BRN Discussion Ongoing

mrgds

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buena suerte :-)

BOB Bank of Brainchip
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Vanman1100

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Definitely think IMU is one to look at. And a few of the contributors are pretty amazing. And I definitely agree a lot to get your head around. I noticed a few of the quality posters from IMU are also on DRE which is an early stage Rare Earth Elements explorer and I am currently researching their projects, much higher risk profile being such early days.
PLL
 
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Sirod69

bavarian girl ;-)
Arm got 7% of the server market.

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Sirod69

bavarian girl ;-)

embedded news: Arm and CEVA ship record chips, SOMs, and more​

August 12, 2022 Nitin Dahad
This week’s embedded news features various announcements around system on modules (SOMs), as well as news from Arm and CEVA on their chip shipment successes, plus more from the industry.

Arm this week reported record quarterly royalty revenue, exceeding $400 million for the first time, at $453 million for Q1 of FY 2022. It announced more record figures too, with a record Q1 total revenue of $719 million, and a record number of Q1 unit shipments, with its partners shipping 7.4 billion Arm-based chips in Q1. Arm said it has now achieved four quarters of more than 7 billion Arm-based chips shipped.

 
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Sirod69

bavarian girl ;-)

Autonomous Vehicle Deployment: pick up, drop off, and great user experience​

Here is a sample of the companies who’ve entered the commercial arena with driverless cars:

  • Easymile operates autonomous shuttles across the globe, performing thousands of commercial drives.
  • Cruise has been approved to deploy commercial driverless cars in San Francisco through its AV ride-sharing service.
  • Argo-AI has begun piloting autonomous taxis, sans safety drivers, in Miami, Florida, and Austin, Texas.
  • Meanwhile, in China, AutoX robotaxis are operating without them as well.
  • Gatik’s delivery trucks have been conducting driverless hauls successfully for some time without human backup drivers.

While the delivery-robot market is growing, it’s difficult to picture how an unassisted autonomous bot would find the customer at a beach or park. Even as it drives smoothly to a pinpointed destination, it may pass the actual customer standing nearby, or, say, waving to the robot while yelling, “over here!”
 

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Iseki

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Hi all,

Non Brainchip question so feel free to skip over it:

I can finally move a portion of my Super into a SMSF at the end of November. It’s been agonising waiting for this to occur, watching Brainchip and a few others go up whilst unable to put my super into it!

To balance my portfolio I am asking if anyone has any other solid share tips they’d like to share please?

Obviously I’m going to do my own research which is why I’m asking now as it give me a few months to consider my options and investment strategy.

Rather than clog up this thread if you could message me with any advice/share tips it would be much appreciated.

Thanks in advance. :)
IMU - to cure cancer by shrinking tumors
PAB - to cure cancer by stopping it spreading
ACW - to cure Alzheimers
CGS - to diagnose Alzheimers using AI in a phone
PLY - Computer games where you play for crypto
4DS - ReRam currently in TH
WBT - ReRam

Spec stocks only. Not advice, obviously. You need to know your risk profile, set stop losses. Good luck!
 
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Deadpool

Did someone say KFC
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stuart888

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What a boring bunch you are today, just remind me not to come to your funeral when ya dead.

Well it was 32

View attachment 14079
Wow, toilet paper stacks rather than computational graphs. :geek:

I always wondered how the smart folks design their neural networks!

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stuart888

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View attachment 14060
Looks like you can use 3 types of the ZMOD series for digital gas sensor for a fridge.
They state in the system benefits write up that " Dedicated MCU for AI Based powerful sensor data evaluation". Fingers Crossed !!!!!!
The refrigerator sensor inference has a lot of use cases. I would love for my refrigerator's outside display to let me know that my half-n-half cream for my coffee has gone sour. I would not have to stiff-test it ever again!

Also it can monitor for too-much-bacteria of all forms and warn people before they eat the food. Food born-illness is a big deal for lots of people and the elderly.

Plus, auto restocking, like the Amazon Fridge. Consumers are going to want these products, as anything that helps health is going to fly.

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D

Deleted member 118

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jtardif999

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Great post, as for is Akida too good - I think the CNN to SNN automated conversion Akida offers has allowed it to be readily accepted into the DL community. You can’t argue much with being able to take CNN trained models into the sparsity of SNN and finish up with something that is best in class for SWaP and allows continued improvement via on chip learning. That’s a great selling line imo.
Ah, and has been whispered to me - the best example of Akida never being too good is Renesas only licensing 2 nodes. What other technology in this space can be configured in such a way as to be powerful enough to potentially augment server farm performance - when ganged together with other Akida’s (80 + 80 + .. nodes) or split out into as few as 2 nodes for smarting up an air quality sensor?
 
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I just wonder what sort of impact the Climate Bill will have on our little brain chip investment.
It’s a few dollars that they are throwing at this.
We ( 🧠 🍟 ) use less power
Create less heat
Now how can we help save the world 🌎
The mind goes into overtime just thinking of all the applications where we can be involved.
 
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Makeme 2020

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  1. Home
  2. Diabetes
  3. Home
  4. Health informatics

AUGUST 9, 2022

AI + ECG heart trace can accurately predict diabetes and pre-diabetes​

by British Medical Journal
heartbeat
Credit: CC0 Public Domain
An artificial intelligence (AI) algorithm, derived from the features of individual heartbeats recorded on an ECG (electrocardiogram), can accurately predict diabetes and pre-diabetes, suggests preliminary research published in the online journal BMJ Innovations.

If validated in larger studies, the approach could be used to screen for the disease in low resource settings, say the researchers.
An estimated 463 million adults around the world had diabetes in 2019, and picking up the disease in its early stages is key to preventing subsequent serious health problems. But diagnosis relies heavily on the measurement of blood glucose.
This is not only invasive but also challenging to roll out as a mass screening test in low resource settings, point out the researchers.
Structural and functional changes in the cardiovascular system occur early on even before indicative blood glucose changes, and these show up on an ECG heart trace.
The researchers therefore wanted to see if machine learning (AI) techniques could be used to harness the screening potential of ECG to predict pre-diabetes and type 2 diabetes in people at high risk of the disease.
They drew on participants in the Diabetes in Sindhi Families in Nagpur (DISFIN) study, which looked at the genetic basis of type 2 diabetes and other metabolic traits in Sindhi families at high risk of the disease in Nagpur, India.
Families with at least one known case of type 2 diabetes and living in Nagpur, which has a high density of Sindhi people, were enrolled in the study.
Participants provided details of their personal and family medical histories, their normal diet, and underwent a full range of blood tests and clinical assessments. Their average age was 48 and 61% of them were women.
Pre-diabetes and diabetes were identified from the diagnostic criteria specified by the American Diabetes Association.
The prevalence of both type 2 diabetes and pre-diabetes was high: around 30% and 14%, respectively. And the prevalence of insulin resistance was also high—35%—-as was the prevalence of other influential coexisting conditions—high blood pressure (51%), obesity (around 40%), and disordered blood fats (36%).
A standard 12-lead ECG heart trace lasting 10 seconds was done for each of the 1262 participants included. And 100 unique structural and functional features for each lead were combined for each of the 10,461 single heartbeats recorded to generate a predictive algorithm (DiaBeats).
Based on the shape and size of individual heartbeats, the DiaBeats algorithm quickly detected diabetes and prediabetes with an overall accuracy of 97% and a precision of 97%, irrespective of influential factors, such as age, gender, and coexisting metabolic disorders.
Important ECG features consistently matched the known biological triggers underpinning cardiac changes that are typical of diabetes and pre-diabetes.
The researchers acknowledge that the study participants were all at high risk of diabetes and other metabolic disorders, so unlikely to represent the general population. And DiaBeats was slightly less accurate in those taking prescription meds for diabetes, high blood pressure, high cholesterol, etc.
Nor were data available for those who became pre-diabetic or diabetic, making it impossible to determine the impact of early screening.
"In theory, our study provides a relatively inexpensive, non-invasive, and accurate alternative [to current diagnostic methods] which can be used as a gatekeeper to effectively detect diabetes and pre-diabetes early in its course," they conclude.
"Nevertheless, adoption of this algorithm into routine practice will need robust validation on external, independent datasets," they caution.
 
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Makeme 2020

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Short video from the ASX investor.....
 
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S

Straw

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Well there is one solution to waiting on announcements.... take a trip to Malta 🙀
 
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Boab

I wish I could paint like Vincent
Elon explains why Tesla won't use LiDar. Well worth a watch of the video.

 
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mrgds

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Short video from the ASX investor.....

Thnx @Makeme 2020 ,
We have all wondered about "Mr ASX" and his possible presence here on TSEx, and of course his sign off of "Akida Ballista"
when discussing BRN.
Seems to me a very appropriate video clip for the time us holders are experiencing between any positive major announcements.
Im like everyone here, patiently waiting, .............. "pantene comes to mind"

Meanwhile, .......................... "IM STILL FEELIN IT" ( in the hot tub ) ..........................:eek: .........................:cool:

Akida Ballista
 
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