Companies across the board are investing in AI, and automotive companies are no exception. The progress towards autonomous vehicles, more intelligent driving assistance, and features like automated parking and crash avoidance are all evidence of AI impacting the automotive industry in deep ways. The AI Today podcast recently interviewed Samantha (Sam) Huang of BMW iVentures, a strategic corporate VC fund backed by BMW. She shares with us insights into investment trends in AI in automotive, and some thoughts on the challenges and opportunities for emerging companies in the AI landscape. Below is an excerpt from the interview. You can check out the full podcast, and a full transcript, on the Cognilytica site here.
AI Today: Hello and welcome to our podcast. We’d like to start by having you introduce yourself to our listeners. Tell them a little bit about your role at BMW iVentures and your experience in A.I.
Sam: I’m a senior associate at BMW iVentures. We are a 500 million euro fund, that invests in companies and interesting technologies that are related to the automotive ecosystem. What that means is that we invest in all of the technologies and start-ups and services that it takes to really make a car. That could include things like industry 4.0, autonomous driving, enabling technologies for the car — even intelligent systems and so forth. And then we also invest in all of the technologies, services and start-ups that it takes to retain the customer, after the car is in his or her hands and continue to build interesting experiences on top of automotive ownership, or alternative mobility experience as well. That can mean things like automotive insurance marketplaces or used cars, and just generally different modes of mobility.
AI Today: What are some of the challenges to adopting AI in the auto industry that you see?
Sam: The automotive industry is really like any other industry, in how it’s trying to tackle AI. And it really comes down to two things. One is: how do you incorporate AI technology across the enterprise, so that you can streamline workflow and optimize cost reduction and things like that. And then the other way that enterprises are trying to integrate AI technology is, of course, in creating sticky and more addictive products. Those are two general things that any sort of AI company, or any sort of company really, is trying to solve in terms of the big problems.
AI Today: We know that big data plays a role with AI in general. How do you think data issues are impacting artificial intelligence, both good and bad, in terms of adoption?
Sam: Data is a huge problem within the AI field, but it’s also one of the drivers of the proliferation of AI technology today. So what I mean by that is with any sort of AI problem or machine learning problem, you have your algorithms and you have the data. In order to train your algorithms effectively, you need this data. What this means for automotive OEMs like us is: when we don’t have enough data, or we don’t have enough data about the miles of a car that has driven on the road, we’re not able to train our system effectively. The second problem with data is that if you have data that is dirty, you’re going to train your system completely wrong. As an example: if you tell a child growing up, that a picture of a cat is in fact a dog — and the child learns or thinks that that cat is a dog — you’re feeding that child the wrong data. So that child is going to have the wrong output. That’s a very basic machine learning example. That’s the type of problem that really resonates across data and AI. Without solving these key issues of bad quality data and lack of data, we’re not going tobe able to solve the big AI problems down the road.
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