Artificial intelligence (AI) makes it possible for machines to learn from there experience. Machines now can understand there functionality and where there productivity is lagging down with the help of Artificial intelligence. Now with Artificial intelligence they can make there own decisions.
Taking Artificial intelligence one step ahead, a team of US researchers has developed an artificial synapse. Artificial synapse is something that help computers mimic one piece of the brain. It is an artificial version of the space over which neurons communicate. Artificial synapse not only help the machine to process the information like a digital computer do but also mimics the way human brain completes any task.
In today’s world we can’t say confidently we have build a zero error machine composed with Artificial intelligence. The major problem with such machines are that they demand high end hardware configuration and this high end hardware configuration ultimately make the machine high energy consuming devise. With the help of this newly discover Artificial synapse, we can build energy-efficient AI devices that would revolutionize our lives.
Dr Feng Xiong, Assistant Professor of Electrical and Computer Engineering said “The analog nature and massive parallelism of the brain are partly why humans can outperform even the most powerful computers when it comes to higher order cognitive functions such as voice recognition or pattern recognition in complex and varied data sets,”
Even through robots can perform hell complex mathematics, when it comes to processing power, the human brain just can’t be beat. Our brain consist somewhere around 100 billion neurons. A single neuron relay on instructions to thousands of other neurons via synapses — the spaces between neurons. There are more than 100 trillion synapses that mediate neuron signaling in the brain, which help brain to recognize patterns, remember facts, and carry out other learning tasks, at lightning speeds.
I’m not saying it is impossible to make a digital brain, computers can replicate the brain in certain ways. The human brain has hundreds of trillions of synapses for transmitting information, so building a brain with digital devices has a very least chances or we can say it is not scalable. When we will go to to mimic one analog synapse, it will take about a dozen digital device.
An emerging field called “neuromorphic computing” focuses on the design of computational hardware inspired by human brain. Dr Xiong and his team built graphene-based “artificial synapses” in a 2D honeycomb configuration of carbon atoms.
Instead of carrying out computations based on binary, on/off signaling, like digital chips do today. The elements of a “brain on a chip” would work in an analog fashion, exchanging a gradient of signals. In this way, small neuromorphic chips could work like the brain. With the hep of this chip-set they can efficiently process millions of streams of parallel computations that are currently only possible with large banks of supercomputers.
Xiong Lab’s approach provides a possible route for the hardware implementation of large-scale artificial neural networks.
The Graphene synapse demonstrated excellent energy efficiency just like biological synapses, said the study published in the journal Advanced Materials. However, Artificial Synapse still use about 10,000 times as much energy as the minimum a biological synapse needs in order to fire.
The graphene-based neural networks can be employed in flexible and wearable electronics to enable computation at the edge of the Internet. Graphene’s conductive properties allowed the researchers to finely tune its electrical conductance.
Dr Xiong said “By empowering even a rudimentary level of intelligence in wearable electronics and sensors, we can track our health with smart sensors, provide preventive care and timely diagnostics, monitor plants growth and identify possible pest issues, and regulate and optimize the manufacturing process.”
The development of an artificial brain that functions like the analogue human brain still requires a number of breakthroughs, said researchers.
There is a need to find the right configurations to optimise these new “artificial synapses”.
Despite the challenges, Dr Xiong said he’s optimistic about the direction they’re headed.
“We are excited about this progress since it can potentially lead to the energy-efficient, hardware implementation of neuromorphic computing, which is currently carried out in power-intensive GPU clusters,” he noted.
Ability of Artificial Synapse of communicating with live neurons, will ultimately leads to improved brain-machine interfaces. The softness and flexibility of the device also lends itself to being used in biological environments.
What do you think is it possible to build something as powerful as a human brain.
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