Making Intelligent Machines With Artificial Intelligence

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If we want to name one technology that is changing our world rapidly, then that will be Artificial Intelligence.

What is Artificial Intelligence?

Artificial intelligence is a technology that makes machines capable of doing tasks that need human-like intelligence. Human intelligence includes logical reasoning, learning, problem-solving, and perception, and so on. In various industries, there are lots of things that are getting automated using AI. If you want to understand what are the multiple parts of AI, then please keep reading.

Stages of Artificial Intelligence: There are three different states of AI-based on how it is evolving 

Artificial Narrow Intelligence (ANI – Weak AI: These machines can perform narrowly defined tasks. At this stage, devices do not have any thinking ability. It just executes predefined functions or set of instructions. All the AI-based systems built to date come under this category.

Artificial General Intelligence (AGI) – Strong AI: These machines will be able to think just like human beings. There is no example of AI so far. Many scientists predict that this will be a risk to human existence.

Artificial Super Intelligence (ASI) – Super AI: These machines will surpass human intelligence. Self-aware AI is part of ASI. Right now, this seems a possible thing, just like a science fiction movie.

Main Benefits of AI

Increased automation: Increased automation of repetitive and mundane tasks. Example – Recruitment process automation, which is using NLP and deep learning to filter out resumes based on the required skills and scheduling interviews.

Increased productivity: AI application can perform a task in a far lesser time. Example – a legal robot that can read many legal documents using NLP to analyze legal documents and identify errors. It can also compare reports to verify if it is as per industry standards.

Fast Decision Making: It helps rapidly changing businesses. Example – Sales Force’s Einstein Analytics is AI for CRM (Customer Relationship Management), which allows companies to make smarter decisions.

Solve complex problems: Perform large computational tasks on massive data. Example – PayPal uses AI for fraud detection, medical diagnosis, and weather forecasting, and so on.

Personalization: AI helps in customization, which helps in better customer experience and increases business growth. Example – eCommerce websites.

Global defense system:  Defense also uses various AI applications, including war robots.

Disaster Prediction and Management: It uses AI for the weather forecast. Example IBM’s Deep Thunder.

Enhanced Lifestyle: People enjoy orAI based products like Amazon Echo, Google homes, Siri, self-driving cars.

Increase in accuracy: It a by avoiding human errors in the calculation of big data.

Domains of AI

Machine Learning: It is a subset of AI. In these machines, interpret, process, and analyze data to solve real-world problems. Under Machine Learning have categories – Supervised Learning, Unsupervised Learning, and Reinforcement Learning. The limitation of machine learning is processing multidimensional data because it is complicated. Deep learning eliminates this limitation.

Deep learning: It is a subset of machine learning. It uses a neural network to analyze multidimensional data. An example is an algorithm used for face verification on Facebook.

Robotics: It focuses on different branches of robots. Sophiya humanoid is an excellent example of this.

Expert Systems: An expert system learns and reciprocates the decision-making ability of a human expert. It uses if-then logical to solve complex problems. It does not rely on traditional procedural programming. Expert systems are mainly used in information management, medical facilities, loan analysis, and virus detection, and so on.

Fuzzy logic: It is a computing approach that uses the principles of “degrees of truth” instead of the usual Boolean true or false modern computer logic.

Natural Language Processing: It uses text analytics. Analyzing human language and deriving better insights through machines and grow businesses. Examples – Amazon, which uses it to read customer reviews to enhance the customer experience. Twitter also uses NLP to filter out hate speech and terrorist languages.

Computer vision (Object Detection): It deals with making machines understand details in an image or a video by processing it. It automates the human vision task.

Application of AI in daily life

  • Google predictive search is the most popular AI application. When we start typing in the Google search engine, then Google starts recommending words for you. These recommendations are the result of personal data collection that Google does about you, like your location, personal preferences, and interest.o
  • Facebook – Face detection (Machine learning and Deep learning) used for tagging friends
  • Twitter – Twitter uses machine learning to filter out hate speech and terroristic languages. It uses Machine learning, Deep learning, and Natural Language Processing (NLP).
  • Self-driving cars from Tesla Company use computer vision, image detection, and deep learning. Robo taxi is going to run on the roads soon.
  • IBM Watson is helping the healthcare organization for decease analysis.
  • Contract intelligence platform analyzes legal documents and notes down data points in less time, which could take months if done manually.
  • Google AI Eye doctor uses a retina scan to identify deceases which cause blindness.
  • Virtual assistants like Google duplex uses AI. It can respond to calls and book an appointment for you.
  • Chatbots: Companies are implementing Chatbots for customer support. AI Chatbot can answer intelligently to complex queries. Chatbot learns from every conversation with the customers. It goes through previous communication to improve the current response. It helps to strengthen ChatBot’s response efficiency and helps to understand your customer’s choices.

Conclusion

In this article, we covered the high-level ideal of AI and its domains. Each domain of AI is a vast area in itself. We also understood that AI is already around us in different industries. AI implementation is increasing automation in various industries. It will result in the elimination of a few jobs; for example, self-driving cars will eliminate truck driver jobs.

On the other hand, it is generating lots of jobs in various AI domains. People who will embrace this change will prosper in the coming years. We will cover multiple AI-based topics in upcoming articles.