Scientists Invented AI Made From DNA

["Dave Farber" <farber () gmail com>]

Begin forwarded message:

> From: Dewayne Hendricks <dewayne () warpspeed com>
> Date: July 11, 2018 at 8:00:32 AM GMT+9
> To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com>
> Subject: [Dewayne-Net] Scientists Invented AI Made From DNA
> Reply-To: dewayne-net () warpspeed com
>
> 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. It’s 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, “artificial intelligence” here doesn’t 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 algorithm’s 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 network’s 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 don’t have such biological luxuries. By feeding an artificial 
> neural network a ton of handwritten examples of the number four, however, an algorithm can “learn” 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 it’s 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 Qian’s graduate students, Kevin Cherry, has 
> considerably advanced this technique by applying it to the recognition of handwritten “molecular numbers.” 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 doesn’t resemble a grid—it’s all mixed up—and so a molecule’s 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 “winner take all” 
> approach to allow DNA neural nets to distinguish between numbers by synthesizing a so-called “annihilator” 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.”
>
> [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=18849915
Unsubscribe Now: 
https://www.listbox.com/unsubscribe/?member_id=18849915&id_secret=18849915-a538de84&post_id=20180710201728:C95CACBA-849F-11E8-AB92-9A06C95C195D
Powered by Listbox: https://www.listbox.com