Three hundred fifty computers. Two hundred twenty-five monitors, Almost a mile of computer cable. More than 6,500 attendees.

Almost seven months of preparation. Three days of technical setup. Four hours of tear-down.

About 500 researchers, most of them sporting forest-green, short-sleeved polo shirts with a white TechFest 2008 logo on the left sleeve.

"It's a huge coordination effort," says Kendall Martin, director of technology for Microsoft Research, as his team begins the process of unplugging the TechFest 2008 computers, coiling the cables, and storing the gear until next year's event.

"You're trying to get hundreds of individuals to work together, so it's pretty complicated: arranging the card readers, assigning the demos, getting the demoers the equipment they need. It's quite an orchestration. And it's not just the technical staff. The planning starts months out. It's just massive. Every single player ... the content selection, the worldwide tour ... The number of people who touch this is huge."

And, with luck, so is the payoff. James Oker should know. As director of Program Management for Microsoft Research, he heads the team whose responsibility it is to stage this annual celebration of the future of technology.

So, Jim, how'd it go?

"This is an awesome event," he responds. "I've talked to people ranging from senior executives to program managers and developers working on products I've worked with, and everyone was super-excited about what they saw here.

"I have a lot of follow-up to do with a bunch of people after the show that I think is going to lead to better Microsoft products in the next couple of years."

TechFest 2008: It's a wrap. 

OK, consider me dazzled. This afternoon, I got to spend a few minutes with Yuval Peres, principal researcher within Microsoft Research Redmond's Theory Group. In viewing the Theory Group's TechFest demos over the past few years, it has occurred to me that computational theory occupies a space where computer science intersects with philosophy. Maybe that's why that team always seems produce posters and animations that approach the nature of art.

Peres is an amiable, bespectacled man who speaks in the precise, clipped diction of one for whom mental meanderings would represent nothing so much as a waste of time and energy. He speaks in well-formed sentences woven into crystalline paragraphs of model clarity, This afternoon, he discussed his two TechFest 2008 demos--one entitled Algorithms and E-Commerce, the other Probability and Networks. But those mundane descriptions don't do justice to his irresistable, effortless elocutionary logic.

I warned Peres that he'd have to speak simply for me to have a chance to grasp the gist of his work, and he graciously complied. Instead of inserting my comments and interrupting the flow, I present his comments virtually verbatim. In the course of a few minutes, he managed to touch upon the techniques credit-card companies use to make money, college basketball's March Madness, and the complexity of fairness:

"The main two themes of our demonstrations this year are optimization and network formation. In optimization, we're emphasizing the difference between global optimization, when you're trying to optimize the social welfare for everyone, and local optimization, in which you have lots of individuals participating in the system and each one is maximizing their self-interest. You get very different solutions when these two mechanisms are in play, and we have some pictures that demonstrate the different solutions.

"This theme of local versus global optimization is also present in a classical problem we call the overhang problem, how far you can get a sequence of blocks to extend over a table, where the classical solution, which is obtained by recursive local optimization, is much weaker than the optimal solution you get by global optimization. We have a demonstration and even a game where people can try out the difference for themselves.

"The other thing we have is network formation, and that's very basic to a lot of what Microsoft does. It's understanding networks, from the Internet to distribution networks to social networks to telephone networks. They all have different structures. We're analyzing the basic structures of how networks form.

"If you look at a picture of a network, it often looks like a big disarray, but we now understand there are three key ingredients in the formation of a network. If you want to move away from the details and understand the basic themes, they are: underlying geometry, and that's very important in, say, a road network and less important in the Internet, though it still plays a role there. There is optimization, either by some network administrator or central authority, or optimization by individuals joining the network or taking part in the network. And the third ingredient is just randomness, or luck. These ingredients come in different doses, and they yield very different-looking networks. We have some striking examples of that in our booth.

"On the application side, besides understanding networks, we have several applications. One is monetizing social networks where, when you have a network, it's not a huge one, but it's already complicated, and it's changing very fast. The challenge is for the platform owner to monetize the network, to get the return on the investment in building the network, and to do that is more tricky than it seems at first, because if you do it the wrong way, you discourage users from joining your network. You need to understand where funding flows into the network, where it flows out of the network--and both things happen at many places--and make sure, as the platform owner, you only tax where money comes out--from people who sell stuff on the network, from developers--and not from users, not from people who bring money into the network.

"That's one basic insight that's common to many networks. A credit-card company will always put the charge to the seller and not to the buyer. They'll even sometimes reimburse the buyer. It's the same principle: You take money into the system from somebody who makes money from the system. Once you understand that principle, you still have to locate where money is going out of the system, and for complicated networks which evolve all the time, this is itself a challenge where understanding the structure of networks plays a role.

"Another application we have is a joint project with the VIBE group: fantasy-sports prediction. It's designed to help groups that participate in office pools. They make predictions on something like March Madness or other sports contests--or it can also be other competitions, even political contests, where people make predictions--and after you have their predictions in, you want to know what's the chance for each participant to win the pool.

"You could just go over all possibilities if it's a small pool, but in March Madness, with 63 games, you can't go over 2⁶³ possibilities. You have to do a Monte Carlo algorithm. There are many algorithms you could use, and the initial algorithm used took three minutes per query. After we got involved, together with the VIBE group, we have an algorithm that works in one second per query, so we had a huge improvement to make this thing practical."

Yuval Peres in front of a poster that provides a graphical depiction of the effects of global optimization. Best to let him explain it:

"This poster shows what arises from fair allocation done locally. You have a thousand points spread here, and each one is trying to obtain exactly the same area as all the rest--not more than their quota, exactly their quota. The way they do it is each one grows a [color-coded] disc around it, at unit speed, and it captures all the area it reaches first. Some points get all their area nearby, but some points that are hemmed in by others, they grow this disc, but here [he points to a place where a disc's shape is warped by the presence of a previously grown disc nearby], they don't get any area, because this area is already captured by this center. They have to wait, and they somehow resurface out here [he points to an area far from the nexus of the warped disc] and get their fill of area here. At the end of the day, they all get the same area, but some of them have to go very far.

"It's a very simple method. It doesn't require any kind of coordination by the different centers and no central authority, and it gives a fair allocation. But, as you see, the structure is very complex, and some individuals have to go very far to get their quota. What turns out is that this is the hardest to do, when you want a fair allocation. ... Our contribution is: If you consider this scheme with quotas, it yields this very complex picture. Fair allocation is great, but without central authority, it's complicated.

I mentioned the other day that I had run into Feng Zhao, principal researcher in the Networked Embedded Computing group, who was looking particularly happy at the moment. Well, Feng has been demonstrating the reason for his delight over the past three days: a set of small, wireless sensor devices, branded with the Microsoft Research logo, that he and his group plan to utilize in a variety of settings.

"This is an example of a sensor that we use for research and we plan to put into a data center," Zhao says, holding one of the devices up for inspection. "The sensors currently in data centers are a different form factor. This one just got designed. It senses temperature and humidity, the kinds of parameters one cares about in a data center."

The sensors are special, Zhao says, because of their size, their functionality, and their energy efficiency.

"We're working on making sensors easier to manage and interoperable with other devices," he says, "and to make the devices speak the kinds of language and protocols that computers on the Internet speak. For example, on the Internet, computers talk in terms of Web services, TCP/IP, HTTP. But these are designed for much bigger machines, and this one only has 4K of memory. We want to shrink these very big things onto this device with a very small memory and processor, and, furthermore, use as little energy as possible: two AA batteries for a year."

It's part of a research project called Tiny Web Services, designed to develop sensor nets and Web-service techniques that can fit onto these tiny 4K sensors. The sensors are programmed to go to sleep when not needed to send information.

"Web servers anticipate a set of requests, and they have to respond immediately," Zhao explains, "so they stay on all the time, waiting for requests. This one uses AC currency, and Web services can register events to make it turn itself on. A number of these techniques are being used to make the footprint of Web-service processing much smaller and simpler but still comply with the standards of the Internet so the device can talk to other devices."

Zhao's group is one of the first to address this area, and its devices are among the smallest--and least expensive--to talk in terms Web services can understand. Beyond their small footprint, they also contain technology that is advancing the state of the art.

"By building our own devices," Zhao says, "we actually are building a stack of software. We own all the drivers, and we can program. Our goal is to develop this into a reliable system, and we're also interested in making them available to academics, to help people building applications for the environment, for conservation, or for energy savings. If you look at what's currently available, some of those devices are not as reliable or as easy to program as we'd like." 

 With that, he was smiling again. These days, it seems, Feng Zhao just can't help himself.

Rick Rashid, senior vice president of Microsoft Research, shows one of Feng Zhao's new wireless sensors to an audience of invited guests during Tuesday's TechFest keynote speech.

Andrew Herbert, managing director of Microsoft Research Cambridge, was busily reading e-mail when I passed him in the hall a few minutes ago, so I doubled back and asked him to provide a brief statement about the value of TechFest for his lab and for the larger organization. His response:

"The value to us is two things. It's making contact with people here in Microsoft's Redmond product groups. Obviously, being based in Cambridge, it's not so easy for us as it is for our colleagues here in Redmond. The other great thing is to see what people in the other labs are doing with their research, seeing how we can join our ideas together to build even more fascinating technologies."

And for Microsoft Research as a whole and Microsoft as a company?

"It's great for Microsoft Research, because it really is the thing that drives our technology-transfer agenda. And I'm sure, for the company, it's great to show all the employees what Research is doing and some of the exciting things that we'll find in Microsoft products in the future."

Andrew Herbert, managing director of Microsoft Research Cambridge and a Microsoft distinguished engineer.

I just ran into Phil Fawcett, who had an idea seven years ago that became the original TechFest, which has since become Microsoft Research's signature event. The fact that I could barely manage to squeeze through the throngs to get to Phil speaks volumes about his foresight--and the power a simple idea can have.

"I started this as an idea in 2001," says Fawcett, principal research program manager in Microsoft Research's Program Management team, "and it's been amazing to see this grow to the size it has--and the popularity. The energy that's created by the new technology is just awesome, and the opportunity for actually getting the ideas into our products, and the potential there, is awesome, as well. I really love for everybody to come out and see that Microsoft is an innovative company and that we've got a ton of new technology that we're going to get into our customers' hands."

In 2001, Fawcett actually had a hard time convincing his colleagues that the TechFest idea was worthy of consideration.

"When we first started, there was a lot of resistance in Microsoft Research on why we needed to do this," Fawcett recalls, "After I got it sold through, the first TechFest was from 10 a.m. to 7 p.m., and most of the researchers could not talk afterward because it went so long. We've kind of strung it out a little bit, so now what happens is that we get six or seven thousand over two days, which is a lot better."

The idea, shall we say, has taken hold. But before the inaugural show, it was difficult to be sure.

"I had no idea," Fawcett smiles. "I was taking a big risk. They knew I was taking a big risk, but they let me do it, which was great, and now it's turned out to be a great showcase for Microsoft Research and for Microsoft as a whole."

One man, one idea, one legacy of success. Well done, Phil!

Philip Fawcett

Data abounds in the digital age. Bits by the billions are collected on a daily basis from a variety of sources: Web services, financial programs, governmental agencies. Those who specialize in data mining and analysis could spend a hundred lifetimes sifting through such data, looking for patterns and clues that could help explain and fine-tune 21st-century life.

But they can't.

Much of that never-ending stream of data is privacy-protected--as it should be. Nobody wants their personal information accessible to any and all. Privacy is the cornerstone of the Internet era. Without it, society would devolve into digital chaos. But those same privacy protections deny experts access to all that massive, tantalizing data.

Frank McSherry aims to change all that. McSherry, a researcher at Microsoft Research Silicon Valley, is demonstrating, along with colleague Cynthia Dwork, a principal researcher at the same lab, a project called Privacy Integrated Queries, designed to enable the mining of huge data collections while not putting individuals' private information at risk.

"We're looking to put together a privacy-preserving data-mining platform," McSherry says, "tools that analysts can use, even without privacy training, to interact with and mine data from sensitive data sets that they wouldn't otherwise have access to.

"Cynthia Dwork and I have had a lot of prior experience with this privacy-preserving data analysis over the last few years. There's a lot of really formal mathematics behind it, but every time we do something new, we start from scratch in some sense: prove theorems, write papers--this is the model for convincing people things are private. We thought it would be smart to try to factor out the common technology we've been using in each of these results and package it in a framework that people could use to put together their own analyses."

One scenario in which such a platform could play a useful role relates to recent troubles in the financial sector.

"Some folks," McSherry says, "are really excited about finding what went wrong in the subprime collapse. Unfortunately, all that data is locked up--all the mortgage information, who bought what, at what rates--sealed up for privacy reasons. People can't sort out where the next collapse will be and how to counteract it, and that's unfortunate, that privacy is getting in the way, in some sense, of a real common-good happening. They didn't want to know who had what mortgage, but what parts of the country are most at risk."

Perhaps that episode would have developed differently if data analysts had access to the Privacy Integrated
Queries technology devised by McSherry and Dwork.

"We put together something that looks a lot like LINQ, Language Integrated Query, a sort of SQL-style, programmatic data access," McSherry explains. "To the user, it's basically indistinguishable. But under the covers, the privacy thing is going on, instincting about what you're asking for and communicating back with the data center, trying to determine if this is OK and pushing a lot of formal mathematics around, making sure that, at the end of the day, you haven't compromised privacy.

"The goal was to try to make it transparent to the users, so they didn't have to worry about what weird, funny machinations underneath are going on. They could just program against it as if it were LINQ."

But this latest project has one significant distinction that sets it apart from LINQ. 

"Unlike LINQ," McSherry says, "you don't get to just enumerate a data set. You have to stay one step removed. You explain what you'd like to do with the data set and how you'd like it to be aggregated, and the results come back to you, perturbed a little bit. You get a little bit of noise, and the noise introduces uncertainty about the answer, which turns into this formal notion of privacy."

Privacy Integrated Queries could prove beneficial in a number of settings. The medical field, for example, could benefit greatly if the data could be safely aggregated without disclosing the associated personal information. Another potential usage could find takers within Microsoft.

"We have all sorts of data on who searched for what," McSherry notes. "It's good stuff. Microsoft would love to collaborate with external researchers, people interested in Web research who don't have access to the scale of data that we have, but we're really concerned about privacy. That sort of stalls research, to some extent, for Web researchers who don't have anywhere to go. They can't start up their own Web-search engine.

"With this sort of technology, we can really start the ball rolling on this type of work, work that people just haven't been able to do before."  

Frank McSherry and Cynthia Dwork of Microsoft Research Silicon Valley in front of their TechFest poster on Privacy Integrated Queries.

A cool video that captures the energy of TechFest has been posted externally. Enjoy!

Some people look for controversy in the strangest places. As hundreds of computer-science researchers congregated for TechFest on Tuesday, one Web site interpreted a cross-disciplinary but topical demo from a team of Microsoft Research Redmond folks as evidence of a forthcoming news site to "rate media bias."

Michael Gamon, here's your chance to set the record straight about BLEWS.

"What we're trying to do is to give you a way to prioritize your news reading a little bit better," says Gamon, a computational linguist within the Natural Language Processing group.

"The problem is that there's way too much news for anybody to consume. You've got topic aggregators, news aggregators--all wonderful tools. But you still end up with more than you can read."

BLEWS, a research project based on a platform provided by Microsoft's Live Labs, is designed to address this information glut in one specific area of current interest: U.S. politics.

"Specifically," Gamon says, "in the political domain, what you can do is use the blogosphere as an annotation on the news. You can look at the number of conservative bloggers who link to a news article, the number of liberal bloggers who link to a news article, and you can just surface that information. The user, themselves, can then do with it what they want."

If the project ever received real-world implementation, users might be able to sort the resultant information by articles most linked to by those of a particular political persuasion. In addition, they could read news that has a preponderance or a paucity of heated verbiage.

"What we also do is try to look at the language around the news links in the blog and detect whether the language is more neutral or more emotionally charged," Gamon says. "That's not positive or negative--that's a different thing--it's just about emotionally charge. It could be enthusiastic, it could be very antagonistic. We don't make that distinction."

For example, words such as "failure," "progress," "strong," "unbelievable," and "better" would indicate a level of emotion in a blog posting's text, while the absence of such words would indicate a more measured approach.

BLEWS, though, doesn't make those determinations itself.

"It's not words that we pre-identified," Gamon continues. "We take a random sample of blog posts, recategorize it as neutral or emotionally charged, and then we have a machine learn the weights for individual terms and words. They are not manually identified. It's a machine that actually does that.

"We aggregate it all in a UI where you see, at one glance, the number of liberal links to a news article, the number of conservative links, and you have little boxes to the side that indicate the level of emotional charge. You can sort in these dimensions, and the UI is also fully navigational, so you can click on the news article, go read it, form your own opinion, and see what conservatives or liberals are thinking about that article."

In this U.S. political season, with emotions running at full throttle, such a tool could serve to help news fanatics take the partisanship down a notce. Such filters also could prove instructive,

"Oftentimes," Gamon notes, "it's actually more interesting to see what the other side is thinking about an article. Those are the arguments that people might find more challenging."

The BLEWS technology, though, is not limited to political discourse.

"Political stuff is really just one point to illustrate it," Gamon says. "Every piece of news, whether you're talking gardening, entertinment, sports--it works in this context. The context is provided by the blogosphere, and the minimun you can do is look at the link counts from the blogosphere and try to do something intelligent around the links to provide additional information."

Something intelligent to provide additional information: These days, that seems entirely reasonable--unless you're just itching to use the words "media bias" in a headline.

With a grounding in what the European Science Initiative is trying to accomplish, and the reasons why, I then stepped across the aisle in the Rainierr/St. Helens room of the Microsoft Conference Center to get a feel for the individual demos on display in the Science for the 21st Century booth.

• Leeza Pachepsky is showing Visualizing, Modelling and Analyzing Complex Networks, a project devised by Microsoft Research Cambridge colleague Rich Williams:

"I'm presenting 3-D software for understanding complex networks--in particular, describing who eats who in a ecosystem, as simple as lion seeks antelope, antelope seeks grass. But the real networks are much more complex. This software allows you to run different analyses and also to see what happens if you add or remove a species in an ecosystem. This could help us understand potential changes that climate changes could bring to our ecosystem, or, for example, to construct an artifical ecosystem. The network software is general in the sense that the network analogy and analyses can be applied to many other systems, for example, computing and social networks."

• Andrew Phillips discusses Visual Programming of Gene Networks and Biological Pathways, which employs SPiM, functional program code used to constuct graphical representations of biological systems that can be tested in a lab environment.

"Essentially, this stems from a revolution we're seeing in medical research," he says. "Scientists understand more and more molecular details of biological systems, so they're building complicated models, and we're developing a programming language for biology in order to help some of this. We're developing a language that enables a very complicated model to be split up into smaller building blocks that can simulate and analyze these parts of a biological system.

"I'm working with a range of collaborators around the world in building models of biological systems. One of these models is a pathway of the immune system on which we're running simulations. We're basically using techniques from computer science and parallel systems in order to model biological systems and understand reverse-engineering of biological systems."

• Robin Freeman is researching Autonomous Monitoring of Vulnerable Ecosystems:

"This allows us to monitor, in real time, changes in organisms within vulnerable environments. We're mining sensor networks, GPS, and a variety of Microsoft software to bring the data back in real time to researchers on their desktop and enable them to both monitor and analyze changes in these vulnerable environments.

"The advantage that gives us is that though we know some things about changes in climate, we don't know how they affect the organmisms in these sensitive environments. An understanding of that, in a variety of different environments, gives us an idea of an early-warning system for ecological changes which we can make to go in and preserve and manage the organisms better."

• Martin Calsyn speaks about the DISCOVERY environment, a product of intensive development work by Andreas Heil:

"We now have, with the new age of computational science, an unprecedented demand for computational cycles and resources. Luckily, we have an unprecedented ability for computational cycles and resources. But we have a huge chasm between the users and the resources. What I'm trying to do with the DISCOVERY environment is bridge that chasm. You could boil it down to a cliche by saying less plumbing and more science. We don't want our computational climatoligists, biologists and ecologists to become domain experts in computer science. We want them to spend 80 percent of their time on their work.

"DISCOVERY has a couple of main, featured components. One is clouds, which represent communities. A community is something into which you can put people, resources, and data. By resources, I mean a peer-to-peer collection of computers that form a peer-to-peer grid and use the idle cycles on the computers in your community. We have a workspace, a journal that's replicated in space. Everybody in the community gets a copy, and I can go back in time and make strong statements about the provenance of my data and my visualization results.

"I have the ability to build UI, and when I'm happy with the UI, I have the ability to publish that UI, in the form of a standalone .exe or a Web application, so when I do my peer-reviewed paper, it goes up on the Web. That's DISCOVERY."

• Drew Purves, who is leading the Understanding and Predicting Forest Dynamics project wasn't able to make the trip from Cambridge to Redmond for TechFest, but his Web site provides insight:

"Forests harbor around 60 percent of the world’s biodiversity and around half of its terrestrial carbon, so there is an urgent need to predict how forests will respond to continuing anthropogenic perturbations including increased atmospheric CO₂, logging, and land-use change. A new toolbox of techniques for understanding forests--consisting of a new kind of simulation model, new analytical techniques for understanding the model, and new statistical techniques for parameterizing the model using widely available inventory data--is beginning to deliver a fundamentally new, quantitative, and predictive level of understanding of how forests work."

Whew. Forest dynamics, ecosystem modeling, immune-system pathways, complex modeling, tools for computational life sciences. When the European Science Initiative produced its Towards 2020 Science report a couple of years ago, it represented nothing less than a clarion call to the scientific community, and it's fascinating to see how that call is being answered. 

Bryan Barnett (left), business manager for Microsoft Research's External Research group, engages in a TechFest chat with Alexander Brändle, head of technology for the European Science Initiative.

Next, I wandered over to a mega-booth from Microsoft Research Cambridge called Science for the 21st Century, featuring no fewer than six demos. There was a lot to absorb, so before diving in, I found Stephen Emmott, director of the lab's European Science Initiative, to get a quick overview. In four short minutes, he provided a comprehensive look at his group's motivations and priorities, so I thought it best to stay out of his way and let him explain away:

Stephen Emmott

"All the demos we've got here, the thing that ties them all together is the thing that ties all of our work together, which is our focus on developing new conceptual methods, techniques, and tools for helping scientists model and study complex biological systems.

"Understanding complex natural systems is rapidly becoming the most important and active area of science, certainly for the next 50 years, and the reason for that is because it's where the greatest scientific and societal challenges are: understanding biological systems, living systems. After 50 years of spectacular success in molecular biology, we still don't know how a cell works. And that's going to require building models of complex organisms.

"On the other hand, understanding what's happening to climate change and environmental change ... a lot is known about the physical aspects of climate change to do with carbon, but virtually nothing is known about the other important aspect about what regulates climate change, which is the biosphere, the biological components like the Earth's forests. So we've got the Forest Dynamics project here, which is modeling the relationship between forest dynamics and climate regulation.

"Interestingly, complex natural systems are also where the biggest societal, social, scientific challenges are.The biggest technological, economic opportunities for growth over the next 50 years are also around understading complex natural systems. If we can understand biological systems, it would, in medicine for example, revolutionize our ability to understand and treat disease. And it would probably revolutionize our ability to build novel, biological-based energy sources.

"One of the most efficient users and converters of energy on the planet are plants, and we don't know how they do that. If we understood how they do that, we'd have a big energy- and technological-innovation opportunity.

"The one thing that underpins the proper development of a science of complex natural systems is models and modeling. A lot of data is already known, so it's a question of how you bring all that data together. That's why we focus on complex natural systems, and that's why we focus on modeling and new modeling techniques.

"If we can crack even some of those problems, it would give us enormous insights into how we can think about a model and build much more complex engineered systems, software systems. There's an interesting interplay between understanding and doing science and its impact back on computer science and, naturally, our software engineering. That's the main motivator for everything that we're doing."

Now here's technology I could use right now, and I'll bet you could, too.

Ranjita Bhagwan is a researcher at Microsoft Research India who found herself getting frustrated with her home network. And that got her to thinking about her family.

"I am a home-network user," Bhagwan says, "and I’ve faced this problem a lot. I’ve studied computer science, so I have some knowledge on how networking works. And if it’s such a problem for me, then I shudder to think what a problem it must be for my mother or for my grandparents, who have no idea what’s happening.

"This is a way to simplify things for that population."

Her thinking led to NetPrints, a technique for diagnosing home-networking issues with a single click.

"I have a client that works on your machine," Bhagwan explains. "If you have a problem with any application, like to your e-mail service, you just press one button, and it goes to a service and resolves the problem."

One-touch resolution? Sounds like a dream come true.

"Home networks are growing," she observes, "and because of that, there’s this unmanaged environment. You have different companies' routers interacting with different computers, and that causes a lot of problems. That’s where we see this being helpful."

The unmanaged nature of many home networks is the crux of the problem.

"There’s so much diverse configuration network in a home network, Bhagwan observes. "What we finally decided to do was to use shared wisdom to resolve the problem. If you have the same configuration as me, and you’ve resolved your problem, in that case, that information should be ready for me to resolve my problem, as well."

In a nutshell, here's how NetPrints works: When you have a problem with an application, you can open the NetPrints client on your home machine. It automatically detects the application that’s having the problem and then you press the Diagnose button. The client crawls the configuration information from your home network and sends it to the service.

The service uses a structured index database to figure out which configuration-information parameter is wrong and ships  a suggestion to your client. The client then automatically fixes that configuration on the router or on the home network. And, voilà, the application starts working.

"The server side," Bhagwan explains, "is using a machine-learning algorithm to build a decision tree using the configuration, and that’s how it figures out how it can go from a bad configuration to a good one."

NetPrints is still being evaluated, but so far, things look quite promising.

"We have tested it with four or five applications right now, and four or five different routers, Bhagwan reports, "and it seems to work really well. We looked at a few problems that we scrounged around the Internet for, related to VPN clients, to gaming systems, to media players, and we found that for a large majority of them, something like this would be extremely valuable."

I'm sure I speak for millions when l say, the sooner I get my hands on this, the better.

Ranjita Bhagwan in her TechFest booth, March 5, 2008.

Today's session just got under way, and I made a beeline toward the demo entitled An HMM-Based Talking and Singing Head. This is speech-synthesis work out of Microsoft Research Asia, and Frank Soong, a principal researcher and research manager of that lab's Speech Group, was happy to explain it to me.

"HMM" refers to hidden Markov Model, a statistical model that Soong and colleagues have used to improve upon existing, often frustrating speech-synthesis techniques.

"Frankly," Soong said, "we advance the art of text-to-speech synthesis. It sounds fairly natural. I'm biased, as a technologist, but it doesn't sound robotic anymore. It has a natural prosody. This guy can say things highly intelligently, in a very clear way."

 "This guy" amounts to an evolved emoticon, a happy face with arms and legs and an expressive face. It might sound silly, but as we'll learn, such features can prove valuable.

The talking head lip-syncs to the speech synthesis Soong's technology produces. Emotions are reflected in its facial expressions. It gesticulates. And it can sing.

"It gives both the audio and the video part," Soong explains. "In the old days, we were building the playback for speech, but people were having a hard time hearing. But once you can read the lips, that can help you understand the speech in a noisy environment.

"For hearing-impaired people," he adds, "that's totally indispensable."

The improved speech synthesis could prove valuable in hands-busy environments, Soong says, such as listening to your e-mail while you're driving to work or getting an oral reminder of an appointment.

Another potential scenario is in video services. A person could make vocal requests and receive a spoken response, interacting with an animated agent by using both auditory and visual cues.

Sounds like the sort of thing a busy receptionist might consider a true godsend.

A video of yesterday's TechFest 2008 keynote is now available.

Also, the Channel 9 folks have posted a bunch of additional videos:

• Privacy Integrated Queries
• New Search UI
• BEE3: Revitalizing Architecture Research
• Tiny Web Services
• Music Steering

Happy viewing!

Green, as we now know, is, indisputedly, the new black. Seems like you can't turn on the television or pick up a newspaper to read about the latest green initiative. Lots of people are talking.

Feng Zhao is doing something about it.

Zhao's Networked Embedded Computing group is showing a TechFest demo called Auto-Shift: Energy-Aware Server Provisioning, which addresses server resource management for Internet services, such as Live Messenger and Hotmail. Data centers for such services require potentially expensive decisions about how many computers to allocate and how those are deployed.

"No. 1," Zhao says, "you have to buy the servers. No. 2, once you buy a server, you have to manage it. And third, you have to have an infrastructure, such as power supply. In this particular study, we looked at the power usage of the servers that are running one of our largest Web services. If you look at the load as it varies over the course of the day, it peaks around noon and slows down around midnight. That clearly shows that not all the servers are needed all the time. Can we shut down some of the servers? Can we actually save energy?"

Initially, Zhao and colleagues thought the challenge might be straightforward. But complications arose. With a Web service such as Messenger, users are serviced continually. When servers are turned off, users lose access, and when they sign back in, they create a spike in the data-center load.

"We have developed a set of techniques that allow us to intelligently forecast when a load is going to spike," Zhao says, "and to provide a period of buffer time so that we gradually shut down machines--and only shut down machines when the load on the machine is below some small threshold. So we don’t have to migrate too many users, which, in turn, would create too many spikes in the load. The techniques that we have been developing are different ways of managing that process of gradually ramping down a server--and at the same time having some knowledge about what’s coming and actually prepare enough resources."

Some techniques, though, save more energy, some slightly less.

“If you want to be aggressive about saving energy, you are going to incur some penalties," Zhao says. "Maybe the response time will be longer. We’re looking at these tradeoffs. If you care more about energy, it will have to be at the price of a little more degraded user experience. Or you can provide a very good user experience, but the energy saving might be a little bit less. That’s a very interesting set of findings."

Auto-Shift is based on Messenger data over a period of 45 days running on machines used to emulate the Networked Embedded Computing's group's algorithm. Zhao says that algorithm can achieve energy savings of as much as 25 percent without significantly affecting the user experience.

"Energy savings are possible," he says, "but they require some careful scheduling, management, and predicting, and we have algorithmic smarts in the system that allows us to do this."

Work remains. The team needs to ensure sure that its algorithm works with other existing components. Other Internet services need investigation to see if the savings work in other scenarios. In the meantime, the server-provisioning work could prove even more beneficial.

"We also have all these sensors in the data centers," Zhao says. "Some of the machines work harder than others. If we can move the workload around, from hotspots to cool spots, the air conditioning doesn’t have to work as hard, because of the efficiency of cooling the hottest spots. If you move that workload and even out the temperature disparities, that means good energy savings. Incorporating environmental-sensor readings such as temperature and humidity, and couple that with smart scheduling and workload migration, and we believe we can even save more resources."

That sounds green, indeed--and economical, too. 

"What it translates to," Zhao concludes, "is that you use less power and that, with these smarts, we can figure out that maybe we don’t need to buy that many machines to start with, because we can do the same work, with very little difference in performance, and actually run it on a smaller set of machines. Reduce energy cost and reduce hardware investment in the first place--that would reduce service cost, reduce staffing, and reduce the space you need to build."

Amazingly enough, it wasn't long afterward that I actually found myself talking with Alan Alda and Craig Mundie. Representatives for the two graciously arranged for me to get a couple of minutes with the two, so I introduced myself and started by asking what they had seen on the TechFest floor that they found impressive. Alda, as you will see, was as affable and voluble in person as he appears on screen.

Alda: The most interesting thing I saw was Craig Mundie, because he's got in his head all of these exhibits, plus all of the others that the research end of Microsoft is working on. He knows it down to the ground. It's amazing to me. I really enjoyed talking with him, and I've talked to a lot of scientists.

Mundie: Everything [at TechFest] is interesting, but I loved the machine translation, in particular, because it basically breaks down one of the barriers to letting all people collaborate with one another, and I think that one's particularly interesting.

Alda: I agree with that, too, and as I said to Craig when we were over there looking at the machine translation, it's like the hallmark of so many of the exhibits here, which is getting people to be able to collaborate in detail and in real time and to amalgamate their brains--and to aggregate the data in a lively way that has never been possible before, to be able to see it interacting dynamically. That kind of stuff ... we dreamed about that, not too many years ago.

I then asked them both, in slightly different ways, about the value of research and how, over the next decade, technology could transform the future for the better.

Mundie: Obviously, at Microsoft, we believe in research in a very fundamental way. It's the thing that prepares us for the future. We can't define the future. As Alan says, we can dream about it, but we can't predict it accurately. To me, our research investment is all about being prepared for that future and being able to capitalize on it.

Alda: I don't have any idea what the timeline is. Every time I visit a lab, doing Scientific American Frontiers, and they'd show me some fantastic thing that they had a prototype for, or that they almost had a prototype for, I'd say: "This is amazing! When will this be on the market?' And they always said, "Five years." Five years is the magic number, and when you hear it enough, you realize that it means "who knows?" (chuckles)

Mundie: Not tomorrow.

Alda: Not tomorrow, yeah. You can't go out today and buy it.

I was telling Craig at dinner last night, I've been longing for a solid-state hard drive for a long time, and pretty soon, I'm going to be able to have one.

Mundie: You can have it today. That, we can give you today.

Alda: But I want it with a lot of gigabytes in it.

Mundie: Define "a lot."

Alda: (laughing) Well, you can barely get 128 in that.

But what I really want is a pair of glasses with a little camera in the front of it, and I want to be able, when somebody says, "What's the derivation of that word?" I just want to be able to see it on the inside of my glasses. And I want to be able to see movies and stuff, and I want to see my e-mail on the inside of my glasses while I'm in a taxicab. And I want my body to be registered all the time and tell me when I've got too much fat, too much sugar and it's time to take a glass of water, time to take my pill. That might not be for 20 years--or five, one or the other.

(My two-minute interview went two minutes over. Keep it quiet.) 

Craig Mundie (left) and Alan Alda during TechFest 2008.