Dave Farber
2018-07-11 00:17:18 UTC
Date: July 11, 2018 at 8:00:32 AM GMT+9
Subject: [Dewayne-Net] Scientists Invented AI Made From DNA
Scientists Invented AI Made From DNA
Researchers made a neural network out of DNA that can recognize handwritten numbers.
By Daniel Oberhaus
Jul 9 2018
<https://motherboard.vice.com/en_us/article/594mvz/ai-made-from-human-dna>
Last Wednesday, researchers at Caltech announced that they created an artificial neural network from synthetic DNA that is able to recognize numbers coded in molecules. Itâs a novel implementation of a classic machine learning test that demonstrates how the very building blocks of life can be harnessed as a computer.
This is pretty mind blowing, but what does it all mean? For starters, âartificial intelligenceâ here doesnât refer to the superhuman AI that is so beloved by Hollywood. Instead, it refers to machine learning, a narrow form of artificial intelligence that is best summarized as the art and science of pattern recognition. Most of the cutting edge advances in machine learning involve artificial neural networks, which are a type of computing architecture loosely based on the human brain. These neural networks are fed a lot of data as input and then taught how to perform some task with that data; sometimes humans help to guide the algorithmâs learning, and sometimes not.
This is effectively what the Caltech researchers designed, but instead of using silicon and transistors, they used DNA and test tubes as their neural networkâs hardware.
All DNA is composed of four basic nucleotides: adenine (A), cytosine (C), guanine (G), and thymine (T). Strands of these nucleotides can bond with other strands of these nucleotides to form the double helix of DNA, but can only bind in specific combinations (i.e., A-T or C-G). This predictable pattern of combination makes these nucleotide strands ideal computing devices, which can be designed so that they produce specific chemical reactions in the presence of various molecules.
The Caltech researchers applied this sort of DNA-based computer to one of the classic tests in computer vision research: teaching an algorithm how to recognize handwritten numbers. This is tough for a computer to do because humans all write the number four slightly differently. Humans are hardwired to easily see the similarities between the ways different people write four, but machines donât have such biological luxuries. By feeding an artificial neural network a ton of handwritten examples of the number four, however, an algorithm can âlearnâ to generalize qualities from individual examples and form an abstract idea of what a written four looks like. The next time the algorithm encounters something that looks like a four, it will compare this to its abstract representation of four and if itâs a close enough match, it will conclude that it is looking at a four.
In 2011, Caltech bioengineer Lulu Qian created the first artificial neural network out of DNA, but it could recognize only a handful of patterns. In the work unveiled last week, one of Qianâs graduate students, Kevin Cherry, has considerably advanced this technique by applying it to the recognition of handwritten âmolecular numbers.â Each molecular number was based on a handwritten number translated into a 20-bit pattern in a 100-bit (10x10) grid. Each of the bits on the grid was represented by a molecule of DNA, and these molecules of DNA were assigned a place on a conceptual 10x10 grid before being mixed together in a test tube.
The DNA in the test tube doesnât resemble a gridâitâs all mixed upâand so a moleculeâs place on the grid was determined by the concentration of each molecule in the test tube. The DNA neural net was a strand of DNA that produced a specified reaction when added to the test tube only if the 20 DNA molecules assigned to represent a given number are arranged (i.e., in the appropriate concentrations) so that they form that number when translated onto the 10x10 grid.
Cherry began his experiment by building a neural net that could distinguish between handwritten sixes and sevens that had been translated into molecular structures. He tested this approach on 36 different handwritten versions of the same numbers and in each instance the DNA neural network was able to recognize them. Cherry used a âwinner take allâ approach to allow DNA neural nets to distinguish between numbers by synthesizing a so-called âannihilatorâ molecule.
"The annihilator forms a complex with one molecule from one competitor and one molecule from a different competitor and reacts to form inert, unreactive species," Cherry said. "The annihilator quickly eats up all of the competitor molecules until only a single competitor species remains. The winning competitor is then restored to a high concentration and produces a fluorescent signal indicating the networks' decision.â
[snip]
Dewayne-Net RSS Feed: http://dewaynenet.wordpress.com/feed/
Twitter: https://twitter.com/wa8dzp
-------------------------------------------Subject: [Dewayne-Net] Scientists Invented AI Made From DNA
Scientists Invented AI Made From DNA
Researchers made a neural network out of DNA that can recognize handwritten numbers.
By Daniel Oberhaus
Jul 9 2018
<https://motherboard.vice.com/en_us/article/594mvz/ai-made-from-human-dna>
Last Wednesday, researchers at Caltech announced that they created an artificial neural network from synthetic DNA that is able to recognize numbers coded in molecules. Itâs a novel implementation of a classic machine learning test that demonstrates how the very building blocks of life can be harnessed as a computer.
This is pretty mind blowing, but what does it all mean? For starters, âartificial intelligenceâ here doesnât refer to the superhuman AI that is so beloved by Hollywood. Instead, it refers to machine learning, a narrow form of artificial intelligence that is best summarized as the art and science of pattern recognition. Most of the cutting edge advances in machine learning involve artificial neural networks, which are a type of computing architecture loosely based on the human brain. These neural networks are fed a lot of data as input and then taught how to perform some task with that data; sometimes humans help to guide the algorithmâs learning, and sometimes not.
This is effectively what the Caltech researchers designed, but instead of using silicon and transistors, they used DNA and test tubes as their neural networkâs hardware.
All DNA is composed of four basic nucleotides: adenine (A), cytosine (C), guanine (G), and thymine (T). Strands of these nucleotides can bond with other strands of these nucleotides to form the double helix of DNA, but can only bind in specific combinations (i.e., A-T or C-G). This predictable pattern of combination makes these nucleotide strands ideal computing devices, which can be designed so that they produce specific chemical reactions in the presence of various molecules.
The Caltech researchers applied this sort of DNA-based computer to one of the classic tests in computer vision research: teaching an algorithm how to recognize handwritten numbers. This is tough for a computer to do because humans all write the number four slightly differently. Humans are hardwired to easily see the similarities between the ways different people write four, but machines donât have such biological luxuries. By feeding an artificial neural network a ton of handwritten examples of the number four, however, an algorithm can âlearnâ to generalize qualities from individual examples and form an abstract idea of what a written four looks like. The next time the algorithm encounters something that looks like a four, it will compare this to its abstract representation of four and if itâs a close enough match, it will conclude that it is looking at a four.
In 2011, Caltech bioengineer Lulu Qian created the first artificial neural network out of DNA, but it could recognize only a handful of patterns. In the work unveiled last week, one of Qianâs graduate students, Kevin Cherry, has considerably advanced this technique by applying it to the recognition of handwritten âmolecular numbers.â Each molecular number was based on a handwritten number translated into a 20-bit pattern in a 100-bit (10x10) grid. Each of the bits on the grid was represented by a molecule of DNA, and these molecules of DNA were assigned a place on a conceptual 10x10 grid before being mixed together in a test tube.
The DNA in the test tube doesnât resemble a gridâitâs all mixed upâand so a moleculeâs place on the grid was determined by the concentration of each molecule in the test tube. The DNA neural net was a strand of DNA that produced a specified reaction when added to the test tube only if the 20 DNA molecules assigned to represent a given number are arranged (i.e., in the appropriate concentrations) so that they form that number when translated onto the 10x10 grid.
Cherry began his experiment by building a neural net that could distinguish between handwritten sixes and sevens that had been translated into molecular structures. He tested this approach on 36 different handwritten versions of the same numbers and in each instance the DNA neural network was able to recognize them. Cherry used a âwinner take allâ approach to allow DNA neural nets to distinguish between numbers by synthesizing a so-called âannihilatorâ molecule.
"The annihilator forms a complex with one molecule from one competitor and one molecule from a different competitor and reacts to form inert, unreactive species," Cherry said. "The annihilator quickly eats up all of the competitor molecules until only a single competitor species remains. The winning competitor is then restored to a high concentration and produces a fluorescent signal indicating the networks' decision.â
[snip]
Dewayne-Net RSS Feed: http://dewaynenet.wordpress.com/feed/
Twitter: https://twitter.com/wa8dzp
Archives: https://www.listbox.com/member/archive/247/=now
Modify Your Subscription: https://www.listbox.com/member/?member_id=26461375
Unsubscribe Now: https://www.listbox.com/unsubscribe/?member_id=26461375&id_secret=26461375-c2b8a462&post_id=20180710201728:C95CACBA-849F-11E8-AB92-9A06C95C195D
Powered by Listbox: https://www.listbox.com