Thousands of routine decisions daily are made by computer systems that have specialized knowledge of a problem area. In the past, rule changes at a central office — e.g., the IRS, or the headquarters for a corporation — were incorporated slowly into practice. With expert systems, the people making the decisions have the benefit of codified knowledge bases that reflect current policy and practices.

Research on expert systems began in the 1970’s with support from DARPA, the National Institutes of Heath, and NSF. Expert systems have subsequently become an essential part of the IT toolkit for every major company. Help desks, credit checking and equipment troubleshooting are examples of systems that have been replicated many times over and are routinely saving money for business and public institutions.

Expert systems technology is a game changer.

Clearly, this family of related results changed the game, making secure communication and secure commerce a reality for (potentially) all users of the Internet. Without these artifacts, we would have no amazon.com, no ebay, …

The use of logic in various areas of computer science was unexpected, at least for non-theorist. Formal Methods, AI, NLP, … Just take a look at handbook of logic in computer science and handbook of logic in AI and logic programming.

Martin Lof’s Type Theory, Denotational Semantics, …

Open Source movement was/is also an important game changer in a higher level.

And, of course, GPS is definitely a game changer.

There are important related techniques that have been developed, e.g, molecular structure visualization (but > 20 y old)

Over the last 20 years, software has crept in in all aspects of our
lives and now controls everything: computers, phones, networks,
communications, airplanes, power, traffic lights, cars, elevators,
elections, etc. Today, millions of software engineers develop all this
software. Software as a whole is arguably the most complex artifact
ever engineered by human beings. Software engineering jobs
(developers, testers, etc.) might very well outnumber engineering jobs
in all other engineering disciplines combined (???). How is all this
software being written? Well, poorly, some might joke. But the truth
is, the software industry would not have been able to sustain its
tremendous growth over the past 20 years without significant advances
in software engineering, through better languages and abstractions,
common platforms and standardization, shared libraries and the
internet, and modern design and analysis tools which are required to
master complexity and boost productivity.

Just my (biased) 2 cents…

Just for fun, here’s one of my wish list items that I hope is one the follow-on list in 20 years: Autonomous, intelligent cars that can drive us to work or wherever. Not only could these coordinate better with respect to intelligent traffic systems and keep a cooler head and be safer by not being susceptible to distractions, but many of us would get around an additional hour a day, or 250 hours a year to work or rest, adding about 12% or productivity to the typical 2000 hour work-year.

2) Computational Science has become a third pillar of the scientific enterprise, a peer alongside theory and physical experiment. See the PITAC Report: http://www.nitrd.gov/pitac/reports/20050609_computational/computational.pdf

Federally funded research in software design architecture has had a significant practical impact on industrial development of software and software-intensive systems, and a major intellectual impact on software engineering and languages research.

Continuing advances in this area appear to be needed to manage complexity and harness the power of computation in highly complex, cyber-physical-social systems of the future: in transportation, environment, energy, health care, defense, communication, and so on.

Machine Learning
Statistical Machine Translation
Data-driven Natural Language Parsing
Mobile Computing
Relational Databases
Cryptography
Social/Online Networking
Social Media
Collaboration Software (e.g., Wikis)

I am uncertain about the role of NSF in video game development (DOD is big there), but it is a large industry shaping behavior on a global scale; think medical imaging, satellite imaging for weather and climate research; digital photography (pace Kodak).

Should desktop publishing come here too? A major game-changer, though its main impact came when tied with the web, and the role of Federal funding was not seminal here, I suppose.

Other things that we can’t quite do but can almost due, thanks to computing research: in silico drug discovery, and genomics-based medicine.

In response to Rance Cleaveland: I agree that computational science is a changed game rather than a game-changer. But perhaps the title “Computational Science” is the best way to describe the confluence of ideas that went into changing this game: cluster computing, numerical analysis, lots and lots of algorithms research, … In that sense, computational science might be a good argument for CS in general, rather than any specific game-changing aspect of CS.

It dramatically altered how we think about computing applications by introducing the idea that the actions and preferences of other people could be a useful resource in computations intended to support someone else’s activities.

It is easily appreciated by a broad audience (anyone who has used Amazon’s “people who bought this also bought…” or other social features; a somewhat narrower audience will also appreciate that a major improvement in search engine performance occurred when they started taking into account link structures and then click behaviors).

There’s a clear tie to computing research, both in work on algorithms for using data from other people, and in interfaces for collecting it and presenting predictions or recommendations. The idea was first articulated in CACM and in the ACM CSCW and CHI conferences, and there are now thousands of papers about it and for the last two years an ACM RecSys conference devoted just to it.

There has been a lot of NSF-funded university research in this area, including some of the early work. (There has also been a lot of work in industry).

I agree with Suresh that crypto is a game changer.

I would also put forward wireless networking (broadly defined) as a game changer. Being able to connect on a laptop or cell phone without being tethered to a building is certainly changing my game!

Arguably the web would not have been a friendly place for commerce without RSA and the like.

Another area on which the jury might still be out (but is further along than (say) quantum computing in terms of impact) is the computational perspective on game theory and economics, and how it has led to developments in auction theory, search and advertising.