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'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 from AI and what should be done to protect individuals and humanity. View the leaders.
'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.
What is the academic curriculum for a successful career in AI?
Artificial Intelligence is not a profession, it’s a technology that enables the extraction of insights from data. AI requires varied skills and different roles throughout the data science life cycle. There are many specialised roles in AI, the most characteristic ones being:
- AI Architects determine what business problems to solve. They choose the right technology, frameworks, and architect solutions to solve business challenges.
- Data engineers manage the data sources and prepare the data. Well, we can argue that the Data Engineer is not part of AI, but I have included it as it’s an important piece of the puzzle for AI to be successful. A data engineer is a comprehensive data specialist who prepares data and makes it available within your organization. By extracting information from different systems, transforming, cleaning data, and combining different sources into a working database, data engineers become an important piece of the puzzle in the AI process. They analyze and cleanse the collected data from the various systems and tools, used in an ecosystem. To uncover the hidden treasures in a company's data, these individuals need to deeply understand business processes.
- Data scientists train the models using the prepared data. They use machine learning and predictive analytics to collect, analyze, and interpret large amounts of data to gain insights that go beyond statistical analysis. A data scientist builds machine learning or deep learning models to help organisations improve efficiencies.
- AI or Machine Learning Engineers deploy the models. Once the models are built, the process is very similar to the software development lifecycle. The role of an AI/ML engineer is to develop deployable models from models developed by data scientists and integrate the models into the final product. AI engineers also develop secure APIs for delivering models on demand.
- AI Strategists work in research or real-world solutions. They should have hybrid skills in both technology and business. A good AI strategist understands the power of both technology and business; they must be able to explore the art of the possible as the means of transforming the business. They also identify the important AI projects for the organisation and strategize the AI journey for the organization. In my experience, the biggest gap we have in the industry at the moment is the AI Strategists.
Most often the perception is that people working in AI need a Ph.D. It really depends on what exactly the person is interested in. Skills required for different roles in AI vary but they typically include mathematics (statistics, linear algebra, and probability), programming languages (python, java, R, C++), neural network architectures or frameworks, and most importantly domain or industry knowledge.
For a high school student considering a career in the AI space, I would suggest focusing on and learning Mathematics and Programming. It’s important to get your hands dirty so, do some Python projects. Get yourself familiar with some Machine Learning or Deep Learning frameworks like Sci-kit learn, Pytorch, or TensorFlow and try to apply them in solving real-world challenges.
It is important to have a degree in Computer Science which helps understand the basics and provides a strong foundation. A Master's in Data Science will add to your knowledge and give you the tools and skills required for developing a career in Data Science. Also, it is essential to explore an industry you are interested in and look at how AI is transforming that industry. There are a lot of massive open online courses (MOOCs) and university programs with a focus on AI in certain verticals. And lastly, If you are interested in research and improving and building algorithms then you will definitely need a Ph.D. All these if you would like to have a formal education. I have also seen a lot of people without formal education excelling in the AI space by learning from MOOCs.