Totally agree Big-D.....conversation interesting and important but not for this thread. Although, as a small comment before we leave this thread - the connection to BRN regardless of the CC discussion is that there is great value in aiming for a lower energy future and we (and Mercedes) know this is a fabulous part of the Akida package.
Surely the blue oval must be close.There's that piece of string thingy again.
PS. I like to get my Saturday eve thought bubbles straight from Bollinger.
Did you hear about the bloke who poisoned his wife with a razor blade?
www.forbes.com
Any takers for the new S-Class?
So good they're not waiting for the EQXX?
#excitingballista!
(Pure speculation on my end of course)
www.mercedes-benz.com
Really good read there S69, encourage everyone to check it out. Whilst a lot of the commentary is superficial in nature the tone of the article paints BRN very positively against a number of others who are trying to compete.
The AI edge chip market is on fire, kindled by 'staggering' VC funding
Dozens of startups continue to get tens of millions in venture funding to make chips for AI in mobile and other embedded computing uses. The race shows no sign of slowing down.www.zdnet.com
@ManChildreborn😜 ... related research
Neuromorphic Computing for Content-based Image Retrieval
Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency. Here, we explore the application of Loihi, a...arxiv.org
Neuromorphic Computing for Content-based Image Retrieval
Abstract
Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency. Here, we explore the application of Loihi, a neuromorphic computing chip developed by Intel, for the computer vision task of image retrieval. We evaluated the functionalities and the performance metrics that are critical in content-based visual search and recommender systems using deep-learning embeddings. Our results show that the neuromorphic solution is about 2.5 times more energy-efficient compared with an ARM Cortex-A72 CPU and 12.5 times more energy-efficient compared with NVIDIA T4 GPU for inference by a lightweight convolutional neural network without batching while maintaining the same level of matching accuracy. The study validates the potential of neuromorphic computing in low-power image retrieval, as a complementary paradigm to the existing von Neumann architectures.
@zeeb0t . Hi. I just attempted to make a contribution but the system multiplied what I was trying to donate by 1,000. Rather than a decimal, the amount entry field displays a comma like is used in the European system (or that was my interpretation). It obviously was a comma denoting '000s. I'm sorry but I don't have enough BrainChip shares to afford that. How can I specify exactly how many dollars to donate?
? 
