The rest of this post may appear haphazard and random. What started out as an analysis report on the velocity of media consumption has evolved. Actually, evolved isn't the right word. Let's just say that the time has come for me to benchmark my artistic and professional talents and abilities. In other words:
"What I don't know I can learn, what I do know- nobody can teach".
Everything below the break is reposted/copied/pasted here for my convenience. Please feel free to comment, suggest or make recommendations (and yes I am aware of the fact that i really haven't given you much to go on, but i don't want to get ahead of myself. Maybe I'll make this into a game? You know-provide clues, and toolkit with supplies. Make a soundtrack, provide concessions and award prizes? I've got a conference call now.
But that's not all..
from Wired
In just over a day, a powerful computer program accomplished a feat that took physicists centuries to complete: extrapolating the laws of motion from a pendulum’s swings.
Developed by Cornell researchers, the program deduced the natural laws without a shred of knowledge about physics or geometry.
The research is being heralded as a potential breakthrough for science in the Petabyte Age, where computers try to find regularities in massive datasets that are too big and complex for the human mind and its standard computational tools.
"One of the biggest problems in science today is moving forward and finding the underlying principles in areas where there is lots and lots of data, but there’s a theoretical gap. We don’t know how things work," said Hod Lipson, the Cornell University computational researcher who co-wrote the program. "I think this is going to be an important tool."
Condensing rules from raw data has long been considered the province of human intuition, not machine intelligence. It could foreshadow an age in which scientists and programs work as equals to decipher datasets too complex for human analysis.
Lipson’s program, co-designed with Cornell computational biologist Michael Schmidt and described in a paper published Thursday in Science, may represent a breakthrough in the old, unfulfilled quest to use artificial intelligence to discover mathematical theorems and scientific laws:
- Half a century ago, IBM’s Herbert Gelernter authored a program that purportedly rediscovered Euclid’s geometry theorems, but critics said it relied too much on programmer-supplied rules.
- In the 1970s, Douglas Lenat’s Automated Mathematician automatically generated mathematical theorems, but they proved largely useless.
- Stanford University’s Dendral project, was started in 1965 and used for two decades to extrapolate possible structures for organic molecules from chemical measurements gathered by NASA spacecraft. But it was ultimately unable to assess the likelihood of the various answers that it generated.
- The $100,000 Leibniz Prize, established in the 1980s, was promised to the first program to discover a theorem that "profoundly affects" math. It was never claimed.
Unlike the Automated Mathematician and its heirs, their program is primed only with a set of simple, basic mathematical functions and the data it’s asked to analyze. Unlike Dendral and its counterparts, it can winnow possible explanations into a likely few. And it comes at an opportune moment — scientists have vastly more data than theories to describe it.
Lipson and Schmidt designed their program to identify linked factors within a dataset fed to the program, then generate equations to describe their relationship. The dataset described the movements of simple mechanical systems like spring-loaded oscillators, single pendulums and double pendulums — mechanisms used by professors to illustrate physical laws.
The program started with near-random combinations of basic mathematical processes — addition, subtraction, multiplication, division and a few algebraic operators.
Initially, the equations generated by the program failed to explain the data, but some failures were slightly less wrong than others. Using a genetic algorithm, the program modified the most promising failures, tested them again, chose the best, and repeated the process until a set of equations evolved to describe the systems. Turns out, some of these equations were very familiar: the law of conservation of momentum, and Newton’s second law of motion.
"It’s a powerful approach," said University of Michigan computer scientist Martha Pollack, with "the potential to apply to any type of dynamical system." As possible fields of application, Pollack named environmental systems, weather patterns, population genetics, cosmology and oceanography. "Just about any natural science has the type of structure that would be amenable," she said.
Compared to laws likely to govern the brain or genome, the laws of motion discovered by the program are extremely simple. But the principles of Lipson and Schmidt’s program should work at higher scales.
The researchers have already applied the program to recordings of individuals’ physiological states and their levels of metabolites, the cellular proteins that collectively run our bodies but remain, molecule by molecule, largely uncharacterized — a perfect example of data lacking a theory.
Their results are still unpublished, but "we’ve found some interesting laws already, some laws that are not known," said Lipson. "What we’re working on now is the next step — ways in which we can try to explain these equations, correlate them with existing knowledge, try to break these things down into components for which we have clues."
Lipson likened the quest to a "detective story" — a hint of the changing role of researchers in hybridized computer-human science. Programs produce sets of equations — describing the role of rainfall on a desert plateau, or air pollution in triggering asthma, or multitasking on cognitive function. Researchers test the equations, determine whether they’re still incomplete or based on flawed data, use them to identify new questions, and apply them to messy reality.
The Human Genome Project, for example, produced a dataset largely impervious to traditional analysis. The function of nearly every gene depends on the function of other genes, which depend on still more genes, which change with time and place. The same level of complexity confronts researchers studying the body’s myriad proteins, the human brain and even ecosystems.
"The rules are mathematical formulae that capture regularities in the system," said Pollack, "but the scientist needs to interpret those regularities. They need, for example, to explain" why an animal population is affected by changes in rainfall, and what might be done to protect it.
Michael Atherton, a cognitive scientist who recently predicted that computer intelligence would not soon supplant human artistic and scientific insight, said that the program "could be a great tool, in the same way visualization software is: It helps to generate perspectives that might not be intuitive."
However, said Atherton, "the creativity, expertise, and the recognition of importance is still dependent on human judgment. The main problem remains the same: how to codify a complex frame of reference."
"In the end, we still need a scientist to look at this and say, this is interesting," said Lipson.
Humans are, in other words, still important.
Citations: "Distilling Free-Form Natural Laws from Experimental Data." By Michael Schmidt and Hod Lipson. Science, Vol. 324, April 3, 2009.
"Automating Science." By David Waltz and Bruce Buchanan. Science, Vol. 324, April 3, 2009.
Additional links and resources:
Building Dynamic Websites. This 12-part series conducted by Professor David Malan at Harvard includes all you could want to know about building a website that can easily compete in today’s environment.
Operating Systems and System Programming. John Kubiatowicz from Berkeley starts from the basic concepts of operating systems and moves through a variety of topics relevant to system programming.
Introduction to Computer Science and Programming. These instructors from MIT provide a series of lectures geared to people of any background. The goal is for anyone who watches to gain an understanding of how computing can solve problems and how to write simple scripts
.The Structure and Interpretation of Computer Programs. Get an introduction to computer science from this lecture series given by Professor Brian Harvey.
Machine Learning. Andrew Ng from Stanford covers topics such as robotic control, data mining, speech recognition, and text and web data processing in these lectures.
Introduction to Algorithms. Learn the techniques for designing and analyzing algorithms when you watch these lectures given by Charles E. Leiserson at MIT.
Computer Graphics as a Telecommunication Medium. Vladlen Koltun discusses the recent research being done on virtual worlds and some of the ways experts are working to overcome current problems.
Social Annotation, Contextual Collaboration, Online Transparency. Bobby Fishkin lectures on his work to incorporate textual notation within scholarly texts and how the technology works.
Understanding Computers and the Internet. This lecture series is perfect for anyone, no matter their level of familiarity with a computer, to learn the inner workings of computers and the Internet.
Pario: the Next Step Beyond Audio and Video. Professor Todd C. Mowry from Carnegie Mellon describes a joint venture between the university and Intel that is developing a new multimedia technology.
Ray Kurzweil on how technology will transform us. Inventor and entrepreneur Ray Kurzweil talks about the work on reverse-engineering the human brain and where that technology may take us in the near future.
Jamais Cascio on tools for a better world. Cascio examines technology that will help create a greener environment while also providing benefits for humanity.
Gregory Stock: To upgrade is human. Taped in 2003, this video shows a prophetic talk Stock gave on the possible impact of technology on humanity.
Jeff Han demos his breakthrough touchscreen. Han discusses his work creating a touchscreen that may replace the ubiquitous point-and-click technology.
Chris Anderson of WIRED on tech’s Long Tail. Anderson discusses technology trends, forecasting them, and the birth of new technology.
Rachel Armstrong: Architecture that repairs itself?. Armstrong discusses ways to build sustainable architecture by using materials that can "grow" and repair themselves.
Yves Behar’s supercharged motorcycle design. Yves Behar and Forrest North share their past and show how that contributed to their collaboration to create a fully electric motorcycle, which they show to the audience.
Janine Benyus: Biomimicry in action. Benyus suggests inventors look to nature for inspiration when trying to come up with ideas. She also discusses current technology that has done just that.
