I don't think anybody that enters in has that expectation, because I had an accounting, a marketing background, I used to work for PriceWaterhouse. The reason I got into computing is I was the youngest in most accounting departments and what they asked me to do was convert manual accounting systems into automated, which ended up being a corporate-wide thing. So I got exposure to a broad range of businesses and well as their computational needs, as well as the challenges -- the social challenges, the community challenges, the 'we're gonna do things different now' challenges, of taking a manual system and automating it. That's how I got my own background in computing and I was thrown into the fire!
I look at Microsoft as an interesting company, mostly back then is was MS DOS and they had a few applications such as multi-plan, so they really needed someone with a strong accounting background to be able to answer phones! So that's how I started, but things were growing so much that I ended up helping start the test department at Microsoft, so I helped start the quality assurance side of how we did things then and I shipped about 25 or 30-odd products, including the first versions of Word and Excel.
Back then, the competition was also as heavy as it is now, because we had to compete against Lotus 1-2-3, Corel WordPerfect, Borland and the likes. In a lot of cases the product shipping cycles were 7 days a week, for 6-8 months at a time and are pretty intense things. The product release cycles were more like competitive campaigns, with a focus on a specific competitors set of features. The reason I enjoyed Microsoft so much was that the company allowed me to move as I acquired skills and became better technically as well as managerially and as well from a visioning perspective, I was able to adjust and help spend the last nine years in research.
At Microsoft Research my focus is on R&D. I do technology transfers, so I help take the ideas of researchers worldwide and put the ideas into our products.
I never thought that we'd have sensors to determine what you're feeling and adjust the interface accordingly. I never though we'd be able to handle as much data as we can now. And I never thought that we'd be able to use our hands and more natural interfaces to be able to interact with devices. Admittedly, we still haven't got to the point where we have the singularity, where computers can think as much or as fast as human beings, but it was a bit of a different world. And also a separate world -- data processing was a separate world from our day to day lives. Now technology has been integrated and I hadn't anticipated that. Then there are the effects like the ability to respond to somebody worldwide and all those kinds of things. We had an inkling about that, but never thought those systems could come together.
Typically, research money is best in something that has potential, but needs a little extra push to move it forward. The problem is in many cases you have to ask yourself -- have you ever scheduled a breakthrough?
I have to ask that question because people have a sense that if you just throw the money into research something will come out and you'll see the benefits right away. Well, it's a long term investment and it's usually put in places where you don't necessarily know the outcomes, but you'd like to see that field move forward.
So, $38.2 million, it's not about the numbers but it's really about what you get out of the numbers and if you moved things forward. If I looked at any governmental program, that's how I would evaluate it: what are the results?
What I like about it is the evaluation process of the National Institute of Health, the National Science Foundation and some of the other government funded agencies is that they're willing to do public-private partnerships, except for things that affect national security. So we do a lot of work with a research lab which is an established security lab over in Washington -- the Pacific Northwest National Laboratory -- in high-performance computing data analysis because they're working on cyber security.
It's definitely the way to go; you have to have a balance between public and private. The thing that I would like to see in terms of that investment is a benefit to the tax payers in some form, whether it's cleaner water or whatever. I see a lot of that coming from our NASA space program and from some of the military technology. But the problem with any new breakthrough is there are always ethical issues. There's a lot of technology sitting in labs, I'm sure --ours included -- that may or may not make it to the market and the timing is critical.
I'm a PHD student at the University of Washington's Information Science school and part of a project called 'ICT for Developing Countries' so my focus is on how to take information and turn it into a development activity. There are so many development questions, I don't think I could create the ultimate model, but we can always do more. We also have to adjust what our existing product sets are to fit into those environments. Here's an example: we have a research lab in Banaglore, India. If we took our existing products and gave it to folks who didn't have enough training, they would soon revert back to where they were. Instead we are creating text-free interfaces using symbols that they can understand even though the literacy rate is low.
There are other projects as well, like the Gates Foundation. We still do some active work with them on the HIV project -- a huge project to take a spam filter that was shipped in Office and actually turned it into and algorithm that can analyse protein sequences and show you the highest probability of where the HIV vaccine should go, and then also derive a vaccine from that customised for each human being.
I think there are 5 billion people that make under $5 per day - is our investment and research helping them directly? I think it is. We just did a study at the university of 35 countries and analysed their cyber café's and their telecentres. And what we found was that the technology is starting to get an increase in numbers, so the technology that computers have created is actually having an impact. It isn't going to ultimately solve the digital divide, but there are exemplar places where they're having a huge impact.
I can't commit to a date, but I think we're being conservative saying 2019. Sensor array systems and other things are already getting close.
We also have to ask ourselves, sociologically: what level of technology do we want integrated into our lives? There are plus and minuses and although I don't think the minus is anything scary, you just have to ask yourself, do you have the ability to turn it off and get away from it if you don't want it.
GPS was an R&D investment which is now being used by everyone worldwide. Now the investment is around how do you do an indoor GPS and how can you make it as accurate as the one that sits outside. The augmented reality part of Microsoft's 2019 vision means I can go through a space and have a viewer in my phone or something else that can actually delineate things. If I need to go from A to B for example, how can I get there in the fastest way?
One of the things that makes research investment effective is to make sure you hire some of the smartest people in the world. It doesn't necessarily guarantee a breakthrough, but it increases the probability. If you're going to create a quantum computer, you're going to hire someone who has won a Fields Medal in mathematics, form a team around them.
So that's what we have done. We have 10 researchers at UCSB (University of California, Santa Barbara) and [Fields Medal winner] Michael Freeman who's working with 10 other physicists and mathematicians to not only create the theoretical framework, but also turn it into a real prototype. There are ethical issues with creating a quantum computer in terms of cryptography. There's also computational things like transmitting something from Point A to Point B and recomposing it.
The limitations that exist, either in the infrastructure or computationally, go away with quantum computing. And new sets of issues come around as well. That's the extreme of some of the more high-end things we do in the research realm.
We also work on stuff that's going to help developers, like how to make compilers better, how to write a spec at the same time you're writing the code, how to only write the code once and reuse it many times and all sorts of things. We're developing new languages and new operating systems. Some may or may not see the light of day, but concepts from those get over into the products.
We have prepared a concept demo of what it would be like to have your own personal robot. Microsoft has a robotics studio and we're actually doing some consulting with some of the main robotics users, like car manufacturers and the like, and trying to make improvements there. In our labs we have a flying robot, it's had problems with its sensors and determining glass and transparency, we've had a few crashes with it! But we anticipate this kind of personal companion idea will be there by the time we get to the 2019 office vision, and I think that will be part of it as well.
I don't think the robots will have personalities, but they will be able to offer a lot of functions. To me, there's this vision that somehow we can create how human beings are, but at the moment we have a big enough challenge trying to get computers to be effective at speech recognition and turning them into something that can emulate human vision, which is very hard to do, and getting robots to speak in a way that's very similar. Right now, you're nodding your head, your eyes are flickering, there's all sort of cues that I'm taking -- how do we create this same experience within the computational. So, putting personalities in robots I think is nice for Star Trek and those other things, but we have so many other problems to get to, if we solve those we might be able to work on computer devices that have more human attributes, but until we can get computers to see, hear and speak better than they do today, and I'm talking about 100 or 200 times better, then maybe this will change.
I am most fond of the projects that have had health effects; how you take a computational model and put it in an organic being and create a model of how that disease propagates. You can then take the existing analytical tools and apply it and solve that problem. It's not just about solving diseases that have existed -- it's also about treating and fixing existing problems. The meta point is combining multiple disciplines. So you take computer science, medicine and biology and put them together to create a solution. I think the age of specialty is great, but you really don't get the breakthroughs until you start combining things.