AI with simulation will continue to be adopted rapidly”, says Prashant Rao, Head of Application Engineering, MathWorks India

In an exclusive conversation with CXOToday, Prashant Rao, Head of Application Engineering, MathWorks India, talks about the adoption levels of AI in India, key AI-focused global trends, challenges in large-scale adoption of AI and MathWorks’ efforts in enabling engineers to streamline their design and development process by effectively implementing AI.


  • Could you give us an overview on MathWorks and its solutions?

MathWorks is the leading developer of mathematical computing software. MATLAB®, the language of engineers and scientists, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Simulink® is a block diagram environment for simulation and Model-Based Design of multidomain and embedded engineering systems. Engineers and scientists worldwide rely on these product families to accelerate the pace of discovery, innovation, and development in automotive, aerospace, electronics, financial services, biotech-pharmaceutical, and other industries. MATLAB and Simulink are also fundamental teaching and research tools in the world’s universities and learning institutions. Founded in 1984, MathWorks employs more than 5000 people in 34 offices around the world, with headquarters in Natick, Massachusetts, USA.

MathWorks India has been serving customers in the country for more than a decade now. The primary offices are in Bangalore and Hyderabad, and we have additional offices in Delhi and Pune.


  • How do you think the adoption of AI has been across industries over the last few years?

There are some applications where large enterprises have a big advantage because of the wealth of data they own to train AI systems – think the likes of Apple, Google, Facebook, and Microsoft.  There are also some easy AI-as-a-Service tools cloud providers offer for someone to learn and get started for specific applications, an example is an objection detection system to recognize your spouse’s car has arrived at your house. One way of looking at it is to see it as the capability of an organization to combine data usage across engineering and business processes to maximize or optimize the organization’s overall business operations. Technologies such as data analytics, Machine Learning, deep learning can derive insight from the data and connectivity can be achieved through Wireless/5G technologies. Further technologies that impact digital transformation range from virtual commissioning, digital twins, IoT, cloud computing, to robotics and autonomous systems.

However, the industrial applications we’re helping customers with are ones where AI is used as part of an overall system that enhances a system design or product. In this context, we’re seeing an interest in AI across all industries, especially auto and aerospace and defense, because every company is looking to see how they can improve their system using AI, and what they find is turnkey AI as a service doesn’t work.

One of our customers, Caterpillar is using MathWorks tools to identify tractors and people in the field. AI-as-a-service cannot meet their requirements because they need a unique solution specific to their scenario. You can watch the Caterpillar presentation outlining their challenge, solution and the results here:


  • What is the expected growth for the AI market over the next 5 years? How do you see this impacting the adoption of AI across key sectors such as manufacturing, healthcare, etc? <referred the AI trends article by MathWorks)

IBM’s Global AI Adoption Index 2022 study found 35% of companies reported using AI in their business, and an additional 42% reported they are exploring AI. AI adoption is growing steadily, up four points from 2021.

We are seeing the following global trends:

  1. Increasing prevalence of AI within engineering and science disciplines, across industry and academia – AI will be one of the first tools engineers and scientists look to for innovative solutions in solving problems and building applications. AI is also used to help address global challenges, from the continuing pandemic to climate change to the opportunity of Electric Vehicles.

AI with simulation will continue to be adopted rapidly – AI is normally one part of a complex system with many components, so there is a need to simulate AI within the context of a larger system. For example, in automated driving the AI algorithm for pedestrian detection must be tested with other design components and everything must be simulated together. Increased reliance on simulation and testing with 3D and increasing realistic scenarios will become more relevant for these applications.

More AI models deploying to more low-power, low-cost embedded devices – Productionizing AI is a major challenge for embedded systems engineers. Whilst a model may show promise during prototyping, power and hardware costs can limit its usefulness when deployed to an edge device. This is a significant development, especially in the medical sector, as companies address real-world challenges in virtual scenarios, e.g., making medical services available in remote areas with unreliable power supplies.


  • What do you think are some of the key challenges in the large-scale adoption of AI by companies?

Most AI projects focus on the AI model, and those who are new to AI usually spend a large percentage of their time learning to develop and fine-tune AI models.

However, in engineering and scientific applications, AI is usually a small piece of a larger system. The AI model needs to work correctly in all scenarios with all other working parts of the product, including sensors, actuators, and traditional algorithms such as control, signal processing, and image processing.

Organizations need to look at AI as a workflow, not just model development

Engineers and scientists have an inherent knowledge about the problem they are trying to solve with their domain expertise. The right tools can help them get started even if they’re not AI experts. Ultimately, engineers are at their best when they can focus on their own area of expertise and can use intuitive tools that help them bring AI into the picture when appropriate.

The AI-Driven Workflow

Figure 1. The four steps that engineers should consider for a complete, AI-driven workflow. © 1984–2022 The MathWorks, Inc.


Limited access to AI skills is another challenge.  – When companies in sectors like manufacturing, healthcare etc are looking at developing AI systems, they are looking at building teams that can bring in the domain engineering and technology skills.

Organizations need to look at the right combination of people, processes, and technology to overcome these challenges.


  • What are your thoughts on AI in embedded devices? How do you see the future of this trend?

Machines are becoming increasingly intelligent. As consumers, we also expect all devices, be it our car, mobile phone or washing machine, to act in “smarter” ways. With computing power and data storage getting cheaper and with access to low-cost hardware, embedding AI into devices is becoming more economical and pervasive.

We see several examples of embedded AI with our clients.

Almost any autonomous system that is performing tasks – from cars to industrial robots – are classic examples. With the factory shop floors getting more modern, we will see more automation and deployment of AI in manufacturing. Beyond the shop floors we also see our clients using robots for doing high precision and safety-critical applications:

  • Visual Inspection: Enabling machines to recognize images and handle visual information is an area we see emerging across many sectors, ranging from automated driving, to robotics, to satellite applications.
  • Medical devices: This development uses embedded AI in multiple ways. With an increasing requirement in early disease identification, many practitioners need to operate in remote areas so we see broad application of AI in this sector.
  • Electrification: Electric vehicles where battery is the main component, often termed as the central nervous system, where there is rapid development in optimizing battery management systems in various external conditions.

In general, we believe almost every application area will see an increase in AI development. We are increasingly seeing the need to incorporate AI seen by design rather than as an afterthought.


  • How can the implementation of AI in embedded devices enable scalability and flexibility for industries?

Embedded systems have a huge role to play in the implementation and success of AI. With computing available on the edge and in the cloud, engineers and architects are continuously looking for ways to optimize performance based on consumer demands.

MathWorks tools accelerate the design and development process by enabling an end-to-end workflow from initial AI model development to implementation on an embedded device by means of automatic code generation. The workflow reduces the need for hand-coding, saving time and reducing the possibility of introducing errors. The ability to target multiple devices enables teams to remain flexible in their decisions to target specific devices and enables them to scale to smaller or larger devices as per their requirements.

In addition to deploying on embedded devices, the AI models developed in MATLAB® can be deployed on edge devices in the field, enterprise systems, or the cloud.

For example, GPU Coder™ can be used to implement and deploy deep learning models on NVIDIA® CUDA® GPUs. Or MATLAB Coder™ and Embedded Coder™ can be used to generate C code for deployment on Intel® and Arm® boards. Vendor-optimized libraries create deployable models with high-performance inference speed.

With MATLAB Production Server™, you can securely deploy to and integrate with enterprise IT systems, data sources, and operational technologies.


  • How is MathWorks helping engineers effectively adopt and implement AI in their design and development processes?

MathWorks is helping engineers adopt and implement AI in multiple ways.

Our products MATLAB, Simulink®, specialized toolboxes for autonomous vehicles, predictive maintenance, and visual inspection are extensively being used by small and large organizations globally to accelerate their development of AI systems

Our Start-up and Accelerator program helps start-ups adopt our tools in their early product development cycles at reasonable investment levels.

We are an active part of the ecosystem working with industry associations and governmental bodies in spreading access to more organization in MSME and SME sectors. We partner with organizations like Foundation for Smart Manufacturing (FSM) to help build real-life prototypes that demonstrate the latest technologies, including simulation and digital twin technologies. These engagements enable organizations to help their customers experience these technologies in action while fostering faster decision-making.

The MATLAB Community is a very vibrant and active community that provides an excellent platform for people to learn from their peers. The ever-increasing number of libraries available through this community help engineers and scientists accelerate their development.


  • Do you think there is a rising need for upskilling of engineers to effectively adapt to the changing technology landscape?

Yes, there is a constant need to upskill engineers to adapt to the ever-changing technology landscape. We look at this in a couple of ways – upskilling the existing workforce of engineers and working with educational institutions to incorporate learning paths into curriculums. It is important that both corporations and educational institutions watch this space actively to develop their employees and students.

We work directly with our clients to understand their current level of expertise through a structured skill assessment. Learning paths and trainings on specific topics are available for engineers in these organizations to help them continuously upgrade their skills. We work extensively with our customers’ Learning & Development organizations to build medium- to long-term plans as we believe education is a continuous journey.

MathWorks collaborates with several industry organizations, including SAE, ARAI, ICAT, AeSI to engage with larger technical communities that provide platforms for individuals to learn and upskill. MathWorks has recently signed an Memorandum of Understanding (MoU) with Skill-Lync and organizations working in this space to extend our tools and training services.

AICTE, the governing body for technical education in India, has clearly stated the importance of training and educating students to meet the requirements of Industry 4.0. We have been working with several educational institutions in big cities and smaller towns to help educators incorporate AI, Electrification, and other technologies into the curriculum.

We closely work with education consortiums like IUCEE and ICT Academy to reach wider audiences.

MathWorks is also involved in several student competitions and hackathons. These platforms provide students with an excellent platform to apply their classroom learnings to solve real-world challenges.

In addition, everyone has access to the plethora of online learning courses available. This repository is continuously updated to incorporate latest technologies and provides excellent starting points for graduate engineers.


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