Feb 16, 2021

LeddarTech and Seoul Robotics partner on Lidar AI vision

AI
computer vision
autonomous vehicles
Machine Learning
William Smith
2 min
The Quebec City-based LeddarTech is to work with Seoul Robotics, which hails from the South Korean capital, on processing Lidar data
The Quebec City-based LeddarTech is to work with Seoul Robotics, which hails from the South Korean capital, on processing Lidar data...

LeddarTech, a provider of environmental sensors for use in autonomous vehicles, has announced a partnership with AI computer vision firm Seoul Robotics.

The Quebec City-based LeddarTech is to work with Seoul Robotics, which hails from the South Korean capital, on combining their hardware and software offerings, utilising the latter firm’s Lidar data processing platform.

Sensing with light

Lidar technology was famously spurned by Tesla in its own pursuit of driverless cars, but is nevertheless used by the majority of competitors. The visual equivalent of radar, lidar involves measuring distances by shining a laser on an object and sensing its reflection. Extant since the 1960s, the technology found uses in many geographical pursuits such as surveying before being harnessed for autonomous vehicles.

“By partnering with Seoul Robotics, a respected leader in 3D perception software for LiDAR sensors, LeddarTech and our customers benefit from Seoul Robotics’ advanced and innovative LiDAR-based perception technology,” said Charles Boulanger, CEO of LeddarTech. 

“The collaboration yields a solution to greatly ease and accelerate the integration of the Leddar Pixell in a wide range of applications in mobility and industrial markets.”

The Leddar Pixell

LeddarTech’s implementation of the technology is known as the Leddar Pixell, which offers a 180 degree sensor capable of detecting objects and other road users and is intended for use in everything from robotaxis to commercial vehicles.

“Robust LiDAR sensors like the Leddar Pixell require the most advanced 3D perception software to process and interpret data in real time,” said HanBin Lee, co-founder and CEO of Seoul Robotics. “Our industry-leading perception platform, SENSR, enables stronger understanding and interpretation of 3D data, better serving the market with highly detailed, LiDAR-based perception. Through our collaboration in the Leddar Ecosystem, we will provide object detection and classification, as well as speed measurement, direction, and location information without a need for map data, all while reducing cost and increasing efficiency.”

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Jun 22, 2021

What is neuromorphic AI?

Intel
neuromorphicai
AI
machinelearning
2 min
Neuromorphic computing – or neuromorphic AI – is the hardware side of artificial intelligence, changing the rules for the future of machine learning

AI is dead. Long live AI?

 

AI is evolving. The first generation of machine learning used ordinary logic and rules to draw conclusions in a very specific manner. A good example would be IBM’s Deep Blue computer, which was trained to play chess to championship standard. That hasn’t disappeared, but it has been augmented by more perceptive deep learning networks that can analyze a broader set of parameters and provide intelligent insights.

 

And neuromorphic AI is next?

 

Correct. Neuromorphic computing is a way of designing hardware – microprocessors, really – to work more like human brains. The idea is that this new iteration of AI hardware will allow machine learning of the future to deal better with ambiguity and contradiction, things that are currently difficult to process for computers.

 

How does neuromorphic AI work?

 

The problem with current chip architecture is that it is not very efficient. Because of the linearity of the process, the chips have to built with a massive amount of horsepower just in case it’s needed. Building a human brain that way would be unfeasible, so engineers have had to rethink the nature of chip design in their quest to get computers to perform more of the tasks human brains are good at. Enter SNNs.

 

What’s an SNN?

 

A spiking neural network (SNN) is, in the words of chipmaker Intel, “a novel model for arranging those elements to emulate natural neural networks that exist in biological brains.” Each ‘neuron’ fires independently, triggering other neurons only when they are required. Intel again: “By encoding information within the signals themselves and their timing, SNNs simulate natural learning processes by dynamically remapping the synapses between artificial neurons in response to stimuli.”

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