are using it when you upload pictures to your social media and they are automatically labeled. We are starting to see a lot of enterprises look at it as a core detection technique. The computer never gets tired looking for anomalies or fraud or other things like that so in a lot of other cases, it's definitely showing promise.
LR: What kind of timeline are we looking at for deep learning to be mainstream in the enterprise?
HH: I think that the type of advancement we're showing here we hope will put us on a different inflection rate on our deep learning capabilities in general across a variety of use cases. The wait time has meant you really have to be using it in a space where it's worth waiting weeks for the answer; where it has to be far more effective than any existing technique to be worthwhile. With the productivity of deep learning science dropping to hours and minutes, we hope this will change the rate of adoption. In general, it's taken several years every time people try to apply deep learning to a new type of data and problem for the model and capabilities to be mature and understood enough to be comfortable deploying them. In a number of use cases we talked about, we're a couple years from deployment, but we hope this type of innovation will help pull those learning times in and help people get to models that are understood and well validated and have high accuracy such that they can be deployed faster.
LR: What type of investment does deep learning require for the average enterprise?
HH: The process of doing deep learning usually starts with data collection. Some enterprises are in excellent shape in terms of that. They have stats and characteristics of what they're running all ready to go and they understand anomalies and what they are looking for. Data is labeled. For others, it's a matter of beginning that process of data collection. That's true of any artificial intelligence, not just deep learning. You have to have your data in order. Once you've done that, the process of applying a deep learning model actually goes relatively quickly. There are a good number of models available in open source, so a lot of teams are refining models to get the end outcomes they are looking for. It's a highly iterative process of applying data to model to see if you got the outcome you wanted and rinse and repeat, effectively. Then actually deploying a model, doing the inference of the model, just means putting it in where the new events are happening. You have a system running already doing some type of monitoring or measurement or prediction, and you just enhance that system with this model. There is a data collection stage, a data discovery and exploration stage to get the model right and the deployment stage where you have some system -- you have something going today already and you are going to essentially increase the accuracy of that, make it better, using the model. For deep learning specifically, that's the big question.
LR: What is the business model for IBM? How will you commercialize deep learning?
HH: We are particularly excited about the commercialization aspect of this project. Deep learning is in its early days with really long wait times, but people are starting to explore new data types using it. It's also a very hot topic in the research community. My team and I sit in the research division and work closely with our cogitative systems organization and IBM server group. We can provide this breakthrough now in a technology preview through IBM's server group. It's available online now for IBM customers to download and try. We deliver it as a binary distribution so they don’t have to worry about how to install it or what all the dependencies are. Open source bases can be complicated and tough to get going with. We're taking an approach of providing in a "download and go" way with people who have IBM Systems. You can get going using it in the cloud with IBM Systems or a customer site if you have data privacy concerns or other restrictions. They can purchase IBM Systems and download software and get going with it.
LR: What is your biggest piece of personal advice for women building their careers in the comms industry?
HH: One of the greatest pieces of advice I have for women is to get a strong set of mentors. I have been privileged to have a number of mentors in my career. They each help me with different types of things I try to deal with be it dynamics in my team with coworkers or where should my career be in a couple of years -- different types of questions. For me, that's been one of the absolute key aspects to being a woman in tech -- to have a set of people, male and female, who have been advocates and have sanity checked what I'm thinking. They have encouraged me when I didn't enjoy what I'm working on and wasn’t sure of next steps and have always been there to talk me through whatever it was I needed to understand better about my career. I think I see a lot of women will hesitate to ask people to mentor them. I encourage you to just do it -- everyone is almost always flattered to be asked to serve in a mentoring role. It really can only help.
— Sarah Thomas, , Director, Women in Comms