Always positive my friend… if you’re negativ on something, stop it, change it, move on, or let it be.You're entitled to your opinion.
And good on you for having a positive opinion when our company's value continues to slide to the negative.
Always positive my friend… if you’re negativ on something, stop it, change it, move on, or let it be.You're entitled to your opinion.
And good on you for having a positive opinion when our company's value continues to slide to the negative.
Altmann is not the messiah ...Some relevant background information here is that Sam Altman (OpenAI) had committed to a $51 million investment in AI chips from Rain AI in a letter of intent signed in 2019. The agreement committed OpenAI to purchase Rain's chips upon production. At that point in time Rain AI was focused on developing neuromorphic chips.
Since then, Rain AI no longer promotes the term neuromorphic technology, choosing instead to describe their chip efforts as novel compute-in-memory technology.
Last I heard they were hoping to bring their first chip (an analogue in-memory digital AI accelerator) to market at the end of this year.
I noticed that Airbus Ventures are listed as backers for Rain AI and I asked ChatGPT what the relationship between Airbus Ventures and Airbus is (see below).
Also, another connection is that our Chairman Mr Antonio Viana is also currently serving on the Board of Directors at Arteris. Arteris and Rain AI are collaborating to develop advanced hardware solutions aimed at enhancing artificial intelligence computing. Rain AI is leveraging Arteris' FlexNoC 5 network-on-chip (NoC) technology.
I wonder if @Diogenese might have an opinion on whether there could be a possibility that BrainChip's and Rain AI's technologies might be complementary, or whether our technology might be able to implemented into theirs in some way for better efficiency and performance outcomes?
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Always positive my friend… if you’re negativ on something, stop it, change it, move on, or let it be.
I wouldn’t give you a life advice for freeWith all due respect, I don't need life advice from you.
That is no true. They designed the things and we investors are following. They know if they plan bigger salaries then holders will ask for more transparency. This way they can hide why they have to pay that much. In reality it is better for holders to pay salary because that will help us tax offset. e.g Sean get half a million worth of shares he pay 200,000 worth of tax by selling his shares. If we pay him 500,000 worth of money he will pay the same tax but we can account that in expenses and claim the same from our profits in future ( if there will be)They get shares because we don’t have money to pay their high salaries.
You are right BOD are accumulating but for the market they are selling on market. If company pay them money in leu of their shares then they may be net buyers. So they sell shares on behalf of company for their tax purposes, if company sell those shares and pay them money, that will mean BOD may but on market and that will be good outcome for share holders.You cannot compare salaries of MDs and CEOs of US based companies to those of Aus based companies.
Sean as part of his package gets circa $US500K cash and the rest as shares and options (81%).
$US500K cash for a CEO is peanuts in the US.
If BRN goes belly up and his shares worthless he has worked for Jack Shiete.
His package is effectively designed to reward performance.
If his 5 year plan plays out he will be very, very wealthy via his package with 81% equities.
If you look at the recent Ann concerning Tony Viana share sales you will see 250k of restricted shares have vested and therefore attract income tax..
Note restricted shares for Tony after the transaction has reduced by the 250k vested shares.
Note that he sold 85k of those 250k shares at 23.5 cents to pay income tax on those 250k vested/he now owns shares.
The net result is that he owns out right an extra 165k shares (250k less 85k sold). The increase in his holding is reflected in the ann.
So in effect Tony has via his salary package purchased an extra 165k shares.
Contrary to what the downrampers on the crapper say these shares are not free as they are paid for via pay package.
Shares as part of a salary package are a great incentive strategy.
The irony is if you believe the company is a dud then senior staff are actually being paid dud money.
If you believe the company will eventually succeed then senior staff will be very, very well off financially via their holdings.
The bottom line is that the BOD is accumulating.
Altmann is not the messiah ...
When he bought the $51M worth of chips off the plan, Rain were touting pie-in-the-sky technology - a spaghetti bowl of self-organizing nanowires which miraculously formed an analog neural network, substituting chaos for complexity.
Rain have since completely changed their design and now offer a hybrid (Frankenstein) digital/analog MAC NN. Analog is very efficient in performing 1-bit multiplications, but, due to manufacturing variability, the output amplitude varies between different neuron multiplier.
To overcome this, Rain interposes ADC and DAC circuits within or between the NN layers.
US2024249190A1 GRADIENT COMPUTATION IN HYBRID DIGITALLY TIED ANALOG BLOCKS WITH ARBITRARY CONNECTIVITY BY EQUILIBRIUM PROPAGATION 20230119
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Having started as a pure analog comapny, Rain are late to the hybrid digital/analog party.
In Rain they use MACs.
US2024143541A1 COMPUTE IN-MEMORY ARCHITECTURE FOR CONTINUOUS ON-CHIP LEARNING 20221028
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A system capable of providing on-chip learning comprising a processor and a plurality of compute engines coupled with the processor. Each of the compute engines including a compute-in-memory (CIM) hardware module and a local update module. The CIM hardware module stores a plurality of weights corresponding to a matrix and is configured to perform a vector-matrix multiplication for the matrix. The local update module is coupled with the CIM hardware module and configured to update at least a portion of the weights.
Akida and Rain are competitors with incompatible technologies.
Thanks for this. Every now and then you just need posts like this to keep the hope aliveInsiders still hold circa 15.5% to 16.5% of SOI - that shows confidence.
LDN (Sean's predecessor) is still in the Top 50 holders - that shows confidence.
Sean takes 81% of his salary in shares and options - that shows confidence.
All the BOD take levels of shares and options as part of their pay- that shows confidence.
The above gives me confidence.
Yes. Those two would understand the new Rain tech. I doubt that either would have joined Rain if they were pushing the original nanowire NN which Altmann signed up for - think Theranos.Thanks Diogenese.
RainAI has attracted some top quality people such as Jean-Didier Allegrucci, who worked on Apple's s SoCs for over 17 years. And also former Meta architecture leader Amin Firoozshahian. And both of these appointments have been within the last 12 months.
I'll admit, I find it a bit of a pity that our technologies seem to be incompatible as they seem like they're shaping up to be a formidable competitor in our midst if I'm not mistaken.
Always positive my friend… if you’re negativ on something, stop it, change it, move on, or let it be.
Well said. Also opportunities for them to buy shares is limited especially if they are in possession of knowledge that would be seen as insider knowledge. I'm hoping they are in possession of a shit load of that knowledge.You cannot compare salaries of MDs and CEOs of US based companies to those of Aus based companies.
Sean as part of his package gets circa $US500K cash and the rest as shares and options (81%).
$US500K cash for a CEO is peanuts in the US.
If BRN goes belly up and his shares worthless he has worked for Jack Shiete.
His package is effectively designed to reward performance.
If his 5 year plan plays out he will be very, very wealthy via his package with 81% equities.
If you look at the recent Ann concerning Tony Viana share sales you will see 250k of restricted shares have vested and therefore attract income tax..
Note restricted shares for Tony after the transaction has reduced by the 250k vested shares.
Note that he sold 85k of those 250k shares at 23.5 cents to pay income tax on those 250k vested/he now owns shares.
The net result is that he owns out right an extra 165k shares (250k less 85k sold). The increase in his holding is reflected in the ann.
So in effect Tony has via his salary package purchased an extra 165k shares.
Contrary to what the downrampers on the crapper say these shares are not free as they are paid for via pay package.
Shares as part of a salary package are a great incentive strategy.
The irony is if you believe the company is a dud then senior staff are actually being paid dud money.
If you believe the company will eventually succeed then senior staff will be very, very well off financially via their holdings.
The bottom line is that the BOD is accumulating.
Sean is all in up to his neck financially with BRN.Well said. Also opportunities for them to buy shares is limited especially if they are in possession of knowledge that would be seen as insider knowledge. I'm hoping they are in possession of a shit load of that knowledge.
SC
New AI Algorithms Streamline Data Processing for Space-based Instruments | Science Mission Directorate
A team of NASA personnel and contractors has prototyped a new set of algorithms that will enable instruments in space to process data more efficiently. Using these algorithms, space-based remote sensors will be able to provide the most important data to scientists on the ground more quickly and...science.nasa.gov
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A team of NASA personnel and contractors has prototyped a new set of algorithms that will enable instruments in space to process data more efficiently. Using these algorithms, space-based remote sensors will be able to provide the most important data to scientists on the ground more quickly and may also be able to autonomously determine which Earth phenomena are the most important to observe.
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The International Space Station, where Steve Chien and his team prototyped a new set of AI algorithms that will reduce data latency and improve dynamic targeting capabilities for satellites. (Credit: NASA/ISS)
Earth-observing instruments can gather a world’s worth of information each day. But transforming that raw data into actionable knowledge is a challenging task, especially when instruments have to decide for themselves which data points are most important.
“There are volcanic eruptions, wildfires, flooding, harmful algal blooms, dramatic snowfalls, and if we could automatically react to them, we could observe them better and help make the world safer for humans,” said Steve Chien, a JPL Fellow and Head of Artificial Intelligence at NASA’s Jet Propulsion Laboratory.
Engineers and researchers from JPL and the companies Qualcomm and Ubotica are developing a set of AI algorithms that could help future space missions process raw data more efficiently. These AI algorithms allow instruments to identify, process, and downlink prioritized information automatically, reducing the amount of time it would take to get information about events like a volcanic eruption from space-based instruments to scientists on the ground.
These AI algorithms could help space-based remote sensors make independent decisions about which Earth phenomena are most important to observe, such as wildfires.
“It’s very difficult to direct a spacecraft when we’re not in contact with it, which is the vast majority of the time. We want these instruments to respond to interesting features automatically,” said Chien
Chien prototyped the algorithms using commercially available advanced computers onboard the International Space Station (ISS). During several different experiments, Chien and his team investigated how well the algorithms ran on Hewlett Packard Enterprise’s Spaceborne Computer-2 (SBC-2), a traditional rack server computer, as well as on embedded computers.
These embedded computers include the Snapdragon 855 processor, previously used in cell phones and cars, and the Myriad X processor, which has been used in terrestrial drones and low Earth orbit satellites.
Including ground tests using PPC-750 and Sabertooth processors – which are traditional spacecraft processors – these experiments validated more than 50 image processing, image analysis, and response scheduling AI software modules.
The experiments showed these embedded commercial processors are very suitable for space-based remote sensing, which will make it much easier for other scientists and engineers to integrate the processors and AI algorithms into new missions.
The full results of these experiments were published in a series of three papers at the 2022 IEEE Geoscience and Remote Sensing Symposium, which can be accessed through the links below.
Chien explains that while it is easiest to deploy AI algorithms from ground computers to larger, rack-mounted servers like the SBC-2, satellites and rovers have less space and power, which means they would need to use smaller, low-power, embedded processors similar to the Snapdragon or Myriad units.
By processing the data onboard, these AI algorithms prevent important or urgent information from being buried within larger data transmissions. A researcher wouldn’t have to downlink and process an entire transmission to see that a hurricane is intensifying or a harmful algal bloom has formed.
“A large image could have gigabytes of data, so it might take a day to get it to the ground and process it. But you don’t need to process all that data to identify a wildfire. These algorithms pre-process data onboard so that researchers get the most important information first,” said Chien.
These algorithms could be useful not only for Earth-observing instruments, but also for instruments observing other planets as well. The proposed Europa Lander mission, for example, could use Chien’s algorithms to help search for life on the Jovian moon.
“There are several missions that are in concept development right now that could use this technology. They’re still in the early phases of development, but these are missions that need the kind of onboard analysis, understanding, and response these algorithms enable,” said Chien.
The team is also testing neural network models to interpret Mars satellite imagery. “Someday such a neural net could enable a satellite to detect a new impact ejecta, evidence of a meteorite impact, and alert other spacecraft or take follow-up images,” said JPL Data Scientist Emily Dunkel. “Rovers could also use these processors with neural networks to determine where it is safe for the rover to drive,” Dr. Dunkel added.
“We used the CogniSat framework to deploy models to the Myriad X, reducing the effort to develop deep learning models for onboard use. This experience helps prove that this advanced hardware and software system is ready now for space missions,” said Léonie Buckley, Senior Engineer at Ubotica.
As climate change continues to alter the world we live in, information systems like Chien’s allow scientific instruments to be as dynamic as the Earth systems they observe.
“We don’t often think about the fact that we’re walking around with more computing power in our cell phones than supercomputers had forty years ago. It’s an amazing world we live in, and we’re trying to incorporate those advancements into NASA missions,” said Chien. View attachment 35765 View attachment 35765