A graduate from the Stanford University, Ritika Dokania is now the Head of Machine Learning, AIML.com. Ritika has 8 years of experience in this field. Through this article, let us take a look at her AI journey.

Can you tell us about your AI journey? 

My AI journey began in 2016 when I joined the Master's in Statistics program at Virginia Tech, USA. The MS Statistics course helped me build a strong foundational knowledge of mathematics, probability, linear algebra, and optimization methods, which are core to the field of Machine Learning and Data Science. I then worked at Funding Circle USA, an online lending platform for small business loans. I built machine learning models at Funding Circle to make real-time approval/decline decisions for online loan applications. I then joined Intuit USA, one of the largest fintech companies in the world. I developed machine learning models and risk strategies at Intuit to detect fraudulent online banking transactions. These hands-on experiences made me appreciate the power of machine learning in industrial applications and decision-making. 

By this time, the field of Deep Learning had emerged as a strong contender to traditional machine learning models, outperforming them in almost every task. This motivated me to deepen my knowledge of the newer deep learning methods. I enrolled in several Deep Learning, Natural Language Processing, and Natural Language Understanding courses at Stanford University, USA and earned an AI Professional certificate.

Empowered by this knowledge, in 2023, I quit my job at Intuit and took the plunge to start my venture, AIML.com. AIML.com is an educational and upskilling platform for learning AI and preparing for machine learning interviews. It is home to over 300 of the most commonly asked machine learning interview questions, providing detailed answers and video explanations. Additionally, there are over 60 practice quizzes across various topics, with insightful commentaries for in-depth learning. AIML.com's machine learning quizzes are being used by several universities in the US, and the website has been visited by people from over 100 countries across the globe. We aim to establish AIML.com as the one-stop resource for everything related to Artificial Intelligence and Machine Learning.

What is your area of expertise in AI, and what made you choose it?

I specialize in NLP (Natural Language Processing) and Generative AI. At the beginning of my professional career, I focused on traditional machine learning, as I mainly dealt with tabular data at the time. I predominantly worked with classification algorithms, including logistic regression, random forest, and boosting algorithms, to predict the rate of loan default and fraud. In the last few years, I have slowly transitioned to specializing in NLP and Deep Learning, focusing on Generative AI.

Generative AI fascinated me as it involved taking natural language as input and passing it through a complex neural network architecture to produce well-written output. This framework could solve various NLP problems, such as sentiment analysis, text generation, text summarization, language translation, etc. Generative AI elevated Machine Learning to a new level. We generate tons of natural language in our daily lives through social media conversations, news articles, books, scientific journals, online websites, customer care chats, etc. Generative AI provides us with the tools and resources to analyze this vast data, learn meaningful connections, and provide user-friendly output. It has genuinely transformed the Machine Learning space and is finding its use in every aspect of human life. ChatGPT is an example of a system powered by Generative AI.

How did generative AI impact your field of work?  

Generative AI has made a significant impact in the field of education. It has become a personal coach cum assistant with whom you can brainstorm problems, discuss ideas and find an optimal solution. Some of the areas where I use generative AI regularly are as follows:

• Code generation and learning new libraries: It is much easier to generate boilerplate code that I can further modify to meet my requirements. GenAI particularly shines when it comes to learning new libraries or frameworks. It provides much more context and examples than official documentation, significantly reducing the learning curve. 
• Image generation: I use GenAI tools to generate images for marketing purposes
• Improving Writing: Thanks to GenAI, I have enhanced my writing skills. Whenever I write something, I ask ChatGPT to identify grammatical and structural errors and ways to fix them.
• Fun Stuff: Finally, when it comes to discovering new recipes, planning vacation trips, or finding kids' bedtime stories, ChatGPT has replaced Google for me.

Describe the challenges you have faced in reaching where you are now.  

The field of Machine Learning has been growing at a very rapid pace. What seemed like the most efficient way to operate yesterday has become obsolete today. Traditional machine learning has paved the way for Deep Learning and Neural Networks advancements. In this scenario, one must always be on their toes to learn more, adapt to new technologies, and provide innovative solutions. 

I aspired to start an educational platform for machine learning. Only when I thought I was equipped with both the theoretical and practical aspects of machine learning did newer algorithms, such as Transformer architectures and Neural Networks, become mainstream and begin dominating the space. I had already built the first version of my website with information on traditional machine learning. However, I realized that I had to update the website with Deep Learning and Neural Networks content to keep it relevant.

Neural networks were a black box to me then and seemed extremely tough to navigate. However, I took this challenge as an opportunity to learn more about the subject and ventured into upskilling my knowledge in deep learning. I enrolled in several NLP and Deep Learning courses at Stanford University, completed the Hugging Face NLP module, and studied Andrej Karpathy's Neural Network series, among several others. For 20 straight weekends, I left my kids with their cousins to study for these courses. My job at Intuit was highly hectic, so I had to work hard on weekdays and even harder over weekends to sail through the demanding schedule. My hard work soon paid off, and I was writing articles on Neural Networks and Deep Learning for my website at AIML.com. What was previously a daunting idea became a fun endeavour for me. I authored over 50 articles on Deep Learning for AIML.com that received great reviews from experts and professors. And to this day, I continue to learn, as this challenge paves the way for new learning opportunities.

What do you want to say to women who wish to build careers in AI and other tech-related fields? 

I encourage every woman to pursue their dreams of building careers in AI and other tech-related fields. In our Indian society, there's somehow a general perception that boys are better suited for science and technical fields, while women are more apt for arts and humanities. As a result, women are often not encouraged to pursue careers in scientific fields. Growing up, even though I excelled in Maths and Science, topping my class, I always felt that I was not good enough and that this was a boy's domain. It took several years of validation before I gained the confidence to realize that I am no less; a woman need not be any less. It all comes down to hard work and dedication; whoever is ready to put in the effort is bound to succeed.

Therefore, I would urge every woman to follow their dreams. Do not be discouraged by stereotypes. I advise women interested in AI to start learning coding early in their lives. Coding is core to machine learning and AI, and the earlier you start, the better it will be. Talk to your seniors, find a mentor, and consult with experts to receive the right educational guidance and a career in this field. 

Do you see enough female leadership roles in corporates? In your opinion, what should change?  

Female corporate leadership becomes significantly less common as you move into senior management. Despite a growing number of companies promoting women to leadership roles, the statistics still fall far below 50%. One reason I've observed for women's career regression in corporate life is maternity leave. Although an increasing number of companies are offering improved maternity benefits, women still face a disadvantage in their professional career growth regarding family planning. In my opinion, maternity for women should be given its due regard. Women should be able to declare their pregnancy plans confidently, and no woman should worry about her promotions and career growth while planning the most beautiful event in her life.