Yael Garten, Director of AI/ML Data Science and Engineering, Apple
Interviewer: Verne Kopytoff, Senior Editor, FORTUNE; Co-chair, Fortune Brainstorm A.I.

Brainstorm A.I. 2022 - How To Turn Good Products Into Great Ones With A.I. 

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How To Turn Good Products Into Great Ones With A.I.

Brainstorm A.I. 2022 - How To Turn Good Products Into Great Ones With A.I.

consumers expect elegant design and products that are intuitively easy to use right beneath the smooth surface. The smooth interface, artificial intelligence already powers much of its operating system from Siri to face I. D. But how is this tech giant leveraging Ai and machine learning into emerging products and services? Will ask the senior Apple executives to share her insights into Ai advances at the world's most valuable company. She is also on the board of a 169 year old retail company and can address how a legacy brand embraces the technology and is able to revitalize its processes and products. Anybody know what that retailer is? Please welcome Yael Garten, director of Ai ml data science and engineering at Apple. And member of the board of directors at Levi Strauss and co for an interview with Fortune Senior editor VErne kopytoff. Hello everyone and thank you for coming. You thank you thank you. Uh to start things off, could you set the stage and list a few? I mean Apple has so many products. Could you list a few that um that use ai your favorites for example. I love them all. Um But yeah, if you think about our hardware, software and services ai and Ml Ds really extensively across them. To name a few of these, a couple of them in the information seeking space uh theory and search research for music and and movies and a lot of great content news um that those are some examples um you already mentioned face I. D. Earlier. Um If you think about the accessibility area, a lot of what we focus on is making technology accessible to everyone and making the world accessible. So one of my favorite features that we recently announced is a door detection which enables people that have low vision or blind to use their iphone camera to detect the door that they're about to near. And it reads two texts that you see on the door, how to open the door handle. So that's really a great example of making the world accessible and using technology for that. And it's powered by machine learning. Um and then maybe a third example in our in our health uh areas of investment um is um what we call walking steadiness. Um So this is an example where your iphone detecting how you walk and your stability and and uh we are able to use machine learning in this case to identify trends in your walking stability um and and identify you when there are changes to those trends and how you might take actions to improve that to help reduce uh risk of falling and other other kinds of things. So really expensively across hardware, software services, those are just a few. And Apple is so big and has billions of users. What are the unique challenges of dealing with data for such a huge operation. Yeah, well I don't know if this is a challenge. I kind of view it as an opportunity and one of the things that we um that we take very, very seriously is is the privacy of our user data. I mean, so that's something that I would just highlight as um really we put a lot of focus and thought and energy into how can we create a great machine learning based products that are also privacy preserving. Um and so that involves right, taking more time, more thoughtfulness in the design of features. How can we do more and more on the device, really leveraging the integrated hardware, software staff that we have thinking about using the hardware using the framework building applications um that enable us to preserve privacy and develop a great machine learning based products. Um so that's one of the areas where I'd highlight as we we spend a lot of time and and focus on that as well as making that something that third party developers can, can leverage as well. And when you're working on your projects? I mean, you've worked on so many, what are the key lessons and sort of pitfalls you've you've come across? Yeah. Um well maybe a company of any size, not just one the size of Apple. Yeah, well if you're if you're talking about how you implement machine learning and maybe more more broadly, I would say there's this life cycle of uh when you want to deploy a machine learning based solution, um how do you take into consideration all of the phases? And this is, again, really speaking pretty broadly, how do you take into account all of the phases of that development life cycle and plan it out? I think I mentioned earlier this lifecycle of machine learning development right. Which starts from what's the problem? We're trying to solve what's the product experience, what's the workflow? Um and this can be really in any company. Right. Apple at Levi's um and really any company. So if you think about what's the product experience and then you move on to what the data that we need to collect um to be able to create that machine learning based experience and then how are we going to train the model and how do we ensure that the data is representative? And how do we then evaluate the model before we deploy it? And how do you then ship it in terms of a really cohesive user experience and how do you later continue to evaluate the performance of that model as it's out there in the real world and as the world is changing and people are changing macro trends are changing and continuing to have that responsibility of thinking through all of those steps. I would say that in general, um underestimating the um the amount of time and investment it takes is probably one of the pitfalls that I would say, you know, entity, small and large industry academics, you know, etcetera underestimate. And so just thinking about that in advance and planning for that is something that's pretty important um, as well, putting in place the structure in terms of, you know, team and organization to have um, adoption of machine learning based technologies as something that is part of the conversation and something that companies and entities are really planning for. So that's probably kind of two of the common pitfalls and what's the level of investment, what's the workflow and then what's the team and structure that we need to have in place for that adoption and what's the, what's the secret to knowing that you're even building the correct models? What steps do you take to make sure you are? That's a great question. Um, you know, I would say it really just comes back to what's the use case, So really what is um and again really broadly, you know, what's the business use case you're trying to solve? What's the product experience you're trying to solve? Um so if I take, for example, if I think about how we develop things that Apple, we really try to think about what is that end user experience that we're trying to deliver and really obsessing over that at the beginning and ideation phase all the way through the end stage. And I think that that's really important because then, you know, she learning for example, which was talking about a lot today is is an an ai part of that, but that's not it. So, having uh, that question really guide us then brings the right people to the table. Right? So if its product managers, designers, engineers have different types of hardware, software, machine learning data engineers coming together uh bringing in, you know, members from from marketing and from the legal team to really think through what are the ethical implications of what we're doing? Um What are the intended consequences and what are the unintended consequences that might come out? So bringing together all of those components as you're developing the feature, as you're developing the solution is gonna be some of the key aspects to ensuring that it does what you want it to do and it doesn't do what you don't want it to do or that you've carefully thought through all those steps as you're going into production. Um and then again really putting in place um mechanisms and rigorous evaluation programs to evaluate after deployment. Um is it working as intended? If not, what are the changes to make? I think a big part of that is really having metrics that help you understand how the models and how the solutions are performing in the real world and being able to adopt and adapt to those metrics as kind of the North Star of are we on the right track? Is it going where we wanted it to, we go into questions in a in a couple minutes, so please have some ready. Uh Now you're on the board of 100 and 70 year old company levi's what are the challenges of getting such a legacy company going with Ai and what are some of the projects they've been able to pull up? Yeah, great. Um yeah, Levi is a really iconic brand and it's one that I would say it's actually been investing in technology and innovation for for many, many years and including in recent years in data and AI. Um and so there's it's been really exciting to see because a piece of it has been, you know, digitizing um data that wasn't digital before. Right? So moving everything from manual to digitized in a way that it can be a basis for building applications on top of it some of it interestingly has been taking data that they've had and making interesting use cases of it. So for example, think about archives of genes going back to the 1850s and how can you take um images of those genes and then start to think about ways and they're doing this in ways of um uh which are ai assisted to design new products and new fabrics based on those archives and based on analysis of those. Um So that's really interesting ways of leveraging existing data and then also great examples of bringing in external data. So for example, um an example I really liked was bringing in weather data um and using building models off of that and showing how um actually when you have differences, for example, slight differences in weather. Um the team found that in Milan versus Rome, the period of time which skirts were worn was different or the color of denim that was purchased was darker due to the winter coming. And so that starts to enable the company to shift strategically in ways that enable different distribution of goods, uh different perch of goods that are coming to the different stores and you start to think about that at a global um you know, basis and it really starts to change how goods are distributed, how you operate distribution centers, how you plan your pricing, um how you order in advance and so it starts to bring in really agile ways of um running the company um in ways that you know, prior were done differently. And so as more and more of the company starts to embrace these different, great you know, examples of great ways that machine learning or uh you know, data data science have been integrated. More of the company starts to um to leverage that in addition to just really great ideas that many companies are doing and many of you that we spoke about earlier are doing bootcamps. Right? So how do you train more and more of the workforce to understand what data science is and what a i is and how you can leverage that so that it's really something that is coming from more and more pockets of the company versus for example, just one centralized team that's developing the solutions and so those are some of the examples of how they're approaching. Um Great, great. Let's go to questions, we have a microphone in the room um just raise your hand please and stand up and identify yourself. There we go. Hi, I'm kai from iron clad uh and I was wondering are there things that you have thought or advances that you've seen in ai that you thought would be coming for many, many years to come? I mean how do you design an organization that works around kind of the unpredictability of how advanced what tasks are going to be possible versus what tasks are um maybe disappointing in how long it's taken them to be accomplished. Yeah. Um you know, I think in terms of how you, how you organize, you have to have a portfolio, a mixed portfolio of investments. Right? So we know that the field of machine learning, artificial intelligence data science are rapidly advancing and in some ways that we can't yet anticipate. Right? And so you have to have an ability to have investments that give you, you know, a steady straight steady stream of wins, um whether it wins your business when your product wins to the team's agility. Right. A lot of um aI can be used now to really help teams be more operationally effective. Um So really having a steady stream of investments that gives you um you know gains while also making some investments in the team to seed uh you know early research or investigations that might yield fruit later but may also change as the trajectory of technology is changing and we've talked about it today, it's changing so rapidly. Um and so really having a mixed portfolio is is probably what I'd recommend. Do you have another question? Anyone, anyone anyone uh Now there's a lot of hype that Ai is a solution to every problem. What do you say about that I mean and what should executives ask themselves to decide whether ai is the solution? Yeah, so I agree with you that there's a lot of hype and I also do think that um while we're at brainstorm ai and there are a lot of great uh innovations and and and enablement that I bring. I don't think that Ai and machine learning is the solution to every problem. So I do really think it comes back to the core of asking what is the business problem, I'm trying to solve? What is the product problem trying to solve and being really clear on that to then be able to map out, you know, what is, what is the hammer that fits my all right um or sorry what is the nail that I need and what anyway, you understand what I'm trying to say but but I don't think it's the solution to everything. So I think it's really important to focus on what is it that you're trying to deliver and identify is very simpler solution or it's actually the right time and space for you to go and invest in a machine learning based approach or an artificial intelligence based approach. Um I think when you've decided that it is the time to invest um and that it is the right solution for you, I do think it comes back to you touched on this earlier, it really comes back to these kind of a couple of areas of investment that it's important to to do. I think one is really thinking through that workflow end to end of, how is if you're going to put a model into production, what is that workflow, who are the end users that are going to be interacting with it and really stretch that out and to end so that you thought about all the consideration going from what data you'll collect, how will this be interacted with. Right. Is it a dashboard? Is that a ui is it a product, is it in the field? You know, what else is going to interfere without being successful? What is going to enable it being successful and really thinking through that whole workflow into. And I think we see a lot of examples where in clinical health care, it's just uh the there are a lot of great ideas on the technology side, but getting it into the clinical workflow um isn't happening and it could be because the doctors and nurses don't know how to interact with it is because it could be because I haven't thought through the financial construct of who's going to pay for it. So I think that thinking through that workflow is really important of how is somebody going to interact with the model doesn't need to be explainable. Um Is it explainable? Right, so a lot of those aspects um the second is really say around the data. Right, so how are you going to the data cleanse the data, train the day to make sure that the data is representative and kind of protect that data and really thinking through that workflow. Um And probably the third area is around um the team, right who is going to be implementing the models and how does that really get structured in terms of your organizational structure for um joint success and joint accountability. Um So thinking through what you have, is this under a general manager working closely with a data leader, where you have shared responsibility for implementing that model in production um and where the outcomes are you know feeding back to your business. So really thinking through that construct is something that helps set it for success. Yeah, thank you so much for coming. Thank you. Thank you all