BRN Discussion Ongoing

[7für7]

You nailed it. Disgusting put down posting behaviour etc is allowed on the crapper which is what the Government is trying to shield kids from.
On a different track we all have regrets. I should have put a heap into a NASDAQ ETF right after the GFC or bought NVIDIA at 30 cents. I have, however bought a heap of BRN though which i think will make up a bit for those blunders.

Here in our civilized forum, where the crème de la crème of tech investors gather, we professionally ask, ‘Are we about to see a boooooom?’—accompanied by the trendiest GIFs to underscore our expertise. Please try again. No one understands such a philistine writing style… I’m asking you nicely.

 

[manny100]

Here in our civilized forum, where the crème de la crème of tech investors gather, we professionally ask, ‘Are we about to see a boooooom?’—accompanied by the trendiest GIFs to underscore our expertise. Please try again. No one understands such a philistine writing style… I’m asking you nicely.

View attachment 73019

Agree, I am very confident that it's getting close for BRN to start landing deals. Predictions for Edge adoption commercially by media/industry commentators have been way overstretched.
However those predictions gave investors expectations of rapid commercial adoption which we are still waiting for.
It's getting close now and the patient holders will be rewarded.

 

[7für7]

Agree, I am very confident that it's getting close for BRN to start landing deals. Predictions for Edge adoption commercially by media/industry commentators have been way overstretched.
However those predictions gave investors expectations of rapid commercial adoption which we are still waiting for.
It's getting close now and the patient holders will be rewarded.

In this case you should say “moon soon” or if you don’t want to show up too bullish you can say it more like “when moon?” Or “when lambo” no need to thank me… it’s my pleasure to help you! ….

And by the way….

 

[Rach2512]

Not sure if this has been shared before?

Are we the backbone?

Saving the best till the very last.

 

[Esq.111]

 

[AARONASX]

I think I have found a new Patent, 6 days ago!

METHODS AND SYSTEM FOR IMPROVED PROCESSING OF SEQUENTIAL DATA IN A NEURAL NETWORK

Abstract​

Disclosed is a system that includes a processor configured to process data in a neural network and a memory associated with a primary flow path and at least one secondary flow path within the neural network. The primary flow path comprises one or more primary operators to process the data and the at least one secondary flow path is configured to pass the data to a combining operator by skipping the processing of the data over the primary flow path. The processor is configured to provide the primary flow path and the at least one secondary flow path with a primary sequence of data and a secondary sequence of data respectively such that the secondary sequence of data being time offset from the processed primary sequence of data.

 

[Esq.111]

Afternoon Chippers ,

Potential sugar rush imminent...

XTX Index ( S&P / ASX ALL Technology Index ) UP+117.40 points or 3.09%.

SHE'S PUMPING

Regards ,
Esq.

 

[7für7]

Afternoon Chippers ,

Potential sugar rush imminent...

Thank god i stopped eating sugar for years now… do you have something out of xylitol? Stevia? Erythritol?

 

[miaeffect]

Afternoon Chippers ,

Potential sugar rush imminent...

XTX Index ( S&P / ASX ALL Technology Index ) UP+117.40 points or 3.09%.

SHE'S PUMPING

Regards ,
Esq.

Do I get to see "green baby" + "beer" + "rocket" soon?!

 

[Diogenese]

I think I have found a new Patent, 6 days ago!

METHODS AND SYSTEM FOR IMPROVED PROCESSING OF SEQUENTIAL DATA IN A NEURAL NETWORK

Abstract​

Disclosed is a system that includes a processor configured to process data in a neural network and a memory associated with a primary flow path and at least one secondary flow path within the neural network. The primary flow path comprises one or more primary operators to process the data and the at least one secondary flow path is configured to pass the data to a combining operator by skipping the processing of the data over the primary flow path. The processor is configured to provide the primary flow path and the at least one secondary flow path with a primary sequence of data and a secondary sequence of data respectively such that the secondary sequence of data being time offset from the processed primary sequence of data.

View attachment 73027

Hi Aaron,

Good pick up. The ink is not yet dry.

The invention provides a parallel path to the output in a multi-layer NN for elements which have been classified in an earlier layer so they are not further processed (overfitting) in subsequent layers. Overfitting can cause NN hallucinations.

[0002] Convolutional neural networks (CNNs) are generally used for various tasks, such as segmentation and classification. Skip connections may be implemented within the CNNs in order to solve problems, such as overfitting and vanishing gradients and improve performance of the CNNs. Skip connections neural networks (skipNN) generally involve providing a pathway for some neural responses to bypass one or more convolution layers within the skipNN.

 

[7für7]

Do I get to see "green baby" + "beer" + "rocket" soon?!

And also it’s kind of discriminating Man… why is SHE pumping? I guess when it’s red he will write “he’s dumping” always the men are responsible when something goes wrong.. thank you

 

[toasty]

Hi Aaron,

Good pick up. The ink is not yet dry.

The invention provides a parallel path to the output in a multi-layer NN for elements which have been classified in an earlier layer so they are not further processed (overfitting) in subsequent layers. Overfitting can cause NN hallucinations.

[0002] Convolutional neural networks (CNNs) are generally used for various tasks, such as segmentation and classification. Skip connections may be implemented within the CNNs in order to solve problems, such as overfitting and vanishing gradients and improve performance of the CNNs. Skip connections neural networks (skipNN) generally involve providing a pathway for some neural responses to bypass one or more convolution layers within the skipNN.

Yes, but what application might this be useful for??

 

[Rach2512]

Hi Aaron,

Good pick up. The ink is not yet dry.

The invention provides a parallel path to the output in a multi-layer NN for elements which have been classified in an earlier layer so they are not further processed (overfitting) in subsequent layers. Overfitting can cause NN hallucinations.

[0002] Convolutional neural networks (CNNs) are generally used for various tasks, such as segmentation and classification. Skip connections may be implemented within the CNNs in order to solve problems, such as overfitting and vanishing gradients and improve performance of the CNNs. Skip connections neural networks (skipNN) generally involve providing a pathway for some neural responses to bypass one or more convolution layers within the skipNN.

So on a scale of 1 to 10, 10 being the final nail in the coffin for any potential competitors, how good is it? Cos me no understand, I got lost on the first path. Thank you for your time, please and thank you.

 

[rgupta]

Good morning,

For anyone to be second guessing their investment in our company, well, stop worrying...we are positioned better than many
of our so-called competitors, we have established a very strong patent perimeter, have actual 100% proven in silicon, have moved
way beyond our first generation NSoC, have been engaged with many leading (already named) companies for a number of years,
and most importantly, we have established a very trusting relationship within our eco-system partners, for keeping our mouth shut.

"More data is heading to the edge — with Gartner predicting more than 50 percent of enterprise managed data will be processed outside the data center or cloud by 2025. Instead, workloads and data will be run across several edge systems and locations."

Texas Instruments is a very interesting prospect, I wonder if Duy-Loan Le has opened any doors for us over the last 2 years ?

Have a positive day ahead.....regards......Tech

Hi Tech
We all hope the same way.
But a matter of fact is we have zero commercial revenue till end of 24, and processing 50% enterprise data is a lot of change not only brainchip but the entire industry. But anyway let us wait and watch, but it looks impossible to process 50% enterprise data on edge in a year. Upto now no big player is talking about brainchip at all.
Let us wait and watch.
Dyor

 

[Diogenese]

So on a scale of 1 to 10, 10 being the final nail in the coffin for any potential competitors, how good is it? Cos me no understand, I got lost on the first path. Thank you for your time, please and thank you.

Hi Rach,

The patent is an improvement on a known NN technique called skip processing. If we conider an input image to a multi-layer NN to be made of of a number of distinct visual elements (eg, bounding boxes (BB)), and input signals representing the whole image are fed to a NN, then different BBs may be classified at different layers of the NN. The invention is an improvement which prevents the duplication of features (hallucinations) in the classification/inference result by stopping the processing of input signals which relate to an element (BB) of the input signals when the element they represent has been classified at an intermediate layer of the NN. The identified element is supplied to the NN output directly from the intermediate layer, bypassing any subsequent NN layers. This stops the further processing of the BB data so it cannot become confused by adjacent portions of the image data. As well as avoiding hallucinations, this technique also reduces the amount of downstream classification operations and hence reduces the power usage at all the downstream layers as there are fewer "events/spikes" to be classified.

This patent relates to the synchronization of the skipped elements with the elements which passed through all the NN layers so the full image can be reassembled at the output.*

There are already techniques for skip processing in NNs, so this is an improvement on existing processes.

In isolation, I would probably rank this invention at 3 if Akida1 is 10 because it is an improvement on existing techniques. In combination with the full BRN patent portfolio, it is a significant improvement as it does improve both accuracy and power consumption. I'm guessing that it could be applied to other NNs in addition to Akida, which would further increase its licensing potential value.

I would rank Akida2, which includes TENNs, at 17+ on the 1 to 10 scale.

Pico also has high value in low power/battery/remote applications. Its value increases when used in conjunction with Akida2/TENNs.

TENNs on its own also ranks above Akida1, as it is able to be used as software (a new income generating product line) and brings the temporal element to both software and hardware. I think TeNNs is the basis of our newish algorithm product line.

*Synchronization is vital for video - recall the many-headed dog video?

 

[Tothemoon24]

Hi Rach,

The patent is an improvement on a known NN technique called skip processing. If we conider an input image to a multi-layer NN to be made of of a number of distinct visual elements (eg, bounding boxes (BB)), and input signals representing the whole image are fed to a NN, then different BBs may be classified at different layers of the NN. The invention is an improvement which prevents the duplication of features (hallucinations) in the classification/inference result by stopping the processing of input signals which relate to an element (BB) of the input signals when the element they represent has been classified at an intermediate layer of the NN. The identified element is supplied to the NN output directly from the intermediate layer, bypassing any subsequent NN layers. This stops the further processing of the BB data so it cannot become confused by adjacent portions of the image data. As well as avoiding hallucinations, this technique also reduces the amount of downstream classification operations and hence reduces the power usage at all the downstream layers as there are fewer "events/spikes" to be classified.

This patent relates to the synchronization of the skipped elements whith the elements which passed through all the NN layers so the full image can be reassembled at the output.

There are already techniques for skip processing in NNs, so this is an improvement on existing processes.

In isolation, I would probably rank this invention at 3 if Akida1 is 10 because it is an improvement on existing techniques. In combination with the full BRN patent portfolio, it is a significant improvement as it does improve both accuracy and power consumption. I'm guessing that it could be applied to other NNs in addition to Akida, which would further increase its licensing potential value.

I would rank Akida2, which includes TENNs, at 17+ on the 1 to 10 scale.

Pico also has high value in low power/battery/remote applications. Its value increases when used in conjunction with Akida2/TENNs.

TENNs on its own also ranks above Akida1, as it is able to be used as software (a new income generating product line) and brings the temporal element to both software and hardware. I think TeNNs is the basis of our newish algorithm product line.

Dio your a TENNs out of TENNs

 

[Rach2512]

Hi Rach,

The patent is an improvement on a known NN technique called skip processing. If we conider an input image to a multi-layer NN to be made of of a number of distinct visual elements (eg, bounding boxes (BB)), and input signals representing the whole image are fed to a NN, then different BBs may be classified at different layers of the NN. The invention is an improvement which prevents the duplication of features (hallucinations) in the classification/inference result by stopping the processing of input signals which relate to an element (BB) of the input signals when the element they represent has been classified at an intermediate layer of the NN. The identified element is supplied to the NN output directly from the intermediate layer, bypassing any subsequent NN layers. This stops the further processing of the BB data so it cannot become confused by adjacent portions of the image data. As well as avoiding hallucinations, this technique also reduces the amount of downstream classification operations and hence reduces the power usage at all the downstream layers as there are fewer "events/spikes" to be classified.

This patent relates to the synchronization of the skipped elements whith the elements which passed through all the NN layers so the full image can be reassembled at the output.

There are already techniques for skip processing in NNs, so this is an improvement on existing processes.

In isolation, I would probably rank this invention at 3 if Akida1 is 10 because it is an improvement on existing techniques. In combination with the full BRN patent portfolio, it is a significant improvement as it does improve both accuracy and power consumption. I'm guessing that it could be applied to other NNs in addition to Akida, which would further increase its licensing potential value.

I would rank Akida2, which includes TENNs, at 17+ on the 1 to 10 scale.

Pico also has high value in low power/battery/remote applications. Its value increases when used in conjunction with Akida2/TENNs.

TENNs on its own also ranks above Akida1, as it is able to be used as software (a new income generating product line) and brings the temporal element to both software and hardware. I think TeNNs is the basis of our newish algorithm product line.

The 17+ sounds good to me, the rest of it I still don't understand I'm ashamed to say, but many thanks for taking the time, I'm sure you've helped many more here.

 

[HopalongPetrovski]

Hi Rach,

The patent is an improvement on a known NN technique called skip processing. If we conider an input image to a multi-layer NN to be made of of a number of distinct visual elements (eg, bounding boxes (BB)), and input signals representing the whole image are fed to a NN, then different BBs may be classified at different layers of the NN. The invention is an improvement which prevents the duplication of features (hallucinations) in the classification/inference result by stopping the processing of input signals which relate to an element (BB) of the input signals when the element they represent has been classified at an intermediate layer of the NN. The identified element is supplied to the NN output directly from the intermediate layer, bypassing any subsequent NN layers. This stops the further processing of the BB data so it cannot become confused by adjacent portions of the image data. As well as avoiding hallucinations, this technique also reduces the amount of downstream classification operations and hence reduces the power usage at all the downstream layers as there are fewer "events/spikes" to be classified.

This patent relates to the synchronization of the skipped elements with the elements which passed through all the NN layers so the full image can be reassembled at the output.*

There are already techniques for skip processing in NNs, so this is an improvement on existing processes.

In isolation, I would probably rank this invention at 3 if Akida1 is 10 because it is an improvement on existing techniques. In combination with the full BRN patent portfolio, it is a significant improvement as it does improve both accuracy and power consumption. I'm guessing that it could be applied to other NNs in addition to Akida, which would further increase its licensing potential value.

I would rank Akida2, which includes TENNs, at 17+ on the 1 to 10 scale.

Pico also has high value in low power/battery/remote applications. Its value increases when used in conjunction with Akida2/TENNs.

TENNs on its own also ranks above Akida1, as it is able to be used as software (a new income generating product line) and brings the temporal element to both software and hardware. I think TeNNs is the basis of our newish algorithm product line.

*Synchronization is vital for video - recall the many-headed dog video?

Our Mr Diogenese is the best boy around.

 

[MrNick]

Hi Aaron,

Good pick up. The ink is not yet dry.

The invention provides a parallel path to the output in a multi-layer NN for elements which have been classified in an earlier layer so they are not further processed (overfitting) in subsequent layers. Overfitting can cause NN hallucinations.

[0002] Convolutional neural networks (CNNs) are generally used for various tasks, such as segmentation and classification. Skip connections may be implemented within the CNNs in order to solve problems, such as overfitting and vanishing gradients and improve performance of the CNNs. Skip connections neural networks (skipNN) generally involve providing a pathway for some neural responses to bypass one or more convolution layers within the skipNN.

 

[Pom down under]

Afternoon Chippers ,

Potential sugar rush imminent...

XTX Index ( S&P / ASX ALL Technology Index ) UP+117.40 points or 3.09%.

SHE'S PUMPING

Regards ,
Esq.