AI or artificial intelligence – as an abstract concept – conjures up multiple images, in part because it has been loved so much by science fiction writers and filmmakers for many years. It certainly wasn’t all good, was it? In my mind, right off the bat, there is the bewildering and ultimately failed HAL of Stanley Kubrik’s “2001: A Space Odyssey”. Or there’s “The Terminator” and the dystopian future where machines have taken over everything, humans have become obsolete and no longer needed. And then there are the many adaptations of robots and androids, machines with human characteristics or, in human form.
Creative minds certainly love this interaction between humans and robots / machines that could possibly do the things that we humans do. There is also something of a debate about what human intelligence or thinking really is and where emotion comes into play. It can get very philosophical.
In the early part of the 21st century, AI has sort of come of age, but we are still in the early days of its development. Definitions vary but the realities of AI in 2021 are a bit more mundane than the wacky products of the imagination of sci-fi writers. IBM (of everyone they should know) defines it as “the use of computers and machines to mimic the problem-solving and decision-making abilities of the human mind.”
In its simplest form, according to IBM, artificial intelligence is a field that combines computing and robust data sets, to enable problem solving. IBM also says it encompasses the subdomains of machine learning (ML) and deep learning and that these disciplines are “made up of AI algorithms that seek to create expert systems that make predictions. or classifications based on input data “. “Deep learning” includes what’s called neural networks, or layers of inputs and outputs, a kind of “scalable machine learning,” but machine learning is the foundation of it all. ML is based on the premise that systems can be designed to “learn” from data, identify patterns, and make decisions with minimal human intervention. Very intelligent algorithms (written by humans, which define the parameters of ML decision making) started this particular movement, and large datasets – like those generated by connected cars – are the abundant raw material that smart chips can process faster and faster.
The advanced high-speed computing capabilities available today enable or facilitate many machine learning applications – and we are seeing more of them. It becomes less “exceptional” and more “general public”. Digital processes that create large data sets act as an enabler. Large volumes of data can be processed quickly in real time for solutions that are described differently as smart or intelligent.
The auto industry is emerging as a major source of AI and machine learning. The importance of artificial intelligence (AI) to the automotive industry over the next decade cannot be overstated. Faced with the existential long-term threats of sustainability, overcapacity and the prospect of shrinking volumes due to the challenge of shared mobility, automotive players must harness the potential of AI. The greatest potential lies in the abundance of data that suppliers and automakers amass and currently do not use effectively.
The volume of data will only grow as the functions of autonomous, software-defined and connected vehicles increase in number and scope.
Data science and machine learning (ML) are designed to quickly assimilate large volumes of data, understand what it means, and quickly apply the information that emerges.
Additionally, saving money and reducing the costs of Moonshot projects caused by the pandemic means that some of the threats (like autonomy and shared mobility) have temporarily abated. It is therefore more than ever time to integrate AI into the automotive value chain.
AI has use cases beyond autonomous vehicles
Autonomous vehicles (AV) are the most publicly available AI application in the automotive industry. AI chips, computer vision, and ML are the main AI technologies associated with autonomous driving. However, AI is important all along the value chain. The upstream (Tier 1, 2 and 3 suppliers and automakers) benefit from computer vision and intelligent robots, along with data science and ML to streamline production, while downstream (sales and the growing aftermarket) are taking advantage of conversational platforms and contextual systems alongside data science and ML.
Most importantly, AI plays a crucial role in closing the feedback loop between upstream and downstream by incorporating vehicle sales and aftermarket data into predictive modeling, thereby regulating production. more closely to demand. Automakers can thus operate in an agile relationship with real-world events, which is necessary to mitigate crises like the pandemic and the auto chip shortage, in addition to the threat of mobility challengers. Automakers and suppliers are finally realizing that they are way behind the software giants and are rightly reluctant to give up value-added opportunities. Developing AI capabilities is now central to the future profitability and survival of automakers.
The technology of “digital twins”
The digital twins use a combination of IoT sensors, real-time analytics, and ML to create a virtual simulation of an asset, factory, or supply chain. Constantly updated with new data collected at the edge, the use of data science and ML in digital twins helps create a virtuous feedback cycle that enables earlier detection and prevention of inefficiencies causing inefficiencies. . Additionally, when the physical environment changes based on this information, new information is then produced for the twin to assimilate and refine.
For automakers, the end-to-end image provided could thus help rebalance supply chains proactively and quickly in the face of rapidly changing situations. As a result, production can move from reactive and siled activities to a holistic, iterative and agile process. AI can therefore allow automakers to operate in a much closer relationship with real-world events, which is exactly what must happen in order to survive and successfully adapt to future crises.
Smart cities overlap
The use of AI in automotive manufacturing will increasingly overlap with the development of sustainable smart cities. 5G connectivity will provide a low-latency vehicle-to-vehicle (V2V) and eventually vehicle-to-everything (V2X) communication platform, opening up a whole range of AI use cases. From a sustainability perspective, road demand forecasting and centralized traffic management will benefit from AI, improving travel efficiency and reducing vehicle energy consumption. A new adoption of AI will occur in real-time fleet management and vehicle routing by mobility providers and enabling ambient commerce in infotainment systems through intelligent infrastructure interaction.
The development of AI is naturally crucial to the potential success of Tier 4 and 5 VAs, which will be heavily scrutinized by regulatory authorities before being adopted by the public. AI chips, computer vision, LiDAR, and advanced computing power are the key technologies that are being rapidly developed for safe and reliable AVs to meet this most acute challenge. A low failure rate is not acceptable or acceptable when scaled up to hundreds of thousands and possibly millions of vehicles.
How AI Can Increase Profits For Auto Manufacturers
AI can play an important role in halting the decline in results for automakers. In the shorter term, it will be critical to use the increasingly granular levels of data available on vehicles, parts usage and driving habits. ML and data science are essential tools that enable flexible demand planning strategies, thereby maximizing cost reduction.
In the long run, as ownership and volume of vehicles decline, automakers will have to build entirely on demand, perhaps under the most advanced circumstances, becoming captive suppliers to fleet operators. This will require smarter production methods and factories to reduce costs and maintain a viable profit margin. Using AI to dictate supply chain management alongside the use of intelligent robots in factories will go a long way in reducing costs in the long run despite the initial capital outlay required to implement the technology.
Income streams are likely to come more and more from value-added services rather than the traditional streams of vehicle sales and spare parts replacement. The biggest prospect is to generate revenue by providing wireless services, features and upgrades made possible by the connected car. There may also be the opportunity to earn a commission on third party purchases made through the vehicle’s infotainment systems. Therefore, the AI systems driving personalization trends in other industries can no doubt be applied to the automotive market and will be essential to address threats of declining volume and profitability. Automakers must strike a balance between utilizing the superior AI and big data capabilities of large tech companies without fully ceding the potential value-added revenue available.
The reason for the growing importance of AI to mitigate these challenges is due to the increasing homogenization of mobility vehicles. This means that in the future, consumers will get used to prioritizing vehicle function over form. They won’t choose the best vehicle, but the best service and AI will help deliver the best services. Manufacturers and fleet managers who deploy AI as efficiently as possible to operate as close as possible to customers’ preferred (and fluctuating) mobility demands will have the edge. AI is therefore a crucial tool for capitalizing on this hyper-premiumization of function over form.