Geoffrey Hinton wins Nobel Prize

The Inventor of Modern AI

AI pioneer and computer scientist Geoffrey Hinton has won the 2024 Nobel Prize for his groundbreaking contributions to deep learning. He is renowned for advancing neural networks and developing backpropagation algorithms, which allow machines to learn.

In 2018, Hinton, along with Yann LeCun and Yoshua Bengio, was awarded the prestigious Turing Award. The trio, often referred to as the "godfathers of AI," has been instrumental in shaping the future of artificial intelligence.

Autonomous 3D vision without lidar

LiDAR (Light Detection and Ranging) uses laser beams to measure distances and create precise, high-resolution 3D maps of objects or environments. Lidar works by emitting laser pulses toward a target and measuring the time it takes for the light to reflect back to the sensor. Based on the time delay, the system calculates the distance to the object. By scanning across a wide area, lidar can generate a detailed, 3D map of the surroundings. This process is often referred to as "time of flight."

Impressive technology but has several shortcomings. First, its high cost makes it less accessible for consumer applications like autonomous vehicles. Additionally, lidar is vulnerable to weather conditions; rain, fog, and snow can interfere with its laser beams, limiting its effective range. It also struggles to accurately detect objects with highly reflective or absorbent surfaces, resulting in unreliable data. Furthermore, processing the large amounts of data generated by lidar requires significant computational power, adding complexity to the system.

In response to these challenges, some companies, like Tesla, have abandoned lidar in favor of cameras and computer vision systems for their autonomous vehicle technologies. However, cameras also have their limitations. They struggle in low-light environments, such as at night, and can be affected by glare from the sun, headlights, or reflections, leading to inaccuracies in object detection. Like lidar, cameras are impacted by adverse weather conditions, including rain, fog, snow, and heavy cloud cover. Additionally, cameras provide only 2D images, making it difficult to gauge the distance of objects. While stereo cameras can create depth maps, they still face challenges in certain situations. Processing all this data in real-time also demands significant computational resources.

A startup in Norway called Sonair aims to revolutionize the field with its innovative ultrasonic 3D imaging technology, which leverages ultrasound already used in medical applications. This groundbreaking approach harnesses sound waves to accurately detect people and objects in three dimensions.

Sonair's 3D ultrasonic sensors enable robots to move beyond the narrow, in-plane perspective of 2D LiDAR, offering an omnidirectional view of their surroundings.

Cost-Effective Solution

Sonair provides a more affordable alternative to traditional technologies without sacrificing performance.

Enhanced Performance

Unlike LiDAR and cameras, ultrasonic sensors are less susceptible to environmental factors such as poor lighting, dust, and fluctuating temperatures. Additionally, Sonair's technology can effectively detect reflective or transparent surfaces, including glass and mirrors.

Lower Power Consumption

Ultrasonic sensors generally require less power, which is crucial for mobile and autonomous applications. Sonair’s sensors operate with a maximum power consumption of just 5 watts.

Safety First

Operating within a frequency range that is safe for both humans and animals, Sonair’s sensors are suitable for use in public and indoor spaces.

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