By: Paul Eichenberg, Managing Director, Paul Eichenberg Strategic Consulting
Artificial intelligence (AI) has come a long way in the last few years, bridging the gap between theoretical conversations and what is now practical possibilities. And, nowhere are the possibilities more exciting than in the automotive industry.
Between the design of the cars we currently drive (and the ones we’ll own in the future) and the process of manufacturing them, there is a lot of room for AI to expand, create efficiencies and make the process of auto-making and driving safer overall. The question is becoming when, not if, artificial intelligence will take over the automotive industry — and will manufacturers, suppliers and automakers be ready?
Four Types of Artificial Intelligence
As it relates to the automotive industry, AI can be broken down into four categories:
- Machine Learning – Algorithms that learn from examples and experience, rather than predetermined processes. Machine learning is one of the essential frameworks of AI and can be found in many of the technologies we already use in daily life.
- Deep Learning – A subset of machine learning, patterned after human neural networks. Deep learning allows computers to make accurate predictions about behavior. Given the increasing amount of data made available via social networks and smartphones, deep learning is the fastest growing part of AI.
- Natural Language Processing – The ability of computers to recognize, interpret and respond to varied types of human speech. This includes accented speech, dialects, and slang — hence the title “natural” — and has come a long way in the last decade.
- Machine Vision – The ability of computers to perceive and process visual cues like images, spatial distance, defects and speed — even when humans cannot.
Autonomous Vehicles – Increasing Safety and Security
Of course, when thinking about artificial intelligence, the first thing that comes to mind are autonomous vehicles, which operate with minimal interference from human drivers. AI processes are now synthesizing data to learn how best to respond — and how humans respond — to driving conditions. For automobiles, this includes predicting how other cars will behave, how to gauge weather conditions, understanding road issues and more. Eventually, this could change many auto-driven industries, from taxis and rideshare vehicles, to delivery companies and public transportation.