Artificial Intelligence and Machine Learning—these two words have been hitting the headlines for all good reasons for quite some time. While they are nestled within each other and are often synonymously used, they have a thin boundary that separates them. They are the two sides of a coin called Big Data.
While artificial intelligence is the ability of the machines to think and act on its own, machine learning is all about training a machine to arrive at a possible solution through analytics and minimal to zero manual intervention.
Do you know that the two technologies, when bundled with Big Data, can create wonders? Yes, we are already witnessing it through humanoid robots, although still restricted to exhibitions. What if AI & ML lead to new technologies? This question looks rhetoric, but it isn’t.
While humanoid robot recently got Saudi Arabia’s citizenship, the US army is making the best use of machine learning for understanding when their combat vehicles require repair. Google went a step ahead and developed a robust architecture that can predict the death of patients at hospitals. Over two lakh patients were studied, and results were 90% accurate.
The Kickstart of Future Technology
Both these technologies, which have vast potential to replace humans, are built on Artificial Neural Networks (ANN). It is through these networks that machines will mimic humans with ease. While ANN continues to expand its wings, various studies are already underway to sub-divide AII into three different technologies.
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Generative Adversarial Networks (GAN)
Convolutional Neural Networks is the proposed technology that will be used in the recognition of images and respond to signals. Recently, a proposal was made for Apple to include CNN in its iOS, which was turned down due to its low latency that doesn’t support cloud services. However, once this technology hits the market, it will undoubtedly make our lives even more sophisticated. For instance, when it’s used to monitor the traffic, it’s capable to detect a number of cars, trucks, two-wheelers etc.
Recurrent Neuron Networks is already in use in Apple’s Siri, Microsoft’s Cortana, Google Voice. It is the ability of the machine to take the voice command, retrieve and read out the information. RNN is equipped with special functionality to remember the input. This is going to dominate the future, said analysts. With the emergence of IoT, RNN technology will be a part and parcel of our lives. Every communicable IoT device, that takes voice commands, is depended on Recurrent Neuron Networks. What next? You will have fancy gadgets that can communicate with you without any manual intervention; something like plug and play.
Generative Adversarial Networks comes] equipped with two unique layers that act against each other and therefore the term adversarial network. Developed by researchers at Montreal University in 2014, GNN machines can mimic human brains. They not only act independently but have artistic skills such as reciting poems on own, ability to play music and dance.
Nanorobotics: Researchers at Campinas University introduced this nanorobotics, which looks promising, and is still under evaluation. If it works out, nanorobots would be injected into the human body, that scavenges throughout the body providing details about every organ and making repairs, such as removing stones from kidney or cleaning out the cholesterol buildup in arteries.
Conclusion: Artificial Intelligence and Machine Learning are still in the initial stages of development, but that day isn’t very far where manual work in every field will be minimized to the core and robots will dominate every field. Why do you want to ignore this promising field? Start exploring AR and MR technologies through IoT. We are here to help you out in the digital transformation. Write to us at firstname.lastname@example.org.