1. Tell us about yourself and your background.
I come from a small farming background in India. After completing my undergraduate education in India, I attended Ohio State University and graduated with my doctorate degree in Quantitative Genetics and Statistics. I joined Monsanto because of its mission to increase agriculture productivity on the farm.
As part of Monsanto’s research and development function, I have led several statistical analytics and genetics teams. I also managed strategy teams responsible for developing strategic investments for Monsanto’s product portfolio. Currently, I lead the digital partnerships, innovation and outreach team for Monsanto and am responsible for establishing long-term partnerships with startups and industry leaders within the emerging technology space to create win-win arrangements for agriculture.
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2. What have you been working on recently?
In the past few years, my team has been heavily focused on bringing external innovations to Monsanto and ensuring internal deployment and adoption for enabling a digital future for Monsanto. We are heavily focused on Artificial Intelligence (Deep Learning, Machine Learning, Natural Language Processing), Robotic Process Automation, Sales and Marketing Automation and Internet of Things, along with Edge computing
At an industry level, Monsanto sees modern agriculture changing. Modern agriculture is an evolving approach to agricultural innovations and farming practices that helps farmers increase efficiency and reduce the amount of natural resources—water, land, and energy—necessary to meet the world’s food, fuel, and fiber needs. Modern agriculture is driven by continuous improvements in digital tools and data, as well as collaborations among farmers and researchers across the public and private sectors.
3. Tell me about the right tool you used recently to solve customer problem?
We recently deployed Machine Learning as a Service Engine for enterprise, and we are seeing great success in helping drive customer solutions. We also recently deployed natural language processing tools to better understand brand sentiments.
4. Where are we now today in terms of the state of artificial intelligence, and where do you think we’ll go over the next five years?
We are still in early years of AI. The space is evolving more rapidly than what the experts would have anticipated because of the unprecedented growth in technologies supporting the advancement of AI (GPUs, low cost of compute power, accelerated growth of open source, rapid explosion of social media channels). The reason I tend to articulate it as ‘early’is because we still tend to focus heavily on the technology supporting AI (Machine Learning, Deep Learning, etc). These technologies and algorithms need to mature significantlybefore they can become mainstream. In the next five years, we expect to see a lot more AI-derived products in the marketplace.
5. There is a negative perception around AI and even some leading technology folks have come out against it or saying that it’s actually potentially harmful to society. Where are you coming down on those discussions? How do you explain this in a way that maybe has a more positive beneficial impact for society?
As a scientist, I do believe that we need to hypothesize, experiment, fail and learn quickly. Every great invention has gone through numerous failures before it succeeded. I see the same thing with Artificial intelligence. We are at a stage right now, where we as humans are experimenting with it. We need to experiment and experiment a lot. We will be failing constantly, but we will be learning as well. I do not believe that it will be harmful for society as long as we are approaching this experimentation with a scientific lens. We absolutely need more diversity within this space. If one thing that will hurt the future of AI in a big way will be not having diversity of thoughts and diverse leaders helping drive the outcomes. Gender diversity is a major gap in this space and we will need to make conscious effort to bridge that.
6. When you’re hiring, what types of people are you hiring? The job market for traditional programmers, engineers is very difficult to get into AI space. Are you hiring from that talent pool or is that a different talent pool? In terms of talent, how do you go about ensuring you get the best AI people at your company?
We do understand that the talent pool in this space is not meeting the demand. Every industry delving into AI will have significant needs for experts. At Monsanto we have implemented a multifold approach to address the talent gap. We are providing enterprise-wide training for data scientists and programmers who are interested in this space for 4 to 6 weeks by partnering with a one of the online educational training providers. We are also working closely with number of startups within this space for some of our projects and they are helping provide all day training for our technical scientists. We have recognized that we need to work with Universities to help train and build talent so we are working Universities to help build curriculums for future workforce. We have been sponsoring several universities to help drive the talent build needed to support the future need.
7. Will progress in AI and robotics take away the majority of jobs currently done by humans? Which jobs are most at risk?
Progress in AI and robotics will generate more jobs than what we can comprehend today, but most likely new types of creative jobs that we can’t imagine right now. One job will potentially be – an AI trainer. Computers will need experts to train them, and train them to become the best. That job doesn’t exist today but that will be in demand in the future. Yes, AI should replace or automate manual, robotic, repetitive tasks. There will be huge focus on moving from local implementation of technology to global implementation at a rapid pace with the help of AI. Emerging markets will be reachable and the potential to increase productivity on the farm globally at its potential will be the highest.
8. What can AI systems do now?
AI systems can and will do what we train them to do. Systems are as good as their training algorithms. We are and we will be spending a lot of time in training of our algorithms with a “Human in the Loop” process. We need to learn how to work with artificially intelligent systems.
9. When will AI systems become more intelligent than people?
Data is and will be core to making AI systems more intelligent than people. In speech recognition and image recognition, we have already seen AI systems becoming better than humans as showed by few industry leaders within that space. The only reason those systems were able to do better than humans is because the systems were trained by humans and were trained a lot!
10. You’ve already hired Y number of people approximately. What would be your pitch to folks out there to join your Organization? Why does your organization matter in the world?
Monsanto is focused on increasing ag productivity on the farm with less impact on the environment. In modern agriculture, digital tools will be the key. Farmers and the agricultural industry are making a concerted effort to keep Mother Nature’s gifts right where they belong. We strive to use less. And data is helping the leaders of modern agriculture create the techniques and technologies to reduce our impact on the environment. (SEE MODERNAG.ORG)
From the Earth’s deepest roots to its highest satellites, we are more connected than ever to our planet and the food it produces. At Monsanto, we believe these connections hold the key to unlocking positive potential for growers and consumers. We help unlock that potential for farmers around the globe. Our global population is growing dramatically, but the resources of our planet are not. We need to significantly increase our food supply by 2050 to keep pace with global population growth (approaching 10 billion), and we must do so safely and environmentally sustainably in the areas of land, water and energy. We believe in helping farmers grow the food we need using fewer natural resources. That means we must preserve natural ecosystems, address water scarcity and soil health, improve farmer livelihoods and mitigate climate change. We continue to develop best in class innovations and demonstrate our commitment to sustainability through our products, processes and partnerships that advance modern agriculture.
11. What are some of the best takeaways that the attendees can have from your “Digital Partnerships: Accelerating the age of aI” talk?
My remarks will be focused on how to develop a digital journey for bringing and adopting external innovation inside a large enterprise to help drive and deploy AI for either efficiency gains or product development. From test and learn to full deployment and adoption, the content will showcase few such examples of use cases where we have gone from test and learn with multiple external partners to a significant investment, training and adoption in a few weeks.
12. What are the top 5 AI Use cases in enterprises?
The top 5 AI use cases around majority of the industries will be around understanding customer behavior, customer churn, predictive maintenance in supply chain, higher accuracy around product performance prediction prior to product launch, and automation of repetitive tasks. I hope to see image recognition, speech recognition and language translation becoming a commodity in next few years for majority of the enterprise to be able to use.
13. Which company do you think is winning the global AI race?
I am not sure if I see this as a global AI race. I see this as a place where all of us are learning together and with each other to make AI better – better for consumption by all. We recently hosted an AI Fellows Symposium at Monsanto and we had leaders from Intel, Google, Microsoft, IBM and few startups. All of us learned a great deal from each other, and we are supporting the betterment of society. There will be competition at the product level, but I expect that industry will adopt the best technologies and help drive the inception of these technologies.
14. Any closing remarks
Machine learning is a mathematical approach to model, integrate and interpret large amounts of data, and will provide valuable acceleration of Monsanto and The Climate Corporation research and development efforts to unlock new value for our customers. Digital tools will revolutionize all industries, but change will be especially be apparent at the enterprise level.