According to the recent study, it is evident that the artificial neural network composed of DNA has the ability to understand numbers using molecules. On the basis of such findings, the modern research claims that with such number recognition ability, the artificial neural network can also diagnose the possibility of diseases.
Therefore on the basis of these findings, we can conclude that artificial neural network has wide-ranging applications in the real world. This network is being readily employed in professions and fields such as:
• Medical science that deals with the diagnosis of high-risk diseases
• Navigation systems
• Recognition of voices and the purposes behind them
• Recognition of handwriting
• Imaging and screening
• Speech therapy programs
In order to be more precise about the diverse nature of the artificial neural network (ANN), it should be noted that it is not restricted towards the digital field. Rather, the AI is increasing its exposure in the biological domains including genetics and complex molecular structuring.
Furthermore, emerging and influential researchers that the scientist at the Caltech is carrying out proves that Al leads to the designing of synthetic biochemical circuits. In this way, they are able to perform processing of information on the basis of the molecular level without any need to a hardware or software.
Keep in mind that artificial intelligence is still at its progressive stage. Many critics claim that AI is at a rebirth stage as it is collaborating with artificial neural network (ANN) presently. Due to this integration, AI has made significant changes in the domains of pattern recognition.
Furthermore, as per the observation, the ANN is a machine learning automated tool that develops upon the principles of neuroscience. It is because the brain and the functions of the nervous system were the driving force behind the emergence of this concept in digital technology. The only difference is that instead of neurons, ANN has artificial nodes. On the whole, they have the same functionality as the nervous system.
Just like the nerves, these nodes are able to transfer messages among the network, receive and process the data into messages and feedbacks.
Now, as far as synthetic biology and genomics are taken into context, it should be noted that they are a new concept. Its background is not too old. Both the concepts revolve around biological technology that makes use of designing and engineering that lead to the restructuring of existing biological concepts.
However, the recent times, people are significantly learning about these two emerging fields in biotechnology. It is because these two fields cater towards:
• Al machine learning algorithms
• An increasing amount of big data
• Reduction in the scope of processing cost and storage of computing data
• Decentralized computing that is cloud-based.
Why there is a need to invent a DNA based on the computer that lives inside a single cell?
Today is the era of molecular computing that paves the way for the creation of different types of medications and diagnosis of diseases. In this way, this technology has achieved milestones in healthcare industries, biotech, chemical, and pharmaceutical fields.
Note: Some information used in this article is from , https://www.psychologytoday.com/au/blog/the-future-brain/201901/ai-created-in-dna-based-artificial-neural-networks , https://motherboard.vice.com/en_us/article/594mvz/ai-made-from-human-dna , https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/neural-net-dna