 Live from Stanford University, it's theCUBE covering Global Women in Data Science Conference brought to you by SiliconANGLE Media. Welcome back to theCUBE. We are live at Stanford University for the fourth annual Women in Data Science Conference. theCUBE has had the pleasure of being here all four years and I'm welcoming back to theCUBE one of our distinguished alumni, Janet George, the fellow chief data officer, scientist, big data and cognitive computing at Western Digital, Janet, it's great to see you. Thank you, thank you so much. So I mentioned, yes, fourth annual Women in Data Science and it's been, I think I met you here a couple of years ago and if we look at the impact, I had a chance to speak with Margo Garrett about an hour ago, one of the co-founders of WIDS, saying we're expecting 20,000 people to be engaging today with the live stream. There are WIDS events in 150 locations this year, 50 plus countries expecting about 100,000 people to engage. The attention, the focus that they have on data science and the opportunities that it has is really palpable. Tell us a little bit about Western Digital's continued sponsorship and what makes this important to you? So Western Digital has recently transformed itself as a company and we are a data-driven company. So we are very much data infrastructure company and I think that this momentum of AI is phenomenal. It's just, it's a foundational shift in the way we do business and this foundational shift is just gaining tremendous momentum. Businesses are realizing that they're going to be in two categories, the have and the have not. And in order to be in the half category, you have to start to embrace AI, you've got to start to embrace data, you've got to start to embrace scale and you've got to be in the transformation process. You have to transform yourself to put yourself in a competitive position and that's why Western Digital is here. We're the leaders in storage worldwide and we'd like to be at the heart of where data is. So how has Western Digital transformed? Because if we look at the evolution of AI and I know you're on a panel stand, you're also giving a break out on deep learning but some of the importance, it's not just the technical expertise, there's other really important skills, communication, collaboration, empathy. How has Western Digital transformed to really, I guess maybe transform the human capital to be able to really become broad enough to be able to harness AI for good? So we are not just a company that focuses on business for AI, we are doing a number of initiatives. One of the initiatives we are doing is AI for good and we are doing data for good. This is related to working with the UN. We've been focusing on trying to figure out how climate change, the data that impacts climate change, collecting data and providing infrastructure to store massive amounts of species data in the environment that we've never actually collected before. So climate change is a huge area for us. Education is a huge area for us. Diversity is a huge area for us. We're using all of these areas as launching pad for data for good and trying to use data to better mankind and use AI to better mankind. One of the things that is going on at this year's whiz, second annual Datathon. And when you talk about data for good, I think this year's predictive analytics challenge was to look at satellite imagery to train the model to evaluate which images are likely to have oil palm plantations. And we know that there's a tremendous social impact that palm oil and oil palm plantations can impact such as, I think, in Borneo, an 80% reduction in the orangutan population. So it's interesting that they're also taking this opportunity to look at data for good and how can they look at predictive analytics to understand how to reduce deforestation, like you talked about climate change, the impact and the potential that AI and data for good have is astronomical. That's right. We could not build predictive models. We didn't have the data to put predictive, build predictive models. Now we have the data to put out massively predictive models that can help us understand what the change would look like 25 years from now and then take corrective action. So we know carbon emissions are causing very significant damage to our environment and there's something we can do about it. Data is helping us do that. We have the infrastructure, economies of scale. We can build massive platforms that can store this data and then we can alienize this data at scale. We have enough technology now to adapt to our ecosystem, to look at disappearing gorillas, to look at disappearing insects, to look at just ecosystem that we live in, how the ecosystem is going to survive and be better in the next 10 years. There's a tremendous amount of power that data for good has. When oftentimes, whether the cube is at technology conferences or events like this, the word trust is used a lot in some pretty significant ways and we often hear that data is not just the lifeblood of an organization, whether it's an industry or academia, to have that trust is essential without it. That's right, no go. That's right. So with data we have to be able to be discriminative. That's where the trust comes into factor, right? Because you can create a very good AI model or you can create a bad AI model. So a lot depends on who is creating the AI model. The authorship of the model, the creator of the model is pretty significant to what the model actually does. Now we're getting a lot of this new area of AI is coming in which is adversarial neural networks and these areas are really just springing up because it can be creators to stop and block bad that's being done in the world. Like so for example, if you have malicious attacks on your website or your malicious data collection and that data is being used against you, these adversarial networks can help build the trust in the data and in the AI. So that is a whole new effort that has started in the latest AI world. Which is critical because you mentioned everybody, I think regardless of what generation you're in that's on the planet today is aware of cybersecurity issues whether it's HVAC systems with DDoS attacks or it's a baby boomer who was part of the 50 million Facebook users whose data was used without their knowledge it's becoming, I won't say accepted but very much commonplace. So training the AI to be used for good is one thing but I'm curious in terms of the potential that individuals have, what are your thoughts on some of these practices or concepts that we're hearing about data scientists taking something like a Hippocratic Oath to start owning accountability for the data that they're working with? I'm just curious what some of your thoughts are on that. I have strong opinion on this because I think that data scientists are hugely responsible for what they are creating. We need a diversity of data scientists to have multiple models that are completely diverse and we have to be very responsible when we start to create. Creators are by default have to be responsible for their creation. Now where we get into tricky areas are when you are the human author or the creator of an AI model and now the AI model has self created because it is self learned. Who owns the pattern? Who owns the copyright to those when AI becomes the creator? And whether it's malicious or non malicious, right? And that's also ownership for the data scientists. So the group of people that are responsible for creating the environment, creating the models, the question comes into how do we protect the authors, the users, the producers and the new creators of the original piece of art? Because at the end of the day, when you think about algorithms and AI, it's just art, it's creation. And you can use the creation for good or bad. And as the creation recreates itself, like AI learning on its own with massive amounts of data after an original data scientist has created the model, well, how do we account for that? So that's a very interesting area that we haven't even touched upon because now the laws have to change, policies have to change, but we can't stop innovation. Innovation has to go and at the same time we have to be responsible about what we innovate. And where do you think we are as a society in terms of catching, as you mentioned, we have to continue innovation. Where are we as a society and starting to understand the different principles and practices that have to be implemented in order for proper management of data to enable innovation to continue at the pace that it needs to. I would say that UK and other countries are kind of better than us, US is still catching up, but we're having great conversations. This is very important, right? We're debating the issues. We're coming together as a community. We're having so many discussions with experts. I'm sitting in so many panels contributing as an AI expert in what we are creating, what we see at scale. When we deploy an AI model in production, what have we seen as the longevity of that AI model in a business setting, in a non-business setting? How does the AI perform? And we are now able to see sustained performance of the AI model. So let's say you deploy an AI model in production. You're able to inform yourself watching the sustained performance of that AI model and how it is behaving, how it is learning, how it's growing, what is its track record. And this knowledge has to come back and be part of discussions and part of being informed so we can change the regulations and be prepared for where this is going. Otherwise we'll be surprised. And I think that we have started a lot of discussions. The communities are coming together, the experts are coming together. So this is very good news for us. So the awareness is there, the momentum forward is building, these conversations are happening. Yes, and policy makers are actively participating. This is very good for us because we don't want innovators to innovate without the participation of policy makers. We want the policy makers hand in hand with the innovators to lead the charter. So we have the checks and balances in place and we feel safe because safety is so important. We need psychological safety for anything we do. Even to have a conversation we need psychological safety. So imagine having AI systems run our lives without having that psychological safety. That's bad news for all of us, right? And so we really need to focus on the trust and we need to focus on our ability to trust the data or AI to help us trust the data or surface the issues that are causing the trust. Janet, what a pleasure to have you back on theCUBE. I wish we had more time to keep talking, but I can't wait until we talk to you next year because what you guys are doing and also your true passion for data science, for trust and AI for good is palpable. So thank you so much for carving out some time to stop by the program. Thank you. It's my pleasure. We want to thank you for watching theCUBE. I'm Lisa Martin, live at Stanford for the fourth annual Women in Data Science Conference. We'll be back after a short break.