Introduction
Artificial intelligence has dawned a new era of technology in our lives and it all started at the cusp of the new millennium. Hardly did we know that our interaction with machines would become so intimate that we would acknowledge the cognitive and mental capabilities of machines outside science fiction. It is difficult to predict the graph of the rise of artificial intelligence in the coming decade but it is fair to conclude that artificial intelligence courses would accentuate the advent of new machines, devices, and systems. In this article, we take a look at the various predictions that are most likely to be witnessed in the domain of artificial intelligence by the end of this decade.
The streamlining of business processes
The role of artificial intelligence involves the automation of different types of tasks related to manufacturing and assembly processes. But this is a very narrow view about the role of artificial intelligence in business processes. In the present times, artificial intelligence helps in the streamlining of different types of business processes. Ranging from data collection to data analytics, artificial intelligence has taken up the role of report generation, documentation, and visualization in a chronological manner. Other types of robotic automation processes help in the effective administration of complex and strategic tasks.
More personalization than ever before
Artificial intelligence lies at the center of the creation of personalized customer grievance systems and mechanisms. This also extends to the personalization of other types of recommendation systems as is the case in e-commerce. The different types of tech giants have prospered by harnessing the power of customer personalization. AI-powered systems have the capability to predict a 360-degree view of customer behavior and prepare a catalog of customer choices and profiles. Based on this customer profile, the recommendation system targets customers with specific products according to their liking.
The accuracy of decision sciences
As more and more data sets become available, their processing becomes increasingly difficult. This is where we require high-quality information to inform our strategic decisions. A highly efficient and effective automated system is extremely crucial if one has to make informed decisions motivated by powerful and quantitative data sets. Artificial intelligence courses not only help in processing diverse data sets but also helps in simulation at an advanced stage. The accuracy, efficiency, and availability of data for real-time analytics are extremely crucial to make customized decisions according to specific situations and circumstances.
AI-powered systems
The devices and systems powered by artificial intelligence are slated to become cheaper and open to the commercialization that we are seeing in the present times. AI-powered systems will not only bridge the gaps in the security infrastructure but will also be the way out for data privacy in near future. AI-powered systems and devices will also find applications in embedded systems, smart applications, and workplace tools. Given the pace at which cloud computing and the internet of things are growing, this could have a proliferating effect on the systems driven by artificial intelligence.
Human AI collaboration
Collaboration between humans and AI-powered systems will increase as more tools and devices take the erstwhile place of repetitive tasks in human lives. We would also witness AI-powered systems acquiring the cognitive and imaginative capabilities of humans. This may not only facilitate better interaction and communication between humans and humanoids but will also lead to superfast analytics and augmentation capabilities through enlarged large data sets. As per a report by Statista, more than 75% of organizations will bridge their skill gaps with the help of AI by the end of 2025.
AI at the edge
Most of the devices powered by artificial intelligence require real-time analytics without much latency. This is where the processing of data very close to the source could lead to a decrease in latency and an increase in efficiency of the devices. Various data-driven algorithms can be employed by powerful processors to derive insights from large data sets close to the point of data generation. In addition to this, the processing of data close to the source will ensure that the data pipeline becomes immune from malware and attacks that have been common when data is processed away from the source.
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Concluding remarks
Artificial intelligence is progressively entering into new sectors like film, music, games, and the like. In this scenario, we need to throw caution to the winds and ensure a robust cybersecurity infrastructure for the free flow of artificial intelligence into various domains.