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On June 15, 2007, Andries van Dam received the degree Doctor of Mathematics, honoris causa from the University of Waterloo in Waterloo, Ontario. The text of Andy's address to the University of Waterloo follows.

"Thank you so much, Mr. Vice-Chancellor, Professor Tompa, and members of the Faculty of Mathematics, for this marvelous honor. Good afternoon, ladies and gentlemen.

As one of the first computer scientists, I saw our discipline begin in the early ’60s with great hope and promise, but also with the decades-long necessity of proving that we were a real science and of fighting for the creation and growth of CS departments comparable to those in math, engineering, and the physical sciences.

Over the next few decades, computing became the 800-lb gorilla: first, with the success of the mainframe, then the PC industries, then with the Internet and the web, and finally with the bubble, followed by the inevitable bust. Computing has infiltrated every area of daily life as users email, surf and find information at their fingertips, and thus has become essential in driving advances in all other fields. Computing has been responsible in large measure for the modern knowledge economy. Also, many entrepreneurs became rich, CS departments flourished, and research money was reasonably accessible...

Now, we are on the defensive again, buffeted by the dot.bomb, off-shoring, increasing competition for scarce research funding, and dramatic decreases in computer science enrollments, at least in North America, Europe, and Japan.

But, it's hugely ironic to view the significant gaps between what we know to be true and popular perception: * the first myth: it's over; computing isn't sexy any more - or worse, not relevant any more - compared to newer quests such as understanding dark matter/energy, global warming, and bio-technology. Effectively, we're victims of our own success due to the exponential price/performance improvements in PCs, which are truly amazing and hugely enabling to all knowledge-based activities, regardless of field.

The truth is that not only isn't it over, it's barely begun.

* the second myth: it doesn't pay to study CS since all the good jobs are going offshore. This myth produces a vicious cycle: because of the scarcity of good people, companies must go offshore to recruit talent.

The truth is that in just about every field, you’re going to have to “think computationally” in order to be relevant/successful in the future, and jobs will continue to be available for those who are skilled.

What is the exciting reality?

Only now are we becoming able to tackle really complex and fascinating technical and societal problems because finally the essential computing horsepower is both available and cheap enough. Most of these grand-challenge-scale problems require both deep multi-disciplinary approaches and computation-intense modeling, simulation, and visualization. Here are just a few examples:

*First, controlling a computer by thought alone. This science-fiction scenario has been implemented at Brown to let a quadriplegic patient's thoughts move a cursor, thereby giving the patient full use of the computer and of a simple robotic arm. A surgically implanted chip transmits the output of about 100 neurons, and machine learning is used to decode the very noisy signal. This astonishing effort involves brain surgeons, neural and cognitive scientists, computer scientists, applied mathematicians, and engineers.

*Second, the successful early completion of the human genome sequencing project, which was due in large part to the recognition that it was essentially a computation- and database-intensive process. Before that, biology was largely descriptive and phenomenological; now it is becoming a truly predictive science where researchers use a modeling-simulation-wet lab test cycle to gain deep understanding not just of the parts list and structure but of the function of DNA and thereby of the cell and its many components. This will lead to personalized medicine based on the understanding of an individual’s characteristics and genetic makeup.

*Third, the recognition that environmental problems such as global warming, hurricane prediction, and water shortages urgently require multidisciplinary amelioration and adaptation strategies that are based on extensive computer modeling and simulation.

In the computer domain itself, many important problems remain, most dating from the dawn of the field. They can't be addressed by simplistic reliance on computers getting faster and cheaper every year because they require creative breakthroughs. Examples include:

* the old bugbear: how to make software more reliable and less expensive to produce

* how to achieve real AI, e.g., common-sense reasoning based on human-like knowledge and judgment

* how to create human-centered user interfaces that take far greater advantage of human abilities. Interacting with my computer today is far more primitive than interacting with a small child: I have to be ridiculously literal and unambiguous, using just keyboard and mouse rather than my voice, hands, and indeed full body language, and I can't rely on shared context and experience to have the computer infer my intent.

* how to deal with cyber fraud and cyberterrorism -- think of the recent denial-of-service attack on Estonia. We must strike an appropriate and implementable balance between protecting ourselves and our civil liberties. This is as much a sociopolitical-economic-legal problem as it is a technical one, one with hair-raising complexity and chilling potential for harm.

Indeed, an overarching theme of the challenges we all face is learning how to manage the complexity of hugely multidisciplinary problems in the face of multiple legitimate but conflicting points of view.

Well, I have just singled out some of many fascinating problem areas that you may participate in solving. As you move on from here, what skills and traits will be required to meet such challenges? Here are a few obvious ones:

* lifelong learning, flexibility and adaptability in a world where the pace of change will continue to increase.

* students graduating today may have many distinct careers in their lifetime. Such a lifestyle will require not just imagination and risk-taking but also bone-deep hard work; competition and collaboration will be global, requiring the ability to learn and adapt not just to new technologies but also to new cultures and even new languages.

* evidence-based decision-making has become a planet-survival necessity. Approach problems with an open and prepared mind, question assumptions, and rationally decide strategies based on the best evidence available worldwide.

In closing, I would add what may be the most critical trait of all (and one familiar to graduates of the pink tie school): a sense of humor and of proportion: don't take anything - me and most of all yourselves - too seriously.

Congratulations and enjoy the ride!!!"