Everything You Should Know About Cognitive Computing

What is Cognitive Computing?

We often mention how traditional computing is reaching its limit--there’s a threshold we can’t move past without making some seriously big changes to the way we structure computers. One of those exciting ways is by making physical computers similar to human brains. I'm introducing the Cognitive Computing concept in additional detail here, but a fast recap: this type of computing is named neuromorphic computing, which suggests designing and engineering computer chips that use an equivalent physics of computation employed by our nervous system. I know it is too technical, but the subject demands it to be.

Difference between Cognitive Computing and Man-made Neural Network.

Neuromorphic computing is different from a man-made neural network, which may be a program run on a traditional computer that mimics the logic of how a person's brain thinks. Neuromorphic computing and neural networks can work together because as we make progress in both fields, neuromorphic hardware will probably be the simplest choice to run neural networks on, but for this blog, we’re getting to specialize in neuromorphic computing and therefore the exciting strides that are made during this field within the past year.

We all know, traditional computers ‘think’ in binary. It is either 0 or 1, a yes or a no. You simply have two options, therefore the code we use and the questions we ask to these computers must be structured in a very rigid way. Neuromorphic computing works more flexibly. Rather than using an electrical signal to mean 1 or 0, designers of these new chips want to form computer neurons, that interact with one another the way biological neurons do.

How Cognitive Computing is possible?

To do this, you would like a sort of precise current that flows across a synapse or space between neurons. Counting on the amount and type of ion, the receiving computer neuron is activated in some way--giving you tons of more computational options than simply your basic yes and no. This ability to transfer the power of understanding, from one neuron to another and to possess all of them to work together, means neuromorphic chips could eventually be more energy efficient than our normal computers--especially for sophisticated tasks.

To realize this exciting potential, we'd like new materials, because what we’re using in our computers today isn’t getting to cut it. The physical properties of something like silicon, for instance, make it hard to regulate the current between artificial neurons...it just quite bleeds everywhere the chip with no organization. So, a replacement design from an MIT team uses different materials-- single-crystalline silicon and silicon-germanium layered--on top of 1 another. Apply an electrical field to the new device and a well-controlled flow of ions will be there.

Researches going on in this field.

A team in Korea is investigating other materials. They used tantalum oxide to offer them precise control over the flow of ions...AND it’s even more durable. Another team in Colorado is implementing magnets to exactly control the way the pc neurons communicate. These advances within the actual architecture of neuromorphic systems are all working toward getting us to an area, where the neurons on these chips can ‘learn’ as they compute.

Software neural networks are ready to do that for a short time, but it’s a replacement advancement for physical neuromorphic devices--and these experiments are showing promising results. Another leap in performance has been made by a team at the University of Manchester, who has taken a special approach. Their system is named Spinnaker, which stands for Spiking Neural Network Architecture.

While other experiments look to vary the experiments we use, the Manchester team uses traditional digital parts, like cores and routers--connecting and communicating with one another in innovative ways. UK researchers have shown that they will use spinnaker to simulate the behavior of the human cortex.

Future scope of Cognitive Computing.

The hope is that a computer that behaves sort of a brain will give us enough computing power to simulate something as complicated as that of a brain, helping us understand diseases like Alzheimer’s. The news is that Spinnaker has now matched the results we’d get from a standard supercomputer. This is often huge because neural networks offer the likelihood of upper speed and more complexity for less energy cost, and with this new finding, we see that they’re edging closer to the simplest performance we’ve been ready to achieve thus far.

Overall, we’re working towards having a far better understanding of how the brain works within the first place, improving the synthetic materials we use to mimic biological systems, and creating hardware architectures that combine with and optimize neural algorithms. Changing hardware to behave more just like the human brain is one of a couple of options we've, for continuing to enhance computer performance and to urge computers to find out and adapt the way humans do.

Conclusion

Furthermore, research is to take place and we will come across a more advanced and powerful computing system. For now, this was what I had to share with you all. 

I hope you enjoyed reading about cognitive computing or shall I say Neuromorphic computing. Do comment down below your views and click on the follow by email button to receive more updates on such interesting topics. Also, check out my blog on Emerging Technologies in 2020.