All You Need to Know About AI-ML and Its Applications
“The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast — it is growing at a pace close to exponential” states Elon Musk, CEO, SpaceX.
A close look at the AI market showcases that automation with AI is trending with all major corporations. The global market value of AI was pegged at USD 40 billion in 2019 and starting 2020, it is slated to grow at a CAGR of 42.2% for 7 years. The introduction of AI will boost corporate incomes by 38%. It is the efficiency of AI-systems which allows for low-cost installations with high productivity.
In 2020, Covid-19 has gravely impacted global economic health. Yet economists view it as an opportunity for greater reform, stating that grave recession leads to greater automation.
How did digital devices vest AI-automation?
From switch operations to coded commands, the transformation of computational powers has been phenomenal. Computers are increasingly smarter whilst processing data from varied sources, self-learning and improving its capacity to retain and read Big Data in microseconds. This gave birth to machine learning or ML. Programmers coded inherent capabilities to computers to react and recreate, and today we call it artificial intelligence or AI. Now, It has branched into subcontexts like Natural Language Processing (NLP), Speech Recognition, and Machine Vision. These are intertwined in various disciplines as applications of AI-ML.
What are the principal subtypes of Machine Learning in use?
What differentiates AI from normal programming is the ML algorithm. It helps computers to analyse and learn from data to perform its task, and it gets better with more experiences and data. Based on AI applications, machines can learn in three principal ways:-
1) Supervised Learning-
It aims to train the machine to observe patterns from a controlled set of input and react as instructed to deliver a desirable output. The way social media recognizes faces and tags is a common application of supervised learning.
2) Unsupervised Learning-
It is not limited to learn from restricted data sets. Rather appointed to learn the pattern from continuous incoming feed and responds to slightest change in usual patterns.. The system works perfectly for cybersecurity inspectors to detect abnormal behavior in server-centric systems.
3) Reinforcement Learning-
An offset of unsupervised learning where the system decides on how to react to a certain sequence of events. It allows the machine to learn from an uncertain environment and capture the concern and basis the pattern, deciding on the response. It enables AI to make business plans, marketing strategies, search recommendations, and others.
What are the levels of AI?
The more advanced machine learning becomes, the better is the impact of AI. Measuring AI’s success in mimicking human intelligence, it can be –
1) Reactive — AI that can respond to stimuli
2) Limited Memory — AI that uses memory to learn and respond
3) Theory of Mind — AI that understands
4) Self Aware — AI that is aware of its existence and needs
The potential that AI can achieve has 3 stratifications –
1) Artificial Narrow Intelligence (ANI) — Represents existing AI devices
2) Artificial General Intelligence (AGI) — Systems that have near-human abilities
3) Artificial Super Intelligence (ASI) — The most capable form of AI to achieve
Within a narrow timeframe, AI has sneaked into our lives in every possible way. Your music application playing your favorite song or your favorite search engine displays accurate predictions before you complete typing the query is not pure luck. The software we use today takes advantage of AI to provide more human-like responses than behaving like mere machines.
What are the trending AI applications in the market?
The thriving AI-digital age is augmenting new technological disruptions with applications like –
- AI IoT (AIoT) — For near-perfect CRM and personal assistance
- AI for NLP — The speech-to-text tool for Speech API Adaptors
- Edge Computing — A cloud storage system to manage data for IoT systems
- Recommender Systems — Offering customized buying choices for visitors
- Computer Vision (CV) — with multiple object detection ability
- Behavioral Artificial Intelligence (BAI) — asserting brand improvement and predicting customer trends
How Industry 4.0 is preparing to be AI-ready?
In our digital frontier, AI has no intention to sabotage human life. Let’s not get carried away by over-exaggeration of science-fiction movies that robots are going to take over. Scientists say that it is AI that will bring back humanity in the mechanized world. Looking at the bright side, there will be 29% growth in AI job listing and India ranks among the top five countries to globally drive AI growth.
The future is AI. Learning AI will give you an advantage and leverage your career in how you want your future to be. If you aspire to boost your career prospects and learn AI-ML applications, IIT Mandi and Wiley offer PG Certification in Applied AI and ML. This course will walk you through Deep AI processing and industry-specific tools for the entire AI lifecycle. You can learn modeling user-friendly APIs with tools like Python, Keras, Tensorflow, NLTK, NumPy, Scikit-learn, Pandas, Jupyter, and Matplotlib.
Plus you will learn to ‘Apply AI’ in real-world scenarios under the guidance of the top industry experts organized by the Wiley Innovation Advisory Council.
For over 200 years, Wiley has been helping people and organizations develop the skills and knowledge they need to succeed. They are dedicated to developing efficient learning products, digital transformation education, learning, assessment, and certification solutions to help universities, businesses, and individuals move between education and employment and achieve their ambitions. In 2020, WileyNXT has been recognized by Fast Company for its outstanding innovations in education.
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