Technology and machines have become an integral part of our life – be it communication, socializing, entertainment, or living in general – and that has drastically increased the amount of data processed through the interactions with these machines. It has to lead to Artificial Intelligence becoming the latest buzzword in a world that has seen a consistently increasing pace of technological evolution.
Contrary to popular perception, the concept of AI has been around for a while – the term was coined in 1956 at Dartmouth College. It has come a long way since, notwithstanding a lag in the 1970s and 1980s, with its subsequent revival in the late 1990s.
AI is the science and engineering of making intelligent machines, especially intelligent computer programs. As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities classified as AI include successfully understanding human speech, competing skillfully in strategic game systems (such as chess and Go), self-driving (autonomously operating) cars, intelligent routing in content delivery networks, and military situation simulations.
It is essential to understand that AI falls under two categories – Narrow AI and Artificial General Intelligence (AGI). What we see today in real life is mostly Narrow AI – it operates within a limited context and focuses on doing a single task extremely well—much more limited than human intelligence. AGI, on the other hand, is the kind of AI we aspire to – a machine with broader general intelligence that it can use to solve any problem (AGI is also what we see in shows and movies and dystopian science fiction).
Nevertheless, even presently existing AI has a wide range of applications and is extremely relevant in the current world. Its importance lies in the fact that it learns by processing vast amounts of data as per specification, and adds an intelligent aspect to existing products and services. AI works through progressive learning algorithms that build accuracy on each iteration – analyzing the data you feed it through low learning models, performing high-volume computations reliably. It is important to note that AI is not self-sufficient – optimal usage and results are obtained only when you aks the right questions, which cannot be done without human intervention yet.
AI is important because data is all around us. That data can be processed to make lives easier through smarter inputs in decision making and automation of tasks for business optimization. It can be used in practically every industry:
- Healthcare – personalized medicine, healthcare assistants, supplementary diagnostic tools
- Manufacturing – factories and plants to have connected equipment on an Internet Of Things (IoT) framework, collecting data for optimization
- Retail – virtual shopping experiences, personalized recommendations, stock handling, site layout planning
- Banking – verifying and processing transactions to identify fraud (detect activity in such as unusual debit card usage and large account deposits), credit scoring as well as replacing manual data management.
- Analytics – charting models, identifying patterns.
- Interior and Appliance design – connecting household devices on IoT frameworks, optimizing smart assistants
- Designing responsive tools like Siri, Alexa, live translation tools, self-driving cars
Knowledge of AI, its components and practical usage is a valuable skill in today’s world, especially when faced with the genuine possibility of people losing jobs due to redundancy prompted by AI tools adopting their jobs, or because of underskilled employees not being qualified enough to develop and/or work with these tools. It is important to note that AI is also giving rise to new jobs that require new skills and profiles.
For those looking to upgrade their skills or reskill themselves and don’t have the time or resources for a formal degree or diploma qualification (courses may charge for a certification) :
- Machine Learning – Stanford University (Coursera), Andrew Ng
- AI for Everyone – Andrew Ng (Coursera)
- Machine Learning Crash Course – Google
- Learning From Data (Introductory Machine Learning) – Caltech (EdX)
- AI for Everyone: Master the Basics – IBM (edX)
For more rigorous degree courses (in India):
- IIT Hyderabad
- IISc Bangalore – MTech in AI
- IIT Bombay – Deep Learning Certificate programme – covers the foundation of deep learning, neural network, computer vision, NLP and other core areas.
- IIT Madras
- Offers dual degree specialisations in data science and robotics
- Inter-Disciplinary Dual Degree (IDDD) program on Data Science. (Dual-Degree is BTech plus specialisation equivalent to MTech in 5 years)
- IIT Ropar – MTech course in the field of AI and ML
- IIT Guwahati – MTech programme in data science
- Vellore Institute of Technology – B Tech in Computer Science & Engineering with specialization in Artificial Intelligence & Machine Learning.
- SRM Institute of Science and Technology, Chennai – B Tech. in Computer Science and Engineering (Artificial Intelligence and Machine Learning).
For more rigorous degree courses (abroad):
- Carnegie Mellon University
- Bachelor of Science in Artificial Intelligence (BSAI)
- Master of Science in Artificial Intelligence and Innovation (MSAII)
- Stanford University
- Graduate Certificate in Artificial Intelligence
- Bachelor of Science in Computer Science which includes courses on AI
- University Of California, Berkeley- Research Opportunities, with the AI lab offering seminars and courses such as Computational Imagining and Robotic Manipulation and Interaction.
- Massachusetts Institute of Technology – Courses on AI at various levels, excellent research opportunities.