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'60 Leaders' is an initiative that brings together diverse views from global thought leaders on a series of topics – from innovation and artificial intelligence to social media and democracy. It is created and distributed on principles of open collaboration and knowledge sharing. Created by many, offered to all.


'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks of AI and what should be done to protect individuals and humanity. View the leaders. 

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'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.

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What is the academic curriculum for a successful career in AI?

Siddha Ganju


Having interviewed many people who have multiple specializations from, Coursera, Udacity, DataCamp, Udemy, etc, the candidates who tend to stand out are those who demonstrate a working knowledge of AI/DL.

How do you do that? With a portfolio of projects that involve different data domains (text, image, multimodal) and tasks (classification, generation).

Assuming that you have a good grasp of the theory and you're ready to take up a small project that allows you to practice your skills, it is a good idea to find a project that resembles something in the industry and work on it to improve the breadth of your knowledge and try things into production. Let's say for example that you decided to develop an image similarity search system that finds the closest image to a given query image. The following list of questions can help you, not only to prepare the project itself but also to get a good sense of the required skills and knowledge required:

- What would your complete end-to-end pipeline look like?
- How would you build a cloud API to serve the web frontend?
- How do you scale from hundreds of images vs millions to billions?
- What would be the cost involved in scaling this up?
- How would you evaluate performance metrics, eg latency, and accuracy for model drift?
- How would you index the new incoming data? Would you rebuild or incrementally update?
- While scaling up how do you make your network and pipeline efficient? How do you reduce the floating-point computations in your network? How would you reduce the size of the embeddings while still having the same representative power?
- What are the potential sources of bias?
- In retrospect, consider the effort you put into this. What would you have done differently if you had one day, one week, or one month to develop a solution?

Follow the trajectory of these questions in your projects and you’ll find yourself approaching problems from different perspectives, acquiring knowledge, developing multiple solutions, and evaluating the merits and demerits of each. Beyond technical skills, you should be looking for opportunities to elevate your profile with ideas such as:

- Communicating your work and its impact effectively, to both specific stakeholders and the general audience.
- Develop and engage in leadership roles be it for organizing the project or organizing people and teams.
- Maintain professional relationships.
- Create an identity for yourselves in the field, and market yourself.

Siddha Ganju is an AI researcher who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. As an AI Advisor to NASA FDL, she helped build an automated meteor detection pipeline for the CAMS project at NASA, which ended up discovering a comet. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices.


Siddha Ganju

"Form a portfolio of projects that involve different data domains and tasks."

Senior Artificial Intelligence Lead




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