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There is a lot of video in the world. Nearly 90% of global internet traffic is video. Getting value out of video data, however, is challenging, time-consuming and expensive. How do you identify the right subset of video data to train your models, and how do you reimagine the annotation process to accelerate vision AI application development are just some of the big questions that are looking for answers. On this episode of the AI in Automotive Podcast, I am delighted to be joined by Dr Jason Corso, Co-founder and Chief Scientist at Voxel51. Voxel51 develops tools that help AI teams curate, visualise and refine visual datasets to build better-performing computer vision models. In today’s episode, Jason breaks down the steps in taking a vision AI application from concept to production, the important role of annotation within the process, and how the company’s tools reimagine annotation.  In this super enlightening conversation, we cover a range of topics - from how Voxel51 started, to the value it delivers, and what is coming down the road for the company. Listen to the episode for a surprise detour into the world of sports broadcasting and what computer vision has to do with it. I hope you enjoy my chat with Dr Jason Corso, and if you do, please do spread the word by sharing the AI in Automotive Podcast with a friend or colleague.  #ai #automotive #mobility #technology #podcast #computervision #visionAI https://www.ai-in-automotive.com/aiia/504/jasoncorso Annotation is dead: https://medium.com/@jasoncorso/annotation-is-dead-1e37259f1714 AI in Automotive Podcast

If you are a fleet manager responsible for electrifying your fleet, prepare for your world to be rocked. The fleet manager’s decision-making approach and framework for a traditional ICE fleet is well-established. It has been in use for decades, and typically takes into consideration the acquisition cost and operating cost of vehicles to calculate total cost of ownership. It then compares that with the revenue generated by the vehicle over its lifetime, and helps fleet managers make informed decisions about their fleet. With EV fleets, the whole process and its associated tools are going to be upended. Not only are acquisition and operating costs for EVs vastly different from ICE vehicles, fleet managers must take into consideration a variety of other factors into their decision-making. For instance, setting up charging infrastructure at your depots. You need to consider the cost of setting up the infrastructure, the constraints of your power connection and the time and cost of upgrading your grid connection, should that be required.  And this is just the beginning. Where things get really exciting is on the revenue side. Unlike an ICE, an EV can generate value for your fleet even when it is not on the road, but plugged into a charger. How, you ask? I invited Mike Kent from GridBeyond and Alex Iriondo from Monta to join me on the AI in Automotive Podcast, and share with us exactly how. GridBeyond is a smart energy company that offers AI-powered energy services, connecting and automating energy demand to balance the grid. Monta, on the other hand, develops and deploys software solutions for the EV charging industry. Together, GridBeyond and Monta are on a journey to help fleet owners and operators maximise the value they derive from their fleet assets, for instance, when they are hooked up to a charger. Fleet electrification can appear intimidating, but after this chat with Mike and Alex, I couldn’t be more excited about the possibilities that electrified fleets hold. We are just scratching the surface at this point in time, using fleet assets to deliver flexibility and grid services. But as the ecosystem matures, the sky is the limit - fleet assets aggregated as a Virtual Power Plant, vehicle to grid and vehicle to building - all of this is coming in the near future, and promises to revolutionise how fleets are acquired, operated and managed.  If you are a fleet owner or operator, or just curious about the opportunities that exist as fleets electrify, you want to spend 40 mins listening to this fantastic conversation with Mike and Alex. If you do enjoy it and know someone who works with fleets, go ahead and share this episode of the AI in Automotive Podcast with them. #ai #automotive #mobility #technology #podcast #fleet #depotcharging #energy #evcharging #smartcharging #gridbalancing #flexibility https://www.ai-in-automotive.com/aiia/503/mikealex  AI in Automotive Podcast

Your EV is so much more than a car to take you from point A to point B. The energy it stores in its battery is a whole world of possibilities when you are not using the car. Which, let’s face it, for most of us, is the vast majority of the day. During these times, your EV is a ‘decentralised energy resource’ - one that can help the grid in a variety of ways. One of the most interesting ways in which EVs are accelerating the mobility-energy convergence is through virtual power plans, or VPPs. On the one hand, EV fleets aggregated as a VPP provide value to the grid by offering flexibility and balancing services. On the other, it allows fleet owners, operators and vehicle owners to benefit financially, reducing their charging bills or their lease payments.  But how does this come to life, and what role does AI play in this orchestration? In this episode of the AI in Automotive Podcast, I invited Johanna Izett from Hive Power, a Swiss software startup that is making our grids smarter by connecting energy assets like EVs to offer grid services and unlock value for all stakeholders. Johanna shared her insights about VPPs, what makes them tick and how traditional automotive OEMs must rethink the proposition that they offer to their customers. Hive Power is one of those fascinating companies at the intersection of automotive, energy and tech that are accelerating the convergence of the mobility and energy industries. Hive Power’s Flexo platform orchestrates the delivery of flexibility and grid balancing services, making the grid smarter and more resilient, while enabling fleet owners and operators to generate additional revenue from their EV fleets. Hive Power’s software suite also allows OEMs to offer so much more than metal, which I believe is going to be an important differentiator in the EV age. If you want a sneak peek into the possibilities EVs hold, then this is the interview you don’t want to miss. If you enjoyed my chat with Johanna, please do share the AI in Automotive Podcast with a friend or colleague. #ai #automotive #mobility #technology #podcast #energy #evcharging #smartcharging #gridbalancing #flexibility https://www.ai-in-automotive.com/aiia/502/johannaizett AI in Automotive Podcast

Range anxiety. Charger anxiety. This anxiety and that anxiety. There seems to be a lot of anxiety in the mainstream adoption of EVs. One of the biggest behaviour changes someone migrating from years of driving an ICE to an EV has to make is the refuelling behaviour. You refuel an ICE when you have. You recharge an EV when you can. You charge every time you park. At home, at your workplace, at your gym, your shopping center, at the restaurant. For this to become reality, charging must become ubiquitous. But it’s not that easy. It never is. So many chargers are likely to burden the grid, which, let’s face it, was never designed to deal with huge loads coming on simultaneously. So what’s the solution? And how does AI help? I invited Teddy Flatau to kick off season 5 of the AI in Automotive Podcast. Teddy is the founder and CEO of Wevo Energy, a company that helps solve exactly this problem with the power of technology and AI. Charging infrastructure sits squarely at the intersection of automotive and energy. It is often touted as the missing piece in the electrification of transport, and so, understandably, it attracts a lot of attention. However, hiding in plain sight is the opportunity that cheap, reliable AC chargers at multi-family homes, commercial complexes, retail parks, hotels presents - any destination with more than an hour of dwell time is ripe for a bank of cheap and cheerful AC chargers. Making these chargers smart in terms of when they charge which car based on what parameter while being friendly to the pocket and the grid is what can truly unlock the ubiquity of EV charging. This is the transformation that Wevo Energy is bringing about. As one of the most knowledgeable voices in this space, Teddy shares his view on what it will take for charging to be everywhere, and how Wevo Energy is helping the world get there. If you are interested in the smarts that make a charger go from a dumb plug to a cutting-edge piece of tech that can manage when and how much your car charges to optimise your spend and the load on the grid, this is the chat you want to listen to. I hope you enjoyed my conversation with Teddy, and if you did, why not share theAI in Automotive Podcast with a friend or colleague. We will be back soon with more perspectives on how AI is driving the convergence of automotive and energy. #ai #automotive #mobility #technology #podcast #energy #evcharging #smartcharging https://www.ai-in-automotive.com/aiia/501/teddyflatau AI in Automotive Podcast

If you needed a surprise in the new year, this is it! We are back!! It’s been a longish hiatus. I needed the time to recharge and think about where the industry is headed. We also welcomed a new member to our family, and I have used the time to change over a thousand nappies!! So, what exactly happened in the last year?  The AI in Automotive Podcast quietly - literally quietly - turned five.  AI went properly mainstream. Like BOOM mainstream! The silicon valley AI CEOs turned into demigods, and everything from your toaster to your kids’ toys turned AI-powered, seemingly overnight. AI was always an important enabling technology in the automotive industry, and this is what we had been covering on the show for the last five years. But the buzz around AI pushed some OEMs to make AI in the car more visible - enter features like ‘ChatGPT in the car’. There are interesting applications for a conversational interface in the vehicle - we covered this in our chat with Matt Anderson from SoundHound, for instance. But for them to have any value to the driver, software-defined vehicles with a coherent EE architecture is a key pre-requisite. We’ve covered this in numerous conversations in the past. With Sarah Tatsis from BlackBerry for instance, or Hemant Sikaria from Sibros. If you look beyond what is mainstream, then the biggest theme that is emerging is the rapid convergence of the automotive and energy industries. We are seeing energy utilities turn into EV lease companies, and automotive OEMs turning into energy companies, thanks to the energy in the batteries of their EV parc. This represents significant opportunities for energy companies, automotive OEMs, fleet owners / operators and hundreds of other players in the automotive and energy ecosystems.  Needless to say, AI, as an enabling technology, is a key accelerator of this convergence. This is what we will be covering in future episodes of the AI in Automotive Podcast.  As always, I have an incredible roster of knowledgeable experts sharing valuable insights and unique perspectives. I am sure you will enjoy what is coming up. If you feel you have benefited from the content on this show in the past, please do share the AI in Automotive Podcast with your friends and colleagues who might find it valuable as well. I promise you there are no more babies on the way, and hence, no more long breaks!! AI in Automotive Podcast

Till a few weeks back, Cruise was considered one of the big three of autonomous general driving. It was licensed to run a robotaxi service in San Francisco, and my LinkedIn feed was full of folks gushing over the magical experience of being driven around in a car without a driver. Then the proverbial shit hit the fan. One of Cruise’s robotaxis got caught in a classic edge case, with a road user who was hit by another vehicle, falling in its path. So far so bad, but then things got worse. In the last few weeks, heads have rolled. Cruise has seen the departure of its CEO and other key execs. The company, owned by GM, has decided to get rid of a quarter of its staff, and finds itself in a proper existential crisis. How did things come to this, and could they have been avoided? To find out, I invited Alex Roy to the AI in Automotive Podcast. Alex is one of the most recognised voices, and an absolute authority in this space. He wears many hats, amongst which is hosting the very popular Autonocast podcast. Previously, Alex worked as an exec at Argo, and was key to their thoughtful approach to operationalising self-driving cars on public roads. While my conversation with Alex started talking about Cruise, the theme is not about Cruise alone. Because there is a long tail of edge cases, and things are going to go wrong as this very nascent technology is brought to market. This is also a very new space, and as one might expect, regulation needs to find the right balance between encouraging innovation and guaranteeing safety. The technical scale of the problem can not be underestimated, and it rarely is. But it is the human side of the problem that often does not get the attention it deserves. My chat with Alex underlined for me that getting the human and cultural piece right is going to be as critical to the success of autonomous driving as solving the technical problem. With this, we season 4 of the AI in Automotive Podcast is a wrap. I am certain you enjoyed listening to my chat with Alex on season four’s final episode. Please do share the episode with your friends or colleagues, or drop a note on your socials - I always appreciate your support. #ai #automotive #mobility #technology #podcast #selfdriving #autonomousdriving #safety #leadership #cruise https://www.ai-in-automotive.com/aiia/406/alexroy AI in Automotive Podcast

Since the beginning of time, cities have been incredibly important to civilization. Today, the World Bank estimates that cities contribute 80% of global GDP. Cities are central to our growth and prosperity, but every single major city in the world is facing challenges ranging from poor air quality to creaking infrastructure.  So how do cities evolve to prepare for the future? And what role does AI play in this evolution? On this unique episode of the AI in Automotive Podcast, I invited the CEOs of two companies that are enabling our cities to become safer, smarter and more sustainable using the power of artificial intelligence.  Andrew Fleury is the CEO of Luna Systems, a company that is making mobility smarter using their computer vision capabilities. They are putting cameras on micromobility scooters, and using AI to help micromobility operators give their riders a safer experience. Chris Tingley runs EVWare, a company whose hardware and software platform makes vehicles safe, connected and intelligent. They do this by bringing high-tech features and functionality to vehicles of all shapes and sizes, including micromobility scooters. The modern city generates bucketloads of data. It has been for a while now. Till a few years back, there was limited use for this data. Perhaps the quality of data was suboptimal. Perhaps it was in a form that was not adequately usable to identify patterns and generate insights. Maybe we did not have enough tools and infrastructure to leverage this data. All that is changing fast. With the rise of AI and the commoditisation of cloud infrastructure, the data that cities generate carries immense potential in improving decision making and crashing decision time by orders of magnitude. Companies like Luna Systems and EVWare are - in their own way - creating a collaborative ecosystem of partners that can make our cities smarter, safer and more sustainable. I hope you enjoy listening to my chat with Andrew and Chris. If you do, go ahead and rate the AI in Automotive Podcast wherever you get your podcasts.  #ai #automotive #mobility #technology #podcast #machinelearning #urbandesign #cities #infrastructure #vision #micromobility AI in Automotive Podcast

Vehicle quality issues that lead to recalls and lawsuits cost automotive OEMs tens of billions of dollars in cost and lost revenue each year. Given the explosion of connected vehicle data, one might expect that this data could be leveraged to reduce this cost. Things are rarely that straightforward. Why is that? I invited David Hallac, CEO of Viaduct to the AI in Automotive Podcast to find out more. David’s 5-year old startup finds patterns and relationships amongst billions of connected vehicle data points, and delivers two powerful, commercially sound use cases to automotive OEMs. One, it helps automotive OEMs proactively identify and address quality issues, saving hundreds of millions of dollars in warranty costs and recalls. Two, it helps predict failures, call vehicles in for proactive maintenance, and helps bump up up-time - a god-send, especially for fleet customers.  The big penny drop moment for me during my conversation with David was that connected vehicle applications don’t have to be bold, visible and sexy, delivering massive incremental revenue at near 100% margin. In fact, the connected vehicle applications most likely to succeed in the near-term are those that deliver commercial value today, often by way of substantially reduced costs. Viaduct’s quality management and maintenance prediction use cases check those boxes, and how. Listen to my chat with David to find out more. If you enjoyed my chit-chat with David Hallac, please give the AI in Automotive Podcast a solid five stars on Apple Podcasts and Spotify - I am always thankful for your support. #ai #automotive #mobility #technology #podcast #machinelearning #unsupervisedlearning #warranty #recalls #maintenance #quality AI in Automotive Podcast

Autonomous Driving is a big enough paradigm shift. But after years of research and billions of dollars spent trying to get cars to drive themselves, perhaps it is time for a paradigm shift within a paradigm shift. What might this look like? Daniel Langkilde, CEO of Kognic joins me on the AI in Automotive Podcast to discuss exactly this. Daniel and I talk about the current approach to autonomy, which involves breaking down a very complex problem into its components - perception, prediction and planning - and its limitations. Based on a better understanding of how humans actually go about accomplishing the task of driving, we ask if perhaps it is time to take a different approach to delivering autonomy at scale. We discuss a key component of this approach - the world model - or the ‘common sense’ that a machine must be equipped with to make sense of the complex world around it. Daniel also talks about alignment, what it means to steer a system towards accomplishing its stated goal, and its relevance to autonomous driving. I am convinced that we are far from done with solving autonomy. On the contrary, I feel there is a lot of unexplored territory yet, which can dramatically change how we approach this opportunity. I hope my chat with Daniel gave you a sneak peek into what the inception of paradigm shifts looks like, and what it means for the future of autonomous driving. If you enjoyed listening to this episode of the AI in Automotive Podcast, do share it with a friend or colleague, and rate it wherever you get your podcasts. AI in Automotive Podcast

Radars have been evolving at a really rapid clip, helped in no small part by innovative companies like Arbe Robotics. On today’s episode of the AI in Automotive Podcast, I am talking to Ben Rathaus, VP of AI and Perception at Arbe. Ben talks us through the history of radars, and how and why they found their way onto cars. We discuss how Arbe’s silicon and software is creating an order of magnitude improvement in the resolution and performance of automotive grade radars. We talk about the composition of radars, and their output - a mapping of the free space around the vehicle - an absolutely key building block of AD and ADAS algorithms. Ben and I started at cosmology and ended up at what the humble radar might look like in the future! Just another fascinating conversation that allowed me to understand the past and future of radars a lot better, as well as the very important role they play in making our cars smarter and safer. I think of them as the invisible, unsung heroes - working away diligently in the background, making everything around them work a lot better. If you have ever wondered whether future radars can wholly replace cameras on the car… well, you will find out at the end of my chat with Ben. So go have a listen, and if you like what you hear, do share the AI in Automotive Podcast with a friend or colleague. #ai #automotive #mobility #technology #podcast #radar #sensors #sensorfusion AI in Automotive Podcast

Connected cars have been around for a while, but in-car services have strangely not really taken off. I learnt in my chat with Todd Thomas, Chief Revenue Officer at AiDEN Auto that there’s a good reason. Or four. Todd spoke to me about the evolution of the modern car to become a more mature connected device from its current state, and in the process we unearthed some real gems of insight.  After today’s chat with Todd, I have a much better understanding of what connected vehicles 2.0 might look like, and why we may just be at the cusp of an explosion in true in-car services, many of them powered by AI. I am also convinced that the way we interact with our cars is going to undergo a significant shift, substantially changing our relationship with our cars. It’s going to be an exciting next few years in this space. Subscribe to the AI in Automotive Podcast to stay in touch with the technologies shaping our industry’s future. AI in Automotive Podcast

Designing a car is hard. 🚙 🏎️ 🏁 It involves solving a series of multi-dimensional problems under a variety of constraints. I am oversimplifying here, but a problem with 8 dimensions and 5 values for each dimension will have 390,625 combinations to be experimented with. Real-world problems are usually way more complex, and testing every unique combination of inputs in an experiment is often not viable. So what is the solution, and what role does AI play? Gary Brotman, CEO of Secondmind, joined me on the AI in Automotive Podcast to share the answer to that question, and talk to us about the role of AI in accelerating the automotive design process. Think of Secondmind’s platform as a co-pilot for automotive engineers. It uses the power of AI to help them design the right experiments to solve complex multi-dimensional problems of the sort they encounter in their day jobs. This focuses their limited resources on a subset of the problem space, and gets them to the optimal solution in the most time-efficient and resource-efficient manner, accelerating the design process. This is a fascinating conversation with one of the most experienced and knowledgeable blokes out there when it comes to AI in Automotive. I hope you enjoy my chat with Gary as much as I did. And if you do, go ahead and rate the AI in Automotive Podcast wherever you listen to podcasts. #ai #automotive #mobility #technology #podcast #engineering #designofexperiments #machinelearning https://www.ai-in-automotive.com/aiia/305/garybrotman AI in Automotive Podcast

Have you ever thought about how autonomy came to be? What were the origins of the idea of autonomous vehicles? Have you ever wondered what is next for autonomy, and how will this space evolve in the future? Well, wonder no more. On this episode of the AI in Automotive Podcast, I am delighted to host an OG member of the autonomy gang, whose connection with autonomous driving goes way back to the heady days of the DARPA Grand challenge almost two decades ago. Bibhrajit Halder, Founder & CEO of SafeAI joins me to share how the idea of autonomy originated, how it evolved from 1.0 to 2.0, and how it might evolve in the future to 3.0 and 4.0. We also talk about the application of autonomous driving in the mining and construction industries, and how SafeAI is using AI to seriously disrupt the mining industry. I never thought I would be discussing mine economics on this show, but we did, and it was so much fun! I hope you enjoy my chat with Bibhrajit, and if you do, why don’t you go ahead and share the AI in Automotive Podcast with a friend or colleague who carries an interest in this space. AI in Automotive Podcast

In the world of autonomous driving, high-compute GPUs are all the rage. So I was incredibly delighted to learn of a company that is taking a very counter-intuitive approach to the perception stack. These guys have identified a number of use cases that do not require the 100% accuracy that autonomous driving demands, and are focused on making their vision perception stack work on smartphones you can buy for a hundred dollars. In this episode of the AI in Automotive Podcast, I am pleased to host Jorit Schmelzle, co-founder and Chief Product Officer of Peregrine Technologies. This is such a wide-ranging conversation, covering topics from sensor fusion, to understanding the automotive perception stack, and how ordinary smartphones can deliver interesting use-cases for the vision and perception stack. While the world of autonomy started with the desire to make a fully-autonomous vehicle that would then power a fleet of robotaxis, the way the space has evolved has created some very compelling use cases, even if full autonomy is still a few years away. Peregrine Technologies is at the forefront of delivering real value today, while also inventing the technology of tomorrow. I am sure you will enjoy my chat with Jorit, so can I ask you to please share the AI in Automotive Podcast with your friends and colleagues who carry an interest in AI, or the automotive industry. #ai #automotive #mobility #technology #podcast #perception #selfdriving #autonomousdriving AI in Automotive Podcast

LiDARs are an important piece of the autonomous driving and ADAS puzzle. While they boast impressive resolution and frame rates, they have also built a reputation for being big, bulky and expensive. Can there be another way? Paul Drysch, CEO of PreAct Technologies certainly thinks so. PreAct has been working behind the scenes for a number of years to develop their short-range LiDAR which aims to deliver all the functionality of a LiDAR at short distances while addressing the biggest drawback of the technology - its cost. Their software-definable LiDAR is to the world of LiDARs what the software-defined vehicle is to traditional cars. Join Paul and me on this episode of the AI in Automotive Podcast as Paul gives us a crash course on LiDARs, their types and flavours. We also talk about what the sensor suite in future cars might look like, and where PreAct’s low-cost, short-range LiDAR fits in. Paul believes LiDARs’ time in automotive is yet to come. I am so excited about how technologies like PreAct’s can expand LiDARS’ use cases, and accelerate their mainstream adoption. https://www.ai-in-automotive.com/aiia/302/pauldrysch AI in Automotive Podcast