BRN Discussion Ongoing

[Space Cadet]

I wonder how long ISL had been playing with Akida before they got the Airforce radar SBIR?

This patent was filed in mid 2021, so 5 months before the announcement.

US11256988B1 Process and method for real-time sensor neuromorphic processing 20210719

View attachment 61118
View attachment 61119

[0003] The invention relates to the general field of deep learning neural networks, which has enjoyed a great deal of success in recent years. This is attributed to more advanced neural architectures that more closely resemble the human brain. Neural networks work with functionalities similar to the human brain. The invention includes both a training cycle and a live (online) operation. The training cycle includes five elements and comprises the build portion of the deep learning process. The training cycle requirements ensure adequate convergence and performance. The live (online) operation includes the live operation of a Spiking Neural Network (SNN) designed by the five steps of the training cycle. The invention is part of a new generation of neuromorphic computing architectures, including Integrated Circuits (IC). This new generation of neuromorphic computing architectures includes IC, deep learning and machine learning.



1. A method of providing real-time sensor neuromorphic processing, the method comprising the steps of:
providing a training cycle and a live operation cycle;
wherein the training cycle includes:
(1) the establishment of a build portion or training cycle of a deep learning process with AI;
wherein the build portion or training cycle begins with the process taking performance requirements in the form of generation of a scenario as inputs;
(2) selecting a sensor model application, with associated performance specifications set forth in the generation of a scenario;
(3) providing a Hi-Fi Radio-Frequency (RF) sensor model which is used to augment any real data for training;
(4) providing a computer model surrogate which is used instead of, or in addition to, a non-surrogate computer model;
(5) the sensor model application being one or more of radar, sonar or LIDAR;
(6) specifying an operating environment details wherein the Hi-Fi sensor model generates requisite training data and/or training environment;
(7) the Hi-Fi sensor model generates training data in a quantity to ensure convergence of DNN neuron weights, wherein as an input enters the node, the input gets multiplied by a weight value and the resulting output is either observed, or passed to the next layer in the neural network;
(8) raw sensor training data is preprocessed into a format suitable for presentation to a DNN from a DNN interface, the sensor model application training data is forwarded to the DNN;
(9) the training environment information is then output from the DNN to a DNN-to-SNN operation through a DNN-to-SNN conversion; and
(10) thereafter, the DNN is converted to an SNN and the SNN outputs the SNN information to an neuromorphic integrated circuit (IC), creating a neuromorphic sensor application;
(11) providing and utilizing a statistical method which ensures reliable performance of the neuromorphic integrated circuit, wherein reliability is 99%.

Maybe they were one of the reasons AKD1000 was delayed because early adopters wanted changes such as CNN2SNN converters. Makes sense to me.

SC

 

[Diogenese]

Diogenes, just a query concerning tge original patent that expires in 2028.
It's been improved over and over with several more patents added over the years until we got GEN 2 with GEN 3 next year with possibly another patent application.
How do you see the expiry of the original patent effecting our business?
My view is that its old tech now with ongoing further patents improving the original, but it will still enable others to add their own improvements.
I guess it depends mainly on how much market traction we can gain over the next few years with Edge AI starting to take off.

Hi manny,

The 2008 patent is of great value in that the main claim is very broad in scope and is directed to ML:

US2010076916A1 Autonomous Learning Dynamic Artificial Neural Computing Device and Brain Inspired System 20080921

An information processing system intended for use in artificial intelligence, consisting of a plurality of artificial neuron circuits connected in an array, comprising:

a first plurality of dynamic synapse circuits, comprising a means of learning and responding to input signals by producing a compounding strength value simulating a biological Post Synaptic Potential,

a temporal integrator circuit that integrates and combines individually simulated Post Synaptic Potential values over time, and thus constitutes an artificial membrane potential value,

a second plurality of dynamic soma circuits each capable of producing one or more pulses when the integrated membrane potential value has reached or exceeded a stored variable threshold value,

That said, there are a number of subsequent developments which greatly improve performance, chief of these (until TeNNs) is N-of-M coding which greatly increased sparsity and reduced power usage and latency.

Another landmark patent is ...

US11468299B2 Spiking neural network 20181101

A neuromorphic integrated circuit, comprising:
a spike converter circuit configured to generate spikes from input data;
a reconfigurable neuron fabric comprising a neural processor comprising a plurality of spiking neuron circuits configured to perform a task based on the spikes and a neural network configuration; and
a memory comprising the neural network configuration, wherein the neural network configuration comprises a potential array and a plurality of synapses, and the neural network configuration defines connections between the plurality of spiking neuron circuits and the plurality of synapses, the potential array comprising membrane potential values for the plurality of spiking neuron circuits, and the plurality of synapses having corresponding synaptic weights,
wherein the neural processor is configured to:
select a spiking neuron circuit in the plurality of spiking neuron circuits based on the selected spiking neuron circuit having a membrane potential value that is a highest value among the membrane potential values for the plurality of spiking neuron circuits;
determine that the membrane potential value of the selected spiking neuron circuit reached a learning threshold value associated with the selected spiking neuron circuit; and
perform a Spike Time Dependent Plasticity (STDP) learning function based on the determination that the membrane potential value of the selected spiking neuron circuit reached the learning threshold value associated with the selected spiking neuron circuit.


... which showcases my all-time favourite drawing. As you can see, this has a priority of 2018, so it will last well into the 2030s.


CNN2SNN is certainly a vital commercial development, but, for me, not as exciting as N-of-M or TeNNS.

This patent introduced a number of "minor" improvements:

WO2020092691A1 AN IMPROVED SPIKING NEURAL NETWORK 20181101

[0038] But conventional SNNs can suffer from several technological problems. First, conventional SNNs are unable to switch between convolution and fully connected operation. For example, a conventional SNN may be configured at design time to use a fully-connected feedforward architecture to learn features and classify data. Embodiments herein (e.g., the neuromorphic integrated circuit) solve this technological problem by combining the features of a CNN and a SNN into a spiking convolutional neural network (SCNN) that can be configured to switch between a convolution operation or a fully- connected neural network function. The SCNN may also reduce the number of synapse weights for each neuron. This can also allow the SCNN to be deeper (e.g., have more layers) than a conventional SNN with fewer synapse weights for each neuron.
Embodiments herein further improve the convolution operation by using a winner-take-all (WTA) approach for each neuron acting as a filter at particular position of the input space. This can improve the selectivity and invariance of the network. In other words, this can improve the accuracy of an inference operation.

[0039] Second, conventional SNNs are not reconfigurable. Embodiments herein solve this technological problem by allowing the connections between neurons and synapses of a SNN to be reprogrammed based on a user defined configuration. For example, the connections between layers and neural processors can be reprogrammed using a user defined configuration file.

[0040] Third, conventional SNNs do not provide buffering between different layers of the SNN. But buffering can allow for a time delay for passing output spikes to a next layer. Embodiments herein solve this technological problem by adding input spike buffers and output spike buffers between layers of a SCNN.

[0041] Fourth, conventional SNNs do not support synapse weight sharing. Embodiments herein solve this technological problem by allowing kernels of a SCNN to share synapse weights when performing convolution. This can reduce memory requirements of the SCNN.

[0042] Fifth, conventional SNNs often use l-bit synapse weights. But the use of l-bit synapse weights does not provide a way to inhibit connections. Embodiments herein solve this technological problem by using ternary synapse weights. For example, embodiments herein can use two-bit synapse weights. These ternary synapse weights can have positive, zero, or negative values. The use of negative weights can provide a way to inhibit connections which can improve selectivity. In other words, this can improve the accuracy of an inference operation.

[0043] Sixth, conventional SNNs do not perform pooling. This results in increased memory requirements for conventional SNNs. Embodiments herein solve this technological problem by performing pooling on previous layer outputs. For example, embodiments herein can perform pooling on a potential array outputted by a previous layer. This pooling operation reduces the dimensionality of the potential array while retaining the most important information.

[0044] Seventh, conventional SNN often store spikes in a bit array. Embodiments herein provide an improved way to represent and process spikes. For example, embodiments herein can use a connection list instead of bit array. This connection list is optimized such that each input layer neuron has a set of offset indexes that it must update. This enables embodiments herein to only have to consider a single connection list to update all the membrane potential values of connected neurons in the current layer.

[0045] Eighth, conventional SNNs often process spike by spike. In contrast, embodiments herein can process packets of spikes. This can cause the potential array to be updated as soon as a spike is processed. This can allow for greater hardware parallelization.

[0046] Finally, conventional SNNs do not provide a way to import learning (e.g., synapse weights) from an external source. For example, SNNs do not provide a way to import learning performed offline using backpropagation. Embodiments herein solve this technological problem by allowing a user to import learning performed offline into the neuromorphic integrated circuit.


The TeNNs patents:


WO2023250092A1 METHOD AND SYSTEM FOR PROCESSING EVENT-BASED DATA IN EVENT-BASED SPATIOTEMPORAL NEURAL NETWORKS 20220622



WO2023250093A1 METHOD AND SYSTEM FOR IMPLEMENTING TEMPORAL CONVOLUTION IN SPATIOTEMPORAL NEURAL NETWORS 20220622

Disclosed is a neural network system generally relates to the field of neural networks (NNs). In particular, the present disclosure relates to event-based convolutional neural networks (NNs) that are trained to process spatial and temporal data using kernels represented by polynomial expansion. The event-based convolutional neural networks (NNs) are spatiotemporal neural networks. According to an embodiment, an explicit temporal convolution capability is added through Temporal Event-based Neural Networks (TENN) models, or TENNs in the spatiotemporal neural networks. The TENNs includes a plurality of temporal and spatial convolution layers that combine spatial and temporal features of data for low-level and high-level features. The TENNs as disclosed herein are configured to perform in a buffer mode and recurrent mode that effectively learns both spatial and temporal correlations from the input data.


The FIFO (first-in - first-out) buffer acts like a data conveyor belt which facilitates comparison of incoming data with previously temporarily stored data.

 

[Pmel]

https://www.linkedin.com/posts/geof...DH?utm_source=share&utm_medium=member_android

 

[IloveLamp]

Exclusive: Mercedes becomes the first automaker to sell autonomous cars in the U.S. that don't come with a requirement that drivers watch the road

Mercedes is the first automaker to sell level 3 autonomous cars to U.S. consumers, after announcing plans to do so last year.

fortune.com

 

[IloveLamp]

 

[Food4 thought]

View attachment 61144

Sounds to me like something good is going down, mass retail environments
Does that equate to mass sales ??

 

[Bennysmadness]

 

[Boab]

New on the website

 

[manny100]

Hi manny,

The 2008 patent is of great value in that the main claim is very broad in scope and is directed to ML:

US2010076916A1 Autonomous Learning Dynamic Artificial Neural Computing Device and Brain Inspired System 20080921

An information processing system intended for use in artificial intelligence, consisting of a plurality of artificial neuron circuits connected in an array, comprising:

a first plurality of dynamic synapse circuits, comprising a means of learning and responding to input signals by producing a compounding strength value simulating a biological Post Synaptic Potential,

a temporal integrator circuit that integrates and combines individually simulated Post Synaptic Potential values over time, and thus constitutes an artificial membrane potential value,

a second plurality of dynamic soma circuits each capable of producing one or more pulses when the integrated membrane potential value has reached or exceeded a stored variable threshold value,

That said, there are a number of subsequent developments which greatly improve performance, chief of these (until TeNNs) is N-of-M coding which greatly increased sparsity and reduced power usage and latency.

Another landmark patent is ...

US11468299B2 Spiking neural network 20181101

View attachment 61140

A neuromorphic integrated circuit, comprising:
a spike converter circuit configured to generate spikes from input data;
a reconfigurable neuron fabric comprising a neural processor comprising a plurality of spiking neuron circuits configured to perform a task based on the spikes and a neural network configuration; and
a memory comprising the neural network configuration, wherein the neural network configuration comprises a potential array and a plurality of synapses, and the neural network configuration defines connections between the plurality of spiking neuron circuits and the plurality of synapses, the potential array comprising membrane potential values for the plurality of spiking neuron circuits, and the plurality of synapses having corresponding synaptic weights,
wherein the neural processor is configured to:
select a spiking neuron circuit in the plurality of spiking neuron circuits based on the selected spiking neuron circuit having a membrane potential value that is a highest value among the membrane potential values for the plurality of spiking neuron circuits;
determine that the membrane potential value of the selected spiking neuron circuit reached a learning threshold value associated with the selected spiking neuron circuit; and
perform a Spike Time Dependent Plasticity (STDP) learning function based on the determination that the membrane potential value of the selected spiking neuron circuit reached the learning threshold value associated with the selected spiking neuron circuit.


... which showcases my all-time favourite drawing. As you can see, this has a priority of 2018, so it will last well into the 2030s.


CNN2SNN is certainly a vital commercial development, but, for me, not as exciting as N-of-M or TeNNS.

This patent introduced a number of "minor" improvements:

WO2020092691A1 AN IMPROVED SPIKING NEURAL NETWORK 20181101

[0038] But conventional SNNs can suffer from several technological problems. First, conventional SNNs are unable to switch between convolution and fully connected operation. For example, a conventional SNN may be configured at design time to use a fully-connected feedforward architecture to learn features and classify data. Embodiments herein (e.g., the neuromorphic integrated circuit) solve this technological problem by combining the features of a CNN and a SNN into a spiking convolutional neural network (SCNN) that can be configured to switch between a convolution operation or a fully- connected neural network function. The SCNN may also reduce the number of synapse weights for each neuron. This can also allow the SCNN to be deeper (e.g., have more layers) than a conventional SNN with fewer synapse weights for each neuron.
Embodiments herein further improve the convolution operation by using a winner-take-all (WTA) approach for each neuron acting as a filter at particular position of the input space. This can improve the selectivity and invariance of the network. In other words, this can improve the accuracy of an inference operation.

[0039] Second, conventional SNNs are not reconfigurable. Embodiments herein solve this technological problem by allowing the connections between neurons and synapses of a SNN to be reprogrammed based on a user defined configuration. For example, the connections between layers and neural processors can be reprogrammed using a user defined configuration file.

[0040] Third, conventional SNNs do not provide buffering between different layers of the SNN. But buffering can allow for a time delay for passing output spikes to a next layer. Embodiments herein solve this technological problem by adding input spike buffers and output spike buffers between layers of a SCNN.

[0041] Fourth, conventional SNNs do not support synapse weight sharing. Embodiments herein solve this technological problem by allowing kernels of a SCNN to share synapse weights when performing convolution. This can reduce memory requirements of the SCNN.

[0042] Fifth, conventional SNNs often use l-bit synapse weights. But the use of l-bit synapse weights does not provide a way to inhibit connections. Embodiments herein solve this technological problem by using ternary synapse weights. For example, embodiments herein can use two-bit synapse weights. These ternary synapse weights can have positive, zero, or negative values. The use of negative weights can provide a way to inhibit connections which can improve selectivity. In other words, this can improve the accuracy of an inference operation.

[0043] Sixth, conventional SNNs do not perform pooling. This results in increased memory requirements for conventional SNNs. Embodiments herein solve this technological problem by performing pooling on previous layer outputs. For example, embodiments herein can perform pooling on a potential array outputted by a previous layer. This pooling operation reduces the dimensionality of the potential array while retaining the most important information.

[0044] Seventh, conventional SNN often store spikes in a bit array. Embodiments herein provide an improved way to represent and process spikes. For example, embodiments herein can use a connection list instead of bit array. This connection list is optimized such that each input layer neuron has a set of offset indexes that it must update. This enables embodiments herein to only have to consider a single connection list to update all the membrane potential values of connected neurons in the current layer.

[0045] Eighth, conventional SNNs often process spike by spike. In contrast, embodiments herein can process packets of spikes. This can cause the potential array to be updated as soon as a spike is processed. This can allow for greater hardware parallelization.

[0046] Finally, conventional SNNs do not provide a way to import learning (e.g., synapse weights) from an external source. For example, SNNs do not provide a way to import learning performed offline using backpropagation. Embodiments herein solve this technological problem by allowing a user to import learning performed offline into the neuromorphic integrated circuit.


The TeNNs patents:


WO2023250092A1 METHOD AND SYSTEM FOR PROCESSING EVENT-BASED DATA IN EVENT-BASED SPATIOTEMPORAL NEURAL NETWORKS 20220622



WO2023250093A1 METHOD AND SYSTEM FOR IMPLEMENTING TEMPORAL CONVOLUTION IN SPATIOTEMPORAL NEURAL NETWORS 20220622

View attachment 61141


View attachment 61142


Disclosed is a neural network system generally relates to the field of neural networks (NNs). In particular, the present disclosure relates to event-based convolutional neural networks (NNs) that are trained to process spatial and temporal data using kernels represented by polynomial expansion. The event-based convolutional neural networks (NNs) are spatiotemporal neural networks. According to an embodiment, an explicit temporal convolution capability is added through Temporal Event-based Neural Networks (TENN) models, or TENNs in the spatiotemporal neural networks. The TENNs includes a plurality of temporal and spatial convolution layers that combine spatial and temporal features of data for low-level and high-level features. The TENNs as disclosed herein are configured to perform in a buffer mode and recurrent mode that effectively learns both spatial and temporal correlations from the input data.


The FIFO (first-in - first-out) buffer acts like a data conveyor belt which facilitates comparison of incoming data with previously temporarily stored data.

Thanks Diogenese, it's important for holders to understand the playing field is still not not even when our 1st patent expires in 2028.
It's been 'playing' on my mind given 'it's a matter of when not if BRN takes off'. Time is marching on.
Subsequent improvements will maintain our lead.
By 2028 we should be on Gen 4 or 5.

 

[BrainShit]

Evening BrainShit ,

1 , if one could find a more salubrious forum name would be great.
But yes , truely sense & feel ones torment.

2, Not 100% certain , but pretty sure this is a rehashed announcement from some time ago..... with the pressent day date attached , purely to confuse.

Regards,
Esq.

Fully agreed (the name as well), but consider... it could be:

1.1.) a shit
1.2) the shit

... but in both ways it's correct, which Wolowitz would confirm.

2.) right, the first announcement was in 2022.... the second: today, but the message is still valid.
If I'm wrong please throw a stone. (In memory of "Life of Brain") .... what a name .... Brain *giggles*



For futher introduction into shit:

Pls.:
It's about shit ;-)

 

[SharesForBrekky]

Not sure if this article has been posted but here is another one about Intel's super neuromorphic computer:

Intel unveils largest-ever AI 'neuromorphic computer' that mimics the human brain

Intel's Hala Point neuromorphic computer is powered by more than 1,000 new AI chips and performs 50 times faster than equivalent conventional computing systems.

www.livescience.com

"Powered by 1,152 of Intel's new Loihi 2 processors — a neuromorphic research chip — this large-scale system comprises 1.15 billion artificial neurons and 128 billion artificial synapses distributed over 140,544 processing cores."

"Neuromorphic computing is still a developing field, with few other machines like Hala Point in deployment, if any. Researchers with the International Centre for Neuromorphic Systems (ICNS) at Western Sydney University in Australia, however, announced plans to deploy a similar machine in December 2023.

Their computer, called "DeepSouth," emulates large networks of spiking neurons at 228 trillion synaptic operations per second, the ICNS researchers said in the statement, which they said was equivalent to the rate of operations of the human brain.

Hala Point meanwhile is a "starting point," a research prototype that will eventually feed into future systems that could be deployed commercially, according to Intel representatives.

These future neuromorphic computers might even lead to large language models (LLMs) like ChatGPT learning continuously from new data, which would reduce the massive training burden inherent in current AI deployments."

 

[7für7]

BrainChip selected by U.S. Air Force Research Laboratory to develop AI-based radar

2024-04-19 17:52

BrainChip, the world's first commercial producer of neuromorphic artificial intelligence chips and IP, today announced that Information Systems Laboratories (ISL) is developing an AI-based radar for the U.S. Air Force Research Laboratory (AFRL) based on its Akida™ Neural Network Processor Research solutions.

ISL is a specialist in expert research and complex analysis, software and systems engineering, advanced hardware design and development, and high-quality manufacturing for a variety of clients worldwide.

Military and Space are always leaders of new technologies

https://inf.news/en/science/791bf988657af0e134c475ca748067c6.html

I postet the same here from the German forum and deleted it because it’s old news with new date

 

[TECH]

https://www.linkedin.com/posts/geof...DH?utm_source=share&utm_medium=member_android

View attachment 61143

Excellent post...thank you.

Many would have noticed how our share price has started it's usual, slow, possibly deliberate slide south again.
An never ending cycle, why many would be asking ?

• Q1 4C results due within the next 6 business days.
• LDA Capital off-loading their recent allocated shares.
• Geo-Political mounting issues.
• Worldwide Economic headwinds.
• Robotic manipulation attempting to trigger "stop-loss" positions...i.e. 0.35....0.30....0.25

I personally feel that all within the company would agree that the journey to revenue has been a slower process than hoped, some
could go as far as saying, our 'real' business journey didn't really commence until Sean joined the company, meaning, considering
everything that has been put in place over the last 2.5 years, it's taken 5.

Remember I have been banging on about Digimarc Corporation for at least 4 years now, just check out the latest X feed posted on
the company website, it possibly sends a little subtle message in my opinion.

Cheers....Tech


https://twitter.com/BrainChip_inc

 

[DK6161]

Excellent post...thank you.

Many would have noticed how our share price has started it's usual, slow, possibly deliberate slide south again.
An never ending cycle, why many would be asking ?

• Q1 4C results due within the next 6 business days.
• LDA Capital off-loading their recent allocated shares.
• Geo-Political mounting issues.
• Worldwide Economic headwinds.
• Robotic manipulation attempting to trigger "stop-loss" positions...i.e. 0.35....0.30....0.25

I personally feel that all within the company would agree that the journey to revenue has been a slower process than hoped, some
could go as far as saying, our 'real' business journey didn't really commence until Sean joined the company, meaning, considering
everything that has been put in place over the last 2.5 years, it's taken 5.

Remember I have been banging on about Digimarc Corporation for at least 4 years now, just check out the latest X feed posted on
the company website, it possibly sends a little subtle message in my opinion.

Cheers....Tech


https://twitter.com/BrainChip_inc

I think the revenue in this 4C is critical for a lot of us shareholders.
Given a small dip in the entire stock market, some may choose to bail and switch to a company with better financial positions.
Sean and the board will have a lot of convincing to do if the revenue is dog . Promises were made, and they weren't achieved.
Some of the directors are also doing SFA!

 

[hotty4040]

New on the website
View attachment 61146

This new website reveal is interesting from a few perspectives, one notably is that this is dated 25 April !!!

So, perhaps we are 3 - 4 years and 5 days ahead of the pack with our tech. I can live with this I reckon.

SoCs featuring TENNs will be the way forward I hope, and introducing of course BRAINCHIP ( the latest offering ) with all it's MAGNIFICENCE included. Oh, How sweet it is, how sweet it is.

Let's hope it's a hit within the IoT world as we know it, I'm sure it will be, It's just a beautiful thing/picture IMO.


Akida Ballista >>>>> Lets' hope it appeals to the masses before tooo long ---- please ---- let it be so <<<<<


hotty...

 

[Justchilln]

Sounds to me like something good is going down, mass retail environments
Does that equate to mass sales ??

Excellent post...thank you.

Many would have noticed how our share price has started it's usual, slow, possibly deliberate slide south again.
An never ending cycle, why many would be asking ?

• Q1 4C results due within the next 6 business days.
• LDA Capital off-loading their recent allocated shares.
• Geo-Political mounting issues.
• Worldwide Economic headwinds.
• Robotic manipulation attempting to trigger "stop-loss" positions...i.e. 0.35....0.30....0.25

I personally feel that all within the company would agree that the journey to revenue has been a slower process than hoped, some
could go as far as saying, our 'real' business journey didn't really commence until Sean joined the company, meaning, considering
everything that has been put in place over the last 2.5 years, it's taken 5.

Remember I have been banging on about Digimarc Corporation for at least 4 years now, just check out the latest X feed posted on
the company website, it possibly sends a little subtle message in my opinion.

Cheers....Tech


https://twitter.com/BrainChip_inc

Monday will be brutal IMO
Nvidia down 10% last nite
Nasdaq Dow 2%

 

[Bravo]

Here's a LinkedIn post from Cerence two days ago about Mercedes "wake-up word".

If Mercedes do continue working with Cerence in the future, doesn't that by default mean we'll eventually be working with them together (likewise with all the other companies that have technology intertwined in the Mercedes infotainment/voice control side of things such as OpenAI's 'sChatGPT, Apple Car Play, Google Maps).?

After-all Magnus Ostberg's confirmed in his Linkedin post from a month ago, that Mercedes are determined to be the first to use neuromorphic computing in the automotive industry.

Ahhhhh, remember good ol' the days when every second word that I uttered was "Cerence".

Cerence AI on LinkedIn: #heymercedes #wearecerence

A cool moment for those who are passionate about advancing AI- and voice-powered interaction in the car: Mercedes-Benz' MBUX wake-up word #HeyMercedes was…

www.linkedin.com

[

URL unfurl="true"]https://www.linkedin.com/in/magnus-%C3%B6stberg/recent-activity/all/[/URL]

https://www.eetimes.com/cars-that-think-like-you/

 

[IloveLamp]

Laurent Hili on LinkedIn: "AI Hardware & Edge AI Summit 2022" pictures. I will attend the 2024…

"AI Hardware & Edge AI Summit 2022" pictures. I will attend the 2024 edition in San Jose CA. To my view one of the best conference in the domain 😁…

www.linkedin.com

AI Infra Summit 2025

The only event bringing together the AI & ML ecosystem under one roof.

www.aihwedgesummit.com

 

[IloveLamp]

Sign Up | LinkedIn

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

www.linkedin.com

 

[Diogenese]

Here's a LinkedIn post from Cerence two days ago about Mercedes "wake-up word".

If Mercedes do continue working with Cerence in the future, doesn't that by default mean we'll eventually be working with them together (likewise with all the other companies that have technology intertwined in the Mercedes infotainment/voice control side of things such as OpenAI's 'sChatGPT, Apple Car Play, Google Maps).?

After-all Magnus Ostberg's confirmed in his Linkedin post from a month ago, that Mercedes are determined to be the first to use neuromorphic computing in the automotive industry.

Ahhhhh, remember good ol' the days when every second word that I uttered was "Cerence".

View attachment 61158

Cerence AI on LinkedIn: #heymercedes #wearecerence

A cool moment for those who are passionate about advancing AI- and voice-powered interaction in the car: Mercedes-Benz' MBUX wake-up word #HeyMercedes was…

www.linkedin.com

View attachment 61157
[

URL unfurl="true"]https://www.linkedin.com/in/magnus-%C3%B6stberg/recent-activity/all/[/URL]



View attachment 61160

https://www.eetimes.com/cars-that-think-like-you/

Cerence's canoe was, at least until recently, paddleless up the scatological creek.

Their voice recognition system was internet based.

This patent is a continuation of an application filed in 2013 which was updated in 2022:


US11676600B2

[0002] This application is a continuation of U.S. application Ser. No. 15/238,238, filed Aug. 16, 2016, which is a continuation of U.S. application Ser. No. 13/795,933, filed Mar. 12, 2013, the disclosures of which are hereby incorporated in their entirety by reference herein.

1. A mobile device comprising:

at least one input configured to receive acoustic input from an environment of the mobile device while the mobile device is operating in a low power mode; and

at least one processor configured to

perform, while in the low power mode, at least one processing stage on the acoustic input to evaluate whether the acoustic input includes a voice command, the at least one processing stage including

transmitting at least a portion of the acoustic input from the mobile device to an automatic speech recognition (ASR) server via a network for processing by the ASR server to convert the at least a portion of the acoustic input into a text, and

transmitting the text from the ASR server to a natural language processing (NLP) server for processing by the NLP server to determine whether the text includes a voice command; and

responsive to receiving the voice command from the NLP server, initiate responding to the voice command.


Even as recently as 2021, Cerence were hedging their bets on in-car v web-based voice processing, without anything more than a nod to NNs:

US2022343906A1 FLEXIBLE-FORMAT VOICE COMMAND 20210426

[0066] Referring to FIG. 3, in one implementation, the system is integrated into a vehicle 310 , with the microphone 101 , camera 103 , etc., monitoring the driver (not shown). The components illustrated in FIG. 1 are hosted on a computer 312 (i.e., an embedded computation system with a general purpose or special purpose processor), which may include non-transitory storage holding instructions to be executed by a processor of the computer to implement the functions described above. In some examples, the computer 312 connects to a user's smartphone 314 , which may host the assistant or one of the sub-assistants known to the system. In some examples, the smartphone 314 provides a communication link via a communication system 320 (e.g., via a cellular telephone system, e.g., a “5G” system) to a remote assistant 330 , such as a home-based assistant in the user's home remote from the vehicle. It should be understood that certain functions may be implemented in dedicated hardware rather than being performed by a processor, for example, with audio processing being performed by a hardware-based (i.e., dedicated circuitry) component. In some implementations, some or all of the functions that might be performed by the computer 312 are performed by a remote server computer in communication with components hosted in the vehicle.

How to do wake word and speech recognition at the edge?

[ PYMNTS: In-Car Voice Makes Itself Heard at CES - BrainChip

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In-Car Voice Makes Itself Heard at CES BY PYMNTS | JANUARY 4, 2022

Before CES even opened to the industry, in-car voice technology was making itself heard. On Monday (Jan. 3), the first of two media days preceding the opening of the show, connected mobility supplier Cerence received an award for its in-car voice assistant, Mercedes-Benz released details about two voice technologies featured on its new prototype electric car and the Consumer Technology Association (CTA) projected that auto tech will grow 7% in 2022.

Read more: Connected Cars to Strut Their Stuff at CES

These companies are among more than 200 from the transportation and vehicle technology industry — a record number for the event — represented at this year’s edition of the annual tech event. A total of 2,200 companies are taking part in person or in the event’s digital venues.

Proactively Delivering Information to Drivers

On Monday, Cerence received a CES 2022 Innovation Award for Cerence Co-Pilot, its new in-car voice assistant that is powered by artificial intelligence (AI).

The assistant not only responds to voice commands, but also uses data from the car’s sensors to understand situations inside and outside the vehicle and proactively deliver information when it’s needed. For example, as the vehicle nears the driver’s home, Cerence Co-Pilot may ask if they’d like it to initiate a smart home routine. This in-car voice assistant also integrates with cloud services.

“AI is deeply fundamental to the future of mobility, and we see our role as critical, not only in bringing convenient, enjoyable and safe experiences to drivers, but also giving OEMs the ability to maintain control of their brands and data while still giving drivers the secure, seamless and personalized connected experiences they want,” Cerence CEO Stefan Ortmanns said in a press release.

Sounding Impressively Real, Natural and Intuitive

On the same day, Mercedes-Benz previewed two voice technologies that will be displayed on its VISION EQXX, a research prototype car featuring an electric drivetrain and advanced software. The automaker says this prototype demonstrates its transformation into “an all-electric and software-driven company.”

One voice technology featured on the VISION EQXX makes it “fun to talk to,” Mercedes-Benz says. Developed in collaboration with voice synthesis experts Sonantic and with the help of machine learning, this version of the “Hey Mercedes” voice assistant has a distinctive character and personality.

“As well as sounding impressively real, the emotional expression places the conversation between driver and car on a completely new level that is more natural and intuitive,” Mercedes-Benz said in a press release.

The second voice-related technology previewed by Mercedes-Benz features neuromorphic computing, a form of information processing that reduces energy consumption, and AI software and hardware from BrainChip that is five to 10 times more efficient than conventional voice control.

“Although neuromorphic computing is still in its infancy, systems like these will be available on the market in just a few years,” Mercedes-Benz said. “When applied on scale throughout a vehicle, they have the potential to radically reduce the energy needed to run the latest AI technologies.”