I was a biologist once, before I got into television, so I find these times particularly trying when I see schoolteachers and otherwise intelligent people calling evolution into question. That's part of the reason that I jumped at the chance to co-produce a story about bio-inspiration (the other reason being that I LOVE geckos…which will make more sense if you watch our QUEST Bio-inspiration segment).

Bio-inspired design borrows its creative inspiration from models and systems in nature, that is, plant and animal parts that have been slowly tweaked for over 3.8 billion years. But that doesn't mean that nature's designs are perfect. In fact, that's what makes the process of engineering things based on natural models so difficult. You have to figure out how to pull the aces from the evolutionary discard pile. As professor Bob Full at U.C. Berkeley explained in our first phone conversation, that's also why scientists now use the term "bio-inspiration" rather than the more commonly known term "biomimicry." Biologists and engineers are not looking to simply mimic nature, because there are all kinds of dead ends and redundancies in natural systems that would be pointless to recreate in an optimized, man-made piece of technology. One of the examples he gave me is a kind of grasshopper that if you were to copy it, you would copy neurons that go to nothing, they don't connect to any muscles, and that's because during evolution the adults lost their ability to fly. The neurons going to the muscles are still there, but the muscles aren't there anymore. No need to copy that, right?

So what a biomimeticist does is look to nature to find plants & animals with remarkable performance abilities, and studies their adaptations for inspiration to design something new. For example, if you want to make a tiny robot that can fly, then look at the best fliers. If you want to design a blade that moves quickly through fluids, or an Olympic swimsuit that minimizes drag, then look to the most efficient swimmers. Now that's what I call "intelligent design!"

Watch the Bio-Inspiration: Nature as Muse television story report online.

The term "artificial intelligence", was coined in the summer of 1956, on the bucolic grounds of Dartmouth College in Hanover, New Hampshire. There, John McCarthy (who would later go on to teach at Stanford), Marvin Minsky, Claude Shannon, Nathan Rochester and six other conference participants came together to lay out the framework for this exciting new field which would "…find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." (McCarthy et al., 1955)

Though it was McCarthy who persuaded his nine other colleagues at the conference to adopt the term "artificial intelligence" to describe the nascent field, the seeds of artificial intelligence were planted earlier. Alan Turing, who was instrumental in breaking the German's Enigma code during WWII, published a paper in 1950 that laid out what came to be known as the "Turing Test:" if a machine could carry out a conversation with a human in such a sophisticated manner as to trick the human into thinking that he or she was conversing with another human, then the machine would have displayed true "intelligence."

But nearly 60 years later, the world still awaits a machine capable of exhibiting "general A.I.", instead of the "narrow A.I." demonstrated by IBM's chess-playing Deep Blue or Stanford University's Stanley, an autonomous robotic vehicle, or other impressive albeit limited applications of A.I. For example, Deep Blue may be able to beat Gary Kasparov at chess but can it beat a 10 year-old at a game of checkers? The lack of a general A.I. is made even more stark when juxtaposed with Moore's Law, a maxim that goes back to 1965 when Intel founder Gordon Moore postulated that the number of transistors on a computer chip would double roughly every 18 months.

There's even a term – "Singularity" – that is being used to describe the moment when technological progress will leapfrog and herald the creation of computers that not only achieve human-like intelligence, but also give rise to a progeny of computers who will be smarter then their digital forbears. Though he didn't coin the term (sci-fi writer Vernor Vinge did), the most famous exponent of this belief is inventor Ray Kurzweil. He places the Singularity as occurring sometime before 2050 and believes that with the advent of this unheralded technological progress, mankind may solve some of our society's most pressing ills, such as global warming, and even conquer death, by uploading one's consciousness into a virtual medium.

Though this seems a far stretch from engineering a domestic robot like Stanford's Artificial Intelligence Robot, top A.I. researchers like Stanford's Andrew Ng and Daphne Koller do believe that computing systems will some day be as smart or smarter than humans. When I spoke with Dharmendra Modha about his work into cognitive computing at IBM, he talked effusively about creating an "i-Brain," a digital accessory that people could carry around, making decisions and processing information like its human cousin. But if you're like me, and lament those moments when you've misplaced your keys or other instances of poor neural performance, you can't help but think that such a device can't arrive soon enough. On second thought, I'll wait until v2.0 hits the shelves.

Watch the Artificial Intelligence: Thinking Big television story report online.

And don't miss our Web Extra: A Dose of A.I. In this QUEST web exclusive, Stanford University computer science professor and artificial intelligence (A.I.) researcher Daphne Koller provides an elegant explanation of how A.I. can be employed in the examining room to diagnose a patient's illness more accurately than a human clinician. Find out more and learn how medical diagnosis is just the tip of the iceberg when it comes to tasks that rely on making sense of a sea of data to arrive at an informed conclusion.

Guest blogger Lisa Croel of The Tech Museum in San Jose, CA sits in for Dr. Barry Starr this week.

I remember loving science class as a kid. The paper-maché messes, the bubbling baking soda, all of the wonderful experiments… I loved it all. Now, many grammar school kids are lucky to get 15 minutes of science education a week. Hardly enough time to get them imagining future careers as scientists, engineers and inventors.

Between the lack of time given to science education, and the structure imposed by curriculum standards, museums need to be part of the education equation. My boss has a saying: "Give random a chance." I love this quote because it speaks to the role informal educational resources like science museums need to be playing. By exposing young people to the experiences and programs in a museum, who knows what might really resonate and inspire?

For over 20 years, The Tech's Tech Challenge program has presented kids with an open-ended problem for which there is no one right answer. It forces participants to use their knowledge and ingenuity to solve the problem. For example, this year the Challenge (called Water Works) is all about moving water from a stream up to a village without electricity. There is no one right answer, and there are lots of ways to solve this problem.

Participants are 5^th to 12^th graders who will work in teams of 2-6 to explore solutions to solving this real world problem. Along the way, they will hit some roadblocks and come up with some duds. And that's OK because it is here that kids will learn that failure is an important part of problem solving. We have a great quote etched into a wall on the outside of The Tech from Intel co-founder and philanthropist Gordon Moore that says, "If everything you try works, you are not trying hard enough." Through failure, many of the Tech Challenge teams will come up with a far superior solution.

This year we're going international for the first time by partnering with the City of San Jose's Sister City program. On the final event day, where all of the teams come together to present and demonstrate their solutions, we'll be webcasting in teams from far-away locations, and look forward to seeing and hearing how kids from other countries have tackled the challenge. Hopefully the involvement of other cultures will drive home how important it is to be inclusive to come up with better ways to solve problems.

I just looked at the U.S. Census Bureau web site for the latest world population number, and today there are 6,650,846,379 people on Planet Earth. One in five people on Earth don't have access to safe, clean drinking water, which means that 1.3 billion people are suffering from lack of water. As this year's Tech Challenge participants work on solutions to a global water problem, I hope they get excited (or more excited) about science and remain engaged, even they don't get to study it much in the classroom.

Lisa Croel is the Marketing Director at The Tech Museum of Innovation in San Jose, Calif.