An A+ ETF For AI and AV Technology | ETF Trends

That pair of acronyms stand for artificial intelligence and autonomous vehicles, two themes that serve as cornerstones of the ARK Autonomous Technology & Robotics ETF (NYSEARCA: ARKQ).

Among other things, ARKQ is known for having one of the largest weights to Tesla (NASDAQ: TSLA) among all ETFs, but there’s much more to the ARK fund’s story.

Global automotive industry observers believe electric vehicles will reach comparable price points to traditional internal combustion engine vehicles sometime in the next several years, making it more compelling for drivers to make the switch to electric vehicles. Importantly, ARKQ is ideally situated to capitalize on booming AI and AV trends.

“Autonomous driving is one of the key application areas of artificial intelligence (AI). Autonomous vehicles (AV) are equipped with multiple sensors, such as cameras, radars, and lidar, which help them better understand the surroundings and in path planning,” said IHS Markit in a recent note. “These sensors generate a massive amount of data. To make sense of the data produced by these sensors, AVs need supercomputer-like, nearly instant processing capabilities. Companies developing AV systems rely heavily on AI, in the form of machine learning and deep learning, to process the vast amount of data efficiently and to train and validate their autonomous driving systems.”

ARKQ Angles Into Two Exciting Trends

Whether society is ready for it or not, robotics, AI, machine learning, or any other type of disruptive technology will be the next wave of innovation.

“Companies developing AV technology are mainly relying on machine learning or deep learning, or both,” according to IHS Markit. “A major difference between machine learning and deep learning is that, while deep learning can automatically discover the feature to be used for classification in unsupervised exercises, machine learning requires these features to be labeled manually with more rigid rulesets. In contrast to machine learning, deep learning requires significant computing power and training data to deliver more accurate results.”

Other ARKQ holdings, including NVIDIA CORP (NVDA), reach well into the deep learning theme.

“According to a blog by NVIDIA, a specialist in deep learning, if a DNN is shown images of a stop sign in varying conditions, it can learn to identify stop signs on its own,” said IHS Markit. “However, companies developing AVs are required to write not just one but an entire set of DNNs, each dedicated to a specific task, for safe autonomous driving. There is no set limit of how many DNNs are required for autonomous driving; the list is actually growing as new capabilities are emerging. To actually drive the car, the signals generated by the individual DNN must be processed in real-time, which is done by high performing computing platforms.”

For more on disruptive technologies, visit our Disruptive Technology Channel.

The opinions and forecasts expressed herein are solely those of Tom Lydon, and may not actually come to pass. Information on this site should not be used or construed as an offer to sell, a solicitation of an offer to buy, or a recommendation for any product.