BRN Discussion Ongoing

[Tothemoon24]

Anyone had a look at a company called
Seeing Machines



In the first half year to the end of December 2022 the group saw revenues up 54% from $15.8m to $24.4m, with an end period cash balance of $52.2m ($40.5m end June).

The net loss was reduced by 47% to $5.4m ($10.1m), while the annual recurring revenue was increased nearly 17% to $11.9m ($10.2m).

The safety focus tailwinds are boosting the expansion of the company’s addressable market, adding to the attractive structural growth drivers in the Automotive, Aftermarket and Aviation sectors.

The Business

This group has come a long way over the last 23 years, from the 1997 research work into vision-based human-machine interfaces undertaken by Volvo Technology of Sweden and researchers at the Australian National University.

That work was to develop technologies that enable computers to detect, track and interpret human faces and facial features, with the initial aim to improve driver safety.

Three years later the company was set up.

Today the group, headquartered in Australia, is a global industry leader in vision-based monitoring technology that enable machines to see, understand and assist people. Seeing Machines is revolutionising global transport safety.

Its technology portfolio of AI algorithms, embedded processing and optics, power products that need to deliver reliable real-time understanding of vehicle operators.

The technology spans the critical measurement of where a driver is looking, through to classification of their cognitive state as it applies to accident risk.

Reliable “driver state” measurement is the end-goal of Driver Monitoring Systems (DMS) technology.

Seeing Machines develops DMS technology to drive safety for Automotive, Commercial Fleet, Off-road and Aviation.

The company has offices in Australia, USA, Europe and Asia, and supplies technology solutions and services to industry leaders in each market vertical.

The group is continuing to grow as an automotive technology leader in driver and occupant monitoring system technology, having now won a total of 15 automotive programs spanning 10 individual OEMs, covering more than 160 distinct vehicle models.

The cumulative initial lifetime value of all OEM programs that Seeing Machines has won to date now stands at $321m.

The company reported a total of 701,049 cars on road as at 31 December 2022, representing an increase of 188% over the 12 months period (243,722) spanning six individual programs with four global OEMs.

Guardian, Seeing Machines’ Aftermarket driver distraction and fatigue technology, is now installed into and monitoring 46,018 individual vehicles, compared to 36,933 in December 2021 representing a 25% increase over the 12-month period.

 

[Bravo]

Forgive me if I sound like a broken record because everyone who doesn't live under a rock knows about my unhealthy obsession with Cerence and everyone knows that "Cerence's Immersive Companion" for in-car voice assistant is going to be rolled out in FY23/24 and everyone knows that I would be shocked and smack-gobbed( (thanks for that one @Diogense) if it didn't involve AKIDA in some way.

So, obviously I'm eager to share this news release hot off the press.

Optimized for the in-car experience, advanced biometric capabilities in Cerence Assistant empower drivers and passengers with safe, secure interactions​

Cerence has introduced its next-generation, AI-powered biometrics engine in Cerence Assistant

• March 16, 2023

FacebookTwitterLinkedInShare via EmailPrint
Cerence Inc. (NASDAQ: CRNC), AI for a world in motion, today introduced its next-generation, AI-powered biometrics engine in Cerence Assistant, providing enhanced personalization and security capabilities for the mobility experience. Powered by the latest advances in AI, Cerence Voice Biometrics is now more accurate at creating driver profiles; identifying who is speaking; and enabling deeper opportunities for safe, secure, and individualized interactions with the in-car assistant.
Available across a multitude of global languages, Cerence’s next-generation biometrics solution is optimized for automotive and mobility use cases, putting the user at the forefront with a frictionless experience, providing the highest identification accuracies in a low-footprint, embedded solution. Going beyond voice, multimodal cabin biometrics are designed to support multimodal identification throughout the cabin, offering a more convenient and secure identification and authorization process. This allows users to automatically log in to their preferred biometric modality and configure step-up authentication for sensitive tasks, providing a frictionless authentication and authorization solution that can adapt based on context. For example, drivers can identify themselves with facial recognition when starting the car, and authorize a payment with their voice while on the road.
Architected to be embedded within the car for security and convenience, Cerence Voice Biometrics is core to Cerence’s multimodal biometrics capabilities, powering a range of authenticated, personalized experiences, including:

• Transparent, proactive enrollment and easy sign-in – The in-car assistant can learn speakers and their unique voices with only a few interactions and proactively offer to create a voiceprint once enough voice data has been gathered. This eliminates the need for drivers and each of their passengers to set up personal profiles with prescriptive phrases. From there, each time users enter the car, they are authenticated upon their first interaction with the voice assistant, whether it’s “Good morning, Cerence,” or “Drive me to the office.”
• Authenticated interaction from outside the car – Cerence Exterior Vehicle Interaction (EVI) is a suite of AI and voice-powered innovations that enables drivers to interact with their cars from the outside. To maintain safety and security, Cerence EVC now also leverages voice biometrics to limit certain capabilities to approved users only. For example, for security-relevant functions such as unlocking the car or opening the trunk, Cerence Voice Biometrics verifies the speaker’s identity to provide an additional level of protection and security.
• Convenient and secure payments – When integrated with in-car commerce apps, Cerence Voice Biometrics is a powerful tool for authorizing payments with no extra legwork by the user. Perfect while driving, your voice interaction is passively verified, offering an uninterrupted, secure transaction.
• Next-level control – Using the technology’s age detection capabilities, in-car assistants can offer parental controls to voice interactions, limiting what functions children can activate and what content they can access, including ensuring safe web searches in the car via Cerence Browse. Similarly, smart-home connectivity commands such as “Open the garage door” can be restricted by driver profile, user, age and speaker identity.
• Easy language switch – Using Cerence Voice Biometrics, Cerence Assistant can automatically adapt the dialogue and infotainment system language to match the user’s spoken language. This allows users to interact naturally with the in-car assistant, providing a seamless and personalized experience.

“Biometrics is a critical piece of virtual assistant interaction, especially in cases where multiple users are interacting with a single assistant,” said Prateek Kathpal, Chief Technology Officer, Cerence. “Our next-generation biometrics engine in Cerence Assistant represents an important step in our ability to serve automakers and their drivers with a AI-powered, frictionless experience that takes minimal effort from the user but provides maximum impact in terms of personalization and security.”
Several major global automakers have signed on to deploy Cerence Voice Biometrics in their cars. For more information about Cerence biometrics, visit https://www.cerence.com/cerence-products/core-technologies. To learn more about Cerence, visit www.cerence.com, and follow the company on LinkedIn and Twitter.
SOURCE: Cerence

Optimized for the in-car experience, advanced biometric capabilities in Cerence Assistant empower drivers and passengers with safe, secure interactions | Automotive World

Cerence has introduced its next-generation, AI-powered biometrics engine in Cerence Assistant

www.automotiveworld.com

Check out how many people at Mercedes like Cerence's latest announcement on LinkedIn!

Cerence Biometrics | Cerence AI

Today we’re unveiling our enhanced, AI-powered biometrics engine for deeper security and personalization. Optimized for the in-car experience, the advanced biometric capabilities in Cerence Assistant empower drivers and passengers with safe, secure interactions. Available across a multitude of...

www.linkedin.com

 

[Bravo]

I wonder if Cerence are getting some of their state of the art technology from Valeo??

 

[Bravo]

Conversant AI Redefines Audio Exploration - Radio World

The integration of voice assistants into automobiles is opening new avenues for how drivers and passengers interact with the dashboard.

www.radioworld.com

Extract

 

[Steve10]

Anyone had a look at a company called
Seeing Machines



In the first half year to the end of December 2022 the group saw revenues up 54% from $15.8m to $24.4m, with an end period cash balance of $52.2m ($40.5m end June).

The net loss was reduced by 47% to $5.4m ($10.1m), while the annual recurring revenue was increased nearly 17% to $11.9m ($10.2m).

The safety focus tailwinds are boosting the expansion of the company’s addressable market, adding to the attractive structural growth drivers in the Automotive, Aftermarket and Aviation sectors.

The Business

This group has come a long way over the last 23 years, from the 1997 research work into vision-based human-machine interfaces undertaken by Volvo Technology of Sweden and researchers at the Australian National University.

That work was to develop technologies that enable computers to detect, track and interpret human faces and facial features, with the initial aim to improve driver safety.

Three years later the company was set up.

Today the group, headquartered in Australia, is a global industry leader in vision-based monitoring technology that enable machines to see, understand and assist people. Seeing Machines is revolutionising global transport safety.

Its technology portfolio of AI algorithms, embedded processing and optics, power products that need to deliver reliable real-time understanding of vehicle operators.

The technology spans the critical measurement of where a driver is looking, through to classification of their cognitive state as it applies to accident risk.

Reliable “driver state” measurement is the end-goal of Driver Monitoring Systems (DMS) technology.

Seeing Machines develops DMS technology to drive safety for Automotive, Commercial Fleet, Off-road and Aviation.

The company has offices in Australia, USA, Europe and Asia, and supplies technology solutions and services to industry leaders in each market vertical.

The group is continuing to grow as an automotive technology leader in driver and occupant monitoring system technology, having now won a total of 15 automotive programs spanning 10 individual OEMs, covering more than 160 distinct vehicle models.

The cumulative initial lifetime value of all OEM programs that Seeing Machines has won to date now stands at $321m.

The company reported a total of 701,049 cars on road as at 31 December 2022, representing an increase of 188% over the 12 months period (243,722) spanning six individual programs with four global OEMs.

Guardian, Seeing Machines’ Aftermarket driver distraction and fatigue technology, is now installed into and monitoring 46,018 individual vehicles, compared to 36,933 in December 2021 representing a 25% increase over the 12-month period.

We discussed them yesterday.

Not the sales figures though which indicate they are selling their tech for about $106 per vehicle.

701,049 cars on 31/12/2022 - 243,722 cars on 31/12/021 = 457,327 in 12 months / $48.8M annual revenue run rate = $106.70 per car.

457.3k / 125M cars/commercial vehicles pa = 0.37% market share so heaps of room for revenue growth. With laws making it mandatory for in-cabin driver monitoring soon will be a fast growing sub-market within automotive market.

125M vehicles pa x $106 = $13.25B TAM to be split between Seeing Machines, Nviso, emotion3D, Smart Eye etc. Most likely some unknown Chinese companies as well for Chinese market.

BRN could target 50M cars per year via Seeing Machines, Nviso, emotion3D & Smart Eyes. It looked like Nviso was using one Akida chip with Nvidia Jetson for Mercedes demos so 50M chips per year x 30-50c IP royalty = $15-25M pa BRN revenue for this type of application in this sub-market in automotive market. Will become more or less annual recurring revenue for BRN.

 

[Falling Knife]

Check out how many people at Mercedes like Cerence's latest announcement on LinkedIn!

Cerence Biometrics | Cerence AI

Today we’re unveiling our enhanced, AI-powered biometrics engine for deeper security and personalization. Optimized for the in-car experience, the advanced biometric capabilities in Cerence Assistant empower drivers and passengers with safe, secure interactions. Available across a multitude of...

www.linkedin.com

I laughted when I saw this in the Cerence clip.
Who would have thought that this location would have even been a contender?
Its at the 31 second mark
https://www.linkedin.com/feed/update/urn:li:activity:7042138142958571521/

I did a 'WTF' when it flashed up, rewound to confrim my eyes were not deciving me, sure enough.
Hidden messages Im sure

 

[Tothemoon24]

Menu

Sanjeev Aggarwal, CEO, Everspin Technologies & the Non-Volatile Memory Markets​

In the embedded memory space, STT-MRAM is replacing NOR Flash, and it is also bringing new levels of performance to SoC and ASIC designers quotes Sanjeev Aggarwal | CEO | Everspin Technologies Inc​

Niloy Banerjee March 16, 2023
5 minutes read

In the embedded memory space, STT-MRAM is replacing NOR Flash, and it is also bringing new levels of performance to SoC and ASIC designers quotes Sanjeev Aggarwal | CEO | Everspin Technologies Inc. while in an interview with Niloy from BISinotech. In this interview the veteran underlines various aspects inking the future of memory technology including MRAM, STT-MRAM, the potential of neuromorphic computing. Also he highlights Everspin’s dominance and growth trajectories in the coming fiscal years. Edited Excerpts Below.

As MRAM moves into the embedded space, MRAM is also known to emerge as a persistent memory for numerous applications. How instrumental will Everspin be in driving the growth in this space?​

Everspin has been producing STT-MRAM for more than 5 years. The first products we introduced to the market were persistent DRAM-like products, starting with our 256Mb DDR3, and following that with the 1Gb DDR4. Since then, we have optimized our STT-MRAM technology to support the persistent SRAM-like products for more extreme requirements of the industrial electronics market. Last year we announced volume production of the EMxxLX family of xSPI STT-MRAM that supports industrial temperature range and has longer data retention with unlimited write cycle endurance. The EMxxLX is compatible with NOR Flashserial memories but also offers the performance of Static RAM with byte addressability. This product opens up the NOR Flash market for customers needing faster writes and lower power.

STT-MRAM finally comes to market replacing embedded NOR Flash. Everspin’s dominance in this growing market?​

In the embedded memory space, STT-MRAM is replacing NOR Flash, and it is also bringing new levels of performance to SoC and ASIC designers. NOR Flash has not scaled well below 40nm CMOS nodes and STT-MRAM has stepped in to fill the gap. Everspin has licensed its STT-MRAM technology to GlobalFoundries and partnered with them to offer embedded STT-MRAM at 22nm and below. Customers can now integrate this into their own designs getting the benefits of non-volatility but with faster write speeds and higher write cycle
endurance compared to NOR Flash.

What are the challenges confronting the fabrication and testing of STT-RAM?​

Emerging technologies in early stages are typically challenged with manufacturing costs and availability of specific manufacturing tools, metrology tools, and maturity that impact its adoption rate in the industry. Everspin has been in volume production of STT-MRAM since 2017 at GlobalFoundries improving manufacturing efficiency
and yields. Manufacturing tools providers now offer MRAM-specific tools which, in turn, enables volume production scaling and cost reduction. Further cost reductions will be a function of demand and adoption in the broader industry.

May it be connected cars, integration of ADAS or sublime autonomous vehicles, automotive electronics systems are hungry for memory solutions. How does Everspin look into this domain and key offerings to cater for this market?​

As connected devices get smarter and more remote, the need to have data protected in harsh environments as well protected in the event of power loss becomes paramount. EVs may have thousands of data collection points including instrument clusters, cabin preferences, safety systems, battery monitoring, motor control, assisted driving and more. The subsystems controlling this typically have a need for a reliable, persistent memory to capture and store the data from these points. In many case, the data volume of these systems is not huge and can be handled by multiple MRAMs in the various subsystems. Examples of where MRAM gets used are as a personality” memory in infotainment and cabin preferences, BMS health monitoring, security alarm systems, event recorders. This is true in personal vehicles as well as commercial transportation modes such as trains, aircraft and the coming eVTOL market.

SRAM segment still has a viable dominance in the consumer electronics market such as smartphones and wearable devices. Do you see a technological shift in the coming time with MRAM replacing the existing SRAM segment?​

SRAM is used broadly in electronic systems, both as discrete and embedded memory. In the industrial segment, SRAM is often backed up with a battery and this is where MRAM has excelled due to a combination of non-volatility and random-access performance. In the consumer electronics segment, particularly in wearable applications, SRAM suffers from relatively high leakage currents in the advanced CMOS process nodes which affects battery life. This is where MRAM can play a huge role with an inherently lower leakage memory cell,
but also a “zero standby power” capability because the MRAM can be totally shut down without losing its data. There is a big effort underway by the foundries to refine eMRAM for the performance of caching for processors.

This industry has been spectating for joint ventures and collaborations with other manufacturers to create a sophisticated and broad value chain also to have a competitive advantage over other competitors. What has been Everspin’s strategy to be competitive in the market?​

Our ability to compete comes from the unique combination of practical MRAM research with a learned know-how of manufacturability for commercial success in MRAM products. Establishing partnerships and collaborations is necessary to be able to scale up the production in advanced process nodes and to continue to make the process and materials advance that drive down cost. We have a rich history of partnering, having collaborated with GlobalFoundries since 2014 to develop and produce STT-MRAM on 300mm and advanced CMOS nodes for several generations. The embedded MRAM offering from foundries, including GlobalFoundries, are driving wider adoption of MRAM as it becomes a preferred technology over Flash and SRAM. Partners have licensed our technology and augmented the inherent capability of MRAM to make radiation-hardened memories. Recently
we have announced an effort with QuickLogic to develop MRAM technology for use in high-reliability FPGA technologyin a DoD-sponsored program. Everspin is at the forefront of enabling the broader ecosystem for MRAM manufacturing and bringing the technology to additional value-added markets.

Everspin Technologies Inc. is known to be planning to expand its domestic MRAM manufacturing in the state of Indiana. Kindly tell our readers about these strategic expansion plans.​

Everspin is working to build a trusted manufacturing line in the state of Indiana to add domestic capacity for Everspin’s Commercial MRAM and increased capability to act as a foundry for the manufacture of Toggle and STT-MRAM. Plans include working with the local research community to enhance domestic research for MRAM technology development, creating a Technology Development Center at the proposed Indiana-based location.
From Press Coverage:
SIBR 1/19/23: Westgate One will be a combined effort of NHanced Semiconductors, Everspin Technologies, Trusted Semiconductor Solutions and Reliable MicroSystems. The four semiconductor companies plan to invest, with assistance from the U.S. government, up to $300 million in the campus.

In recent years, neuromorphic computing has emerged as a promising technology in the post Moore’s law era. Your comments on this promising technology.​

Replicating the processing power of the human brain has led to research in highly distributed memory systems that both compute and transmit data in attempts to mimic the synapses of the brain in massively parallel neural networks. This is generally in a research phase and MRAM has real potential to be a core technology in this
field. MRAM can be developed to provide multiple states as opposed to the traditional binary 1 and 0 of conventional memory. This can be architected in arrays that provide new abilities in AI learning and inference to modify weights in tables very rapidly and maintain the states until they change. In collaboration with Prof. Joseph Friedman at the University of Texas, Dallas Everspin has done an experimental demonstration of a neuromorphic network with STT magnetic tunnel junction (MTJ)synapses, which performs image recognition via vector-matrix
multiplication1. Practical implementations are being explored in which some processing logic is combined with the MRAM array in a common silicon chip, basically in-memory computing where table weighting or signal processing is distributed, not having to go back to the CPU for each step in a calculation. It will be an exciting new frontier for the industry and MRAM could very well be a key enabler.

 

[Schnitzel lover]

His back

Couldn't help myself..

Go to ARM interview at 1hr 29-30mins.. Seriously how can you not get excited when you watch this?

 

[GazDix]

After a massive shake up this week with Banks SVB/Credit Suisse Struggling/Collapsing; very surprised to see such a great recovery on World markets so rapidly! ..... Good news! ​

Hopefully a GREEN day for BRN today​

SYMBOL	PRICE	CHANGE	%CHANGE
DJIA	32,246.55	+371.98	+1.17
NASDAQ	11,717.28	+283.23	+2.48
S&P 500	3,960.28	+68.35	+1.76

​

​

Stock Indexes​

SYMBOL	PRICE	CHANGE	%CHANGE
*FTSE	7,410.03	+65.58	+0.89
*DAX	14,967.1	+231.84	+1.57
*CAC	7,025.72	+140.01	+2.03
*STOXX600	441.64	+5.19	+1.19
*AEX	727.06	+10.59	+1.48
*BEL 20	3,652.55	+22.64	+0.62
*FTSE MIB	25,918.76	+352.92	+1.38
OMXS30	2,125.24	+28.43	+1.36
*SMI	10,719.1	+202.7	+1.93
HEX	10,561.41	+61.48	+0.59
*PSI20	5,865.99	+53.12	+0.91
OMXC 25	1,721.24	+5.06	+0.29

Also the leaders in last night's action were... Intel, NVidia and AMD. I can't access the article on this device, but I read an article that wrote chip stocks and the industry have now turned a corner.
We should hopefully follow very soon. Institutions aren't getting my shares with all the shenanigans.
Happy St Paddies Day all.

 

[Tothemoon24]

We discussed them yesterday.

Not the sales figures though which indicate they are selling their tech for about $106 per vehicle.

701,049 cars on 31/12/2022 - 243,722 cars on 31/12/021 = 457,327 in 12 months / $48.8M annual revenue run rate = $106.70 per car.

457.3k / 125M cars/commercial vehicles pa = 0.37% market share so heaps of room for revenue growth. With laws making it mandatory for in-cabin driver monitoring soon will be a fast growing sub-market within automotive market.

125M vehicles pa x $106 = $13.25B TAM to be split between Seeing Machines, Nviso, emotion3D, Smart Eye etc. Most likely some unknown Chinese companies as well for Chinese market.

BRN could target 50M cars per year via Seeing Machines, Nviso, emotion3D & Smart Eyes. It looked like Nviso was using one Akida chip with Nvidia Jetson for Mercedes demos so 50M chips per year x 30-50c IP royalty = $15-25M pa BRN revenue for this type of application in this sub-market in automotive market. Will become more or less annual recurring revenue for BRN.

Cheers Steve10
I’m in holiday mode at the minute & feeling as sharp as a bowling ball

Cheers chippers

 

[Yak52]

I laughted when I saw this in the Cerence clip.
Who would have thought that this location would have even been a contender?
Its at the 31 second mark
https://www.linkedin.com/feed/update/urn:li:activity:7042138142958571521/

I did a 'WTF' when it flashed up, rewound to confrim my eyes were not deciving me, sure enough.
Hidden messages Im sure

View attachment 32419

Good spotting there Falling knife.

Did anyone also spot the MAKE of the vehicle in the video ??? YEP...........MERCEDES BENZ.

Yak52

 

[Diogenese]

Forgive me if I sound like a broken record because everyone who doesn't live under a rock knows about my unhealthy obsession with Cerence and everyone knows that "Cerence's Immersive Companion" for in-car voice assistant is going to be rolled out in FY23/24 and everyone knows that I would be shocked and smack-gobbed( (thanks for that one @Diogense) if it didn't involve AKIDA in some way.

So, obviously I'm eager to share this news release hot off the press.

Optimized for the in-car experience, advanced biometric capabilities in Cerence Assistant empower drivers and passengers with safe, secure interactions​

Cerence has introduced its next-generation, AI-powered biometrics engine in Cerence Assistant

• March 16, 2023

FacebookTwitterLinkedInShare via EmailPrint
Cerence Inc. (NASDAQ: CRNC), AI for a world in motion, today introduced its next-generation, AI-powered biometrics engine in Cerence Assistant, providing enhanced personalization and security capabilities for the mobility experience. Powered by the latest advances in AI, Cerence Voice Biometrics is now more accurate at creating driver profiles; identifying who is speaking; and enabling deeper opportunities for safe, secure, and individualized interactions with the in-car assistant.
Available across a multitude of global languages, Cerence’s next-generation biometrics solution is optimized for automotive and mobility use cases, putting the user at the forefront with a frictionless experience, providing the highest identification accuracies in a low-footprint, embedded solution. Going beyond voice, multimodal cabin biometrics are designed to support multimodal identification throughout the cabin, offering a more convenient and secure identification and authorization process. This allows users to automatically log in to their preferred biometric modality and configure step-up authentication for sensitive tasks, providing a frictionless authentication and authorization solution that can adapt based on context. For example, drivers can identify themselves with facial recognition when starting the car, and authorize a payment with their voice while on the road.
Architected to be embedded within the car for security and convenience, Cerence Voice Biometrics is core to Cerence’s multimodal biometrics capabilities, powering a range of authenticated, personalized experiences, including:

• Transparent, proactive enrollment and easy sign-in – The in-car assistant can learn speakers and their unique voices with only a few interactions and proactively offer to create a voiceprint once enough voice data has been gathered. This eliminates the need for drivers and each of their passengers to set up personal profiles with prescriptive phrases. From there, each time users enter the car, they are authenticated upon their first interaction with the voice assistant, whether it’s “Good morning, Cerence,” or “Drive me to the office.”
• Authenticated interaction from outside the car – Cerence Exterior Vehicle Interaction (EVI) is a suite of AI and voice-powered innovations that enables drivers to interact with their cars from the outside. To maintain safety and security, Cerence EVC now also leverages voice biometrics to limit certain capabilities to approved users only. For example, for security-relevant functions such as unlocking the car or opening the trunk, Cerence Voice Biometrics verifies the speaker’s identity to provide an additional level of protection and security.
• Convenient and secure payments – When integrated with in-car commerce apps, Cerence Voice Biometrics is a powerful tool for authorizing payments with no extra legwork by the user. Perfect while driving, your voice interaction is passively verified, offering an uninterrupted, secure transaction.
• Next-level control – Using the technology’s age detection capabilities, in-car assistants can offer parental controls to voice interactions, limiting what functions children can activate and what content they can access, including ensuring safe web searches in the car via Cerence Browse. Similarly, smart-home connectivity commands such as “Open the garage door” can be restricted by driver profile, user, age and speaker identity.
• Easy language switch – Using Cerence Voice Biometrics, Cerence Assistant can automatically adapt the dialogue and infotainment system language to match the user’s spoken language. This allows users to interact naturally with the in-car assistant, providing a seamless and personalized experience.

“Biometrics is a critical piece of virtual assistant interaction, especially in cases where multiple users are interacting with a single assistant,” said Prateek Kathpal, Chief Technology Officer, Cerence. “Our next-generation biometrics engine in Cerence Assistant represents an important step in our ability to serve automakers and their drivers with a AI-powered, frictionless experience that takes minimal effort from the user but provides maximum impact in terms of personalization and security.”
Several major global automakers have signed on to deploy Cerence Voice Biometrics in their cars. For more information about Cerence biometrics, visit https://www.cerence.com/cerence-products/core-technologies. To learn more about Cerence, visit www.cerence.com, and follow the company on LinkedIn and Twitter.
SOURCE: Cerence

Optimized for the in-car experience, advanced biometric capabilities in Cerence Assistant empower drivers and passengers with safe, secure interactions | Automotive World

Cerence has introduced its next-generation, AI-powered biometrics engine in Cerence Assistant

www.automotiveworld.com

Probly said this before, but Cerence has several patents which refer to NNs, but they do not describe the circuit configuration for an NN. So either they are keeping their NN circuit secret (improbable), or they are getting their NN from someone else (probable).

 

[Fullmoonfever]

Great to see so much happening.

Just wait till formal Ann's start rolling out

Bit going on personally so skimming posts here and there.

Liked the find on ANT61.

Not sure if this posted already but found this article about them on Medium.

Awesome to see the AWS / Amazon accelerator connection as well as Mass Robotics.

While no mention of Akida at this point, you'd like to think ANT61's partners well aware of us through ANT now as well.

ANT61 accelerated by AWS​

AWS accelerating ANT61 development in space

ANT61​

ANT61 is a robotics & AI startup founded in the second half of 2021. Our main goal is to enable sustainable development in space and back on Earth; we believe that autonomous robots are the way to go about it.

Naturally, as a young company, we’ve started by simultaneously running into multiple directions: creating prototypes of our future technology, researching problems worth solving, sizing opportunities and building partnerships.

We have applied to several acceleration programs, and several of them accepted us, so we were fortunate to pick the ones we wanted to do. AWS Robotics, led together by AWS and Mass Robotics from Boston.

Here we would like to share our experience with AWS Robotics and talk about the program and how it challenged and propelled us further than we thought.
Following the program, we’ve been selected by Amazon to exhibit our first on-orbit service prototype at Re:MARS 2022 . If you also plan to be there, we’d be happy to catch up and talk the AWS Robotics program, our takeaways and future plans. Find us in the Tech Showcase area!

The program​

AWS Robotics is a 4-week program that balances the workshops on business, technology, marketing and customer acquisition with dedicated 1:1 mentorship sessions. One of the program’s goals is to build something new and move your MVP or POC one step forward on technology and product lines.

The outcomes​

We have received three primary outcomes from this program.

First and foremost, we’ve met a great mentor, Matthew Hanson, who has decades of experience in robotic system architecture and is one of the key robotic experts in AWS. His contribution to our success is invaluable and deserves a separate article!

Secondly, we’ve built our first digital prototype of the satellite repair robot and have validated our autonomous control system technology in accurate simulation.

The third and precious outcome: we’ve tapped into AWS ML engineers’ wisdom, significantly reduced our robot’s training costs, and took our AI game to the next level.

All of that in the record-break time and with the intense focus!

Outcome #1: The mentor​

Every startup in the AWS Robotics Accelerator program is assigned a mentor.

We have been very fortunate to have the one and only Matthew Hanson.

Matthew Hanson

Matt has a great experience in robotics architecture, everything from control systems, planning, behaviour, autonomy, simulations and of course, everything at AWS that has Robotics.

He understands the startup’s unique challenges and has helped us find the quickest path to our goal. Matthew is also very supportive of our cause of sustainable development in space, which means a lot.

As a new business in a conservative industry like space, forming a support group around your team is very important.
Matt was one of the first people who believed in us from the start when we didn’t have much to show except for our prior experience, ambition, hard work and tenacity. He has contributed to everything we did during the program and was kind enough to continue working with us after the program, doing everything he could to help us succeed. We will always remain grateful to Matt, and hopefully, one day, we’ll find a great way to reward him in return.

Outcome #2: The space MVP​

The period of our AWS Robotics acceleration program coincided with our Space Accelerator run with the support of the Australian Space Agency in Adelaide. We were actively looking into the on-orbit repair and servicing and thought it would be great to build the first virtual prototype of our future space robot with AWS.

Since the AWS acceleration program is just four weeks, we won’t have time to develop all systems from scratch. Our main goal was to learn as much as possible about the spacecraft’s control systems and how robotic manipulators can be used for docking and repairing the satellite. And we’ve decided to learn from the best and used NASA’s Astrobee robot that has already flown in ISS as the base for our on-orbit servicing machine.

Astrobee robot on ISS

As a result, we’ve been able to apply our ML-based control systems to demonstrate that our robot can autonomously match orbit with a satellite and safely reach out to it.

You can see the simulation video from our AWS Robotics Accelerator Demo day.

Outcome #3: The next-level AI training​

The price of the experiment

At ANT61, we use Deep Reinforcement Learning to teach our robots skills that they can apply for autonomous installation. This approach means that our robots get better at their tasks simply by trying many-many times, rather than us writing slightly better code for them every day.

Reinforcement Learning is still a very new area of Deep Learning. It is akin to gold mining: you have the general directions where to look, but the rich vein can only be found by trying to dig in many places and analysing the results. Our AI engineers always experiment with various algorithms, reward regimes and parameters to get better and more consistent results. The more experiments we can run, the higher our chances of success. On average, one robot takes about two weeks of 24x7 work to learn an essential skill like drilling a hole in a wall with reasonably quality. It will then take several times more training hours to perfect this skill.

Time is our most precious resource, and compressing the time it takes us to determine whether the experiment is successful is very important in our business.

AWS=scale​

Even before the accelerator, we have already been using multi-agent training, where experience from several robots is accumulated to train one brain, which is then deployed to the whole fleet, and the training continues. However, our budget severely limited how many robots we could run in parallel.

One of the great perks of the accelerator is direct access to senior engineers at AWS. Our mentor, Matthew Hanson, has linked us to the engineers in the AWS Sagemaker team, who provided us with some paths we could try to reduce our costs and run the distributed training of thousands of robots on the same budget.
Now the experiments take only hours instead of weeks, and we can run multiple experiments in parallel without breaking the bank.

Quantity ➩ Speed of Innovation

The ability to run multiple experiments, generating years-worth of the data in just a few hours, allowed us to significantly step up our game and use the RL techniques that are usually unavailable to robotics due to the high cost of each data point. Now we can run unsupervised hyper-parameter tuning and neural architecture search.

Essentially, instead of humans coming up with a new experiment to improve our training regimes, AI generates these experiments.

AI that is training other AIs.

As Károly Szolnai-Fehér says, What a time to be alive!

 

[Bravo]

Just posting this here so I don't forget that Mercedes Drive Pilot must be first be approved by various legislations before being rolled out. With Nevada being the first in the US to certify the use of the Level 3 feature.

 

[stuart888]

Here ya go. Mega Brainchip synergy for partnerships.

 

[Deleted member 2799]

Hey!!
I know that reports from the HC forum are not popular here… sorry for that in advance, however, this post caught my attention... NVIDIA patents
“
https://patents.google.com/patent/US20220067531A1/en

US20220067531 - EFFICIENT IDENTIFICATION OF CRITICAL FAULTS IN NEUROMORPHIC HARDWARE OF A NEURAL NETWORK


https://patents.google.com/patent/EP3745318A1/en

Training a neural network using selective weight updates”

Speculations about possible licensing with brainchip…!?
Has this been discussed here before?
What do you think about it?

 

[Fullmoonfever]

Great to see so much happening.

Just wait till formal Ann's start rolling out

Bit going on personally so skimming posts here and there.

Liked the find on ANT61.

Not sure if this posted already but found this article about them on Medium.

Awesome to see the AWS / Amazon accelerator connection as well as Mass Robotics.

While no mention of Akida at this point, you'd like to think ANT61's partners well aware of us through ANT now as well.

ANT61 accelerated by AWS​

AWS accelerating ANT61 development in space

ANT61​

ANT61 is a robotics & AI startup founded in the second half of 2021. Our main goal is to enable sustainable development in space and back on Earth; we believe that autonomous robots are the way to go about it.

Naturally, as a young company, we’ve started by simultaneously running into multiple directions: creating prototypes of our future technology, researching problems worth solving, sizing opportunities and building partnerships.

We have applied to several acceleration programs, and several of them accepted us, so we were fortunate to pick the ones we wanted to do. AWS Robotics, led together by AWS and Mass Robotics from Boston.

Here we would like to share our experience with AWS Robotics and talk about the program and how it challenged and propelled us further than we thought.
Following the program, we’ve been selected by Amazon to exhibit our first on-orbit service prototype at Re:MARS 2022 . If you also plan to be there, we’d be happy to catch up and talk the AWS Robotics program, our takeaways and future plans. Find us in the Tech Showcase area!

The program​

AWS Robotics is a 4-week program that balances the workshops on business, technology, marketing and customer acquisition with dedicated 1:1 mentorship sessions. One of the program’s goals is to build something new and move your MVP or POC one step forward on technology and product lines.

The outcomes​

We have received three primary outcomes from this program.

First and foremost, we’ve met a great mentor, Matthew Hanson, who has decades of experience in robotic system architecture and is one of the key robotic experts in AWS. His contribution to our success is invaluable and deserves a separate article!

Secondly, we’ve built our first digital prototype of the satellite repair robot and have validated our autonomous control system technology in accurate simulation.

The third and precious outcome: we’ve tapped into AWS ML engineers’ wisdom, significantly reduced our robot’s training costs, and took our AI game to the next level.

All of that in the record-break time and with the intense focus!

Outcome #1: The mentor​

Every startup in the AWS Robotics Accelerator program is assigned a mentor.

We have been very fortunate to have the one and only Matthew Hanson.

Matthew Hanson

Matt has a great experience in robotics architecture, everything from control systems, planning, behaviour, autonomy, simulations and of course, everything at AWS that has Robotics.

He understands the startup’s unique challenges and has helped us find the quickest path to our goal. Matthew is also very supportive of our cause of sustainable development in space, which means a lot.

As a new business in a conservative industry like space, forming a support group around your team is very important.
Matt was one of the first people who believed in us from the start when we didn’t have much to show except for our prior experience, ambition, hard work and tenacity. He has contributed to everything we did during the program and was kind enough to continue working with us after the program, doing everything he could to help us succeed. We will always remain grateful to Matt, and hopefully, one day, we’ll find a great way to reward him in return.

Outcome #2: The space MVP​

The period of our AWS Robotics acceleration program coincided with our Space Accelerator run with the support of the Australian Space Agency in Adelaide. We were actively looking into the on-orbit repair and servicing and thought it would be great to build the first virtual prototype of our future space robot with AWS.

Since the AWS acceleration program is just four weeks, we won’t have time to develop all systems from scratch. Our main goal was to learn as much as possible about the spacecraft’s control systems and how robotic manipulators can be used for docking and repairing the satellite. And we’ve decided to learn from the best and used NASA’s Astrobee robot that has already flown in ISS as the base for our on-orbit servicing machine.

Astrobee robot on ISS

As a result, we’ve been able to apply our ML-based control systems to demonstrate that our robot can autonomously match orbit with a satellite and safely reach out to it.

You can see the simulation video from our AWS Robotics Accelerator Demo day.

Outcome #3: The next-level AI training​

The price of the experiment

At ANT61, we use Deep Reinforcement Learning to teach our robots skills that they can apply for autonomous installation. This approach means that our robots get better at their tasks simply by trying many-many times, rather than us writing slightly better code for them every day.

Reinforcement Learning is still a very new area of Deep Learning. It is akin to gold mining: you have the general directions where to look, but the rich vein can only be found by trying to dig in many places and analysing the results. Our AI engineers always experiment with various algorithms, reward regimes and parameters to get better and more consistent results. The more experiments we can run, the higher our chances of success. On average, one robot takes about two weeks of 24x7 work to learn an essential skill like drilling a hole in a wall with reasonably quality. It will then take several times more training hours to perfect this skill.

Time is our most precious resource, and compressing the time it takes us to determine whether the experiment is successful is very important in our business.

AWS=scale​

Even before the accelerator, we have already been using multi-agent training, where experience from several robots is accumulated to train one brain, which is then deployed to the whole fleet, and the training continues. However, our budget severely limited how many robots we could run in parallel.

One of the great perks of the accelerator is direct access to senior engineers at AWS. Our mentor, Matthew Hanson, has linked us to the engineers in the AWS Sagemaker team, who provided us with some paths we could try to reduce our costs and run the distributed training of thousands of robots on the same budget.
Now the experiments take only hours instead of weeks, and we can run multiple experiments in parallel without breaking the bank.

Quantity ➩ Speed of Innovation

The ability to run multiple experiments, generating years-worth of the data in just a few hours, allowed us to significantly step up our game and use the RL techniques that are usually unavailable to robotics due to the high cost of each data point. Now we can run unsupervised hyper-parameter tuning and neural architecture search.

Essentially, instead of humans coming up with a new experiment to improve our training regimes, AI generates these experiments.

AI that is training other AIs.

As Károly Szolnai-Fehér says, What a time to be alive!

ANT also exhibited at the Aus Space Forum in Oct jointly with these guys.

Haven't looked at their connections yet.

https://www.google.com.au/url?sa=t&source=web&rct=j&url=https://forum.andythomas.foundation/wp-content/uploads/14ASF%2520A5%2520Program%2520Book%25202022%252026102022.pdf&ved=2ahUKEwjX15O46uH9AhXEpOkKHViCAJIQFnoECBsQAQ&usg=AOvVaw2TX078bCK0kljTVHN9DOYv

 

[Fullmoonfever]

ANT also exhibited at the Aus Space Forum in Oct jointly with these guys.

Haven't looked at their connections yet.


View attachment 32426

https://www.google.com.au/url?sa=t&source=web&rct=j&url=https://forum.andythomas.foundation/wp-content/uploads/14ASF%2520A5%2520Program%2520Book%25202022%252026102022.pdf&ved=2ahUKEwjX15O46uH9AhXEpOkKHViCAJIQFnoECBsQAQ&usg=AOvVaw2TX078bCK0kljTVHN9DOYv

As well as appearing to hitch a ride on a SpaceX rocket via Space Machines.

 

[DingoBorat]

Jim Chalmers is doing more than changing the way we tax super – he is also changing the way we tax capital gains

In drawing up plans to more effectively tax large superannuation accounts, Treasurer Jim Chalmers might have stumbled upon an idea that, applied more broadly, could tax rich Australians like the rest of us, writes Peter Martin.

www.abc.net.au

The Henry Review said collecting tax only on "realisation" (when assets were sold) rather than "accrual" (as they grew in value) encouraged investors to hold on to shares and property to delay paying tax — a response it called "lock-in".

This is pretty sucky for us, as this article is saying we could have to pay capital gains tax, on the increase in "value" of shares and property..

It will certainly be a bit of a leveler..

So if BrainChip has a breakout year, due to traction in revenue, NASDAQ listing, multiple IP deals etc, for that tax year.

Then you will have to pay Capital Gains tax, for that year, regardless of whether you sold shares.

In our case, these gains could be substantial and the only way to pay, will be by selling shares or other assets.

The tax man sucks eggs..

 

[gex]

Jim Chalmers is doing more than changing the way we tax super – he is also changing the way we tax capital gains

In drawing up plans to more effectively tax large superannuation accounts, Treasurer Jim Chalmers might have stumbled upon an idea that, applied more broadly, could tax rich Australians like the rest of us, writes Peter Martin.

www.abc.net.au

The Henry Review said collecting tax only on "realisation" (when assets were sold) rather than "accrual" (as they grew in value) encouraged investors to hold on to shares and property to delay paying tax — a response it called "lock-in".

This is pretty sucky for us, as this article is saying we could have to pay capital gains tax, on the increase in "value" of shares and property..

It will certainly be a bit of a leveler..

So if BrainChip has a breakout year, due to traction in revenue, NASDAQ listing, multiple IP deals etc, for that tax year.

Then you will have to pay Capital Gains tax, for that year, regardless of whether you sold shares.

In our case, these gains could be substantial and the only way to pay, will be by selling shares or other assets.

The tax man sucks eggs..

WTAF