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Artificial Intelligence (AI) and Machine Learning

Artificial intelligence and machine learning have been pushing the boundaries of the software and hardware industries over the last few decades. They've progressed from being a figment of our imagination in science-fiction films to performing more complex, accurate, human-error-free, automated, precise, and innovative tasks. Over the last few decades, the application of Artificial Intelligence in science, research, business, marketing, stock markets, economics, medicine, defense, and engineering has grown dramatically. Artificial intelligence and machine learning have brought about revolutionary changes and global impacts on B2B and B2C businesses, societies, and governments, prompting them to perform systematically. From "Alexa, turn on the hallway light" and "Hey Siri, play Baby Shark" to unlocking our phones with facial recognition, AI-based systems have a significant impact on our lives.

What is Artificial Intelligence?

The simulation of human intelligence into machines to perform programmed tasks and learn skills and experiences so that it can perform or mimic human-like actions and tasks is known as artificial intelligence. To put it simply, think of it as a human-created robotic entity capable of performing errands rationally, quickly, and intelligently without being told what to do. So it behaves like a human without being a human, or it can augment and amplify humans without replacing them.

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Various types of Artificial Intelligence

Type 1: Based on capability

a) Artificial Narrow Intelligence (ANI) or Weak AI

This type of AI can only solve a single problem or task exceptionally well and has limited capabilities, such as tasks performed by automated voice recognition AI like 'Alexa' and 'Siri' which can perform single tasks like weather prediction and which we have only been able to build as of now.

b) Artificial General Intelligence (AGI) or General AI

This is based on the development of an AI system capable of closely mimicking human cognitive functions such as reasoning, language and image processing, and comprehension. Achieving something of this level necessitates the use of computing systems and ANI systems that can collaborate to mimic human reasoning and abilities, which is a time-consuming process due to the immense complexities.

c) Artificial Super Intelligence (ASI) or Super AI

The ASI system (fictional for the time being) outperforms all other realms in terms of advancement; such a system would be capable of making human decisions with greater accuracy, rationalism, and understanding. This system is still a long way off in the future.

Type 2: Based on Functionality

a) Reactive machines

They can only perform the tasks that are given to them and have no memories that can be used to make current decisions. The IBM chess computer "Deep Blue" is a prime example of a reactive machine.

b) Limited memory

This type of machine only retains a recent piece of information for a transient amount of time. The machine only observes the current data to perform the task. This is used in pre-performed Self-driving cars which analyze the speed and direction of the other cars and also have information on traffic lights and other information.

c) Theory of mind

These machines can think themselves and can take decisions based on their thoughts. Thus they are said to mimic human behavior and can place themselves well in society.

d) Self-awareness

The self-awareness machines will be the representation of the human being itself performing the entire cognitive task with understanding, reasoning, thoughts, and consciousness. They will have an inner desire and can make a decision based on past experiences. These machines will be a replica of human beings themselves which perform actions base on others' feelings and their own experiences.

APPLICATIONS OF ARTIFICIAL INTELLIGENCE

AI can transform many industries as all the industries are data-driven and efficient data processing can be done easily by Artificial intelligence. The abilities of AI in different industries are listed below let's have a look at them.

Healthcare: AI can analyze various medical tests, provide virtual assistance using chatbots, and monitor the vital stats of the patients. They can assist in diagnosis, telemedicine, and administrative works, perform robotic surgeries with high accuracy, and help in research studies.

Finance and Insurance: They can help in financial fraud detection, money laundering, and identity theft issues especially in the banking sectors, use of chatbots for personalized customer management, leads generation, better policy selection, claims processing and settlements, and better customer experiences

E-commerce: Managing stocks and supply chain to meet the demand, filtering and removing malicious and fake reviews, optimization of the search for social media management and chat-bots to manage consumers, manufacturing and packaging of goods.

Education: AI can provide digitalized online classes, customization of education according to the student, connecting global classrooms, grading and reviewing students. Counseling and guiding students in their career path.

Cybersecurity and Defense: AI can help in detecting cybersecurity frauds; it can develop advanced pieces of equipment in naval and air defense systems. Robots can be used for combat missions and also defense system security.

Agriculture: Small scale tasks can be performed by the robots like sowing and harvesting crops, predicting the weather for a better harvest.

Human resource: AI can help in hiring and maintaining work culture, selecting and assigning projects to teams, collecting feedback about the workplace and the team.

The application is vast in different industries and these are only a few of many.

EXISTING APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN CURRENT TIME

  • Virtual assistance
  • AI-driven autopilot flights and self-driven cars like Tesla
  • Facial recognition features for phones, laptops, banking, and passwords
  • Personal assistance like Apple's Siri and Android's Alexa
  • Spamming filters for Emails
  • Chatbots
  • Plagiarism check tools
  • Sports Analytics
  • Surveillance and security
  • Algorithms will be solved in less time
  • Reduced dependency

DEBATABLE CONSIDERATION

  • Increase in unemployment - Increase use of AI may lead to loss of jobs in most of the sectors Self-driven cars may replace taxis and taxi drivers while industries can replace skilled labours with machines.
  • Increase demands of resources – AI are machines and for running them they require power sources and servers. The serves will be required to store a vast amount of data and for this skilled computing service with massive storage space will be required.
  • Privacy concerns- Some scientists believe that increased use of AI machines can lead to privacy breaches as these machines may hack into the privacy of an individual and this can be weaponized later. There is also a threat that they can evolve more than human beings and can take over civilization.
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Machine learning

It is a subset of Artificial Intelligence (AI). It is described as a set of algorithms that are used to solve problems of the real world with the use of computational resources. Machine learning uses statistics along with Deep Learning to complete a task. Deep learning consists of multiple layers of processing that mimic the works performed by the human brain in processing the data in recognizing and detecting objects, translating languages, and making decisions accordingly. It is just like the extraction of data from existing computation algorithms. Although the terms machine learning and artificial intelligence are frequently used interchangeably, Artificial Intelligence includes machine learning, statistical learning, and other techniques to achieve the desired result

CATEGORIZATION OF MACHINE LEARNING

Machine learning is categorized into the following types-

  1. a. Supervised learning

It is the most simplest and popular form of machine learning. It is task-oriented learning in which labeled sets of data build a model predicting the output labels using regression in case of numerical and classification when there is a variable. The outcome is adjusted in case of incorrect answers to fetch accurate output. Few examples of supervised learning are face recognition in Facebook, Spam filtration in emails

  1. b. Unsupervised learning

It is the opposite of supervised learning which involves a large set of data with no labeling as in supervised learning. The large set of data is fed into the algorithm and if the data is organized into groups and clusters into the more organized data done similarly to human brains. These are intelligent algorithms that organize unlabelled data. Some areas where we can see this learning working is the Recommender system in YouTube and Netflix, buyer recommendations.

  1. c. Reinforcement learning

This machine learning is behavior-driven. The algorithm is based on signals which can differentiate good behaviours as positive signals and bad behaviours as negative ones and over time following this algorithm make fewer mistakes. Video games like Mario, the industrial simulation uses this form of machine learning.

USES OF MACHINE LEARNING IN THIS ERA

  • Facial recognition
  • Voice simulation
  • Self driven cars technology
  • Recommendation systems
  • Image recognition
  • Navigation systems
  • Gaming technology
  • Stock market predictions
  • Search engines
  • Word suggestions in keyboards
  • Robotic technology

Conclusion

In the field of artificial intelligence and machine learning, there is a tremendous amount of research and development going on. The resurgence of Artificial Learning will soon be seen impacting human beings' daily lives, and we will not be far away from seeing Artificial Intelligence walking among us in society.

This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.

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