Artificial Intelligence Revolution: The Guide to Superintelligence

What is Artificial Intelligence Revolution?

Artificial Intelligence or AI Revolution proves the basic changes of collecting and analyzing data. Moreover, it includes components like domain knowledge, data creation, and machine learning. 

Further, Domain knowledge refers to the comprehension and proficiency of real-time situations to identify the reason and method of completing a task. Moreover, the component of data approaches the technique of generating databases to integrate with learning algorithms. Finally, machine learning identifies patterns within the training data to envisage and execute tasks mitigating human interference. 

Here are the Major Elements of AI Technology:

• Intelligent Decision Making: AI often uses data analytics and machine learning advancements to learn and procure new insights. Moreover, it requires a suitable amount of data input to make accurate and deliberate decisions. Hence, it enhances productivity and decreases dependencies by automating processes.
  

• Intentionality: It refers to the technical and phenomenological creatures of a machine and its programs. It also depends on algorithms and comprehension abilities that develop the machine’s computing abilities. Moreover, it enables the AI ability to offer insights using real-time data and information. It also reacts and responds to the queries of users and developers. Hence, it uses processes like data ingestion, storage ability, analysis, and processing capabilities to enhance the sophistication of responses.
  

• Versatility and Conjecture: Machine Learning enables AI to determine programmable data patterns to enhance its functions. Therefore, it helps AI improve its analysis to provide better decision-making insights. It also helps interpret the capabilities of AI and use patterns to deliver better suggestions and results.

What Factors Influence and Power Artificial Intelligence Revolution?

AI revolution brings about several changes for various industries and businesses. Hence, here are six phenomena that influence and power Artificial Intelligence Revolution:

• Big Data:

Gartner defines it as, “Big data is high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

Data is everywhere, it is a fuel that powers our daily lives. Moreover, computers, the internet, mobile devices, etc are all connected by data. The devices also transfer, store, and process the data using various software applications. Further, the advancements in AI, Machine Learning, and Deep Learning enable the rapid processing of data. It also identifies patterns, makes more accurate predictions, offers better insights for decisions, etc. 

Moreover, AI helps segregate data into structured and unstructured formats. It uses the formats to categorize the data according to their volume, velocity, and variety.

• Processing Power:

Gordon Moore, the co-founder of Intel Corporation and the author of Moore’s law states that the number of transistors on a microchip doubles about every two years, though the cost of computers is halved. Another tenet of Moore’s Law says that the growth of microprocessors is exponential.

As a result, the law brought tremendous transformations in the computing powers of processors in the last few decades. Therefore, it has given rise and opportunities for newer technologies that use AI to advance computations.

For instance, GPUs or Graphical Processing Units have large amounts of cores than CPUs. Moreover, GPUs main function is processing a plethora of computations simultaneously. However, the technology enables graphics-intensive gaming requirements. However, this computing feature of GPUs fulfills Deep Learning requirements to process large data sets. It also helps improve the quality of predictions and analysis. 

Therefore, it helps AI researchers and developers offer better Cloud Computing functions. Firstly, it enables the local devices and machines to outsource processing capabilities. Further, it builds models for cloud services that improve computing powers. 

As a result, there has been a rise in Dedicated Processors to support basic AI applications. 

• Syndicated World:

We live in a world where we are not just connected by nature but also by technology. The emergence of smartphones, the internet, etc. has created a syndicate for humans. Although hyper-connectivity brings various changes and benefits to society, it has its repercussions. Accessing and procuring information is now just a click away for everyone. It may be information about multiple topics, research studies, news, etc. 

That is to say, AI helps build solutions for people to connect with information, knowledge, or other people. It also generates solutions to process the data that enables this flow of information. For example, platforms like Medium, Twitter, and GitHub enable the flow of various information. The platforms also encourage communities and individuals to share their experiences and solutions. Hence, it influences sharing and collaborating activities to connect and build better opportunities. 

• Open-Source Solutions:

According to opensource.com, “The term open source refers to something people can modify and share because its design is publicly accessible.” It also states, “Open source software is software with source code that anyone can inspect, modify, and enhance. “Source code” is the part of the software that most computer users don’t ever see; it’s the code computer programmers can manipulate to change how a piece of software—a “program” or “application”—works. Programmers who have access to a computer program’s source code can improve that program by adding features to it or fixing parts that don’t always work correctly.”

Open-source solutions and data enable the development of AI applications and software. It also creates opportunities for developers, programmers, and engineers to access the “source code” and innovate a newer solution. As a result, there is an increase in individual access code and customize their own AI tools as per their requirements. 

• Enhancements in Algorithms:

Researchers and developers are making colossal enhancements in Artificial Intelligence. Moreover, the significant developments in algorithms and methods enable better anticipations for new AI solutions. Moreover, there have been tremendous breakthroughs in technologies with approaches like Deep Learn, NLP, Computer Vision, etc. 

The research for AI solutions also depends on Reinforcement Learning (RL). It refers to the capability of AI to learn with minimum input data. Moreover, it employs trials and errors to maximize functional outputs for better results. 

For example, Google’s DeepMind is a pioneer in AI research and heavily uses RL for games. Moreover, other research labs like Open AI and Facebook AI explore and continuously make breakthroughs. 

• Maximizing Results:

AI, Machine Learning, and Deep Learning are no longer science fiction concepts, but a reality. Moreover, these phenomena offer competitive advantages for companies and enhance productivity. As a result, various companies leverage AI capabilities to enhance productivity and improve services. 

Further, streaming channels like Netflix use AI to manage and analyze large datasets. It also uses data to offer recommendations for a watchlist. As a result, it makes better personalizations for its users. Similarly, Amazon uses AI to manage product, seller, and user information. It also offers insights into the products and uses ratings and customer reviews to offer better services. Moreover, pharmaceutical companies like Pfizer use AI to research and develop drugs to help fight diseases.