AI has become a buzzword. The general public has an exaggerated or a misplaced idea about the same. It is important to demystify AI and view it from the perspective of what kind of business benefits it can bring.

AI can be defined as machine’s ability to perform some cognitive functions we usually associate with human minds (e.g., perceiving, reasoning, learning and problem solving). It can be viewed as consisting of three different areas: ANI, GEN AI and AGI.

• ANI: Artificial Narrow Intelligence
• GEN AI: Generative Artificial Intelligence
• AGI: Artificial General Intelligence

Artificial Narrow Intelligence and Business Applications

Artificial Narrow Intelligence or ANI (consisting of Machine Language (ML), Robotics & Computer Vision) has been used in the business world to improve productivity since more than four decades. There are number of courses, particularly in ML, that prepare our students extensively in applying the same in various business domains. For example:

• Based on past data, the ML algorithm can create a model between age, marital status, asset ownership and income of an applicant and determine whether a person paid back a loan or not. This model can then be used by those in the banking business to predict whether to give a loan or not.
• Based on past purchases by number of customers in an off-line grocery store, ML algorithms can conclude that items like milk and orange juice are bought together quite often. This can then be used to determine optimal storage design as well as bundling various items.
• Based on various events happening in quick succession, like for example: vibration levels of some parts increasing beyond a certain level and temperature of some parts falling below certain value, one can predict the failure of the machine in advance and schedule preventive maintenance.

GEN AI and Business Applications

GEN AI is relatively a new area but has already shown a great potential for improving productivity. Typically, based on a huge amount of information (textual, images, etc.), LLM (Large Language Model) is created, using a Neural Network with a large number of parameters. Besides other things, such models can predict the next word/next interaction. In conjunction with additional and specific information fed by uses, one can answer context and domain-specific queries.

These models are used for a number of applications, and they keep on learning more based on additional information that is created externally as well as through user-fed information and interaction.

• Conversation: It can converse with the users in a natural language, say in English. These can be used for:
  • Customer Support: For example, a bank can feed information regarding various services with details. LLM with this customised information can answer various customer questions, say, what is required for opening a bank account. One can create much better Chatbots this way.
  • Virtual Friends: Imagine that an Alexa-type of system is fed with customised information regarding an elderly person. The system can interact with the person on topics which interest him. For example, if the person had deep interest in cricket, it can talk about a cricket match that is going to be played in the near future. The system can also judge the mood of the person, and in case of detecting deep depression, it can alert his relatives or doctor.

  These kinds of applications combine several features like:

  • Virtual Assistants
  • Sentiment Analysis
  • Interactive Storytelling
  • Language Translation
• A specific application like driver-less cars: An LLM is created using a large number of photos (images) of various road conditions. Now with the current position of traffic, one can generate instructions to the accelerator/break/ steering wheel that can be understood by the car’s electronics & mechanical systems.
• Code Generation: A good programmer can generate code with multi-fold productivity, test it and fix the bugs
• Summarisation: It can summarise the information based on desired size of the summary
• Sentiment Analysis: It can judge the sentiment of various conversations on social media and judge what people feel about a certain product or service

Applications of Generative AI is only limited by one’s imagination. Our students will learn all the concepts and will be able to apply the same in specific situations.

Artificial General Intelligence: The Future

This is a futuristic scenario, that may or may not materialise. AI systems of today do not have real intelligence, feelings or understanding. It certainly does not have consciousness, ideas about right and wrong, fairness or otherwise.
An AGI system is supposed to have all of these. One would be creating a creature like a human being or maybe even superior. Either human being controls AGI systems or vice-versa is something that only the future can answer.

In Summary

The global generative AI market size conservatively was $14.59 billion in 2024 and is projected to grow to $283.37 billion by 2034. Total global AI market will soar from $189 billion in 2023 to $4.8 trillion by 2033.
At SP Jain Global, students pursuing the Bachelor of Data Science will not only learn about the discussed technology but also business applications of these technologies, and thus help create a newer, and a more fascinating world.

About the author:

Dr Sunil D Lakdawala is an adjunct faculty at SP Jain Global. He completed his PhD at Yale University, USA and is a domain expert in the areas of Marketing Audit and Evaluation, Logistics (Ports & Cargo) and Database Management Systems to name just a few.

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