You'll have to get a helicopter with floaties, 'cause you've put the pool in the helipad spot.
BRN Discussion Ongoing
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Smoothsailing
Regular
Bravo
If ARM was an arm, BRN would be its biceps💪!
jtardif999
Regular
Cheap is the only way to eventually proliferate. 54% of companies surveyed recently said they would have to incorporate AI to help their business grow by 2030 or be left behind. (Paraphrasing a story I read on Apple News a few days ago.) There is a greater proportion of companies that won’t be able to afford the expensive infrastructure changes to incorporate the AI we currently have and this includes edge technology. Selling cheaper, lower power tech to that proportion could be the most lucrative thing we could do. BRN has the top down approach selling IP to bigger companies that can then produce large quantities at scale, but through Edge Impulse and similar and the university programs imo are also tackling proliferation from the bottom up. I think the Edge Box and the Cup Cake server are examples of plug and play for a larger audience and the Edge Impulse projects then provide real world examples of use cases that in time will take hold. We’ve seen in the last couple of days a medical tech example detecting pneumonia and now the defect monitoring example. The more these kinds of examples are shown the more Akida will be thought of as a go to imo. It feels like we are getting much closer to that tipping point right now!
? #neuromorphiccomputing #brainchip #akd1000 #techinnovation #rnd | Vasyl Chumachenko, PhD
Started working on an R&D project in Neuromorphic Computing with Akida from BrainChip. Exploring an uncharted field is always peculiar and exciting. Any tips or tricks from the community? #NeuromorphicComputing #BrainChip #AKD1000 #TechInnovation #RnD
Bravo
If ARM was an arm, BRN would be its biceps💪!
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Sora | Tejas Kulkarni
OpenAI's new video model is unbelievable - https://openai.com/sora Do we still think generative AI is not going to disrupt production workflows in image, video and 3D? Times are dramatically changing. Every year is going to feel even more magical than the one before. Super excited for the...www.linkedin.com
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Fullmoonfever
Top 20
Seems their job ad legit then as same group it appears.View attachment 59401 View attachment 59402
#neuromorphiccomputing #brainchip #akd1000 #techinnovation #rnd | Vasyl Chumachenko, PhD
Started working on an R&D project in Neuromorphic Computing with Akida from BrainChip. Exploring an uncharted field is always peculiar and exciting. Any tips or tricks from the community? #NeuromorphicComputing #BrainChip #AKD1000 #TechInnovation #RnD
BRN Discussion Ongoing
https://www.upwork.com/freelance-jobs/apply/Machine-Learning-Engineer-Neuromorphic-Computing-Akida-London_~01e0cd3804f2f5bf56/ Intriguing update on the job opening at that mystery employer’s Central London location: Now I am not sure whether the following location still qualifies as Central...
thestockexchange.com.au
H2 goes up
Member
Anyone seen the new Dyson hairdryer that learns a users preference - on device?
https://www.dyson.com.au/products/hair-care/hair-dryers/dyson-supersonic-nural
"It will remember your go to settings, and select your preferred heat and airflow preferences"
I wonder......
The next generation if hairdryers!
This is brilliant.
I reckon it's running standard AI on a CPU. The CPU fan is vented out and the dumped heat is used to dry your hair.
My foliclely challenged self is still not buy one.
No, I'll buy the akida one when it comes out and use it for a cooling fan in summer. No wasted energy there.
Cheers,
Stable Genius
Regular
What caught my eye @H2 goes up was the self learning and TOF sensor as per this article description. But unfortunately I’ve been overly excited on too many misses so this is probably another one.
Like my hair I’m also technically challenged so without an announcement I’ll keep the cork in the champagne.
https://www.gbnews.com/tech/dyson-supersonic-nural-hair-dryer
MaxwellAnne
Regular
In memory of a great song, and the man who performed it,
And something BRN will make us all do, ............................
AKIDA ( make me smile ) BALLISTA
One of my favorites. Sad to hear that Steve Harley passed away
HopalongPetrovski
I'm Spartacus!
What caught my eye @H2 goes up was the self learning and TOF sensor as per this article description. But unfortunately I’ve been overly excited on too many misses so this is probably another one.
Like my hair I’m also technically challenged so without an announcement I’ll keep the cork in the champagne.
https://www.gbnews.com/tech/dyson-supersonic-nural-hair-dryer
DingoBorat
Slim
Anyone seen the new Dyson hairdryer that learns a users preference - on device?
https://www.dyson.com.au/products/hair-care/hair-dryers/dyson-supersonic-nural
"It will remember your go to settings, and select your preferred heat and airflow preferences"
I wonder......
The next generation if hairdryers!
It says "attachment learning" but it doesn't really learn, it just "remembers" the last settings when a particular attachment is fitted (which the dryer will "know" from a tab or something on the attachment).
They are trying to make it sound "smart" when it isn't really, it's just marketing.
It has a lot of sensors, but the "brain" is nothing special.
It doesn't need something like AKIDA, for what they are doing IMO.
DingoBorat
Slim
I'm not sure I would want to walk around nakedin front of these internet connected robots, which is where AKIDA would come in handy IMO.
From 22 seconds, the Isaac Lab Sim they developed, looks more like Robot Hell!
Here, see how you go with this one GROOTs
Last edited:
Pom down under
Top 20
Hi DB,
Their patent application refers to a generic NN:
WO2023228008A1 HAIRCARE APPLIANCE
A haircare appliance is described that comprises a body for engaging hair in use, a sensor arrangement and a control unit. The sensor arrangement is configured to output a plurality of signals, each signal being indicative of a presence of an object at a respective region of the body. The control unit is configured to determine whether the object is hair based on temporal differences between the signals.
...
In some examples, the control unit is configured to determine whether the object is hair using a trained machine learning model. For example, the trained machine learning model may be or comprise a regression model. In some examples, the machine learning model may be a trained neural network, although other trained machine learning models may be used.
This may be analog (eg, Synsense low hanging fruit) or it could be Akida. I'm pretty sure they did not develop a NN in-house.
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