Watch Episode 2 of Future Proof now on YouTube.
Read the automated transcript below, which has been condensed and edited for clarity:
Microsoft’s lab for experimentation
Lauren: Thank you so much for joining us. Our goal for this Future Proof series is to help create new knowledge around these invisible forces in society of data science and AI and how they’re changing science and the world around us. We’re very excited to have you here today to share all your expertise as a design and engineering leader. And perhaps to start, it would be great to learn about your goals at The Microsoft Garage, where you serve as a Director and Envisioner.
M Pell: Well, first of all, thank you for having me. I love that you said invisible forces. It’s almost as if you read my books ‘cause I always talk about surfacing the invisible, right?
M Pell: But you know what, innovation is actually an invisible force, just like anything else, right? It’s not something you can put your finger on. Lots of people like to put it in a box and say, this is how we do innovation–we press the button, and something pops out. It’s not like that. And you know that very well.
So here at The Microsoft Garage, it is a worldwide innovation program for Microsoft, but what it really is, is an opportunity for our employees and some of our customers to take their ideas and make them real. So that’s this sort of mechanism that we’ve built. There are physical garages in 14 places around the world, but it’s really the program that’s the more important part. Just knowing that if you have an idea, if you band with some of your teammates or friends, then you want to try to make it into a working prototype, just to see if it could actually work. That’s what the Garage program is all about. So it’s very exciting. And for me, there is a lot of that invisible forces stuff because there’s people’s inspiration, you know, their passion. That goes along with innovation.
What the most unexpected and impactful outcomes are born out of
Lauren Xandra: Fascinating. It sounds like what’s most important is this program around prototyping and ideation. So what are some of the examples of the most unexpected or impactful experiments that have been born out of the Garage?
M Pell: It’s funny. We’ve been running The Microsoft Global Hackathon for the last nine years. So that’s this gigantic worldwide event where employees from all over the place form teams, they sort of hack up whatever it is into some form, they don’t have to code. It could be anything. It could be ideas. Business processes.
But the most interesting projects to me and a lot of my teammates on the Garage team are the things that are super personal to people. They’re not about the greatest new technology or how we can apply what we’ve already built to something new. It’s about trying to help their friends, their family, their communities. I mean, people are doing this out of their heart, it’s something that’s really important to them. And so those kinds of projects are always the most interesting, hands down.
One of the examples that you and I have talked about over the years was something that turned out to be the Microsoft adaptive controller, right, for the Xbox. It unlocked accessible gaming. But that started off as a single person in the garage in Redmond tinkering with an XBox D-pad, trying to help his buddies to be able to game. Because they were veterans, some of them weren’t able to use a controller in the way that abled people can. And so he just had this idea to help his friends and he started hacking on it. And, you know, we saw him working on this idea and said, Hey, there’s a hackathon next week. Why don’t you join? Why don’t you form a team? So like those, those kinds of projects, and he did.
And fast forward and that’s turned into this amazing product that’s unlocked gaming for tens of thousands of people around the world and actually turned into the Microsoft Super Bowl ad that year. So from a single person wanting to help his friends to, to something amazing for lots and lots and lots of people around the world. So those are always the best kind of projects.
How AI and spatial computing will transform communications
Lauren Xandra: That’s a really interesting example. And I’d love to shift back to something that you alluded to earlier, which is really the books that you’ve written around data and how that’s shaping communications. Your book, The Age of Smart Information delves into how AI and spatial computing will play a part in transforming communications and it would be great to hear more about your thesis in that book as well.
M Pell: So it’s funny, isn’t it? That I wrote that book five years ago and the stuff that I wrote about still hasn’t quite happened. We’re rapidly approaching that time.
But the book was written from the future. It was sort of a note to myself, you know, a conversation about the things that I felt were gonna happen and many of them have already. The central notion is that we all know data can’t do anything by itself. Right. It has to be cleaned and analyzed and looked at and applied in different ways. But the truth is everything that we create, whether it’s our reports, our charting, you know, music, a video, a tweet; it doesn’t matter. All of that information has the ability to be seen from different altitudes, right? We could sort of, if you had a slider, you could slide it all the way up to, summary or metadata, or all the way down to the very, very detailed data.
We as people know exactly how to describe anything in those terms. We could go all the way to the very abstract keyword sort of description all the way down to a super detailed description of something. The general thesis of the book is that information or data itself will know how to do that.
You know, we won’t have to make sense out of everything anymore. The data, whether it’s a tweet, a video, a song, a report, anything–will be able to communicate with you and sort of make that connection at the right altitude for your context and for you personally, to be able to get the point across.
So if you’re like running around, busy, you’ll get a very quick summary. If you are like you, you’re doing deep research, you’ll get all the data, all the information that you need. Information will be able to tell you its own story. Which is the other thing that we’re very rapidly approaching, right? With generative AI and all these amazing experiments. Like every day, there’s something brand new. It’s just mind blowing. We are getting to the point where the model’s going to turn inside out for data. We, as people, will no longer have to act upon the data ourselves; the data will start to actually be able to figure out how to communicate and how to get its point across.
Lauren Xandra: Really interesting, I especially liked how you position this as a note from the future, one that perhaps one we haven’t quite arrived at yet. As we’re approaching that imminent state, perhaps as you see it…
M Pell: Do you mean, do you mean Skynet? No.
Lauren Xandra: Yep, exactly. You know, what are the steps that you are seeing unfold now as it relates to AI transforming business insights, communication, management?
M Pell: Yeah, this is so exciting, right? Hasn’t just the last few months been incredible? I’ve been doing this for a very, very long time. I have never seen the pace of innovation I’ve seen in the last two months. It’s astounding.
So, what I see happening now is generative AI is amazing–it’s really good at what it’s for. But the most amazing thing to me is what people are figuring out to do with it. That’s just fascinating. Being able to write AI tools, to make it so that you don’t even have to do anything right. You don’t have to learn how to use a new tool to be able to, you know, get an app to exist. Like being able to write all the code, be able to debug it, be able to deploy it, know to package it, to design for it.
So I think the systems of tools and processes that are coming together in order to do things on our behalf, that maybe we don’t have the time or the expertise to do ourselves. It’s mind blowing now that we’re getting to that point.
Advice for entrepreneurs taking advantage of compute
Lauren Xandra: Great. And you have a front row seat to how all of this wild speed of innovation is playing out, with Microsoft’s investment into OpenAI. You’re of course right where it’s happening when it comes to generative AI. Any insight that you can share into what it’s like really being on the inside of a company with such a powerful AI advantage? And what’s your advice for others out there who are considering more deeply integrating AI into their businesses?
M Pell: It’s very exciting to be here at this point in history. I mean, this has been coming for quite a while. This is not like an overnight thing, right? It took the world by storm, you know, perhaps overnight, but many, tens of thousands, hundreds of thousands of people have worked on this for a very long time around the world, at many companies, many institutions, universities. So it’s incredibly exciting to be at Microsoft. I have a lot of respect for Microsoft. I think as a company, we have a lot of integrity. Even to the point where we started working on responsible AI guidelines and publishing them like six years ago. So Microsoft published a book called The Future Computed, I believe, that laid out what we thought, AI was going to bring, all the different changes to both people’s careers and industries.
But Microsoft has been very responsible as a company in trying to be transparent, trying to let people know exactly what our stance is, how we’re approaching this? Trying to publish the principles that we use ourselves in how we design all of these AI services and systems. So I’m very proud of what we’ve been trying to do as far as being transparent, open, and collaborative in the way that we’re doing this.
As far as advice for other people. Wow. You sort of have to keep your eyes open, you know, I’ve started in the mornings, trying to just sort of look at all the different innovations, in your social media feed. You’ll see all kinds of stuff come by. It’s just so fun to see what people are doing. So I would suggest if you really want to start using AI with data, you have to pay attention to all the experiments that are happening out there. Even if you’re not quite ready to do some of them, they’ll give you ideas. And you don’t want to be caught by surprise. Because that’s what’s going to happen with a lot of people. They don’t realize, like you sort of have to zoom out a bit and understand where we are in history right now.
There’s a gigantic sea change happening right in front of our eyes. And we all have to be aware of it. Our jobs are not going to be the same. Our roles are not gonna be the same. Our tool sets, our systems, our communities, the things that we interact with in society–it’s all changing. You know, maybe for the better, maybe not. We’ll see. But it is changing.
And so I would say, try to learn as much as you can experiment. Go look at these image generators, right. Look at all the different versions of GPT that are out there in various forms and get familiar with them. Just try them, see what you can do or what you can’t do. Look at what Microsoft is doing with the Copilot series, almost across our entire product line, being able to get our tool sets to do things on our behalf. It’s pretty astounding. We’ve been working toward that for a very long time and now it’s here.
Lauren Xandra: Excellent. And you’ve been tracking these experiments across data science and AI and where that future is headed for quite some time. Any specific resources to look out for, or to follow for someone who wants to be “future-proofed” as we say, in, in this area?
M Pell: Mm-hmm. Yeah. So I’ll, I’ll break that into two parts. The first part is what tools to pay attention to. And again, I’m like, sorry, I don’t mean to sound like a Microsoft commercial. But look carefully at the videos that have come out recently for M365 Copilot. So what you’re able to do with data, with reporting different types of graphs and charts. Being able to analyze, having the AI Copilot be able to look at something and tell you what’s happening, so not just to recap a document–it’s what’s happening in this meeting and strategically, what do we need to do about this and how does this affect what’s about to happen next quarter. Being able to look at spreadsheets and databases and actually analyze. So you can talk to it like a person, like one of your coworkers and say, “Hey, not only what were the most profitable products this quarter, but what should we do to ramp up manufacturing next quarter?” You know, use it to do forecasting and prediction.
Because the next big thing for business, and that’s what I talk about in my new book, Visualizing Business, is the use of simulation, right? So not only doing forecasting, but actively simulating what’s about to happen–that’s sort of the next big chapter that we’ll get to in AI.
Lauren Xandra: I appreciate that advanced look. So exciting to talk to you. Thank you so much for your time, M!
M Pell: Always! See you soon.