The next article of our Amazing AI-series talks about Tesla. Surprised? Tesla is a company admired by millions for its innovation & drive in excellence in the automotive industry. But what is actually behind their success is their focus on AI. Integrating AI in every aspect like product features, manufacturing process & customer experience, gives Tesla an unparallel edge over others. Read our take on this here.

Just when the caterpillar thought the world was over, it became a butterfly — a proverb that aptly befits the technological evolution and the subsequent revolution brought forth by AI. Favorably, the prevalent employment of AI has paved the way for a world where “mere possibilities” are swiftly transforming into “practicalities.”

No stranger to ushering in new technology itself, Tesla is foreseeing this imminent revolution and integrating the power of artificial intelligence into its various processes. These developments can be seen not just in their self-driving automobiles but also in a variety of their other functions, from manufacturing to end-use.

But rather than give you a rundown of its technology and progress, today we’ll delve deeper into specific use-cases to see how the Elon Musk-led company is putting AI to work and creating an unparalleled value proposition.

A Step Light Years Ahead of the Competitors

“Tesla’s Autopilot AI is so far ahead of the competition it’s like comparing Google to other search engines” — this is what an article on Business Insider from May 2020 read, and rightly so. Tesla’s transition from ‘human-assisted AI’ to ‘AI-only,’ which began with Tesla Autopilot V7.0, has given the company a notable edge over its competitors.

Who are these competitors? You (might) ask — from Mercedes to BMW and Audi to Porsche, almost every automotive giant that has been tapping into luxury markets and, at the same time, yearning to swiftly adopt and assimilate AI into their workings.

Consider this; Tesla’s fully self-driving (FSD) vehicles rely on imitation learning. The process entails algorithms to fetch data from millions of other Tesla cars running on the roads while emphasizing drivers’ reactions and the overall driving process. Comparatively, competitors employ synthetic data generated from real-world emulations, such as video games. Naturally, the former is a more reliable method of optimizing the conglomeration of advanced sensors spread across the vehicle’s cameras, steering, braking system, etc. 

An AI-powered Operational Approach for Interminable Growth by Tesla

One of the most profound uses of AI lies in enabling more automatic features that make Tesla cars smarter and safer. For instance: In 2019, Tesla launched a new software update for the Model 3, which enabled an automatic rain-sensing wiper feature. It essentially leveraged cameras to eliminate the need to manually operate the wipers and switch between wiper fluid modes. Tesla made this possible by using deep neural networks (DNNs) designed to extract information from unstructured data.

The Case for Tesla’s AI Chips

The new Tesla microchip can perform 72 trillion operations per sec, second only to the human brain (1 quadrillion operations per sec). That’s almost 21 times powerful than the previously employed Tesla Nvidia microchips. To that end, it won’t be an exaggeration to mention that these artificial brains, as reported by Select Car Leasing, can power the US’s F-35 fighter jet 180 times. 

The Case for the Computer Vision in a Tesla vs. LIDAR

Some of the most advanced projects Tesla has implemented, such as its camera-based, machine vision-based, and radar-based vehicle identification systems, are being used to identify vehicles and objects on the road. Contrastively, competitors like Audi, Uber, and Toyota are utilizing LIDARs to achieve the same.

As it stands, Tesla’s computer vision system (CV) powered by radars and a camera system is ideal for real-time identification of stationary and moving objects. The moving objects, however, are not something that LIDAR can efficiently detect. Things go further downside for the latter technology (which usually costs $7,500) when you consider the fact that a few stereo cameras worth $5 can achieve equally accurate 3D object detection

The Case for AI-powered Manufacturing at Tesla

While most of today’s operations rely on massive number-crunching capabilities, manufacturing companies often face bottlenecks due to the limitations of algorithms alone. At times, AI systems are prone to data inaccuracies which may lead to millions worth of damages. To circumvent the situation, Tesla uses AI to verify its production line for any defects and further parses online forums to gather comprehensive insights into customers’ issues.  

In a Nutshell

The ability to take on the most complex tasks while being cost-effective is one of AI’s greatest strengths. And Tesla has been leveraging this advantage fundamentally through effective deployment of its FSD cars, associated software and hardware updates, and a slew of other features.