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Creative Machines; however, are they truly without a built in bias due to their own creator/s?
Despite nature’s bewildering complexity, the driving force behind it is incredibly simple. ‘Survival of the fittest’ is an uncomplicated but brutally effective optimization strategy that has allowed life to solve complex problems, like vision and flight, and colonize the harshest of environments.
Researchers are now trying to harness this optimization process to find solutions to a host of science and engineering problems. The idea of using evolutionary principles in computation dates back to the 1950s, but it wasn’t until the 1960s that the idea really took off. By the 1980s the approach had crossed over from academic curiosities into real-world fields like engineering and economics.
Applying natural selection to computing
Evolutionary algorithms are numerous and diverse, but they all seek to replicate key features of biological evolution, such as natural selection, reproduction and mutation. Typically these methods rely on a kind of trial and error — a large population of potential solutions to a problem are randomly generated and tested against a so-called “fitness function.” This lets the system rank the solutions in order of how well they solve the problem.
In Brief
Science once again reaches a milestone in technology by modeling it after nature. Researchers have devised a new type of highly efficient photocell by studying photosynthesis in plants.
Nathan Gabor, assistant professor for physics and astronomy at the University of California, Riverside, led research spurred by a simple question as to why plants are green. This eventually led to a quest to mimic plants’ ability to efficiently harvest energy from the Sun regardless of how erratic the sunlight is.
The US Army Research Laboratory (ARL) are at an advanced stage of with their synthetic biology research. The work could see bacteria being used to send signals and sense in a way similar to computers, the advantage being that it could potentially provide a more intuitive sensory experience to a piece of tech, and bypass some of the pitfalls unique to electrical structures. The research also has application for new 3D printing materials.
The goal of roboticists has long been to make A.I. as efficient as the human brain, and researchers at the Massachusetts Institute of Technology just brought them one step closer.
In a recent paper, published in the journal Biology, scientists were able to successfully train a neural network to recognize faces at different angles by feeding it a set of different orientations for several face templates. Although this only initially gave the neural network the ability to roughly reach invariance — the ability to process data regardless of form — over time, the network taught itself to achieve full “mirror symmetry. Through mathematical algorithms, the neural network was able to mimic the human brain’s ability to understand objects are the same despite orientation or rotation.
Move over, chemists. Thanks to proteins from Icelandic bacteria, scientists at Caltech have managed to coax microbes into making silicon-carbon bonds, a feat that until now has been achieved only by humans in the lab.
The findings, published last week in the journal Science, could open the door to new avenues in organic chemistry and drug development — and could help scientists investigate essential mysteries, such as whether life could be based on silicon instead of carbon on other planets.
A University of California, Riverside assistant professor has combined photosynthesis and physics to make a key discovery that could help make solar cells more efficient. The findings were recently published in the journal Nano Letters.
Nathan Gabor is focused on experimental condensed matter physics, and uses light to probe the fundamental laws of quantum mechanics. But, he got interested in photosynthesis when a question popped into his head in 2010: Why are plants green? He soon discovered that no one really knows.
During the past six years, he sought to help change that by combining his background in physics with a deep dive into biology.
A synthetic metabolic pathway developed by Tobias Erb and his colleagues at the Max Planck Institute for Terrestrial Microbiology in Marburg converts CO2 from the atmosphere into organic matter more efficiently than plants are able to through photosynthesis. We asked the researcher what significance this process could have for climate protection, discussed the hurdles the research team had to overcome to achieve their goal, and looked at the new perspectives that synthetic biology opens up.
Does the synthetic metabolic pathway that fixes CO2 now represent an effective means of curbing climate change?
Firstly, we are aiming to understand the fundamental biological and chemical principles of how CO2 in gaseous form can be converted into organic molecules. Our primary motivation is not stopping climate change. We are seeking to develop atmospheric CO2 as a source of carbon for the future using biological methods. Producing a CO2-neutral process or even one that removes CO2 from the atmosphere and has a positive impact on the climate would be a fantastic secondary effect.
Scientists have managed to coax living cells into making carbon-silicon bonds, demonstrating for the first time that nature can incorporate silicon — one of the most abundant elements on Earth — into the building blocks of life.
While chemists have achieved carbon-silicon bonds before — they’re found in everything from paints and semiconductors to computer and TV screens — they’ve so far never been found in nature, and these new cells could help us understand more about the possibility of silicon-based life elsewhere in the Universe.
After oxygen, silicon is the second most abundant element in Earth’s crust, and yet it has nothing to do with biological life.
The mere mention of “quantum consciousness” makes most physicists cringe, as the phrase seems to evoke the vague, insipid musings of a New Age guru. But if a new hypothesis proves to be correct, quantum effects might indeed play some role in human cognition. Matthew Fisher, a physicist at the University of California, Santa Barbara, raised eyebrows late last year when he published a paper in Annals of Physics proposing that the nuclear spins of phosphorus atoms could serve as rudimentary “qubits” in the brain — which would essentially enable the brain to function like a quantum computer.
Isher’s hypothesis faces the same daunting obstacle that has plagued microtubules: a phenomenon called quantum decoherence. To build an operating quantum computer, you need to connect qubits — quantum bits of information — in a process called entanglement. But entangled qubits exist in a fragile state. They must be carefully shielded from any noise in the surrounding environment. Just one photon bumping into your qubit would be enough to make the entire system “decohere,” destroying the entanglement and wiping out the quantum properties of the system. It’s challenging enough to do quantum processing in a carefully controlled laboratory environment, never mind the warm, wet, complicated mess that is human biology, where maintaining coherence for sufficiently long periods of time is well nigh impossible.
Over the past decade, however, growing evidence suggests that certain biological systems might employ quantum mechanics. In photosynthesis, for example, quantum effects help plants turn sunlight into fuel. Scientists have also proposed that migratory birds have a “quantum compass” enabling them to exploit Earth’s magnetic fields for navigation, or that the human sense of smell could be rooted in quantum mechanics.