Mechanical engineer to data scientist

Idris Khan has solved many real-world business problems for enterprises through the application of data science and AI expertise.

He is particularly interested in AI in the healthcare sector. In addition, he has published numerous research articles in this field.

INDIAai interviewed Idis to get his perspective on AI.

How did a mechanical engineer become interested in AI? How did it all start?

In my engineering days, I was very interested in subjects such as Operations Research, Mathematics and Statistics, but primarily we studied all the theoretical concepts. I was happy and started looking for real-time use cases for these topics. Then I learned how a criminal named Chota Rajan was arrested using facial recognition and how Google Maps can show us a streamlined path using math and concepts that interest me. These use cases were fascinating and I developed a keen interest in this domain. At that time I only knew all these things as data analysis. After learning these topics, I learned more about the domain and eventually delved further into Data Science and Artificial Intelligence.

What initial challenges did you face during the transition?

Coming from a hometown with almost no IT presence, I needed a senior who could show me where to go and what to do. Finding a mentor was a challenge.

We now have a lot of information and knowledge available online. But which things had to be consumed was a significant problem. Data science is an ocean, you don’t have to know everything, and humanly it’s almost impossible to my knowledge, because we can learn everything.

Another major challenge is learning programming languages. Coming from a non-IT background, we focus on learning the language rather than learning the logic build. Once you know how the logic is built, languages ​​are no longer a barrier.

What is your role at Accenture as a data scientist?

For Accenture’s internal project, we are building a recommendation system for Accenture Data MarketPlace. (Place where you can sell or buy data)

There is more client-side work that policy cannot disclose publicly.

Who is your inspiration in AI research, specifically Explainable AI?

Overall my inspiration in AI is Andrew NG. Especially with Explainable AI, it is not a single person, but the organizations Accenture and IBM are doing great work in this area.

Are coding skills required for professionals interested in a job in artificial intelligence?

Yes, you must be proficient in the Python programming language.

What advances in explainable AI can we expect soon?

Advances in explainable AI we will see shortly:

  • We have an explanation for every algorithm you use in AI/ML. We can know what factors contributed to the outcome.
  • Once we know why the AI ​​is producing certain results, we can further correct the AI.

What advice would you give to students and professionals interested in a career in artificial intelligence?

For professionals and students:

  1. Start building logic with Python programming. (Python is a simple language, you can learn Python quickly in a short time)
  2. Pay attention to the mathematical intuition behind algorithms and try to understand how things work behind the scenes.
  3. You don’t need to be a math genius to become an AI engineer, but you do need to know the concepts.
  4. Practice on real-time data as much as possible and build your strong work portfolio on GitHub.

Can you recommend AI books and research articles for people starting out in this field?

  1. Head-First Python: A Brain-Friendly Guide.
  2. Artificial Intelligence – Achyut Godbole.
  3. Artificial intelligence – a modern approach.