Quantum Computing And Machine Learning: The Future

With the rise of the fourth industrial revolution, digital innovation became the precursor to global evolution; with the tech industry’s universal value shooting up as various companies adopted the algorithm-based artificial intelligence (AI) technology.

Artificial Intelligence (AI) and Machine Learning (ML), two closely related terms unarguably the most adopted technologies needed to structure intelligent, human like thinking systems.

According to CrunchBase listed company, JavaTpoint:

“AI is a bigger concept to create intelligent machines that can simulate human thinking capabilities and behaviour, whereas machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly”.

The evolvement of the 21^st century, with the rise of digitalization has made the adaptation and the growth of the tech industry exponentially straightforward, with Machine learning one of the key factors for this growth, it is expected that AI will help sustain its escalation in the coming years.

And this brings us to the subject and discussion on the future of machine learning, what it portends for the global world among other uses.

Machine learning has the capacity to remarkably alter various sectors future path, with the technology market expected to reach a $117.19 billion valuation by 2021, a ‘magical’ increase from its $8.43 billion valuation in the year 2019.

A large chunk of many business enterprises have started the adoption of machine learning algorithms to help with predictions and business decisions as ML adoption presented a vital chance to structure the future with different fields like Quantum computing, Auto Machine Learning (AutoML), diversified sectors, and much more.

Quantum Computing and Its reliance on Machine Learning

 A lot of machine learning Big tech companies are aware of the importance of this are investing heavily in quantum machine learning, a pool of hardware for machine learning by an entirely new type of computing device known as the quantum computer.

Quantum computing, a computation method that make use of the collective properties of quantum states to perform calculations can be said to be one of the few elements with high-level capabilities of taking machine learning abilities to greater heights. The speed of this process helps in faster data processing while delivering a sophisticated level of quantum mechanics to resolve complicated issues.

Despite the fact that there is no commercially deployed quantum computer available, there seems to be heavy flow of investment into the fast growing industry.

Automated ML and the potential to alter future landscape

Auto Machine learning, is an aspect of machine learning that self-regulates the process of using algorithms to perform life tasks and an example of these tasks is when Auto ML is drafted to locate an algorithm that can be worked on or to know if some algorithms are  missing.

AutoML can also be used to automate certain machine learning models like data pre-processing to improve data quality, feature engineering to assist in creating more adjustable features on input data, feature extraction to deliver new features to enhance predictions, and much more.

The Incorporation of ML with different sectors

What has been perceived as the first small leap into the future is the direct incorporation of the Machine Learning technology into our everyday lives, with ML identified as the definite overpass that would propel the world’s digital fate.

With the various adoption of machine learning technologies by different industries to deliver radical advancements in specific functionalities, the future of full automation is nigh.

Machine learning for instance play a very pivotal role in the health sectors, which included the pharmaceutical sub-sector, as the healthcare industry helps in provision of intense data sums. With the implementation of machine learning strategies there can be improved prediction and treatment of diseases. But the deployment of this technology can only happen by the enablement of analysis of a very large range of data that were drawn on previous studies, individual demographics, and health reports to deliver accurate predictions.

Many industries have already optimized their business strategies with the assistance of ML techniques, others in the R&D phase are gearing up to reach such premise.

In the year 2020, many manufacturers in spite of  being in the very early stages of ML deployment have began drafting the technology into their business strategies, with the technological tools  in the technology helping to examine equipment performance and state, predict product quality, and estimate energy usage.

Various industries are expected to latch on the ever-expanding advancement in technologies to have more machine-learning programmed robots into their premises in the upcoming future.

The two concept AI innovation (deep and machine learning) 

To have a better grasp of the different layers of Artificial Intelligence, we must distinguish between AI classification and machine learning, then deep learning.

Machine learning itself is a subfield of AI, while deep learning is a subdivision of machine learning. It connotes that the three terms are intertwined, as they cannot exist without each other.

To have a better understanding of the terms and contrasts is to know that  deep learning is an evolution of machine learning.

Deep learning help to organize the algorithms in tiers, as it  structures an ‘artificial neural network’ to learn and make decision on  its own, while it employs neural networks that are programmable to support machines in delivering better forecasts and decisions in the absence of human interference and assistance.

The deep learning in AI is a representation of how AI can replicate the human brain to have a specific genre of knowledge with the use of machine learning, as the more the algorithms work, and the higher knowledge it gains, the more accurate its prediction is.

AI principally deploys a neural network that helps replicate animal intelligence with three tiers of neurons, which are: the input layer, the hidden layer(s), and the output layer.  The interweaving relationship between the neutrons are related to weight and assessing the significance of the input value.

Machine learning has the leverage to alter the future in every sense of it as the  intelligent technology has shown it can act as the leading artificer in the emergence and optimization of technologies, like robotics, manufacturing, various sectors, the Internet of Things, others yet to come.

With the continued evolution and development of AI-induced technology, machine learning has  a very vital role to play, with its contribution pivotal in its delivery of technological breakthroughs in different fields in the future.