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A Career in Artificial Intelligence

Introduction to Artificial Intelligence

Artificial Intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks that typically require human intelligence. AI systems demonstrate at least some of the following behaviours associated with human intelligence: planning, learning, reasoning, problem-solving, discover meaning, knowledge representation, perception, motion, learn from past experience, manipulation and, to a lesser extent, social intelligence and creativity. They are tied to a system’s ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios.

According to the current system of classification, there are four primary AI types: reactive, limited memory, theory of mind, and self-aware. These four types aren’t all created equal. Some are far more sophisticated than others. Some of these types of AI aren’t even scientifically possible right now.

Let us understand these four AI types in more detail

Reactive AI
The most basic type of artificial intelligence is Reactive AI, which is programmed to provide a predictable output based on the input it receives. Reactive machines always respond to identical situations in the exact same way every time, and they are not able to learn actions or conceive of past or future. Examples of reactive AI include:

  • Deep Blue, the chess-playing IBM supercomputer that bested world champion Garry Kasparov.
  • Spam filters for our email that keep promotions and phishing attempts out of our inboxes. The Netflix recommendation engine

Limited Memory AI
Limited memory AI learns from the past and builds experiential knowledge by observing actions or data. This type of AI uses historical, observational data in combination with pre-programmed information to make predictions and perform complex classification tasks. It is the most widely-used kind of AI today. For example, autonomous vehicles use limited memory AI to observe other cars’ speed and direction, helping them “read the road” and adjust as needed. This process for understanding and interpreting incoming data makes them safer on the roads. However, limited memory AI – as its name suggests – is still limited. The information that autonomous vehicles work with is fleeting, and it is not saved in the car’s long-term memory.

Theory of Mind AI
Want to hold a meaningful conversation with an emotionally intelligent robot that looks and sounds like a real human being? That’s on the horizon with theory of mind AI. With this type of AI, machines will acquire true decision-making capabilities that are similar to humans. Machines with theory of mind AI will be able to understand and remember emotions, then adjust behavior based on those emotions as they interact with people. There are still a number of hurdles to achieving theory of mind AI, because the process of shifting behavior based on rapidly shifting emotions is so fluid in human communication. It is difficult to mimic as we try to create more and more emotionally intelligent machines.

Self-aware AI
The most advanced type of artificial intelligence is self-aware AI. When machines can be aware of their own emotions, as well as the emotions of others around them, they will have a level of consciousness and intelligence similar to human beings. This type of AI will have desires, needs, and emotions as well. Machines with this type of AI will be self-aware of their internal emotions and mental states. They will be able to make inferences (such as “I’m feeling angry because someone cut me off in traffic”) that are not possible with other types of AI. This type of AI is yet to be developed and the hardware or algorithms to support it also yet to be developed.

Today, AI is a very popular subject that is widely discussed in technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it’s not the future – it is the present. Unlike general perception, artificial intelligence is not limited to just IT or technology industry; instead, it is being extensively used in other areas such as medical, business, education, law, and manufacturing.

AI is being used widely to provide personalised recommendations to people, based for example on their previous searches and purchases or other online behaviour in ecommerce sites. AI is also hugely important in e-commerce: optimising products, planning inventory, logistics etc. Search engines learn from the vast input of data, provided by their users to provide relevant search results. Smartphones use AI to provide services that are as relevant and personalised as possible. Virtual assistants answering questions, providing recommendations and helping organise daily routines have become ubiquitous. Language translation software, either based on written or spoken text, relies on artificial intelligence to provide and improve translations. This also applies to functions such as automated subtitling. AI systems are also helping recognise and fight cyberattacks and other cyber threats based on the continuous input of data, recognising patterns and backtracking the attacks.

Introduction to Retail

AI Engineer
AI engineers are problem-solvers who develop, test and apply different models of AI. One can get business insights with these models and this helps the company to make effective business decisions. Undergraduate or postgraduate degrees in the field of data science, computer science or statistics are mandatory. Any kind of certifications on Machine Learning or Data Science adds to the advantage. Proficiency in programming languages, such as Python, R or C++, is essential. The applicants should have a strong grasp in statistics, natural language processing, applied mathematics, and analytics.

Machine Learning
Engineer Machine learning engineers are involved in building and maintaining self-running software that facilitates machine learning initiatives. They are in continuous demand by the companies and their position rarely remains vacant. They work with huge chunks of data and possess extraordinary data management traits. They work in the areas of image and speech recognition, prevention of frauds, customer insights, and management of risks. To become a machine learning engineer, one must have sound command in applying predictive models dealing with cleaned data. Programming, computing, and mathematics are essential to becoming successful as a machine learning engineer.

Robotics Scientist
Robotics Scientists are mainly responsible for developing, designing, application and operation of the robots and robotic systems. Robots can be used in many situations for many purposes, but today many are used in dangerous environments (including inspection of radioactive materials, bomb detection and deactivation), manufacturing processes, or where humans cannot survive (e.g. in space, underwater, in high heat, and clean up and containment of hazardous materials and radiation). There are three engineering disciplines at work in this field namely – mechanics, electronics, and computer science.

Natural Language Processing Engineer
Natural Language Processing (NLP) Engineers are responsible for the interaction between everyday human language and a computer’s ability to process and analyze natural language data. NLP comes from an amalgamation of computer science, Information sciences, AI, and linguistics. They are responsible for the development and design of language understanding systems and for the effective use of text representation techniques. This can involve dealing with speech patterns, AI speech recognition, understanding of natural languages, and natural language generation.

Computer Vision Engineer
Computer Vision (CV) engineering lies at the intersection of artificial intelligence and machine learning. CV engineers help computers process visual data and recognize objects and people. Thus, a CV engineer’s purpose is to help computers “see”.

Education Pathway
Undergraduate Post-Graduate Some Colleges
B.Voc. Retail Management
B.Voc. Retail Management & IT
B. Voc. Retail and Logistics Management
BBA Retail Management
BBA Entrepreneurship
BBA General
B.Des. Fashion Mgmt./ Fashion
Communication/ Retail
MBA Retail Management
MBA
MBA (Sales/ Marketing/ HR/ IB/ Logistics)
Masters in Fashion Mgmt./ Brand Mgmt.
  • Ambedkar University, Delhi
  • G.G.D.S.D. College, Chandigarh
  • Maharshi Dayanand University, Rohtak
  • Jesus & Mary College, Delhi
  • College of Vocational Studies, Delhi
  • Symbiosis University of Applied Sciences, Indore
  • St. Joseph's College of Commerce, Bangalore
  • Bennett University, Noida
  • IIM Indore / Rohtak/ Jammu/ Bodh Gaya
  • Christ University, Bangalore
  • University of Delhi
  • Guru Gobind Singh IP University, Delhi
  • National Institute of Fashion Technology
  • MITID, Pune
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