http://www.wired.com/2009/04/newtonai/ http://www.wired.com/2009/04/newtonai/
http://www.theguardian.com/science/2009/apr/02/eureka-laws- nature-artificial-intelligence-ai http://www.theguardian.com/science/2009/apr/02/eureka-laws-nature-artificial-intelligence-ai Data scientist Michael schmidt sees the world filled with intricate beauty -- the flowering of a rose, the veins branching on a leaf, the flight of a Bumblebee. But below the surface of nature's wonders, Michael also sees a treasure trove of uncharted mathematical complexity. Schmidt: Well, I love coming out here. Nature is beautiful. There are equations hidden in every plant and every bee and the ecosystems involved in this garden. And part of science is figuring out what causes those things to happen. Freeman: Science is our effort to make sense of nature, and this quest has given us some very famous discoveries. In Newton's time, he was able to figure out a very important rule in physics, which is the law of gravity. It predicts how this apple falls and the forces that act upon this apple. Today in science, we're interested in similar problems but not just about how the apple falls but the massive complexity that follows from this very simple dynamic to the world around us. For example, when I drop this apple, the apple stirs up dust. This dust could hit a flower, and a bee may be less likely to pollinate that flower. And the entire ecosystem in this garden could change dramatically from that single event. Freeman: Scientists understand the basic forces of nature, but making precise predictions about what will happen in the real world with its staggering complexity is overwhelming to the human mind. So, one of the reasons why it's extremely difficult for humans to understand and figure out the equations and the laws of nature is literally the number of variables that are at play. There could be thousands of variables that influence a system that we're only just beginning to tease apart. In fact, there are so many of these equations, we'll never be able to finish analyzing them if we do it by hand. Freeman: In 2006, Michael began developing intelligent computer software that could observe complex natural systems and derive meaning from what seems like chaos. So, what I have here is a double pendulum. If you look at it, it consists of two arms. One arm swings along the top axis, and the second arm is attached to the bottom of the first arm, and it's two pendulums that are hooked together, one pendulum at the end of the other. Now, the pendulum is a great example of complexity because it exhibits some of the most complex behavior that we're aware of, which is called chaos. So, when you collect data from this sort of device, it looks almost completely random, and there doesn't appear to be any sort of pattern. But because this is a physical deterministic system, a pattern does exist. Freeman: Finding a pattern amidst the chaos of the double pendulum has stumped scientists for decades. But then Michael had a flash of inspiration. Why not grow new ideas the same way nature created us, using evolution? He called his program Eureka. Eureka starts with a primordial soup of random equations and checks how closely they fit the behavior of the double pendulum. If they don't fit, the computer kills them. If they do, the computer moves them into the next generation, where they mutate and try to get an even closer fit. Eventually, a winning equation emerges, one that Archimedes would be proud of. Eureka! Schmidt: And I'm running our algorithm now. On the left pane are the lists of the equations that Eureka has thought up for this double pendulum. Walking up, we can see we increase the complexity, and we're also increasing the agreement with the data. And eventually, as you go up, you start to get an extremely close agreement with the data, and eventually you snap on to a truth where you get a large improvement in the accuracy. And we can actually look in here and see exactly what pops out. For example here, you might notice we have a 9. 8, and if you remember from physics courses, that is the coefficient of gravity on earth. What's very important is the difference between the two angles of the double pendulum. This pops out. Essentially, we've used this software and the data we've collected to model chaos, and we've teased out the solution directly from the data. Freeman: Eureka has not only discovered a single equation to explain how a double pendulum moves. It has found meaning in what looks like chaos -- something no human or machine has done before. Schmidt: So, we could collect an entirely new data set, run this process again, and even though the data is completely different -- we could have different observations -- we can still identify the underlying truth, the underlying pattern, which is this equation. Freeman: To Michael, the future of scientific exploration isn't inside our heads. It's inside machines. Whether they're looking at patterns of data from genetics, particle physics, or meteorology, programs like Eureka can evolve inspiration on demand, finding basic truths about nature that no human ever could. We're gonna reach a point where we decide what we want to discover and we let the machines figure this out for us. Eureka can find these relationships without human bias and without human limitations. We created robots to serve us. --- <salyavin808@...> wrote : Fascinating. I read a book about genetics recently, it was unnerving to have every part of all known life and everything we are capable of including thoughts and consciousness, coming down to just four nucleotides and how they arrange themselves. But what complexity arises! It would take forever and a day to work this out by hand but now the machines can do it they'll be unraveling our mysteries in no time. Maybe even rewriting us? That'll be something to look forward to, Maybe.. --- <turquoiseb@...> wrote : Well, *that* horse is out of the barn. A recent paper published in the journal PLOS Computational Biology was completely developed by an Artificial Intelligence, and solves a mystery that humans have previously been unable to explain. Computer independently solves 120-year-old biological mystery (Wired UK) http://www.wired.co.uk/news/archive/2015-06/05/computer-develops-scientific-theory-independently http://www.wired.co.uk/news/archive/2015-06/05/computer-develops-scientific-theory-independently Computer independently solves 120-year-old biological my... http://www.wired.co.uk/news/archive/2015-06/05/computer-develops-scientific-theory-independently For the first time ever a computer has managed to develop a new scientific theory using only its artificial intelligence, and with no help from human beings View on www.wired.co.uk http://www.wired.co.uk/news/archive/2015-06/05/computer-develops-scientific-theory-independently Preview by Yahoo
