Building A Career In Artificial Intelligence - Career Foundations Featuring Akshay Chandrasekhar

Be a part of the AI revolution. Learn how you can build a career in AI, understand the foundations of AI, and become a successful AI engineer

Artificial Intelligence - A Revolution

Modern society is an industrial society. Industrial revolutions have paved the way for a better, more connected, and prosperous world. But the current revolution, driven by Artificial Intelligence and Big Data, will potentially have the greatest impact on humankind. Artificial Intelligence is at the forefront of technological advancement today, and it has never been a better time to be a part of the AI juggernaut.

This is what we addressed in ‘Building A Career In Artificial Intelligence’, the first in our Career Foundations series. Featuring Akshay Chandrasekhar , the Co-Founder and CTO of edisn.ai , ex-Enterprise Engineer at Intel, and an alumnus from the Electronics and Communications branch, 2006.

Highlights of the event

The event covered the following topics:

  • What is artificial intelligence, and its impact today
  • The subdomains of artificial intelligence
  • The various paths of a career in artificial intelligence
  • Common job roles in AI and career progressions
  • Upskilling and breaking into the artificial intelligence industry
  • Akshay's Insights

    The Impact of Artificial Intelligence

  • Artificial intelligence is being adopted across all industries today
  • The availability of large datasets and advancements in CUDA GPUs by NVidia have paved the way for easier AI computing
  • A survey by PwC shows that a large section of CEO/CTOs believe that AI will have an impact larger than the internet itself
  • The Gartner Hype Cycle for emerging technologies in 2020 has AI-based technologies dominating other technologies
  • Classification of Artificial Intelligence, based on Learning Methodology

  • The way the AI network learns can be classified into reinforced learning, supervised learning, unsupervised learning, and deep learning
  • Supervised learning includes the usage of a labeled dataset to train the network to perform a particular task
  • TUnsupervised learning is mostly based on clustering, to perform the required task in the absence of labeled data
  • Reinforcement learning is teaching a network to perform a set of tasks upon which there is a set reward.
  • Deep learning uses neural networks to achieve the set goal, and each of the above three can work in conjunction with deep learning for better results
  • The 3 Fundamental Pillars of AI

    The holy trinity comprises of Applied Mathematics, Domain Expertise, and Tools/Programming Languages

    Applied Mathematics

    Applied Mathematics is the base for most of the algorithms used for processing data. DIfferential equations, calculus, probability, etc. are used regularly

    Domain Expertise

    Domain expertise in your industry is essential to adapt AI into your product.

    Programming Languages

    Python expertise is critical to a career in AI

    Career Paths in AI

    The different career paths can broadly be classified into AI Research, Applied AI Research, and Applied AI

    • AI Research focuses on producing research papers, theoretical advancements, and research output. This stream is extremely math-heavy, and job positions are mostly limited in number and limited to Ph.D. holders.
    • Applied AI is the most predominant career path. Application of research and creating an actual end-user experience using AI is the focus of this career path. Software engineering is a predominant part of this job stream.
    • Applied AI Research is a mix of both these streams. Jobs are seen in startups or domain-specific product companies.
    • The entry-level jobs could be as data analysts, data/AI engineers, or a deep learning engineer
    • This could progress into a data scientist or a deep learning scientist role.
    • The importance of software engineering cannot be overstated in the big picture. Senior data scientists would choose the algorithms to use for the solution, while a large number of software engineers are needed to build the actual product by implementing the said algorithms.
    • A strong base in software engineering, along with knowledge of the AI foundations will pave the way for a successful career in AI

    Upskilling and building domain expertise in AI

    Do not focus on paid courses. The best content on AI is available for free in the YouTube channels by MIT, Stanford and IIT Madras, and other online channels
    Focused research, getting information, and building knowledge from the industry leaders will enhance your skillset.
    A post-graduation degree is a formal way of building domain expertise. However, Akshay feels that a degree is not a factor unless domain expertise and knowledge are compromised.
    A Non-Computer Science background is never a deterrent to a successful career in AI. There is no entry barrier with regards to the branch, as long as the base foundation of mathematics, programming tools, and domain expertise is set in concrete.

    Wrap Up

    The event covered these topics and also covered how Akshay broke into AI, and how his learning path and career path were curated for the same. Akshay also showed us his work at edison.ai, where they’re building a real-time engagement platform based on AI.
    And that’s a wrap on the first ‘Career Foundations’ event by BMSCE Alumni Club. Who are we, and why are we doing this?

    We’re just a bunch of people from different walks of life with one common factor - BMSCE. Folks from different industries, geographies, career paths, etc. put under a single umbrella, with the objective of connecting with fellow alumni, expanding our networks, giving back to our community and supporting our cause.

    Do you think you can contribute to your tribe, bring something new to the table, or just have a good time with your fellow BMSians, join us!

    Link to event video

    Wanna join the Club?

    You got to be from BMSCE though 😃