It seems that Y Combinator and TechStars-like incubators are popping up everywhere. BoomStartup just launched an incubator in Utah and TechStars is expanding to other cities in the U.S., as is The Founder Institute. Chicago has a new incubator that recently launched, called Excelerate Labs.

Excelerate is the brainchild of OKCupid entrepreneur Sam Yagan, Kapil Chaudhary, Kelli Rhee, and Troy Henikoff. Yagan says the Chicago-based incubator has a similar model to TechStars and Y Combinator. Six to ten startups will be chosen for a three month long program, where founders will be given resources to build their products, access to mentors and funding. Each startup will receive anywhere from $15,000 to $20,000 (depending on the number of founders) for five percent of equity.

The program is currently accepting applications until April 2. Yagan says that one of the reasons that we wanted to start the program was to help make Chicago become “the Silicon Valley of the Midwest.” Sandbox Industries and i2a Fund have invested in the incubator, but Yagan says that other venture funds have taken an active interest in Excelerate, including DFJ Mercury. Mentors include TechStars founder David Cohen, OpenTable’s Chuck Templeton, Apex Ventures Partners’ Lon Chow and a host of other notable entrepreneurs and investors.

Groupon directors Eric Lefkofsky and Brad Keywell, also recently launched Lightbank and will invest as much as $10 million annually in early-stage technology companies through a new fund dubbed. Similar to Excelerate, the fund aims to help establish Chicago as a technology hub.

It’s always great to see investors and former tech executives investing time (and money) in promising startups and ideas. And we are seeing a plethora of innovative startups emerging from a variety of incubators around the country and world, including Y Combinator, TechStars, The Founder Institute, Launchbox Digital and more.

Silicon Valley has long been heralded as the Mecca for startups, but it isn’t the only city in California to give rise to promising tech companies.

Los Angeles has a growing startup community, and is home to startups like DocStoc and a few much larger businesses, like MySpace and CitySearch. One program looking to help foster  that community is Launchpad LA, which has just opened applications for the second round of its mentorship program.

The program was created by Mark Suster of GRP Partners, who previously founded BuildOnline (acquired by SWORD Group) and later Koral (acquired by Salesforce, where he became VP Product Management).  The criteria for interested companies: startups can’t have raised more than $1 million (or institutional venture capital), and are based in (or willing to move to) Los Angeles, where the program is based.

As with incubator programs like Y Combinator and Techstars, Launchpad LA invites VCs and other mentors from the area to help mentor participating companies.  But unlike those programs, it doesn’t directly invest in the companies — it’s purely for mentorship. That said, many VCs and angel investors in the Los Angeles area have some involvement, so it has played a role in those companies getting funding down the road.

Mentors for the last program included Mike Jones, who is now co-President of MySpace, DocStoc’s Jason Nazar, and Adam Bain of Fox Media Interactive. The last program included 13 companies, including Mobile Roadie,  Movoxx , and GumGum.

Editor’s note: Big Data has been around for a long time between credit card transactions, phone call records and financial markets. Companies like AT&T, Visa, Bank of America, Ebay, Google, Amazon and more have massive databases they mine for competitive advantage. But lately, Big Data is finding its way to the smallest startups. The Web and cloud computing brings Big Data everywhere. But what exactly is pushing Big Data forward?

To answer that we brought in an expert, Bradford Cross. Bradford is the Co-Founder and Head of Research at FlightCaster. FlightCaster is backed by Y Combinator, Tandem Entrepreneurs and Sherpalo Ventures. The company analyzes large data sets to predict flight delays. Bradford is chair of the Dealing with Big Data track at Cloud Connect this week.

We are in a Renaissance for computer science, engineering, and learning from data right now. The scale of data and computations is an important issue, but the data age is less about the raw size of your data, and more about the cool stuff you can do with it. Now that there is so much data, it is time to unlock its value. Really neat things are happening already—like the way the people of the world can educate themselves on all manner of issues and topics, or the way data and computing serves as leverage in other scientific and technical endeavors. There will be lots of amazing stuff on the web, but innovation will come in other domains as well.

The recent big data trend is about the democratization of large data more than its growth. In articles like the Economist’s recent piece on the data deluge, we hear about big data everywhere. We hear about what big data and the cloud mean for the enterprise, but they have had big data for a long time. eBay manages petabytes in its Teradata and Greenplum data warehouses. Sophisticated startups extracting value from big data is also nothing new—it has been happening at least since the days of Yahoo! and Google, and they have done it without the data warehousing folks.

Now focused early stage startups can get up and running faster than ever. Less technical analysts at companies like Facebook and Twitter can access massive amounts of data easily. Even individuals can undertake cool projects with big data, such as Pete Skomoroch of Data Wrangling did with trending topics for Wikipedia.

Why Now?

We do not have to build all our own hardware and software infrastructure anymore.

Pioneers such as Amazon have given us the cloud, where we have the capability to run very large server clusters at a low startup cost. Pioneers like Google have paved the way for open source projects like Hadoop and HBase, that are backed by big company contributors like Facebook.

The combination has paved the way for a new class of data driven startup like Aardvark (just acquired by Google) and Factual, it has reduced both cost and time to market for these startups, as we showed with Flightcaster. And, it has allowed startups that were not necessarily data driven to become more analytical as they evolved, such as Facebook, LinkedIn, Twitter, and many others.

So we have big data, the cloud, and open source facilitating new data-driven startups. I like to break this trend down from the technical perspective into three chunks; storing data, processing data, and learning from data. I define “learning from data” to mean data mining, AI, machine learning, statistics, and so on.

Supersize my data. Oh wait, I’ll just have a Medium.

The first time I heard the “Medium Data” idea was from Christophe Bisciglia and Todd Lipcon at Cloudera. I think the concept is great. Companies do not have to be at Google scale to have data issues. Scalability issues occur with less than a terabyte of data. If a company works with relational databases and SQL, they can drown in complex data transformations and calculations that do not fit naturally into sequences of set operations. In that sense, the “big data” mantra is misguided at times. For instance, a GigaOm article about big data in the cloud states:

What is becoming increasingly clear is that Big Data is the future of IT. To that end, tackling Big Data will determine the winners and losers in the next wave of cloud computing innovation.

The big issue is not that everyone will suddenly operate at petabyte scale; a lot of folks do not have that much data.

The more important topics are the specifics of the storage and processing infrastructure and what approaches best suit each problem. How much data do you have and what are you trying to do with it? Do you need to do offline batch processing of huge amounts of data to compute statistics? Do you need all your data available online to back queries from a web application or a service API?

Once your data and its processing are large enough to require distributing the data and the work among machines across network boundaries, things get a lot harder. You have to deal with distributed computing and make tradeoffs like a real computer scientist.

Big Data & The Cloud: Viral Buzzwords 4.0!

The cloud, and hosted services, present very interesting opportunities. One of the greatest is that people can leverage the a la carte economics of elastic computing to do things that were prohibitively expensive due to the requirements of building and maintaining their own hardware infrastructure. The interesting parts about the current cloud are its lack of entrance friction and elastic cost efficiency, the speed with which new entrants can set up, and the elastic capability to run 100 machine clusters for 1 hour if that is what is needed.

We started Flightcaster almost a year ago, and it is a good example of how startups can leverage cloud compute and storage resources, mix some open source like Hadoop with some data mining, and create interesting new technologies with relatively low capital upfront.

The cloud is not cheaper in general. Once people scale to a certain point, they move off the cloud onto dedicated hardware—not the other way around. That may change, and better hosted services may play a role in the transition, but that will take a while. In the meantime, the interesting part of the cloud is the use of elastic resources and the ability to get up and going quickly. The interesting part is the freedom it gives startups to try things they would never otherwise do.

Another notable thing about the cloud is the new architectures emerging as a result of economic and resource tradeoffs.

Storage of large amounts of data in the cloud is much cheaper with blobstores like Amazon S3 than it is to maintain an always-up cluster for a distributed datastore. If you do mostly offline batch processing and you do not need bulk storage to be online, then it is an attractive setup.

Storage and NoSQL

Taking another glimpse from the future of big data in the cloud.

A Big Data stack…will also need to emerge before cloud computing will be broadly embraced by the enterprise. In many ways, this cloud stack has already been implemented, albeit in primitive form, at large-scale Internet data centers, which quickly encountered the scaling limitations of traditional SQL databases as the volume of data exploded. Instead, high-performance, scalable/distributed, object-orientated data stores are being developed internally and implemented at scale…large web properties have been building their own so-called “NoSQL” databases, also known as distributed, non-relational database systems (DNRDBMS).

There are several misguided points here. First, there is not going to be a big data or cloud stack. Distributed systems are about making trade offs and a move toward problem-specific solutions rather than one-size-fits-all stacks. Second, enterprises already have their solution—expensive data warehousing and consulting support. Will open source projects like Hadoop supported by people like Cloudera take a chunk of the business? Sure. But as I mentioned earlier, the most interesting part about big data and the cloud is not cheaper alternatives for the enterprise, it is the opportunities it facilitates for data-driven startups.

There is a lot of talk about the NoSQL movement. The big idea here is that distributed systems are hard, require tradeoffs, and sometimes we are better off with data storage and processing that are specific to what we are doing with the data. Sometimes even with a small amount of data on a single node, there are better alternatives to SQL queries and relational databases—time series data has long been a good example.

Processing and Hadoop: The Elephant In The Room

There is a broad range of needs for processing large amounts of data. These range from simple needs like calculations for log analysis that just need to occur at scale, to middle of the road needs like BI, to complex needs like scalable modern machine learning and retrieval systems.

There are a different approaches one can use to service specific needs. Again, we see the pattern of moving away from one-size-fits-all stacks, and toward building for your needs. That said, there are very generic abstractions like Map-Reduce that work well for a lot of use cases. Distributed systems are hard to get right, so when something like Hadoop gets a lot of momentum, it retains that momentum until alternatives have the time to mature enough to solve the hard problems with fault tolerance, performance, and so forth. Not everyone is Leonardo da Vinci, so people should not attempt to create these systems on their own unless they really know what they are doing. In that sense, the cloud and big data are facilitators of open source.

An important aspect of processing at scale is abstraction. Writing complex or even simple computations in raw Map-Reduce is verbose for programmers and intimidating for others who might want to play with the data. Abstractions over Map-Reduce like Pig and Hive make simple things easy, and abstractions like Cascading make hard things possible. The Map-Reduce paradigm, and Hadoop in particular, have been a big success. That said, Map-Reduce is not the only important piece of compute infrastructure. Message queues serve as the backbone of a lot of compute architectures – implementations of AMQP, such as rabbitmq, are a prime example. You can accomplish a lot with producers, consumers, and a messaging system. Distributed storage and processing systems can also be very tricky to configure and deploy, requiring a pretty deep understanding of the system – hence the business case for folks like Cloudera.

Learning from Big Data

Hal Varian, Google’s Chief Economist, recently said,

The sexy job in the next ten years will be statisticians… The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it

Unfortunately for those of us working on these problems in real life, it is not so simple. The archetypal data-renaissance man is mathematician, statistician, computer scientist, machine learner, and engineer all rolled into one. There are opportunities where you can lack some of these skills and work with a team that supplements your weak points—a startup is not one of those.

Now that we can store so much data, it is attractive to do previously unimaginable things with it. We are sure to see cool applications in fields from the internet to biotechnology to nanotechnology and fundamental materials science research. Almost all advances in every field of science and technology are now heavily dependent upon data and computing. Machine learning is serving a fantastic role as a bridge between mathematical and statistical models and the worlds of AI, computer science, and software engineering. We are exploring applications in learning from text, social networks, data from scientific experiments, and any other data sources we can get our hands on.

The data renaissance does present some difficult issues. There are not many places one can recieve a good education on working on these problems at large scale. Scaling our modeling and optimization algorithms is hard. We need to figure out how to partition and parallelize, or sometimes trade speed and scale for approximately correct calculations. Another issue is that we are often using simplistic models, albeit with pretty good results in many cases. We would like to move toward a deeper approximation of real intelligence.

But the data renaissance is here. Be a part of it.

While California and New York tend to get the most attention as technology hubs, other states are quietly hosting their own vibrant communities around technology and innovation. Utah is one of these states. Utah is home to tech giants Omniture (which was acquired by Adobe for $1.8 billion), Novell, Symantec. And today, Utah is getting its very own startup incubator, BoomStartup, which is a seed capital and mentor-focused investment program for web and software start-ups based in Utah.

Based in Orem, Utah at the Canyon Park Technology Center (the original site of WordPerfect Corporation), BoomStartup is a full-time program that will run from May to August and provides each selected company with seed capital (up to $15,000), mentoring from entrepreneurs and technologists, free office space and resources, and education that takes them through the various steps of getting a tech startup off the ground. For its first rounds, the organization will choose eight startups to participate in the program. Applicants for BoomStartup must have a founding team (two or more individuals) and an idea with a focus on web, mobile, software, and non‐hardware tech. Startups can apply here.

BoomStartup was founded by Utah angel investor John Richards who invested in Omniture. BoomStartup is made up of seven other mentors and investors in the fund. Each investor-mentor has contributed $15,000 in the fund. Investors include Omniture co-founders Josh James and John Pestana, Ralph Yarro, Nobu Mutaguchi, Martin Frey, and Rod Watson.

It’s always great to see investors and former tech executives investing time (and money) in promising startups and ideas. And we are seeing a plethora of innovative startups emerging from a variety of incubators around the country and world, including Y Combinator, TechStars, The Founder Institute, Launchbox Digital and more.

Continued Richards: “This group of investor-mentors has a track record of growing successful businesses and creating innovative technologies. Their expertise and vision will be invaluable to the selected companies, and give them the know how to overcome the obstacles they might confront, whether that be on the business or technological side.”

BoomStartup will host a series of “Meet the Investor-Mentor Days” through the April 12, 2010 deadline; the first will be held Friday, March 12, 2010 at 4 p.m. at the Canyon Park Technology Center, Building J (1401 N. Research Way, Orem, Utah). Investors-Mentors will be on hand to talk with prospective applicants about business, technology and discuss strategies for their businesses.

Affiliate fees are all over the web and often we don’t even know that we are clicking on affiliate links when we click through to make purchases at our favorite online retailers. On average, affiliate fees can range from 3 to 10 percent of the price of a product. Browsarity is hoping to put money collected from affiliate programs to philanthropic use, and keep a portion for itself in the process. The Y Combinator-incubated company has launched a Firefox plug-in that will rewrite any unclaimed links to a participating online retailer with an affiliate link, and donate any fees collected towards the charity of your choice.

Once downloaded, Browsarity will automatically scan any links to determine if there is an affiliate program and link associated with the retailer and will underline the link in red. So if you search to buy an iPhone on Google or read a blog post with a link to a book on Amazon, Browsarity will underline the affiliate links for retailers which could result in fees that go to charity. If you purchase the item through the affiliate link, the fees will be deposited into a PayPal account operated by Browsarity. Most of the fees in that account will be donated to a charity of your choice. Browsarity will take a 10 percent cut of each affiliate fee, so 90 percent is donated to the charity.

Currently Browsarity offers nine different charities to choose from, including The Red Cross, Doctors Without Borders, and the Black Eyed Peas’ charity PeaPod. The startup says it has partnerships with at least ten “big-name” e-retailers to collect affiliate fees for charity.

There is also a viral component to the service. You can send your friends and family links to install copies of Browsarity pre-set for a certain charity, and the system will keep track of how much money has been generated for that charity as a result of your efforts. In an effort to preserve users’ privacy, Browsarity doesn’t track individual purchases.

While only available for Firefox at the moment, Browsarity will be launching plug-ins and extensions for Safari, Chrome and Internet Explorer in the coming weeks. The startup faces competition from Browse For A Cause, which has a similar model.

Posterous, founded in 2008 by Sachin Agarwal, Garry Tan and Brett Gibson, is a Y Combinator company that began as a way for users to very easily post pictures online. Its appeal lies in its simplicity – users can just email a photo to post@posterous.com and an account is immediately created for them. But today people are using Posterous for videos and text blogs as well. Users can change the CSS and even use their own domain names – see Guy Kawasaki’s HolKaw blog, for example, which is run by Posterous.

The company does have revenue, such as this early deal with Coca Cola for a branded site, but has stood firm in keeping the “nickel and dime” consumer fees out of the product. There are no restrictions on usage, storage, CSS customization or using your own domain, says the company.

Later this year Posterous will launch a pro version of the service for bigger brands, and allow things like Javascript and site monetization for a fee, they say.

All that simplicity and freeness has resulted in a lot of growth for the company. They have 12 million unique monthly visitors, they say, and 25 million page views. And they grew 30% per month in 2009, all with just 4 employees (they are up to 6 now).

Posterous regularly releases new products, such as Post.ly a month ago. Post.ly lets users easily share media on their Twitter account.

CEO Sachin Agarwal says that they want to continue to add new products that make it dead simple to post and share content online: “Our goal is to become synonymous with “posting” just like Google is synonymous with “search”. Doesn’t matter if it’s for twitter or a blog, private or public, group or individual. if you need it online, you go to Posterous.”

“We think the iPad is meant to be a Windows killer.”

Okay, yes, that’s slightly taken out of context — but it’s still one hell of a way to rile up developers. And to light a fire under some would-be entrepreneur fanboys. Here’s the full statement around the sentence:

Most people think the important thing about the iPad is its form factor: that it’s fundamentally a tablet computer. We think Apple has bigger ambitions. We think the iPad is meant to be a Windows killer. Or more precisely, a Windows transcender. We think Apple foresees a future in which the iPad is the default way people do what they now do with computers (and some other new things).

Following the iPad’s unveiling in January, people seem fairly evenly split about whether the device will be a failure, or the next big thing (I’m on the record as saying I think it will take some time to catch on, but then will quickly rise in popularity towards the future of computing). This is a smart bet for Y Combinator (and the startups that apply for this RFS) to make. If they’re right, and this is the future of computing, these startups getting to work around the time of the iPad launch (it’s still set to ship at the end of this month) should be well positioned to fully take advantage of the device.

And Y Combinator is thinking big for these startups too. It would be easy to tell companies to make apps for the iPad that are basically ports of current mobile apps, but the RFS points to this post by Facebook’s (and FriendFeed co-founder, and Gmail creator) Paul Buchheit, noting the future iPad applications may be unlike anything we’ve ever seen before.

Something else that is interesting to Y Combinator is how you get this new device in the door in businesses. They seem to think you’ll have to trick your company’s IT department:

One particularly interesting subproblem is how to introduce iPads into big companies. This will probably have to be done by stealth initially, as happened with microcomputers. They’ll have to be introduced as something individuals use, and which doesn’t really count as a computer and thus can’t be vetoed by the IT department. Don’t worry about this; it’s just a little tablet computer.

Just as iPhone app development has exploded, and Android developers are finally starting to see some real money, iPad developers are already in demand. Windows-killer or not, this is certainly an area to watch for the foreseeable future.

Crocodoc is an extremely straightforward service, and you don’t even need to sign up for an account to use it — just upload a document, and a second later you’ll be in the Crocodoc editor. Markup tools include Sticky Notes, a highligher, text strikeout, and the ability to leave your own comments (a ‘pen’ tool is on the way). Editing a document should be very familiar to anyone who has used Adobe Acrobat or Apple’s Preview. If you’d like to try marking up a sample document, you can use this demo.

By default, the service assigns each uploaded a document a unique, “unguessable” URL, which you can use to share the document with friends, who can mark it up and add their own comments. But there’s one catch to the free version of the service: if you lose the document URL, that marked up document is lost for good (remember, you didn’t create an account to sign up).

Fortunately Crocodoc also offers a ‘Pro’ version, which lets you create an account that includes an archive of your previously uploaded documents. It also allows you to password protect your uploaded docs (as opposed to just relying on the hard-to-guess URL for security), and to use SSL encryption. Pro Accounts cost $8/month or $36/year. And for companies that are wary of uploading sensitive files, Crocodoc offers intranet deployments, which means that these customers can run it inside their firewalls on their own servers.

My only gripe about Crocodoc is the limited number of collaboration options  — you can share your documents with as many people as you want, but everyone will be editing the same one, which seems like it would get messy fast. This will be fixed in the near future, when Crocodoc starts allowing you to review edits on a per-user basis.

Crocodoc’s still quite basic, but that might be exactly what its customers are looking for.  And they may well be willing to spend $36 a year if it means they’ll have to print out fewer stacks of paper.

Gift cards can be a double edged sword. I recently got married and received a number of gift cards to stores where I never shop. But at the same time, I don’t want the value of the card to go to waste. There have been a number of auction-like marketplaces, such as Plastic Jungle and Rackup, that have popped up to allow users can buy and sell their gift cards to each other in an eBay like interface (you can also do this eBay itself). Y Combinator startup CardPool is entering the space but with a slightly different twist to its model. Card Pool allows users to both buy and sell gift cards.

CardPool buys people’s unwanted gift cards, and sells gift cards at large discounts. Though the idea isn’t new, Card Pool has an attractive and fair pricing model. They judge the buyback and selling amount by how desirable the cards are. For example, you can sell a BestBuy’s gift card, which is highly desirable, to CardPool for 90 percent of its value. And on CardPool’s site, you can find a Best Buy gift card for 5 percent off its original value. On the other hand, 1-800-Flowers’ gift cards, which are not as popular as Best Buy’s cards, are discounted by 30 percent on the site.

I’m a fan of the site for a few reasons. The plus about CardPool is that it allows returns for cards for up to 100 days, and many of its competitors don;t have an expansive of a return period. Also, CardPool won’t sell gift cards that have expirations or fees. CardPool makes money off the spread between buying and selling cards. The startup is lean, with its two co-founders running the site, keeping overhead low. The company is also looking into forging partnerships with retailers like Barnes and Noble, Best Buy and others to sell their gift cards at discounted prices from CardPool.

CardPool also has talent on its side. Co-founder Anson Tsai developed online music player Anywhere.com, which was acquired by Imeem in 2008.

Running on just sugar and caffeine, 32 teams of students worked non-stop for 18 hours to develop applications that they hoped would blow the judges’ socks off. This was at the UC-Berkeley Hackathon, last weekend. Indeed, many teams succeeded in their mission. They built some amazing software: to provide server-side rendering of games, convert website mockups to HTML/CSS, create sophisticated playlists for Youtube videos, and to analyze Twitter streams. One team even built a gaming interface for a neural headset.

There were so many cool tools that the seven judges, who included representatives from Zynga, Facebook, Y-Combinator (and me), had a hard time picking a winner in each category. The exception was the “social good” category. There was only one team worthy of receiving this prize. The team built a system to enable villagers in developing countries to send SMSs to volunteers across the globe who provide emergency medical advice. But the Silicon Valley judges couldn’t see the value of this technology. One commented, “If the villager has a cell-phone, why doesn’t he just call 911? This is really dumb”. (Most of the judges didn’t understand that 911 services don’t exist in most places in the world, and that SMSs have become the internet of the developing world). Instead, the panel awarded the prize to a team that developed a polling technology for university classrooms and for conferences. The rationale for this decision? “Helping universities is a social good.”

This brings me to the point of this post. What if we challenged these students and Silicon Valley to build businesses that do good for the planet and make a healthy profit doing so? Today, the world faces more problems than perhaps at any point in recent history. The economy is on the brink. Greenhouse gases threaten to turn Earth into a giant steam room. Scarce resources such as food, water, and oil have already become international flashpoints as the developing and developed worlds jockey for position to sustain or improve their standards of living. Drug-resistant bacteria threaten us with doomsday plagues. Yet we have the greatest minds and the deepest pool of investment capital in the world focused on building Facebook and Twitter apps.

Yes, I know that some in Silicon Valley are solving important problems. But these are the tiny minority.  Out of 32 teams at UC-Berkeley, only one was focused on a social cause. That’s probably the same proportion of do-gooders as in the Valley. I’ll bet that most Berkeley students would do anything to better the world if they knew how.  But like the Hackathon judges, they don’t know what problems need to be solved and what they can do to solve them.

There is a way. In 2008, Charles Vest, the president of the National Academy of Engineering brought together a group of prominent deans of engineering schools from around the country to create a list of Grand Challenges that can be solved by engineers, in our lifetime. These were in several broad realms of human concern — sustainability, health, vulnerability, and joy of living. Dr. Vest believed that “the world’s cadre of engineers will seek ways to put knowledge into practice to meet these grand challenges. Applying the rules of reason, the findings of science, the aesthetics of art, and the spark of creative imagination, engineers will continue the tradition of forging a better future”.

Here is the list of the 14 Grand Challenges the deans created:
Make solar energy economical
Provide energy from fusion
Develop carbon sequestration methods
Manage the nitrogen cycle
Provide access to clean water
Restore and improve urban infrastructure
Advance health informatics
Engineer better medicines
Reverse-engineer the brain
Prevent nuclear terror
Secure cyberspace
Enhance virtual reality
Advance personalized learning
Engineer the tools of scientific discovery

Some of these may sound far afield for typical Silicon Valley TechCrunch readers and Berkeley students, but they are not.  I asked Duke University’s dean of engineering, Tom Katsouleas, to help me translate some of these into tangible business ideas. Here are three examples:

1.  Engineer better medicines. You might think this is the purview of the medical researcher or biomedical engineer, and it is, but it is also an electrical-engineering (EE), computer-science and information-technology challenge.  For example, one of the big drivers here is the need to predict and prevent future pandemics of highly resistant diseases.  So a concrete grand challenge is to provide early detection of diseases from a saliva swab.  It turns out that the human body when exposed to diseases such as H1N1 responds with elevated gene expressions almost immediately.  Picking out the protein signal from such an event and distinguishing it from the noise of normal metabolism turns out to be amenable to the same techniques EE’s develop to pick out a weak cell-phone signal.  Duke Professor of EE, Larry Carin, has teamed up with genomicist Geoff Ginsburg and shown that this approach allows disease prediction up to 5 days in advance of symptoms.  Photonics researchers are busy trying to develop rapid on-chip diagnostics that are optical or based on electrical resistance rather than on lab chemistry and that work on saliva instead of blood.  This information can then be fed into dynamically steered computer models of disease propagation and guide both vaccine developers and public-health officials.

2.  Make solar energy economical.  It is that one extra word at the end of the sentence that changes everything.  Without the word economical, this is a physics challenge that we know how to meet: to convert energy from photons to a flow of electrons.   But with the extra word, the challenge cannot be solved without addressing business, policy, human behavior, and of course a spectrum of technologies far beyond the basic physics.  For example, nano-scale plasmonic structures could be critical to making solar cells as “cheap as paint” as well as coating roofs that are as reflective as white paint but still aesthetic.   Wireless technology could assist the adoption of electric vehicles.   Imagine using metamaterial lenses to make wireless chargers in the floor of garages highly efficient.  The leapfrog from EVs’ being less convenient vehicles that have to be plugged in to never having to stop to refuel turns one key obstacle to adoption into an incentive to make a better product.

3.  Reverse-engineer the brain. As brain researcher and Palm inventor, Jeff Hawkins, at Numenta pointed out, there was a time when computer scientists thought they could create artificial intelligence algorithmically.  That hubris is giving way to a recognition that understanding the structure and function of biological neural networks may be essential to achieving applications as mundane as navigating a car down the freeway to as grand as helping individuals optimize their own learning.

If you review the list of challenges, you may be able to develop some great business ideas of your own. Olin College and the Kauffman Foundation have created a competition for students who have completed science and engineering projects that tie directly to the 14 Grand Challenges. Several universities, including North Carolina State University and Duke University, are also holding a series of summits to bring thinkers together to solve problems. I encourage you to participate. My hope is that rather than run business-plan contests and hackathons, our universities will start competing to solve the Grand Challenges. Maybe the excitement and sense of purpose will seep through to my fellow judges and others in Silicon Valley… and maybe we’ll even help save the world.

Editor’s note: Guest writer Vivek Wadhwa is an entrepreneur turned academic. He is a Visiting Scholar at UC-Berkeley, Senior Research Associate at Harvard Law School and Director of Research at the Center for Entrepreneurship and Research Commercialization at Duke University. Follow him on Twitter at @vwadhwa.