AI developer AutoBrains claims to have developed an unsupervised-AI system for the automotive industry, which promises a faster technology trajectory for ADAS and AV system development. Tier 1 supplier Continental is partnering with the company to bring the technology to market.

According to AutoBrains, its unsupervised AI operates in a fundamentally different way from traditional deep learning systems currently used in most ADAS. The company says its process is based on multi-disciplinary research and development that allows the system to independently learn, process and interpret relevant data from the car’s surroundings in real time, leading to perception that operates in much the same way as a human brain.

One of the disruptive technology’s key advantages over traditional deep learning is a reduced reliance on expensive and often error-prone manually labeled training data sets. It is claimed that the unsupervised AI system successfully interprets and navigates unusual driving scenarios and edge cases where traditional supervised learning systems are least reliable.

Reduced reliance on stored data also means the system requires roughly 10 times less computing power than currently available systems and can be produced at lower cost, increasing the accessibility of ADAS across market segments at a time when regulations require more driver assistance capabilities for passenger and commercial vehicles.

“We are thrilled to partner with Continental to bring our revolutionary technology to the market,” said Igal Raichelgauz, CEO of AutoBrains. “Unsupervised AI is backed by more than 200 patents, over a decade of research and development, and a nearly two-year incubation period with Continental, and we are excited to take the next step with Conti as our key partner.”

The potential of the company’s technology has been recognized by Continental, as Frank Petznick, head of the driver assistance systems business unit at Continental, explained: “We are excited to be partnering with AutoBrains to bring to market its advanced and proven AI technology that we believe will disrupt the ADAS and AV marketplace. Historically, AV and ADAS technologies have been limited by their dependence on supervised learning that uses massive labeled training data sets and requires enormous compute power.

“AutoBrains’ AI breaks through those barriers with a different approach that processes relevant signals from the car’s environment in much the same way that human drivers do. This technology boosts performance while saving compute power and energy. With AutoBrains, we intend to push rapidly ahead toward a safer and increasingly autonomous driving experience.”

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As artificial intelligence changes the investment landscape, advisors need to stay one step ahead. Clients crave a human touch, but they also want to save money and improve their returns using new technologies.

Meanwhile, companies across all industries seek to harness AI to boost profits. Using deep learning and neural networks to instantly identify subtle patterns enables tech-savvy organizations to race ahead of the pack, creating opportunities for investors who track firms that are integrating AI into their overall strategy.

For advisors, the challenge is weaving AI into their practice without going overboard. It’s a delicate balance, especially when curious clients question the role of AI on capital markets and its effect on their investments.

“AI is just one piece of the puzzle,” said Anthony Saccaro, an advisor in Woodland Hills, Calif. “It’s one tool to draw a conclusion, but it’s not the conclusion.”

Reflecting on his two decades in the financial planning business, Saccaro notes how consumers increasingly glean knowledge from their computers and phones. He adds that they sometimes put more faith in AI-generated findings and automated tools than is warranted.

“With any AI model, there’s a bias behind it,” he said. “I’ll show a client how that might not make sense in their situation,” even after they plug their data into some analytic platform that spits out an asset allocation or stock selection strategy.

Make Better Decisions

Consumer awareness of AI is soaring. While consumers may not understand the technical underpinnings of self-driving cars, medical devices and other machine learning-enabled technologies, they expect their advisor to take advantage of cutting-edge advances in the field.

“AI does come up occasionally in conversation with clients,” said Vikram Chugh, chief operating officer at Robertson Stephens, a New York City-based wealth management firm. “Everybody has heard of machine learning and other buzzwords of the day.”

He finds that most clients aren’t sure how AI works or what it does. Instead, they ask, “How can I capitalize on it?”

Chugh sees two ways that AI affects clients and enhances their experience working with an advisor. First, there are predictive tools that help portfolio managers assess investors’ feelings about risk and their range of reactions to market swings. Such tools can aid advisors in making decisions and tailoring strategies for clients.

Second, he says that by gathering more information from clients, his firm can deliver better, more customized service. Examples include inputting data about a client’s goals and objectives, household income and other biographical details.

“Getting this information is a back-and-forth process,” Chugh said. “You have to show clients what you’ll do with this information. If we see how much they earn and spend and who owns a Tesla, that can be incorporated into their investment management.”

Focus On Outcomes

Chugh is not alone in his quest to put AI to work to improve client service. Other investment management executives want to scale their AI capabilities so that their advisors deliver more value to clients.

“To me, AI is an enabler of how we can provide more meaningful insights and advice,” said Hamesh Chawla, chief technology officer at Edelman Financial Engines in Sunnyvale, Calif. For example, advisors can spot what Chawla calls “life event triggers” such as a client’s interest in buying a home, which in turn could spark a conversation about mortgage options.

While clients may express concern to their advisor about cybersecurity and safeguarding their personal data, they are less worried about the role of AI in influencing the relationship. They may have only a vague idea of the practical impact of algorithms or analytics on their portfolio.

“Clients don’t have any direct concerns with AI,” Chawla said. “Outcomes are what they care about.”

Similarly, most advisors don’t feel compelled to master the intricacies of data analytics or machine learning. They simply want to put these tools to work with a minimum of fuss.

“If we bombard advisors with new technologies, it takes away from their time with clients,” Chawla said. “So advisors see the dashboard and a trigger, and that enables them to engage better with clients.”

Some startups are pinning their hopes on applying AI in novel ways. For instance, Wedmont Private Capital recently launched a tech-enabled alternative to the traditional wealth management model, charging high-net-worth clients a flat fee that includes direct indexing in which their portfolios aim to replicate a market index.

“It involves us using an AI optimization engine to build custom portfolios that mirror a specific benchmark in the market,” said James Pelletier, co-founder of West Chester, Pa.-based Wedmont. “Very rarely do clients ask about AI, but when we explain how the system works, they tend to be pretty comfortable with it.”

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