Townyj
Ermahgerd
It reminds me of my old Myspace Acc.. just missing some sparkles around the border.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
rgupta
Regular
I assume LKE and BRN is different as both of them lack sufficient revenue.
So asx holders are not very favourable to non income generating companies. May be !
rgupta
Regular
I don't know whether I am right or wrong but Chris Steven had done 1st sales deal for brianchip and that too without any revenue.
I assume it is more like partnership and ecosystem and should be aligned to Rob Telson.
But of late Rob is not in much limelight either!
AARONASX
Holding onto what I've got
It might not be much to look at...but once upon a time their was a small Australia company trying to do the same (while a bit better page) and was mocked back then, still is depending on who you ask. End of the day if they fulfill their role then I am happy.
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Pappagallo
Regular
Why do people overlay the superhero music to things like this? Makes absolutely no sense.
It's a mindset of "let's focus on the product and not the marketing"..be thankful that the English was correct! Israel is the startup nation, don't forget that..Bought another 10K just now..couldn't resist these prices. Might take a while, but we shall get there...
It would be amazing if it's in Scala 3... that would send this to the moon and make me regret for not picking up more at these level... but as I've been buying more to average down (as I averaged up over time) I am invested in brn too muchhhhh
Plzzzzz soooooon
Jesus Christ the website looks awful
I'd be looking for a refund on that MBA !
Stable Genius
Regular
Combining Geologists and NN.
Shadow59
Regular
T
Tony probably, but will he tell?
Fullmoonfever
Top 20
Slowly creeping towards the tipping point.
Management better have damn well positioned us and Akida to be able to smash that "digital wall" when the cracks open
www.advsyscon.com
The volume of data being created continues to rise exponentially. Data is a critical asset for organizations, providing a foundation for digital services, AI, natural language processing (NLP), deep neural networks, and much more. These technologies are also compute-intensive. Organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.
This is what Gartner has termed the “digital wall”. In order to keep pace with competitors and market demands, organizations will begin to test emerging storage and compute technologies, including DNA storage, glass storage, neuromorphic computing, and extreme parallelism.
The challenge for IT teams over the next two to three years is how to integrate new technologies and reliably manage big data across disparate environments.
Management better have damn well positioned us and Akida to be able to smash that "digital wall" when the cracks open
2024: Gartner's IT Automation Trends Revisited | ActiveBatch Blog
It's 2024 and we're revisiting Gartner's IT automation Trends, learn more about hyperautomation, new data storage technologies and an expanding role for IT.
Preparing For The “Digital Wall”
- By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
The volume of data being created continues to rise exponentially. Data is a critical asset for organizations, providing a foundation for digital services, AI, natural language processing (NLP), deep neural networks, and much more. These technologies are also compute-intensive. Organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.
This is what Gartner has termed the “digital wall”. In order to keep pace with competitors and market demands, organizations will begin to test emerging storage and compute technologies, including DNA storage, glass storage, neuromorphic computing, and extreme parallelism.
The challenge for IT teams over the next two to three years is how to integrate new technologies and reliably manage big data across disparate environments.
Isn't that exactly what they've been doing by expanding our ecosystem partners and product offerings?Slowly creeping towards the tipping point.
Management better have damn well positioned us and Akida to be able to smash that "digital wall" when the cracks open
2024: Gartner's IT Automation Trends Revisited | ActiveBatch Blog
It's 2024 and we're revisiting Gartner's IT automation Trends, learn more about hyperautomation, new data storage technologies and an expanding role for IT.
Preparing For The “Digital Wall”
- By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
The volume of data being created continues to rise exponentially. Data is a critical asset for organizations, providing a foundation for digital services, AI, natural language processing (NLP), deep neural networks, and much more. These technologies are also compute-intensive. Organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.
This is what Gartner has termed the “digital wall”. In order to keep pace with competitors and market demands, organizations will begin to test emerging storage and compute technologies, including DNA storage, glass storage, neuromorphic computing, and extreme parallelism.
The challenge for IT teams over the next two to three years is how to integrate new technologies and reliably manage big data across disparate environments.
I think that's exactly what they've been doing by expanding our ecosystem partners and product offerings. IMO we are in the sweet spot, timing wise.Slowly creeping towards the tipping point.
Management better have damn well positioned us and Akida to be able to smash that "digital wall" when the cracks open
2024: Gartner's IT Automation Trends Revisited | ActiveBatch Blog
It's 2024 and we're revisiting Gartner's IT automation Trends, learn more about hyperautomation, new data storage technologies and an expanding role for IT.
Preparing For The “Digital Wall”
- By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
The volume of data being created continues to rise exponentially. Data is a critical asset for organizations, providing a foundation for digital services, AI, natural language processing (NLP), deep neural networks, and much more. These technologies are also compute-intensive. Organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.
This is what Gartner has termed the “digital wall”. In order to keep pace with competitors and market demands, organizations will begin to test emerging storage and compute technologies, including DNA storage, glass storage, neuromorphic computing, and extreme parallelism.
The challenge for IT teams over the next two to three years is how to integrate new technologies and reliably manage big data across disparate environments.
Sirod69
bavarian girl ;-)
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David holland
Regular
2 - I.P sales and duck egg since, we have partnerships but no I.P sales since, but it's all top secret
Fullmoonfever
Top 20
IOT for all vid from about 3 weeks ago.
Not sure if posted previously.
Watch or skip to 19 min mark.
Gil Dror, CTO at SmartSense by Digi quite aware of BRN in the neuromorphic space....nice to hear.
Not sure if posted previously.
Watch or skip to 19 min mark.
Gil Dror, CTO at SmartSense by Digi quite aware of BRN in the neuromorphic space....nice to hear.
Frangipani
Regular
That’s your interpretation only. The way I see it, we are not led to believe that the chip itself is capable of learning - in fact, on-chip learning is simply not required for these solar-powered devices that can send and receive data via the LoRaWAN network, the leading standard for long-range IoT.
I already posted about these wildfire sensors a couple of weeks ago (https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-305877), and this is what the company manufacturing them states on their website:
How can these sensors detect the fire? How can they know it’s a real fire but not just a burning cigarette?
The built-in artificial intelligence (AI) of the sensors is continually trained for the specific ‘smell’ of the target forest on fire. Our customers collect samples from the forest floor and send them to Germany so that we can train our AI in the lab. Over time, we will collect more and more samples from typical forests in the different parts of the world and will eventually no longer have to train the AI for new deployments as there is only a finite amount of forest types. We expect this to be the case within the first two years of operation.Doing so allows our Silvanet system to be continuously evolving and improving and greatly minimizes any risks of false positives within the platform.
———————————————————————————————————
What the Bosch article does say is:
“With the help of artificial intelligence, this sensor also analyzes the data it collects right there on the spot. If it detects a fire, it immediately sounds the alarm, sending a signal to the cloud and notifying emergency services.
(…)
The tiny “nose” that sniffs out forest fires measures just three by three millimeters. The Bosch environmental sensor installed in this forest-fire detection system is the world’s first gas sensor to feature artificial intelligence and the first to be deployed as an early wildfire warning tool. It can detect various gases, including fire gases such as hydrogen, carbon monoxide, and most hydrocarbons. The system is constantly learning and improving. Data collected from all installed sensors serves to continuously train the environmental sensors using artificial intelligence so they can detect and analyze gases with even greater accuracy. The BME AI-Studio Server software was developed specifically for this purpose.”
———————————————————————————————————-
These kind of Edge AI devices would be a perfect use case for Akida, even without utilising its on-chip learning function, especially since the Bosch sensors are collecting other environmental data as well such as temperature, humidity and air pressure, and the company is also looking at future applications, e.g. involving sensors that can detect chainsaw noise to help prevent illegal logging.
Dryad explicitly state on their website they are open-minded about collaborations: “Open for integration of 3rd party sensors and applications, no single-vendor lock-in.”
So maybe we haven’t entirely missed out on that niche just yet?
Is Dryad involved solely in wildfire detection?
While our initial focus at Dryad is wildfire detection, the Bosch BME688 sensors used in Silvanet sensor devices also collect environmental data such as temperature, humidity and air pressure which is periodically sent to the Silvanet Cloud, allowing forest owners to better understand the microclimate of the forest and its influence and development of the forest heath over time.Further planned use-cases involve sensors that can detect chainsaw noise to help prevent illegal logging.
It’s not just about forests either, our technology can also be applied to other ecosystems including lakes, rivers and oceans. At Dryad, we have ambitious plans to connect the natural world and protect our planet.
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Learning
Learning to the Top 🕵♂️
Not sure this has been posted.
A recent patent publication from TCS.
@Diogenese is this TSC patent using Brainchip's Akida?
Abstract
State of art techniques rely of FPGA based approaches when power efficiency is of concern. However, compared to SNN on Neuromorphic hardware, ANN on FPGA requires higher power and longer design cycles to deploy neural network on hardware accelerators. Embodiments of the present disclosure provide a method and system for energy efficient hierarchical multi-stage SNN architecture for classification and segmentation of high-resolution images. Patch-to-patch-class classification approach is used, where the image is divided into smaller patches, and classified at first stage into multiple labels based on percentage coverage of a parameter of interest, for example, cloud coverage in satellite images. The image portion corresponding to the partially covered patches is divided into further smaller size patches, classified by a binary classifier at second level of classification. Labels across multiple SNN classifier levels are aggregated to identify segmentation map of the input image in accordance with the coverage parameter of interest.
Learning 🏖
A recent patent publication from TCS.
@Diogenese is this TSC patent using Brainchip's Akida?
Abstract
State of art techniques rely of FPGA based approaches when power efficiency is of concern. However, compared to SNN on Neuromorphic hardware, ANN on FPGA requires higher power and longer design cycles to deploy neural network on hardware accelerators. Embodiments of the present disclosure provide a method and system for energy efficient hierarchical multi-stage SNN architecture for classification and segmentation of high-resolution images. Patch-to-patch-class classification approach is used, where the image is divided into smaller patches, and classified at first stage into multiple labels based on percentage coverage of a parameter of interest, for example, cloud coverage in satellite images. The image portion corresponding to the partially covered patches is divided into further smaller size patches, classified by a binary classifier at second level of classification. Labels across multiple SNN classifier levels are aggregated to identify segmentation map of the input image in accordance with the coverage parameter of interest.
US20230154154A1 - Energy efficient hierarchical snn architecture for classification and segmentation of high-resolution images - Google Patents
State of art techniques rely of FPGA based approaches when power efficiency is of concern. However, compared to SNN on Neuromorphic hardware, ANN on FPGA requires higher power and longer design cycles to deploy neural network on hardware accelerators. Embodiments of the present disclosure...
patents.google.com
Learning 🏖
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