Scope of BE Artificial Intelligence Study in Bangalore

In this day and age, Artificial Intelligence (AI) has taken over the world with its limitless possibilities and potential to support humanity as a whole. From automating mundane tasks to assisting large-scale technological productions, this marvellous innovation has slowly but steadily captivated the imagination of people worldwide.

As a result, many people have taken an interest in studying this area of specialisation, be it on their own or through a professional course offered by colleges. Today, there is a whole spectrum of courses in this field available across various educational institutions. Most of these are commonly offered through degree programmes in artificial intelligence courses such as B.Tech and BE with additional specialisations, including Machine Learning, Data Science, Data Analytics, to name a few.

Location is also a primary factor to consider. A student wouldn’t necessarily prefer to study an AI course in a secluded city with limited opportunities for placement or career growth; rather, they’d likely choose to pursue their academics in a city that is ripe with job prospects and future learning opportunities. One such example is Bangalore. Known as the Silicon Valley of India, the city plays a prominent role as India's leading information technology (IT) exporter and a major hub for technology companies and startups.

With that being said, let’s delve deeper into this topic and find out the artificial intelligence scope in India by taking the example of BE Artificial Intelligence in Bangalore.

This course basically focuses on two vast classifications of AI.

Classifications of Artificial Intelligence

The classifications within AI are based on two categories: functionalities and capabilities. These two aspects best exemplify the future scope of artificial intelligence while building on the foundation that is currently present.

AI-based on functionalities

• Reactive Machines - AI tends to replicate a human’s ability to react to different types of stimuli. However, it has no memory power of its own, so it really can’t work on using previous data for better decision-making capabilities. In layman’s terms, they are reactive to any kind of action that is performed in front of them and on the basis of that, they are able to produce a reaction.

• Contextual Memory - The usage of AI is immense as the algorithm is able to make decisions based on past data, thereby ensuring that in the future, decisions are made on an informed basis. Basically, AI powers the machines with some form of memory capabilities, albeit with a limited extent.

• Theory of Mind - The AI that powers machines and algorithms are able to use their practical reasoning in interpreting data and coming up with decisions in real-time. Thereby, leading to continued research at strengthening the intelligence, allowing systems to not just analyse data but also assess it for greater gain.

AI-based on capabilities

• Super AI - This is a hypothetical level of AI that exceeds human intelligence in virtually every field, from creativity to problem-solving to emotional intelligence. A defining characteristic of Super AI is its superior cognitive abilities. This entails the ability to solve problems that are extremely complex and do so at a speed and accuracy no human could match.

• General AI - General or Strong AI is a hypothetical form of AI based on the capability to learn, understand, and apply intelligence to a wide range of tasks in the same way that a human can. Theoretically, it can think, plan, learn, and adjust to new obstacles in various surroundings, leading to an improvement in its flexibility and efficiency.

• Narrow AI - Narrow artificial intelligence, sometimes known as Weak AI, is now the most common kind of AI based on capability. It is trained and designed to perform a single task or a small number of tasks. Examples include voice assistants, spam filters and generative AI tools like ChatGPT.