Rise from the ashes
Regular
Excellent Maxy. No fussing about ..just done! Boom!
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JoMo68
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Best ASX shares to buy in June 2022
We asked our Foolish contributors to compile a list of some of the ASX shares they reckon are worth a look at in June. Here's the lowdown...
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Gotta love old mate Aaron.
Rise from the ashes
Regular
Got to love Tim
Fact Finder
Top 20
I recently posted about the lead AKIDA had over Loihi 2. To achieve what these researchers have with Loihi 1 they needed 22 x Loihi 1 chips and expect to improve these results with Loihi 2.
As you read this article remember AKIDA 1 is already head of Loihi 2 and AKIDA 2. With LSTM is about to be released on the World.
AKIDA replacing the GPU now there is a market we did not estimate the value of but Peter did.
My opinion only DYOR
FF
AKIDA BALLISTA
Photo by Josh Riemer on Unsplash
Neuromorphic computers should make artificial intelligence more efficient by replicating real neurons. However, processing sequential data, such as sentences, requires additional circuitry and negates the efficiency advantage. Researchers at the Graz University of Technology were able to show that these circuits are not required. Their results were published in Nature Machine Intelligence, a preliminary version available free of charge.
Instead of large matrices, neuromorphic computers use artificial neurons. This goes hand in hand with another network model called the Spiking Neural Network (SNN). In this case, the neurons are inactive most of the time and only generate a short current impulse when their action potential is reached. Only when a neuron triggers an impulse do the potentials of the neurons connected to it have to be recalculated, which saves energy.
However, large neural networks typically use long short-term memory (LSTM). It is only with this that they become manageable at all. Neuromorphic computers such as Intel’s Loihi, which the researchers used, use memory for this purpose.
The researchers tested their approach with the Babi data set, in which the network reads a sequence of sentences and then has to answer a question about their content. To do this, they converted an existing network into an SNN and pitted it against the original, which computed a GTX 2070 Super. The neuromorphic implementation was up to 16 times more efficient, but only for the smallest two-sentence network. If the network had to remember 20 sentences, the Loihis were only four times more efficient.
As you read this article remember AKIDA 1 is already head of Loihi 2 and AKIDA 2. With LSTM is about to be released on the World.
AKIDA replacing the GPU now there is a market we did not estimate the value of but Peter did.
My opinion only DYOR
FF
AKIDA BALLISTA
Artificial Neurons Could be 16 Times More Efficient Than GPUs
Austrian researchers are making the processing of data sequences more efficient by optimizing short-term memory.
Photo by Josh Riemer on Unsplash
Neuromorphic computers should make artificial intelligence more efficient by replicating real neurons. However, processing sequential data, such as sentences, requires additional circuitry and negates the efficiency advantage. Researchers at the Graz University of Technology were able to show that these circuits are not required. Their results were published in Nature Machine Intelligence, a preliminary version available free of charge.
Instead of large matrices, neuromorphic computers use artificial neurons. This goes hand in hand with another network model called the Spiking Neural Network (SNN). In this case, the neurons are inactive most of the time and only generate a short current impulse when their action potential is reached. Only when a neuron triggers an impulse do the potentials of the neurons connected to it have to be recalculated, which saves energy.
However, large neural networks typically use long short-term memory (LSTM). It is only with this that they become manageable at all. Neuromorphic computers such as Intel’s Loihi, which the researchers used, use memory for this purpose.
Even more, copied from the brain
This is not particularly efficient, which is why the researchers used a different approach, also inspired by real neurons. Real neurons, after firing once, are less excitable for a time. According to Wolfgang Maass, a professor at TU Graz, this is believed to be the basis of short-term memory in the real brain. With the artificial neurons, the lower sensitivity is achieved via a small current that counteracts the incoming activating current. It decreases over time, this function is also easy to implement on the Loihi chip. The neuron thus briefly remembers what happened in the past.The researchers tested their approach with the Babi data set, in which the network reads a sequence of sentences and then has to answer a question about their content. To do this, they converted an existing network into an SNN and pitted it against the original, which computed a GTX 2070 Super. The neuromorphic implementation was up to 16 times more efficient, but only for the smallest two-sentence network. If the network had to remember 20 sentences, the Loihis were only four times more efficient.
Even more efficient with a new chip
The more sentences the model had to process, the smaller the gain in efficiency was. The reason for this is that more artificial neurons and thus more of the 32 Loihi chips are required on the Nahuku board used. The network used to process a sequence of 20 sets used 22 chips. The researchers are hoping for further increases in efficiency from the successor to the Loihi used. Since this contains eight times as many artificial neurons, there is no communication between the individual chips.Fact Finder
Top 20
In 2020, the global graphics processing unit (GPU) market was valued at 25.41 billion U.S. dollars, with forecasts suggesting that by 2028 this is likely to rise to 246.51 billion U.S. dollars, growing at a compound annual growth rate (CAGR) of 32.82 percent from 2021 to 2028.
https://www.statista.com › statistics
In 2028 the Global GPU market will be US 246.51 billion dollars.
In Brainchip we trust but importantly so do:
1. Renesas
2. MegaChips
3. Valeo
4. Mercedes Benz
5. NASA
1% of the Global GPU market in 2028 is US 2.47 billion dollars.
0.5% of the Global GPU market in 2028 is US 1.23 billion dollars.
0.25% of the Global GPU market in 2028 is US 600 million dollars.
It seems like I am being ridiculous suggesting Brainchip will only capture a quarter of one percent of this market.
What did Nviso say 1,000 frames per second with a few tweaks.
My opinion only DYOR
FF
AKIDA BALLISTA
https://www.statista.com › statistics
• GPU market size worldwide 2020-2028 | Statista
In 2028 the Global GPU market will be US 246.51 billion dollars.
In Brainchip we trust but importantly so do:
1. Renesas
2. MegaChips
3. Valeo
4. Mercedes Benz
5. NASA
1% of the Global GPU market in 2028 is US 2.47 billion dollars.
0.5% of the Global GPU market in 2028 is US 1.23 billion dollars.
0.25% of the Global GPU market in 2028 is US 600 million dollars.
It seems like I am being ridiculous suggesting Brainchip will only capture a quarter of one percent of this market.
What did Nviso say 1,000 frames per second with a few tweaks.
My opinion only DYOR
FF
AKIDA BALLISTA
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Boab
I wish I could paint like Vincent
I'd be more than happy if our SP could have a CAGR similar to the above prediction but I believe it will be much greater than that.
M_C
Founding Member
Hey Macca,
Sorry for the delayed response to your question. *IF it is AKIDA IP, then it will need to be disclosed once it becomes material in nature (my understanding and interpretation)...........pretty grey area which can be argued either way so hard to say when we would see an ann regarding it (if it isn't through one of our existing licensees eg: Renesas / Megachips)
The article states that the evaluation kit is being presented between the 1st and 3rd of June and starts shipping in June, so.......................
Could we see an ann about it in June? Possibly. OR we may have to wait for Panasonic to pay Socionext before it becomes "material" in the eyes of the company.
OR we may only find out when we get revenue...........
OR......... it just simply may not be our IP..........although I am reasonably confident in our chances involving Socionext............ and Panasonic for that matter
Your guess is as good as mine, just my thoughts
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Mccabe84
Regular
I guess I might have to buy some more shares today so I can retire early
jtardif999
Regular
If we are turning over 800 billion and we still only have 100 staff that’s 8 billion each.. should make how many dividends paid interesting 
JK200SX
Regular
$88/shareIf we are turning over 800 billion and we still only have 100 staff that’s 8 billion each.. should make how many dividends paid interesting
M_C
Founding Member
Fact Finder
Top 20
Using sparsity to advantage allows AKIDA to excel and out perform the incumbents.
The following extract is interesting because it highlights why systems that cannot embrace sparsity have to eventually hit road blocks when sparsity is all there is or you can spend time and energy making up synthetic data to allow traditional Von Neumann deep learning to keep on processing.
Neural Computing and Applications(2022)Cite this article
The following extract is interesting because it highlights why systems that cannot embrace sparsity have to eventually hit road blocks when sparsity is all there is or you can spend time and energy making up synthetic data to allow traditional Von Neumann deep learning to keep on processing.
Special Issue on Deep Learning for Time Series Data
- S.I.: Deep Learning for Time Series Data
- Published: 30 May 2022
CGAN-based synthetic multivariate time-series generation: a solution to data scarcity in solar flare forecasting
Show authorsNeural Computing and Applications(2022)Cite this article
- 1 Accesses
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Abstract
One of the major bottlenecks in refining supervised algorithms is data scarcity. This might be caused by a number of reasons often rooted in extremely expensive and lengthy data collection processes. In natural domains such as Heliophysics, it may take decades for sufficiently large samples for machine learning purposes. Inspired by the massive success of generative adversarial networks (GANs) in generating synthetic images, in this study we employed the conditional GAN (CGAN) on a recently released benchmark dataset tailored for solar flare forecasting. Our goal is to generate synthetic multivariate time-series data that (1) are statistically similar to the real data and (2) improve the performance of flare prediction when used to remedy the scarcity of strong flares. To evaluate the generated samples, first, we used the Kullback–Leibler divergence and adversarial accuracy measures to quantify the similarity between the real and synthetic data in terms of their descriptive statistics. Second, we evaluated the impact of the generated samples by training a predictive model on their descriptive statistics, which resulted in a significant improvement (over 1100% in TSS and 350% in HSS). Third, we used the generated time series to examine their high-dimensional contribution to mitigating the scarcity of the strong flares, which we also observed a significant improvement in terms of TSS (4%, 7%, and 31%) and HSS (75%, 35%, and 72%), compared to oversampling, undersampling, and synthetic oversampling methods, respectively. We believe our findings can open new doors toward more robust and accurate flare forecasting models.Fact Finder
Top 20
M_C
Founding Member
Sshh you stay out of this lolSaid the pot to the kettle.
Shadow59
Regular
Should have rounded up!Yeah sorry, its $88.80 / share
JK200SX
Regular
nah, the triple 8 is auspicious!Should have rounded up!
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