Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, Viacom, and WIRED, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.
WHY THIS MATTERS IN BRIEF
AI neural networks made out of DNA will let us create “AI’s in a test tube” that will usher in the next AI revolution and exponentially increase the number of AI use cases.
Recently we’ve seen significant advances in the development of living biological computers, where organisms, like bacteria, become the computer, capable of storing information and processing relatively complex commands in the same way a traditional computer would. Now though scientists at the California Institute of Technology in the US have created an artificial neural network made from nothing more than DNA and used it to recognise numbers written using molecules.
The teams experiment also suggests that DNA neural networks could also one day be used to help recognise other patterns of molecules, such as ones signalling disease, or identifying pollution, and many more things besides.
In a regular “electronic” neural network, as the team refer to traditional neural networks, like the ones that are run on today’s silicon based computers, components dubbed neurons are fed data and cooperate with each other to solve a problem – such as recognising handwriting, or whatever the particular task is. The neural network then repeatedly adjusts the behaviour and connectivity of these neurons to create new patterns. Over time, the system eventually discovers which patterns work best for solving specific tasks and adopts them as its new defaults, mimicking the process of learning in the human brain.
In 2011, bioengineer Lulu Qian, who also lead the latest study, and her team, developed the first neural net made of DNA, which had just four artificial neurons that each accepted four input signals and altogether were made up of 72 different kinds of DNA molecules. In the new study, Qian and study lead author Kevin Cherry developed a more complex DNA neural net of six artificial neurons that each accepted 100 input signals and together were composed of 225 distinct kinds of DNA molecules.
DNA is made of four kinds of smaller molecules, known as nucleotides which are abbreviated as A, T, C, and G, that are arranged in strands. Nucleotides in one strand of DNA can only bond with nucleotides in another strand in very specific ways, so, for example, A only bonds with T, and C only with G. Using these predictable binding rules, scientists can use interactions between DNA molecules to perform computations.
The DNA Neural Network in action
In their experiment the Qian’s team synthesised both single stranded and partially double stranded molecules of DNA that floated around in saltwater. When single stranded DNA molecules found partially double stranded molecules with complementary sequences, they bonded, causing the partially double stranded molecules to shed the strands of DNA that were previously on them.
The single stranded DNA molecules that successfully bonded with the partially double stranded DNA molecules served as the system’s inputs, while the strands kicked off by the bonding DNA molecule served as the system’s output. Once released, an “output strand” as the team called them can in principle serve as an “input strand” by interacting with yet another partially double stranded DNA molecule, leading to the creation of a neural network of “molecular interactions” that can run complex computations, Qian says.
“Artificial intelligence in a test tube is very different from that in a Hollywood movie,” Qian says. “The question that we are interested in is how much intelligence can be programmed into molecular machines.”
In this new study, each artificial neuron is composed of a collection of these interacting DNA molecules. The behaviour of each neuron is determined by the concentrations of each partially double stranded DNA in the test tube were the interactions take place.
The scientists used their DNA neural network to perform a version of a common challenge for electronic neural networks – recognising handwritten numbers. In this task, neural networks must learn how to recognise numbers, account for variations in handwriting, and then compare an unknown number to their memories and decide the number’s identity.
The DNA neural net was used to accurately identify “molecular handwriting,” in other words molecules that were arranged into shapes. Specifically, the neural net had to recognise a series of patterns each made of 20 unique DNA strands chosen from a set of 100. Each of these patterns corresponded to 20 pixels in a 10-by-10 grid, representing a handwritten digit ranging from 1 to 9.
In experiments, which you can see in the above video, their DNA neural network successfully identified all nine digits, including when up to 30 pixels were added to or subtracted from the numbers, much as if a person had sloppily written a number that might be either a 4 or a 9.
There are many potential applications for a DNA neural network that can recognize complex patterns of molecules, the researchers say.
“Imagine a test tube artificial intelligence that is smart enough to detect complex biochemical signals and make better decisions than humans based on information directly obtained from molecules within the nanoscale world, for example, recognising harmful conditions in foods and air, or recognising diseases in blood samples,” Qian says.
The researchers do caution that their DNA neural nets compute very slowly. Currently, “it takes several hours to recognize a molecular pattern,” Qian says. However, using techniques such as arranging DNA molecules on scaffolds to foster more efficient interactions “could potentially make the DNA neural networks compute much faster, say, in minutes,” she says.
Although a traditional electronic neural network can also in principle recognise patterns of molecules, “if that information is in the molecules in a wet environment, it must first be captured and turned into a format the computer can read, for example, a patient’s disease may be recognised by sequencing their DNA and analysing the data on a computer before deciding on a treatment,” Cherry says. “But what if we built a computer capable of working directly within that wet environment? It could interact with that DNA, analyse it, and make a decision on the spot, without any human intervention.”
All in all, “a DNA neural network is not going to compute as fast as an electronic one,” Qian says. “But it can compute and act in a molecular environment.”
The scientists detailed their findings online in the journal Nature, and if you think the future of AI is only going to be restricted to today’s traditional silicon based computers, then it’s time to think again because asides from new DNA neural networks we’re also seeing the rise of new Quantum AI and Neuromorphic AI platforms, both of which are revolutionary in their own right.