True Automotive Intelligence Unlocked with ‘Liquid AI’

Automotive AI powerhouse Autobrains Technologies new groundbreaking ‘Liquid AI’ solution effectively tackles the three major challenges in today's automotive AI landscape; covering edge cases, achieving a viable cost point, and bridging the gap between perception and decision

Source: Autobrains

Autobrains Technologies, a leader in automotive AI, introduces its latest breakthrough: Liquid AI. A highly advanced solution representing a paradigm shift in autonomous driving and Advanced Driver Assistance Systems (ADAS), it addresses challenges that even the best automotive companies struggle to overcome. It combines Autobrains’ signature-based self-learning approach with a modular and adaptive architecture of specialized, scenario-based end-to-end skills.

“While current technologies perform well in handling average conventional driving tasks, they fall short when faced with unexpected real-world driving scenarios that demand greater precision. By using or implementing our Liquid AI, automotive companies can close their AI gaps,” adds Autobrains’ Founder and CEO, Igal Raichelgauz.

Source: Autobrains

The Challenges Faced by Conventional AI Systems

  • Edge Cases: The infinite variety of unexpected driving scenarios presents conventional AIs with practically unsolvable tasks. Today’s manually trained black-box systems cannot cover edge cases. Attempts to address this by feeding the systems more labeled images result in a loss of trackability and controllability.
  • Cost: Addressing real-world driving problems by expanding existing systems with more data, labeling, layers, and computational resources leads to escalating costs and power consumption, resulting in diminishing returns. Achieving a substantial improvement in system accuracy by a factor of 10 requires 10,000 times more computational resources.
  • Perception-Decision Disconnect: The missing interplay between perception and decision functions hinders effective and precise decision-making. For the AI to make optimal driving choices, it requires specific information. However, when details are missing or overly complex, precision is compromised, leading to incorrect reactions.

Liquid AI – Human Brain-Inspired

Autobrains draws inspiration from the human brain, which consists of specialized areas akin to task-specific narrow end-to-end AIs. Just as our brain adapts its architecture based on context – such as light/weather conditions, surroundings, and relevant road users – Liquid AI follows the same approach.

Here’s how it works:

  • Network of Specialized Narrow AIs: Liquid AI comprises hundreds of thousands of specialized narrow AIs, each designed for specific tasks, making reactions very precise and tailored to the relevant driving scenario. This specialized AI approach enables scalability, ranging from a few tens to hundreds of AIs for ADAS systems, scaling up to thousands for higher levels of automated driving, all the way to hundreds of thousands of AIs for full self-driving.
  • Adaptive Architecture: Unlike fixed systems, Liquid AI’s architecture adapts dynamically to the driving context, activating only relevant modules as necessary. This significantly reduces power consumption and compute requirements, not only resulting in cost savings for the System on Chip (SoC) hardware.
  • Efficiency and Precision: By mimicking the brain’s flexibility, Liquid AI achieves superior performance, cost-effectiveness, and safety.
Source: Autobrains

Driving Change: The Future of Automotive Intelligence

Addressing challenging real-world circumstances is Autobrains Liquid AI innovation. Combining AI-assisted driving with its Autonomous Driving capabilities, this game-changing technology  bridges the gap between conventional AI limitations and the promise of truly intelligent autonomous systems. By mimicking the human brain’s flexibility, Liquid AI achieves superior performance, cost-effectiveness, and more explainability and controllability. It enhances situational awareness and decision-making, providing a safer and more reliable driving experience, which is crucial in building trust and adoption among both drivers and manufacturers. As we continue to integrate AI into our vehicles, the question of generating trust becomes paramount. Traditional AI, with its narrow focus, often falls short when faced with the unpredictable nature of real-world driving. Liquid AI, however, marks a significant departure from this approach. By incorporating principles of human cognition, it learns and adapts in real-time, ensuring that our driving systems are predictable and optimized for any real-world driving scenario.

“The safety debate surrounding AVs is more relevant than ever,” notes Autobrains Technologies Founder and CEO Igal Raichelgauz. “While AVs promise to reduce traffic fatalities by eliminating human error such as distracted driving, there are still significant reliability concerns for both manufacturers and drivers. The ongoing dialogue around AVs is critical, and we’re not only at the forefront of these discussions, but also advancing AI that prioritizes driverless car safety. We believe our Liquid AI technology offers a paradigm shift by mimicking human cognitive processes, thereby improving the system’s adaptability and decision-making in real-time. The automotive industry stands at a crossroad. We are proud to lead this charge, setting new standards for what AI in driving can achieve.”

  • Robust Edge Case Handling: Effectively addresses the long tail of edge cases that traditional AI systems struggle with.
  • Human-Like Cognitive Processing: Mimics human decision-making, allowing for better handling of unpredictable real-world conditions.
  • Efficient Resource Utilization: Lower computational power requirements make it scalable across various vehicle models without compromising performance.
  • Real-Time Learning: Liquid AI adapts in real-time to new driving scenarios, ensuring higher accuracy and fewer false positives.

With a background in AI innovation spanning multiple disciplines, Raichelgauz is a distinguished technology executive who has co-founded several successful businesses, including Cortica—a company renowned for its self-learning technology in visual perception.  Under his leadership, the Autobrains Liquid AI technology is now driving consequential change in the automotive industry by resolving autonomous vehicle reliability.