Ranked as one of India’s 10 top data scientists by Analytics India Magazine, Joy Mustafi has led data science research at tech giants including Salesforce, Microsoft, and IBM, winning 50 patents and authoring over 25 publications on AI.
He was associated with IBM for a decade as Data Scientist involved in a variety of business intelligence solutions, including IBM Watson. He worked as Principal Applied Scientist at Microsoft, responsible for AI research. Most recently, Mustafi was the Principal Researcher for Salesforce’s Einstein platform.
Mustafi is also the Founder and President of MUST Research, a non-profit organization promoting excellence in the fields of data science, cognitive computing, artificial intelligence, machine learning, and advanced analytics for the benefit of society.
Recently Mustafi joined Redwood City-based Aviso, Inc as Chief data scientist, where he will leverage his decades of experience to help Aviso customers accelerate deal-closing and expand revenue opportunities.
What initially attracted you to AI?
I love mathematics a lot, and the same for programming. I did my graduate degree in statistics and post-graduate work in computer applications. When I started my AI research journey back in 2002 at the at Indian Statistical Institute in Kolkata, I used the C programming language to develop an Artificial Neural Network system for handwritten numeral recognition. That was 2500+ lines of code, all written from scratch without any inbuilt libraries apart from standard input / output. It consisted of data cleansing and pre-processing, feature engineering, and a back propagation algorithm with a multilayer perceptron. The entire process was a combination of all the subjects that I studied. At that time AI was not so popular in the corporate world, and few academic organisations were doing advanced research in the field. And, by the way, AI wasn’t new at the time! The field of AI research dates all the way back to 1956, when Prof. John McCarthy and others inaugurated the field at a now-legendary workshop at Dartmouth College.
You have worked with some of the most advanced companies in AI such as IBM Watson & Microsoft. What has been the most interesting project that you have worked on?
I want to mention the first patent I was awarded while working at IBM: a method for solving word problems in natural language, which was an open problem with IBM Watson. The system I developed can understand an arithmetic or algebraic problem stated in natural language and provide a solution in real-time as a natural language answer. To do that, the system had to handle the following key steps: Get the input problem statements and question to be answered; convert the input sentences to a sequence of sentences which are well-formed from a mathematical perspective; convert the well-formed sentences into mathematical equations; solve the set of equations; and narrate the mathematical result in natural language.
There’s also my best project for Microsoft — Softie! I invented and built a physical robot equipped with various types of interchangeable input devices and sensors to allow it to receive information from humans. A standardized method of communication with the computer allowed the user to make practical adjustments, enabling richer interactions depending on the context. We were able to implement a robust system with features including a keyboard, pointing device, touchscreen, computer vision, speech recognition, and so forth. We formed a team from various business units, and encouraged them to explore research applications on artificial intelligence and related fields.
You’re also the Founder and President of MUST Research, a non-profit organization registered under Society and Trust Act of India. Could you tell us about this non-profit?
MUST Research is dedicated to promoting excellence and competence in the fields of data science, cognitive computing, artificial intelligence, machine learning, and advanced analytics for the benefit of the society. MUST aims to build an ecosystem to enable interaction between academia and enterprise, helping them to resolve problems and making them aware of the latest developments in the cognitive era to provide solutions, offer guidance or training, organize lectures, seminars and workshops, and collaborate on scientific programs and societal missions. The most exciting feature of MUST is its fundamental research on cutting-edge technologies like artificial intelligence, machine learning, natural language processing, text analytics, image processing, computer vision, audio signal processing, speech technology, embedded systems, robotics, etc.
What was it that inspired you to launch MUST Research?
My love of sci-fi movies and mathematics means I’m often thinking about how technology can change the world, and I’d been thinking about forming a group of like-minded experts on advanced technologies since 1993, when I was in 9th grade. Once I got my first job, it took 10 years to call for a meeting — and another 10 years to identify a group of suitable experts and form a non-profit society. Now, though, we have around 500 data scientists in MUST across India who are passionately contributing to research on emerging technologies.
Over the past several years the industry has been significant advances in deep learning, reinforcement learning, natural language processing, etc. Which area of machine learning do you currently view as the most exciting?
All machine-learning algorithms are exciting once they are implemented as a product or service that can be used by businesses or individuals in the real world. The Deep Learning era has pros and cons, though — sometimes it helps in automatic feature engineering, but at the same time it can work like a black box, and end up with a garbage-in-garbage-out scenario if proper datasets or algorithms aren’t used. Some of the latest technologies are also resource-hungry and require huge amounts of processing power, time, and data. The key thing to remember is that Deep Learning is a subset of Machine Learning (ML), which in turn is a subset of Artificial Intelligence (AI), and AI is a subset of Data Science — so it’s all connected. And it’s not about Python, R or Scala — I started my AI journey in C, and one can even write AI programs in assembly language code. Building successful AI systems depends first and foremost on understanding the business or research environment, and then connecting the dots between actions and data to build a system which genuinely helps various people in different domains. Whether you’re working with Natural Language Processing, Computer Vision, Video Analytics, Speech Technology, or Robotics, the best way forwards is to start with the simplest possible approach, and then adopt more complex methods iteratively as you experiment with and refine your system.
You are a frequent guest speaker at leading universities in India. What is one question that you often hear from students, and how do you best answer it?
The single question I hear most often is: “How can I become a data scientist?” I always tell young people that it’s definitely possible, and try to guide them towards using their love of mathematics, statistics, or computer science to try to solve real-world business problems. People also ask how they can join MUST, and again, the answer is simple: “Build your profile with multiple projects and focus on thinking outside of the box.” If you want to become a data scientist, you have to also prove that you can innovate. Without innovation, we can’t call ourselves scientists. Of course, being awarded patents or publishing your research in reputed journals and conferences also helps!
You recently joined Redwood City-based Aviso as chief scientist, in order to use your AI/ML expertise. Could you tell us a bit about Aviso and your role with this company?
Aviso uses AI and machine learning to guide sales executives and take the guesswork out of the deal-making process. That’s a fascinating challenge, and my primary responsibility is to help the organization grow in a positive direction, using deep research to set the stage for the customers’ success. I’m using my knowledge and experience in artificial intelligence and innovation to help make our core products and research projects more:
Adaptive: They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time.
Interactive: They must interact easily with users so that those users can define their needs comfortably. They must interact with other processors, devices, services, as well as with people.
Iterative and Stateful: They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must remember previous interactions in a process and return information that is suitable for the specific application at that point in time.
Contextual: They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They must draw on multiple sources of information, including both structured and unstructured digital information.
What was it that attracted you to this position with Aviso?
Aviso is working to replace bloated legacy CRM systems with frictionless, AI-enabled tools that can deliver actionable insights and unlock sales teams’ full potential. Our product is a smart system which understands the pain points of salespeople, does away with time-consuming data entry, and gives executives the suggestions and guidance they need to close deals effectively. I was attracted to the strong leadership team and customer base, but also to Aviso’s commitment to using sophisticated AI tools to solve real-world challenges. Selling is a vital part of any business, and Aviso helps with that by leveraging the power of artificial intelligence. Bulls-eye! What more could you want?
Lastly, is there anything else that you would like to share about AI?
Artificial intelligence makes a new class of problems computable. To respond to the fluid nature of users understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. These systems differ from current computing applications in that they move beyond tabulating and calculating based on pre-configured rules and programs. They can infer and even reason based on broad objectives. In this sense, cognitive computing is a new type of computing with the goal of developing more accurate models of how the human brain or mind senses, reasons, and responds to stimulus. It is a field of study which studies how to create computers and computer software that are capable of intelligent behavior. This field is interdisciplinary: artificial intelligence is a place where a number of sciences and professions converge, including computer science, electronics, mathematics, statistics, psychology, linguistics, philosophy, neuroscience, and biology. That’s what makes it so exciting!