AlpineLife
Member
I see todays price as the Coles-mark-down-sticker on a damn fine wagu eye-fillet. Just Pop in in the freezer & enjoy with a fine red at next announcement.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
yes Dhm they are "milking it" for all its worth. Nothing like the sound of War Drums to cause scared hands to bail and provide traders an excellent opportunity to get both Volume (supply) and lower prices. Many smaller holders will try to capture a gain as well with usually limited success.
I am hoping that the Management are watching both the Markets and our trading with a view to helping support a possible sell off if this Russian sage continues much longer. There is ALWAYs something that can be [posted/released] by a company if it so chooses to help the SP when dark clouds are surrounding the SP.
A company like Brainchip will have so much happening it would have no problem in this way. If it chooses of course!
Wonder how NASA are going with our chips? Have they retained them (kept)or have they been returned? Perhaps they have been supplied with other AKIDA chips or Boards to evaluate? this is an example of normal business going on about its daily course, which could help or boost our overall picture of BRN and Brainchip.
Back to you Tony Dawe.
(hey guys I am being nice with the wording above. ok)
Yak52
Labsy
Regular
I picked up another small parcel todayThe fact is, many here are LTH or have bought in and plan on holding through thick or thin. So how many people here are going to buy more shares? I did today but that was because I sold out on a few stocks last week and as I mentioned "Gift horse" set my buy and now it will be a trading amount. Try and double the $ over the next 12 months. (Last time I did this it took 3 weeks
Dhm
Regular
This reminds me of a skit I saw many years ago that was a Chinese Whispers scenario that started with “Send reinforcements, we’re going to advance” and it came out the other end as “send three and four pence, we’re going to a dance”
Good one Labsy.I picked up another small parcel today
I have my 20ft Bowrider up for sale to buy some more BRN. Damn nice Searay .......BUT brainchip shares are just to inviting when looking at a LTH position!
Yak52
Labsy
Regular
Ill swap you some brn shares for the searay!
Only joking... but have been looking at boats.
Labsy
Regular
Keep accumulating my friend... someone posted a few years back that they consider their shares better than a bank account. How correct they were...
Barring any black swan event, history has shown markets can withstand interest rate pressure. A good reminder of what went on during these years.
Attachments
Dhm
Regular
And this throughout the 1980’s and 90’s. People will see decisive FED action as bullish to beat inflation.
M_C
Founding Member
You lost me at freezer lol
Dolci
Regular
A SELL order for 249,731 has been placed first @ $1.49 and now moved down to $1.485
AND now its GONE! Bang. Swallowed up in a single BUY order! Just shows the manipulation here today on BRN trading.
Someone was very "Keen" to buy that order.
Yak52
....pour bastards getting fleeced all day today ...hourly RSI oversold for hours now...I am suprised that they are still willing to sell at these prices..
Feelings re Share Price are definitely cyclical. While we were raging I felt a terrible sense of Impostor's Syndrome and had to stay off the trading platform. I had to keep reminding myself BRN is just about sensors and there is nothing sexy about sensors. Now it's flattening out for a bit, I realize that there are sensors everywhere, and BRN can make these sensors intelligent enough to be able to communicate self taught contextural data in a clean useable fashion, and that this is something that the world hasn't seen before.. And so now I have something to be mindful about and equanimity is restored!
Vladsblood
Regular
Yes they are really terrified and not in control of their emotions today.....By next week they will have depression as our SP will rise again quickly. Nasdaq should come back up sometime this week and hopefully we get an announcement "transfusion" from our company to boost us back over 2 dollars and beyond.....Vlad.
I just posted this over in - Is it AKIDA? Thread.
Worth throwing it here as well. Besides the question of AKIDA being onboard, this Bike shows exactly what AKIDA should be installed on!
TONY DAWE check this out. Maybe give them a call?
--------------------------------------------------------------------------------------------------------------------------
Interesting Motorbike from DAMON.
mentions onboard neural net, scanning for dangers (same as ADAS) . LIDAR.
says with every ride both bike & rider learn from each other - becoming smarter.
Cloud connected (?) Ai connects you to mapping, live video feeds of your riding etc.
Predictive Ai can track up to 64 objects at one time.
while there is no NEED to connect AKIDA to cloud many applications will be connected and still using AKIDA so this makes this DAMON motorcycle
a real possibility.
Predictive Ai .......where have we heard that before? (remember if it walks, talks, looks and flys like a Duck!)
My take is a strong possibility of AKIDa and if not then..........IT DAMN WELL SHOULD HAVE AKIDA! Especially Akida 2000.
ps. Does 200 mph. Has my interest as I used to race superbikes!
Yak52.
Yak52
Worth throwing it here as well. Besides the question of AKIDA being onboard, this Bike shows exactly what AKIDA should be installed on!
TONY DAWE check this out. Maybe give them a call?
--------------------------------------------------------------------------------------------------------------------------
Interesting Motorbike from DAMON.
mentions onboard neural net, scanning for dangers (same as ADAS) . LIDAR.
says with every ride both bike & rider learn from each other - becoming smarter.
Cloud connected (?) Ai connects you to mapping, live video feeds of your riding etc.
Predictive Ai can track up to 64 objects at one time.
while there is no NEED to connect AKIDA to cloud many applications will be connected and still using AKIDA so this makes this DAMON motorcycle
a real possibility.
Predictive Ai .......where have we heard that before? (remember if it walks, talks, looks and flys like a Duck!)
My take is a strong possibility of AKIDa and if not then..........IT DAMN WELL SHOULD HAVE AKIDA! Especially Akida 2000.
ps. Does 200 mph. Has my interest as I used to race superbikes!
Yak52.
Yak52
Fact Finder
Top 20
Food for thought from Renesas:
Back to top
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Carmelo Sansone
Director of Strategic Business Development
With cloud computing now so ubiquitous, and edge computing so well established, why, then, is endpoint computing becoming so important?
In a word: Latency.
If we're going to make true advancements toward meaningful artificial intelligence (AI) and machine learning (ML) in smart homes, smart health and smart cities, lag and latency is unacceptable.
Efficient AI inference demands efficient endpoints that can infer, pre-process, and filter data in real time. Unlike computing in the cloud or at the edge of the cloud, the endpoint represents, as Renesas CEO Hidetoshi Shibata has said, "the true point of action." For example, a home appliance that can predict when it will need maintenance, or a voice-interactive wearable that can warn a user of a possible heart anomaly, is more useful if it applies ML algorithms at the endpoint with near-zero latency.
And it is precisely at the endpoint where Renesas microcontrollers (MCUs) shine. AI models, trained on industry-standard networks, are embedded on chip to offer design engineers the performance, bandwidth, and responsiveness to effectively realize and enhance emerging smart applications. Advanced endpoint-compiler software enables customers "to test capabilities purely in the cloud before porting them over to our boards, and then, finally, to implement these seamlessly on our chips," Mr. Shibata told McKinsey analysts recently.
"While also providing the flexibility to change the functionality or algorithms," he said, "Such dynamically configurable hardware architectures let customers enjoy the benefit of hardware-processing speeds."
Indeed, users of smart homes, smart health and smart cities applications cannot or will not wait even a few milliseconds for AI data to be processed. Consequently, the latency inherent in transferring data to the cloud threatens to undermine progress in those consumer-focused areas. This may sound obvious, but few competitive MCUs are designed to enable fast processing.
As ML inference moves to device endpoints, "this integrated AI will be the foundation that powers a complex combination of 'sense' technologies to create smart applications with more natural, 'human-like' communication and interaction," Dr. Sailesh Chittipeddi, president and CEO of Renesas Electronics America, recently wrote in Embedded Computing magazine.
"In addition," wrote Dr. Chittipeddi, "A convergence of advancements around AI accelerators, adaptive and predictive control, and hardware and software for voice and vision open up new user interface capabilities for a wide range of smart devices."
Let's take a look at what those might be in the smart home, health and city segments.
For example, a refrigerator will learn over time to adjust its temperature settings. Or the TV will learn to change the audio output based on what's happening in the room at the time -- so voices and music are not deafened during parties or cooking. Voice interfaces can also be used to teach or train devices, as well as to provide information when an appliance is offline.
AI algorithms are also being used to predict when an insulin pump is likely to run out of a patient's specific dosage and to anticipate when a dose is needed. Otherwise, the pump might not deliver the right dosage at the right time, putting patients at risk.
Smart city applications will be equally demanding of low latency. A smart streetlamp that can detect traffic density and adjust its brightness accordingly to prevent traffic accidents could save lives. An AI and ML-infused surveillance camera that can detect alterations in traffic flow, which may mean an accident has occurred and can alert emergency services immediately could also save lives.
In scenarios like city traffic, drivers would benefit from AI alerts that warn of slowdowns ahead. Smart parking garages could use AI algorithms to monitor a car's status and accurately charge or discharge a vehicle depending on a user's preferred levels of privacy or security.
In terms of cities and traffic control, AI algorithms embedded in smart traffic lights could similarly be able to notify drivers, pedestrians and bikers of slowing or stopping traffic ahead. Once again, the information would be relayed in near-zero latency. Smart vehicle routing -- say, to avoid heavy congestion -- may be the most challenging application of all.
The same MCU that enables fast processing today may support a completely different algorithm tomorrow. That makes the MCU platform, which includes a flexible CPU, an ideal choice for enabling endpoint ML applications.
Find the right MCU with this easy-to-use MCU selection tool, or download the Renesas MCU guide app and move your designs to the endpoint today!
Beyond the Edge: AI at the Endpoint
Back to top
Image
Carmelo Sansone
Director of Strategic Business Development
With cloud computing now so ubiquitous, and edge computing so well established, why, then, is endpoint computing becoming so important?
In a word: Latency.
If we're going to make true advancements toward meaningful artificial intelligence (AI) and machine learning (ML) in smart homes, smart health and smart cities, lag and latency is unacceptable.
Efficient AI inference demands efficient endpoints that can infer, pre-process, and filter data in real time. Unlike computing in the cloud or at the edge of the cloud, the endpoint represents, as Renesas CEO Hidetoshi Shibata has said, "the true point of action." For example, a home appliance that can predict when it will need maintenance, or a voice-interactive wearable that can warn a user of a possible heart anomaly, is more useful if it applies ML algorithms at the endpoint with near-zero latency.
And it is precisely at the endpoint where Renesas microcontrollers (MCUs) shine. AI models, trained on industry-standard networks, are embedded on chip to offer design engineers the performance, bandwidth, and responsiveness to effectively realize and enhance emerging smart applications. Advanced endpoint-compiler software enables customers "to test capabilities purely in the cloud before porting them over to our boards, and then, finally, to implement these seamlessly on our chips," Mr. Shibata told McKinsey analysts recently.
"While also providing the flexibility to change the functionality or algorithms," he said, "Such dynamically configurable hardware architectures let customers enjoy the benefit of hardware-processing speeds."
Indeed, users of smart homes, smart health and smart cities applications cannot or will not wait even a few milliseconds for AI data to be processed. Consequently, the latency inherent in transferring data to the cloud threatens to undermine progress in those consumer-focused areas. This may sound obvious, but few competitive MCUs are designed to enable fast processing.
As ML inference moves to device endpoints, "this integrated AI will be the foundation that powers a complex combination of 'sense' technologies to create smart applications with more natural, 'human-like' communication and interaction," Dr. Sailesh Chittipeddi, president and CEO of Renesas Electronics America, recently wrote in Embedded Computing magazine.
"In addition," wrote Dr. Chittipeddi, "A convergence of advancements around AI accelerators, adaptive and predictive control, and hardware and software for voice and vision open up new user interface capabilities for a wide range of smart devices."
Let's take a look at what those might be in the smart home, health and city segments.
Smart Home Applications
The intelligent home is in many ways a "sensor-rich" application. Smart homes will use sensors to collect and process all manner of data, from environmental information to user activity. With the guiding principle of providing "total convenience," these applications tend to use AI for predictive analytics and for customizing user interaction with the home environment.For example, a refrigerator will learn over time to adjust its temperature settings. Or the TV will learn to change the audio output based on what's happening in the room at the time -- so voices and music are not deafened during parties or cooking. Voice interfaces can also be used to teach or train devices, as well as to provide information when an appliance is offline.
Smart Health Applications
Smart health applications will be even more demanding when it comes to latency. A pacemaker that can predict when a battery needs to be changed could well save lives. An electrocardiogram (ECG) sensor that can warn a user of a heart anomaly in real time could save lives as well. ML algorithms embedded in an ECG sensor could detect disruptions in heartbeat or rhythm and sound the alarm immediately.AI algorithms are also being used to predict when an insulin pump is likely to run out of a patient's specific dosage and to anticipate when a dose is needed. Otherwise, the pump might not deliver the right dosage at the right time, putting patients at risk.
Smart City Applications
In a smart city, sensors can collect all manner of information -- from pollution levels to traffic delays -- that could be used to forecast traffic conditions and even determine air quality alerts.Smart city applications will be equally demanding of low latency. A smart streetlamp that can detect traffic density and adjust its brightness accordingly to prevent traffic accidents could save lives. An AI and ML-infused surveillance camera that can detect alterations in traffic flow, which may mean an accident has occurred and can alert emergency services immediately could also save lives.
In scenarios like city traffic, drivers would benefit from AI alerts that warn of slowdowns ahead. Smart parking garages could use AI algorithms to monitor a car's status and accurately charge or discharge a vehicle depending on a user's preferred levels of privacy or security.
In terms of cities and traffic control, AI algorithms embedded in smart traffic lights could similarly be able to notify drivers, pedestrians and bikers of slowing or stopping traffic ahead. Once again, the information would be relayed in near-zero latency. Smart vehicle routing -- say, to avoid heavy congestion -- may be the most challenging application of all.
Flexibility is Key
As ML algorithms come to dominate smart home, health and city applications, the speed at which data is processed will be more critical than ever. For companies like Renesas, that means delivering the most intelligent and flexible MCUs that optimize data processing speed while also providing the flexibility to change functionality or algorithms.The same MCU that enables fast processing today may support a completely different algorithm tomorrow. That makes the MCU platform, which includes a flexible CPU, an ideal choice for enabling endpoint ML applications.
Find the right MCU with this easy-to-use MCU selection tool, or download the Renesas MCU guide app and move your designs to the endpoint today!
Fact Finder
Top 20
Food for thought from Renesas:
Beyond the Edge: AI at the Endpoint
Back to top
Image
Carmelo Sansone
Director of Strategic Business Development
With cloud computing now so ubiquitous, and edge computing so well established, why, then, is endpoint computing becoming so important?
In a word: Latency.
If we're going to make true advancements toward meaningful artificial intelligence (AI) and machine learning (ML) in smart homes, smart health and smart cities, lag and latency is unacceptable.
Efficient AI inference demands efficient endpoints that can infer, pre-process, and filter data in real time. Unlike computing in the cloud or at the edge of the cloud, the endpoint represents, as Renesas CEO Hidetoshi Shibata has said, "the true point of action." For example, a home appliance that can predict when it will need maintenance, or a voice-interactive wearable that can warn a user of a possible heart anomaly, is more useful if it applies ML algorithms at the endpoint with near-zero latency.
And it is precisely at the endpoint where Renesas microcontrollers (MCUs) shine. AI models, trained on industry-standard networks, are embedded on chip to offer design engineers the performance, bandwidth, and responsiveness to effectively realize and enhance emerging smart applications. Advanced endpoint-compiler software enables customers "to test capabilities purely in the cloud before porting them over to our boards, and then, finally, to implement these seamlessly on our chips," Mr. Shibata told McKinsey analysts recently.
"While also providing the flexibility to change the functionality or algorithms," he said, "Such dynamically configurable hardware architectures let customers enjoy the benefit of hardware-processing speeds."
Indeed, users of smart homes, smart health and smart cities applications cannot or will not wait even a few milliseconds for AI data to be processed. Consequently, the latency inherent in transferring data to the cloud threatens to undermine progress in those consumer-focused areas. This may sound obvious, but few competitive MCUs are designed to enable fast processing.
As ML inference moves to device endpoints, "this integrated AI will be the foundation that powers a complex combination of 'sense' technologies to create smart applications with more natural, 'human-like' communication and interaction," Dr. Sailesh Chittipeddi, president and CEO of Renesas Electronics America, recently wrote in Embedded Computing magazine.
"In addition," wrote Dr. Chittipeddi, "A convergence of advancements around AI accelerators, adaptive and predictive control, and hardware and software for voice and vision open up new user interface capabilities for a wide range of smart devices."
Let's take a look at what those might be in the smart home, health and city segments.
Smart Home Applications
The intelligent home is in many ways a "sensor-rich" application. Smart homes will use sensors to collect and process all manner of data, from environmental information to user activity. With the guiding principle of providing "total convenience," these applications tend to use AI for predictive analytics and for customizing user interaction with the home environment.
For example, a refrigerator will learn over time to adjust its temperature settings. Or the TV will learn to change the audio output based on what's happening in the room at the time -- so voices and music are not deafened during parties or cooking. Voice interfaces can also be used to teach or train devices, as well as to provide information when an appliance is offline.
Smart Health Applications
Smart health applications will be even more demanding when it comes to latency. A pacemaker that can predict when a battery needs to be changed could well save lives. An electrocardiogram (ECG) sensor that can warn a user of a heart anomaly in real time could save lives as well. ML algorithms embedded in an ECG sensor could detect disruptions in heartbeat or rhythm and sound the alarm immediately.
AI algorithms are also being used to predict when an insulin pump is likely to run out of a patient's specific dosage and to anticipate when a dose is needed. Otherwise, the pump might not deliver the right dosage at the right time, putting patients at risk.
Smart City Applications
In a smart city, sensors can collect all manner of information -- from pollution levels to traffic delays -- that could be used to forecast traffic conditions and even determine air quality alerts.
Smart city applications will be equally demanding of low latency. A smart streetlamp that can detect traffic density and adjust its brightness accordingly to prevent traffic accidents could save lives. An AI and ML-infused surveillance camera that can detect alterations in traffic flow, which may mean an accident has occurred and can alert emergency services immediately could also save lives.
In scenarios like city traffic, drivers would benefit from AI alerts that warn of slowdowns ahead. Smart parking garages could use AI algorithms to monitor a car's status and accurately charge or discharge a vehicle depending on a user's preferred levels of privacy or security.
In terms of cities and traffic control, AI algorithms embedded in smart traffic lights could similarly be able to notify drivers, pedestrians and bikers of slowing or stopping traffic ahead. Once again, the information would be relayed in near-zero latency. Smart vehicle routing -- say, to avoid heavy congestion -- may be the most challenging application of all.
Flexibility is Key
As ML algorithms come to dominate smart home, health and city applications, the speed at which data is processed will be more critical than ever. For companies like Renesas, that means delivering the most intelligent and flexible MCUs that optimize data processing speed while also providing the flexibility to change functionality or algorithms.
The same MCU that enables fast processing today may support a completely different algorithm tomorrow. That makes the MCU platform, which includes a flexible CPU, an ideal choice for enabling endpoint ML applications.
Find the right MCU with this easy-to-use MCU selection tool, or download the Renesas MCU guide app and move your designs to the endpoint today!
Fact Finder
Top 20
bit of desert:
Thomas Hulsing
System of Systems Engineering
4 days
Industry executives and experts share their predictions 2022 : A Breakthrough Year for Artificial Intelligence https://lnkd.in/eEf-Jgx4
Thomas Hulsing
System of Systems Engineering
4 days
Industry executives and experts share their predictions 2022 : A Breakthrough Year for Artificial Intelligence https://lnkd.in/eEf-Jgx4
2022 a Breakthrough Year for AI
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