What would you define as automotive? Sure there are the cars, motorbikes, vans, trucks and so on. But what about lawn mowers?We are stretching the definition of automotive in this edition of the AI in Automotive Podcast. And you will see why. Today we are speaking to Andres Milioto, a Senior Vision Engineer at Scythe Robotics. This company has developed an all-electric, fully-autonomous commercial mower, which, needless to say, uses machine learning extensively, primarily for perception.The way the Scythe team has identified this very unique - but very large problem - and solved it using machine learning - I find that really cool. They are well on their way to solving two of the biggest problems this very traditional industry is facing - a perennial labour shortage, and pollution.I was keen to bring Andres on the show to gain a deeper understanding of the similarities, and most importantly, the differences between Scythe Robotics’ application of autonomous technologies and what we might consider more conventional autonomous driving applications.I hope you enjoy this very unique episode of the AI in Automotive Podcast. AI in Automotive Podcast

Over ten years ago, an idea that captured our imagination was that software is eating the world. Well, software may not have eaten the automobile, but it is certainly transforming it in very profound ways. The modern car is incredibly complex - not just from a hardware perspective, but also from a software point of view. Today’s software-defined vehicle is made up of numerous subsystems, each run by its own code; hundreds of sensors generating tonnes of data every second; and a number of vehicle functions now automated to a large degree. So how does this all work together?We invited Sarah Tatsis, SVP of IVY Platform Development at BlackBerry to help us understand exactly how. In this episode of the AI in Automotive Podcast, Sarah unpacks the automotive software stack for us - from the foundational operating system software that helps various subsystems communicate with each other, to middleware that uses machine learning models to turn sensor data into actions, to application software on top that will help your car do amazing things like pay for your coffee in the future. I thought my cutting-edge new car was smart, but what stuck out most for me through this conversation is the huge amount of headroom available yet for our cars to get smarter and more connected to the world around us. I hope you enjoy my conversation with Sarah. If you do, please do share the AI in Automotive Podcast with your colleagues who might enjoy it as well.https://www.ai-in-automotive.com/aiia/201/sarahtatsisAI in Automotive Podcast

It has been just over three years since I started the AI in Automotive Podcast. With all of the interesting ways in which artificial intelligence and machine learning are shaping the future of the automotive and mobility industries, I am happy to have hosted quality conversations exploring this theme further. My intention with the AI in Automotive Podcast was - and continues to be - to invite interesting people doing interesting things with AI in the automotive industry, and engage them in thought-provoking and enlightening dialogue.The twenty episodes we have published since have had over five thousand downloads, and I am incredibly grateful to the regulars amongst you who have kept us going.A lot has changed since 2019, and I imagine the pace of change will only accelerate in the coming years. With this, I am aiming to publish more regularly. We will soon release the long-overdue Season 02 of the AI in Automotive Podcast. 6 episodes, published once every alternate week. The same conversations, only better.I might be slightly biased here, but I think if you are associated with the automotive and mobility industries in any capacity, then this podcast is essential listening for you to stay connected with the fascinating changes we are seeing around us. So, do me a favour - go ahead and share a link to the AI in Automotive Podcast with your friends and colleagues. You can find us wherever you get your podcasts.Continue to tune in, and hear from some of the smartest people in our industry.AI in Automotive Podcast

A number of companies are trying to crack the autonomous driving puzzle, and a variety of approaches have evolved. Some companies are taking a software-first approach, building an AD software stack that can work on any hardware environment. Others are taking a hardware-first approach, creating a sensor and hardware environment that can adopt any software stack. Others still are creating a ‘walled-garden’ with software and hardware designed in close conjunction, allowing each to work only with the other for a specific use case. Imperium Drive, a UK-based autonomous driving startup, has a radically different take on the autonomous driving problem. Imperium Drive believes that a ‘human-in-the-loop’ is a critical stop on the journey to full autonomous driving. The company is focused on bringing commercially and operationally viable products to the market, even as it pursues its ultimate goal of making autonomy a reality.We caught up with Koosha Kaveh, CEO of Imperium Drive on this episode of the AI in Automotive Podcast. Koosha shares his view of the evolution of AD, and progress on the autonomy journey. He also introduces us to Fetchcar, their super interesting driverless, human-in-the-loop car rental service.I hope you enjoy listening to this episode, and if you do, please do share it with your network on LinkedIn and rate our show wherever you get your podcasts.https://www.ai-in-automotive.com/aiia/120/kooshakavehAI in Automotive Podcast

The digital screens in the car are the latest battleground in the battle for the driver’s attention. The main competitor? The mobile phone. Each time you choose to connect your smartphone to your car’s infotainment system using CarPlay or Android Auto, the mobile phone wins. The car’s digital surfaces are reduced to dumb screens that mirror the phone’s apps and use the phone’s processing power. Your usage data and analytics are no longer accessible to the car maker, putting them at a significant disadvantage.So, can car makers, famously still on the software learning curve, compete with the Apples and Googles of the world - companies that build software for a living - in this software-led automotive world?In this episode of the AI in Automotive Podcast, we are hosting Alex Barth, VP of Automotive and Mobility at Mapbox. Mapbox is a mapping and location cloud platform that offers the building blocks; the SDKs and APIs to power navigation. Their new product, Mapbox Dash, helps car manufacturers create brand-specific in-car digital experiences anchored to navigation, and generate subscription revenue. Alex shares some amazing insights on how automotive manufacturers can use the power of AI to deliver compelling in-vehicle digital experiences. AI in Automotive Podcast

90% of all the data in the world was generated in the last 2 years. I have read this statistic in a news report from 2012, and a news report from 2019 and a news report from 2021. If this statement is true every year, you can begin to imagine the rate at which data generation and use is expanding. Actually that number is so big, you probably can’t imagine it. As someone famous once remarked: with great data comes great responsibility. Infinite amounts of data raise questions about the ownership of data, the uses of data and the ethics surrounding data. These are all themes we explore with Richard Barlow, Founder & CEO of wejo. Wejo is on a mission to be the data ecosystem and the comms stack for the automotive industry. Richard brings some really powerful counter-intuitive perspectives to our discussion, that will challenge all the cliches you have heard about data, data usage, data ownership and data security.If you liked my chat with Richard on this episode of the AI in Automotive Podcast, please do share this episode with your colleagues and connections, and be sure to rate us on Apple Podcasts.AI in Automotive Podcast

Automotive product development is not just incredibly expensive, but also an extraordinarily complex process. Everything you see on a production vehicle is, at the end of the day, the result of hundreds of tug-of-war contests. Design and cost, manufacturability and serviceability, power and weight. The list goes on. Dozens of teams with hundreds of engineers pursuing conflicting design objectives, but eventually finding an optimum through a series of very difficult trade-offs.Any benefit an automaker can take in the design and development process that accelerates take-to-market and improves margins is an absolute no-brainer. So what role can machine learning play?In this episode of the AI in Automotive Podcast, I am joined by Dr Richard Ahlfeld, CEO of Monolith. We discuss how machine learning can be a potential game-changer in automotive R&D, and deliver some serious acceleration and cost benefits in the design and development process. Complex mathematical equations, ketchup bottles and a million dollar prize. This episode of the show has it all!AI in Automotive Podcast

In this episode of the AI in Automotive Podcast, we are following the incredible journey of Ocado from a retailer to a global technology business that offers a cutting-edge technology platform to other retailers. We talk to David Sharp, Head of Head of Autonomous Mobility at Ocado Technology, and he talks to us about their journey - bold and audacious in more ways than one.Three things really stood out for me from this very enlightening conversation with David.One, the way Ocado experiments with, integrates into and scales AI technology for various use cases in their business.Two, the way Ocado is using the composite of traditional statistics, new generation AI and time-tested human intelligence to deliver dramatic order-of-magnitude impact in their business. Three, the autonomy experiments Ocado is running in moving goods from their warehouses to their customers, and what lessons might that have for the larger automotive and mobility industries.Ocado’s journey to this point, and their ambition for the future offers so much inspiration for the larger automotive industry. Will we ever see an automotive retailer create a technology stack that they sell to OEMs and other retailers? Never say never!AI in Automotive Podcast

Have you ever thought about all the things that go on behind the scenes when you file an insurance claim for your car? Probably not. And that’s a good thing, because if you did have to think about the complexity that lies behind, you would not be a happy customer.The reality, however, is that every insurance claim triggers hundreds of processes and transactions that work in harmony to verify and pay out your claim. The fascinating thing is that AI is starting to play an increasingly influential role in this ecosystem.To understand this better, we invited Marc Fredman to the AI in Automotive Podcast. Marc is the Chief Strategy Officer at CCC Intelligent Solutions. CCC’s technology connects thousands of companies that make the insurance economy run and powers over a hundred billion dollars of insurance commerce.In today’s conversation, Marc takes us inside the insurance economy, and shares with us the variety of ways in which artificial intelligence is helping power the insurance ecosystem.If you like this episode of our podcast, do share it with your friends and colleagues, and rate us wherever you get your podcasts.AI in Automotive Podcast

If Elon Musk says something is hard, you can bet your bottom dollar that it is bloody hard. Automotive applications are some of the toughest, most unforgiving environments to deploy AI in. Most AI applications in automotive are mission critical - they just can’t go wrong. This requires that all the input they process is of a very high resolution or quality. They also need to operate in real-time, often perceive the environment around them and respond to it in a matter of milliseconds. Lastly, they need to operate with a very low power requirement, in an environment that is subject to dust, vibration and wide temperature variations. This intersection of constraints means that deep learning algorithms for automotive applications need to be designed right, and optimised constantly. This is where Deci comes in. Think of Deci as an easy-to-use platform that offers a set of tools to optimise your machine learning algorithms, and deliver order-of-magnitude performance improvements. In this episode of the AI in Automotive Podcast, I am joined by Yonatan Giefman, Co-founder & CEO of Deci. Yonatan tells us how AI applications are brought to life, and why they need to be optimised. We discuss the Deci platform, and how it can help data scientists and deep learning architects to optimise their algorithms, using the company’s proprietary neural algorithm search technology. We also talk about machines building machines, and even take a peek into science fiction :)If you liked my chat with Yonatan today, do subscribe to the AI in Automotive podcast, and give us a shout on your social media.https://www.ai-in-automotive.com/aiia/114/yonatangeifmanAI in Automotive Podcast