BRN Discussion Ongoing

Diogenese

Top 20
Hi FF,

Personally I disagree with all the comments said here about this. The word inference has different meanings depending on which industry you are referring to. In the context of machine learning, inference refers to the data being fed into the machine learning model or algorithm (see link below).

So in the context here, processing inference simply refers to processing sensor data. Nothing further to read here, definitely no reference to LSTM's.
On top of this, since English isn't Anil's primary language, I think some leniency may be required, so there's not much point reading too deeply into how his sentences are structured.

Pure speculation, DYOR


Machine learning inference is the process of running data points into a machine learning model to calculate an output such as a single numerical score. This process is also referred to as “operationalizing a machine learning model” or “putting a machine learning model into production.”
Hi IDD,

You've left out the outference "to calculate an output".

The inference is the estimating of the output from the input data.

infer
[ɪnˈfəː]
VERB

  1. deduce or conclude (something) from evidence and reasoning rather than from explicit statements:


Inference, from my understanding, is the classification of an object from data which is similar to, but not identical with already known data.

Akida has been processing inference for donkeyears, as exemplified by the use of the word 11 times in this 2019 presentation.

https://brainchip.com/wp-content/uploads/2019/10/BrainChip-Linley-Akida-Presentation_v5.pdf


For example, Akida may determine the classification of an object if it has a sufficient percentage of similarities to a known object, or if a part of the new object is sufficiently similar to a corresponding part of a known object.
 
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uiux

Regular
For what it's worth:


Python:
def infer(self):
        while True:
            input_array = camera.get_input_array()
            predictions = self.model_ak.predict_classes(input_array, num_classes=NUM_CLASSES)
            if predictions[0] in SAVED:
                self.camera.label = LABELS.get(predictions[0], predictions[0])
                self.camera.shots = "{} shot/s".format(SHOTS.get(predictions[0]))
            time.sleep(1 / INFERENCE_PER_SECOND)


This is logic to get a frame from a webcam to process with akida, eg. the one shot learning demo
 
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Anyone else remember what Ryad Benosman the founder of Prophesee said on social media when asked by a poster here about Brainchip and AKIDA.

Be interesting to have it reposted here in light of what has been announced today.

My opinion only DYOR
FF

AKIDA BALLISTA
 
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Foxdog

Regular
Still no major pop on the SP - is the market blind 😆
 
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Mccabe84

Regular
Still no major pop on the SP - is the market blind 😆
Is it getting held back again, insto’s still accumulating?
 
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M_C

Founding Member
I was saving these for a rainy day............

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Lex555

Regular
Playing devils advocate for a minute, my calculate revenue by royalties x price of royalties. With all these partnerships coming out and interconnecting with their own ecosystems it would be easy to imagine market(s) saturation. @MC🐠 thanks.

What I’m not 100% convinced on is average price per royalty, could we be potentially over estimating here? @Fact Finder what do you think?

Comparing ARM Holdings, they have 2021 revenue of $2.7b from a total of ~$30b chips sold this year which gives an average of $0.10 revenue per chip. Why is this so low?


Makes me wonder if we’re doing IP, and agreements aren’t disclosed, then what are the chances we could be overstating this 10x.

Or does it even matter, with a conservative $60b market by 2025 and Akida being best in class/ first in class we achieve a more likely 50% share like @chapman89 says, then multiply by 15x (ARM ranges from 10-25x) we get $450bn USD MC. Hmm…
 
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Is Akida in this product?

Roborock vacuum cleaner

The Roborock S7 MaxV in test

Beyond LiDAR - Introducing Reactive AI 2.0, Roborock’s upgraded navigation technology

Roborock, with its always-eager & ambitious group of engineers, has decided that even the almighty LiDAR has its flaws and has introduced its own proprietary Reactive AI 2.0 obstacle recognition and avoidance system in Jan 2022.


Reactive AI 2.0 - How Effective Is It?
The 3D structured light acts as a ruler, accurately measuring the distance between an object in the S7 MaxV’s path and the S7 MaxV itself. It is also able to make sense of the dimensions of the obstacles.

The RGB camera plays the role of the human eye, capturing detailed images of the obstacles, including texture and color.

Both the 3D structured light and the RGB camera feed information onto the onboard processor, powered by a NPU chip (the brain), which then allows the pre-set machine learning algorithms to kick into play. The S7 MaxV will then clean around different objects/ obstacles at an optimal distance - meaning the less risky the object is, the closer the S7 MaxV will clean around the objects. This also means less chances of collision while at the same time, less chances of missed cleaning spots.
I'm not sure, why they keep making these round? 🤔..
I'm yet to see a house, that doesn't have corners..
 
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AARONASX

Holding onto what I've got
I'm not sure, why they keep making these round? 🤔..
I'm yet to see a house, that doesn't have corners..
Here you go, the one and only :rolleyes:🤪

1655689601527.png
 
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F

Filobeddo

Guest
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TECH

Regular
Yet ANOTHER great partnership announced this morning.

Are things going to plan ? Are things following a disciplined, yet flexible pathway ?

As I mentioned months ago, it was my opinion that, positivity attracts positivity, successful proven technology is always going to attract others whom also have successful proven technology.

As Sean mentioned recently, basically we ALL feed off each other, technology isn't just about "one layer" it's about a lot of different layers ALL coming together to produce brilliant, futuristic products that benefit the masses.

Egos, arrogant attitudes must be buried, IF companies wish to succeed...Brainchip Holdings Ltd has ALWAYS conducted itself in a professional manner, led first and foremost by Peter and Anil.

Partnerships in our field of expertise are only going to accelerate in my opinion, each and every company joining us has their own brilliant IP..BUT to open up the gateway to success, their own clients may have asked for that little something extra, to enable them to produce products that the masses will go for, and guess who can assist in that process, that's right....

BRAINCHIP'S SOMA.........OUR EVENT BASED PROCESSOR.....AND WITHOUT IT, THEY WON'T SUCCEED.

BRAINCHIP HAS THE KEY TO THE KINGDOM......AND THIS IS WHAT WILL MAKE US THE "DE-FACTO PARTNER FOR MANY"

Keep positive, enjoy the journey, the road has many more ups and downs, but ultimately we will arrive, still carrying the Aussie flag :geek:

Cheers for now...Tech x
 
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M_C

Founding Member
What else have you got stashed up your sleeve MC?
Don’ be shy 😉

Here's a little teaser for you mate............patience is a virtue ........... :p



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Bravo

If ARM was an arm, BRN would be its biceps💪!
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Quiltman

Regular
I was saving these for a rainy day............

View attachment 9694 View attachment 9695 View attachment 9696
There seems to be credence to the fact that Rob Telson and Anil Mankar carefully allocate their likes on LinkedIn. To date they have proven highly predictive of BrainChip relationships ... the list on the Iceberg has many below the water nominees that have been "liked" by these two. I'm feeling pretty confident they will rise above the surface !
 
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Bravo

If ARM was an arm, BRN would be its biceps💪!
Yummy...😛

Screen Shot 2022-06-20 at 12.09.23 pm.png
 
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F

Filobeddo

Guest

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For what it's worth:


Python:
def infer(self):
        while True:
            input_array = camera.get_input_array()
            predictions = self.model_ak.predict_classes(input_array, num_classes=NUM_CLASSES)
            if predictions[0] in SAVED:
                self.camera.label = LABELS.get(predictions[0], predictions[0])
                self.camera.shots = "{} shot/s".format(SHOTS.get(predictions[0]))
            time.sleep(1 / INFERENCE_PER_SECOND)


This is logic to get a frame from a webcam to process with akida, eg. the one shot learning demo
Do you work for Brainchip, now Uiux 🤔..
Not from the coding, just you barely post anymore..

Don't give a Shareman answer 😛
 
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MADX

Regular
We're you impressed with the Agm and do you think Brn akida will be involved in a billon products long-term
Certainly was impressed as I posted on my return. Since then, in my new self-appointed role as "BRN Influencer", I have created two more BRN share purchasers.
If FF says a billion, that's it. :)
 
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BaconLover

Founding Member
Even have a space ready for Brainchip.

The spider web is starting to spread over the entire automotive and robotics industry.

The CEO Sean Hehir is probably already looking forward to being judged on his performance at next years AGM.

My opinion and speculation only but @BaconLover has a few adjustments to make to the Iceberg.
FF

AKIDA BALLISTA
Hmm not sure the exact date when, but Prophesee is already on top of the iceberg. So I assume we must have seen some confirmation for us to move it up.

Goes to show 1000 one eyed eyes find things with their research before anyone else. What a great community 👏 👌
 
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