>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the key. We air live at Stanford University for the fourth annual Women in Data Science Conference. The Cube has had the pleasure of being here all four years on I'm welcoming Back to the Cube, one of our distinguished alumni Janet George, the fellow chief data officer, scientists, 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, Annie will women in data science. And it's been, I think I met you here a couple of years ago, and we look at the impact. It had a chance to speak with Margo Garrett's in a about an hour ago, one of the co founders of Woods saying, We're expecting twenty thousand people to be engaging today with the Livestream. There are wigs events in one hundred and fifty locations this year, fifty plus countries expecting about one hundred thousand 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 distal 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 off A 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 have not. And in order to be in the half category, you have started to embrace a 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 Vest Initial is here, where the leaders in storage worldwide and we'd like to be at the heart of their data is. >> So how has Western Digital transform? Because if we look at the evolution of a I and I know you're give you're on a panel tan, you're also giving a breakout 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 ableto tow harness. Aye, aye, for good. >> So we're not just a company that focuses on business for a We're doing a number of initiatives One of the initiatives were doing is a I for good, and we're doing data for good. This is related to working with the U. N. 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 a eye to better mankind. >> One of the things that is going on at this year's with second annual data fun. 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 air likely tohave oil palm plantations. And we know that there's a tremendous social impact that palm oil and oil palm plantations in that can can impact, such as I think in Borneo and eighty percent reduction in the Oregon ten 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 and the impact in the potential that a I 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 boats predictive models. Now we have the data to put put out massively predictive models that can help us understand what change would look like twenty five 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. Alan, it's the state at scale. We have enough technology now to adapt to our ecosystem, to look at disappearing grillers, you know, to look at disappearing insects, to look at just equal system that be living, how, how the ecosystem is going to survive and be better in the next ten years. There's a >> tremendous amount of power that data for good has, when often times whether the Cube is that technology conferences or events like this. The word trust issues yes, a lot in some pretty significant ways. And we often hear that data is not just the life blood of an organization, whether it's in just industry or academia. To have that trust is essential without it. That's right. No, go. >> That's right. So the data we have to be able to be discriminated. That's where the trust comes into factor, right? Because you can create a very good eh? I'm odder, or you can create a bad air more so a lot depends on who is creating the modern. The authorship of the model the creator of the modern is pretty significant to what the model actually does. Now we're getting a lot of this new area ofthe eyes coming in, which is the 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 next. So, for example, if you have malicious attacks on your website or hear militias, data collection on that data is being used against you. These adversarial networks and had built the trust in the data and in the so that is a whole new effort that has started in the latest 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 H vac systems with DDOS attacks or it's ah baby boomer, who was part of the fifty million Facebook users whose data was used without their knowledge. It's becoming, I won't say accepted, but very much commonplace, Yes, so training the A I 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's >> more, I have a 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 divorce, 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 off, then you are the human auto or the creator ofthe Anay I model. And now the marshal has self created because it a self learned who owns the patent, who owns the copyright to those when I becomes the creator and whether it's malicious or non malicious right. And that's also ownership for the data scientist. So the group of people that are responsible for creating the environment, creating the morals the question comes into how do we protect the authors, the uses, the producers and the new creators off the original piece of art? Because at the end of the day, when you think about algorithms and I, it's just art its creation and you can use the creation for good or bad. And as the creation recreates itself like a learning on its own with massive amounts of data after an original data scientist has created the model well, how we how to be a confident. 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? Is a society in terms of catching As you mentioned, we can't. We have to continue innovation. Where are we A society and society and starting to understand the different principles of practices that have to be implemented in order for proper management of data, too. Enable innovation to continue at the pace that it needs. >> June. I would say that UK and other countries that 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 Aye aye expert in what we're creating. What? We see its scale when we deploy an aye aye, modern in production. What have we seen as the longevity of that? A marker in a business setting in a non business setting. How does the I perform and were now able to see sustained performance of the model? So let's say you deploy and am are in production. You're able inform yourself watching the sustained performance of that a model and how it is behaving, how it is learning how it's growing, what is its track record. And this knowledge is 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 will be surprised. And I think that we have started a lot of discussions. The community's air coming together. The experts are coming together. So this is very good news. >> Theologian is's there? The moment of Edward 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 policymakers. We want the policymakers 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 a >> I >> 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 a right to help us trust the data or surface the issues that are causing the trust. >> Janet, what a pleasure to have you back on the Cube. I wish we had more time to keep talking, but it's I can't wait till we talk to you next year because what you guys are doing and also your pact, true passion for data science for trust and a I 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 the Cuba and Lisa Martin live at Stanford for the fourth annual Women in Data Science conference. We back after a short break.