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  1. #1
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    Cool Brn - brainchip holdings

    Brainchip Holdings - Thought i would bring this to your attention

    We are looking at the Worlds first and autonomous learning technology -


    1) Patented in 2008 by Peter Van Der Made
    They currently talking with fortune 500 companies about developments and Joint venture opportunities

    2) Essentially BRAINCHIP replicates the biological behaviour of the human brain and uses 1/1000 of the consumption of power - The name of 3) The technology is SNAP (spiking neuron ADAPTIVE PROCESSOR )
    Validated by some of the worlds pre- eminent neuroscientists

    Spiking Neuron Adaptive Processor (SNAP)

    BrainChip’s inventor, Peter van der Made, has created an exciting new Spiking Neural Networking technology that has the ability to learn autonomously, evolve and associate information just like the human brain. The technology is developed as a digital design containing a configurable “sea of biomimic neurons’.

    The technology is fast, completely digital, and consumes very low power, making it feasible to integrate large networks into portable battery-operated products, something that has never been possible before.

    BrainChip neurons autonomously learn through a process known as STDP (Synaptic Time Dependent Plasticity). BrainChip’s fully digital neurons process input spikes directly in hardware. Sensory neurons convert physical stimuli into spikes. Learning occurs when the input is intense, or repeating through feedback and this is directly correlated to the way the brain learns.

    “Peter’s technology has the ability to learn autonomously and evolve.
    It operates at the speed of the brain and has low power consumption.”
    – Dr. Nick Spitzer, Director, Kavli Institute, California
    Computing Artificial Neural Networks (ANNs)
    The brain consists of specialized nerve cells that communicate with one another. Each such nerve cell is called a Neuron,. The inputs are memory nodes called synapses. When the neuron associates information, it produces a ‘spike’ or a ‘spike train’. Each spike is a pulse that triggers a value in the next synapse. Synapses store values, similar to the way a computer stores numbers. In combination, these values determine the function of the neural network. Synapses acquire values through learning.

    In Artificial Neural Networks (ANNs) this complex function is generally simplified to a static summation and compare function, which severely limits computational power. BrainChip has redefined how neural networks work, replicating the behaviour of the brain. BrainChip’s artificial neurons are completely digital, biologically realistic resulting in increased computational power, high speed and extremely low power consumption.

    The Problem with Artificial Neural Networks
    Standard ANNs, running on computer hardware are processed sequentially; the processor runs a program that defines the neural network. This consumes considerable time and because these neurons are processed sequentially, all this delayed time adds up resulting in a significant linear decline in network performance with size.

    BrainChip neurons are all mapped in parallel. Therefore the performance of the network is not dependent on the size of the network providing a clear speed advantage. So because there is no decline in performance with network size, learning also takes place in parallel within each synapse, making STDP learning very fast.

    A hardware solution
    BrainChip’s digital neural technology is the only custom hardware solution that is capable of STDP learning. The hardware requires no coding and has no software as it evolves learning through experience and user direction.

    The BrainChip neuron is unique in that it is completely digital, behaves asynchronously like an analog neuron, and has a higher level of biological realism. It is more sophisticated than software neural models and is many orders of magnitude faster. The BrainChip neuron consists entirely of binary logic gates with no traditional CPU core. Hence, there are no ‘programming’ steps. Learning and training takes the place of programming and coding. Like of a child learning a task for the first time.

    Software ‘neurons’, to compromise for limited processing power, are simplified to a point where they do not resemble any of the features of a biological neuron. This is due to the sequential nature of computers, whereby all data has to pass through a central processor in chunks of 16, 32 or 64 bits. In contrast, the brain’s network is parallel and processes the equivalent of millions of data bits simultaneously.

    A significantly faster technology
    Performing emulation in digital hardware has distinct advantages over software. As software is processed sequentially, one instruction at a time, Software Neural Networks perform slower with increasing size. Parallel hardware does not have this problem and maintains the same speed no matter how large the network is. Another advantage of hardware is that it is more power efficient by several orders of magnitude.

    The speed of the BrainChip device is unparalleled in the industry.

    For large neural networks a GPU (Graphics Processing Unit) is ~70 times faster than the Intel i7 executing a similar size neural network. The BrainChip neural network is faster still and takes far fewer CPU (Central Processing Unit) cycles, with just a little communication overhead, which means that the CPU is available for other tasks. The BrainChip network also responds much faster than a software network accelerating the performance of the entire system.

    The BrainChip network is completely parallel, with no sequential dependencies. This means that the network does not slow down with increasing size.

    Endorsed by the neuroscience community
    A number of the world’s pre-eminent neuroscientists have endorsed the technology and are agreeing to joint develop projects.

    BrainChip has the potential to become the de facto standard for all autonomous learning technology and computer products.

    Patented
    BrainChip’s autonomous learning technology patent was granted on the 21st September 2008 (Patent number US 8,250,011 “Autonomous learning dynamic artificial neural computing device and brain inspired system”). BrainChip is the only company in the world to have achieved autonomous learning in a network of Digital Neurons without any software.

    A prototype Spiking Neuron Adaptive Processor was designed as a ‘proof of concept’ chip.

    The first tests were completed at the end of 2007 and this design was used as the foundation for the US patent application which was filed in 2008. BrainChip has also applied for a continuation-in-part patent filed in 2012, the “Method and System for creating Dynamic Neural Function Libraries”, US Patent Application 13/461,800 which is pending.

    Hardware only application so no software to impair its performance - milestone 1 ACHIEVED

    https://www.google.com/patents/US20100076916

    APPLICATIONS:

    FIRST STAGE DEVELOPMENT APPLICATIONS
    Obvious applications for this technology are in speech recognition, speaker recognition, and extraction of speech and sound from a noisy background environment. Other experiments show that the devices can also be successfully applied in applications such as visual image recognition, robotics and autonomous learning machines used in exploration and unmanned vehicles.

    The advantages of using a Spiking Neuron Adaptive Processing (SNAP) device over a traditional programmed device are a shorter development path, faster response times, better quality recognition, persistent learning after the initial commission of the system and the re-usability of training models.

    BrainChip’s ongoing development work is focused on the commercialisation of key applications that were prioritised after consultation and direction from potential technology partners located in California.The basis for these first stage developments is related to the requirements for specific applications from such companies. These discussions have focussed the BrainChip engineering department on:
    Smart Phone
    Smart Phone technology applications use unique voice and image identification capabilities to give the user a far greater interactive experience than ever before. These high volume applications, when development is completed, are expected to be licensed and generate royalties from leading smartphone chip manufacturers and will be used in smartphones, smart television sets and tablets.
    Internet of Things (IoT)

    IoT is a term used to describe the network of objects and people to the internet. ABI research (ABI) estimates there will be 30 billion devices connected to the Internet of Things by 2020. Applications for the IoT devices are broad and include Environmental monitoring, Manufacturing, Energy Management and many more. The estimated value of the IoT sector according to Cisco is US$14.6 trillion.

    Robotic Technology
    Hardware that is designed to interact with users visually and aurally could give the user an interactive experience which would be adapted for the individual and give a different and unique experience every time. Toys would be a good example of this application and as an added value, safety warning systems could also be inbuilt into the toys.

    Limitless Possibilities
    The BrainChip team has developed further interest for a more diverse range of applications from sectors including cyber security, gaming, driverless vehicles, and medical however, the capability of the technology is not limited to these sectors alone.

    Read about BrianChipSolves the most fundamental problem in our neuro computing today

    Applications are limitless:


    The technology will be licensed which will provide "BRAINCHIP WITH A HIGH MARGIN BUSINESS"

    https://www.google.com/patents/US20100076916
    \"if women didn,t exist , all the money in the world would mean nothing\" Aristotle Anasis.

    \"The trend is your friend\"

    \"A mans reach should always extend beyond his grasp" J.F Kennedy

  2. #2
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    Arrow Wait and see if it develops and TA signals for me

    Highly speculative, interesting concept which may come to something and then again, may not.

    No Income yet?
    Lots of shares and stuff [Read this]

    No trades yet since it came to market after reversing into Aziana (probably a failed miner/explorer)

    Best Wishes
    Paper Tiger
    om mani peme hum

  3. #3
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    Also like the look of this, picked up a parcel last week. If it shoots up i'll sell some and de-risk a bit

    When you say no trades do you mean no trades PT? Over 30m volume changed hands last week

  4. #4
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    Red face Best you not know what other software I have written

    Quote Originally Posted by clip View Post
    Also like the look of this, picked up a parcel last week. If it shoots up i'll sell some and de-risk a bit

    When you say no trades do you mean no trades PT? Over 30m volume changed hands last week
    You are right and my market data says your right but my charting software says no trades - I have filed a bug report and I will fix it as soon as possible.

    Best Wishes
    Paper Tiger
    om mani peme hum

  5. #5
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    Brainchip - has gone well today and i EXPECT SOME NEWS REGARDING PARTNERSHIPS VERY SOON !! or milestones that are also on their way which could make sense of recent buying yesterday and today
    Nice volume and should close around 27c if we get some late buying
    Last edited by SCHUMACHER; 29-09-2015 at 05:21 PM.
    \"if women didn,t exist , all the money in the world would mean nothing\" Aristotle Anasis.

    \"The trend is your friend\"

    \"A mans reach should always extend beyond his grasp" J.F Kennedy

  6. #6
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    Happy with my Friday parcel and AUZ as well.. first entry points in some number of months. Funnily (or unfunilly enough) it seems scoping the daytraders thread on HC can be fruitful to pick up on tickers with expected news & movements

  7. #7
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    Highly speculative could go either way in the short term.
    I personally maintain the opinion that no revolutionary tech company will be backdoor listed onto the ASX.
    In addition less than half of companies that under go a reverse merger survive after two years. Even less provide a reasonable return.
    Not a recommendation or advice.

  8. #8
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    Stoplosses set to cover exchange changes + brokerage and turn a (even if a teensy tiny) profit.. have learnt from past mistakes! Good volume and green stocks in a sea of red showing positive signs IMO

  9. #9
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    Quote Originally Posted by clip View Post
    Stoplosses set to cover exchange changes + brokerage and turn a (even if a teensy tiny) profit.. have learnt from past mistakes! Good volume and green stocks in a sea of red showing positive signs IMO
    i Think you will find this is bigger than what you may think - they are talking with fortune 500 companies at the moment and they are working on the next milestones - i expect this to head back up now as we seeing today and have a target of 30c by end of week
    \"if women didn,t exist , all the money in the world would mean nothing\" Aristotle Anasis.

    \"The trend is your friend\"

    \"A mans reach should always extend beyond his grasp" J.F Kennedy

  10. #10
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    Quote Originally Posted by SCHUMACHER View Post
    i Think you will find this is bigger than what you may think - they are talking with fortune 500 companies at the moment and they are working on the next milestones - i expect this to head back up now as we seeing today and have a target of 30c by end of week

    Some aggressive buying today - wiping out lines up to 28c - looks like we will see more of this price action today and I'm revising my target to 30c today - by end of day if not 30c + - something up and last few days been some good accumulation
    \"if women didn,t exist , all the money in the world would mean nothing\" Aristotle Anasis.

    \"The trend is your friend\"

    \"A mans reach should always extend beyond his grasp" J.F Kennedy

  11. #11
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    Quote Originally Posted by SCHUMACHER View Post
    i Think you will find this is bigger than what you may think - they are talking with fortune 500 companies at the moment and they are working on the next milestones - i expect this to head back up now as we seeing today and have a target of 30c by end of week
    Tech is probably the one sector where i'm able to read and understand the announcements due to my career, not underestimating this just being careful not to repeat last mistakes. Haven't bought anything in a year or so, picked up gold/miner, tech and biomed in the past few days. Very much green amongst the sea of ASX red this week

  12. #12
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    Quote Originally Posted by clip View Post
    Tech is probably the one sector where i'm able to read and understand the announcements due to my career, not underestimating this just being careful not to repeat last mistakes. Haven't bought anything in a year or so, picked up gold/miner, tech and biomed in the past few days. Very much green amongst the sea of ASX red this week

    Fair comments - pays to be risk adverse but commodities for me are scarier - the CMI commodities index is sick looking and globally growth is worsening - technology sectors is where the experts are saying the best performing sector even biotech moves slower.

    Technology sector is doing well - anyway my prediction came true we hit 31c so i would expect a bit of a sell off now after a run from 27c
    29.5c is now strong support so should hold there
    \"if women didn,t exist , all the money in the world would mean nothing\" Aristotle Anasis.

    \"The trend is your friend\"

    \"A mans reach should always extend beyond his grasp" J.F Kennedy

  13. #13
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    Yep I have taken some profits am getting close to free carrying now, dropping a bit now but not planning to sell off with expected milestone ann's this month

  14. #14
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    BRN have released an investor presentation today which is well worth reading for anyone mildly interested, it summarizes their technology/goals/key upcoming delivery dates in a way that is not too technical/should be understandable by most people. Up another 14% today, haven't had a red day in almost 2 weeks. http://www.asx.com.au/asx/statistics...idsId=01668917

    Until they meet milestone 2 (putting the technology onto a hardware chip) which is expected in the next month, the technology is still experimental/ideological but all information they have released so far suggests they are on track. They have demonstrated it's capabilities in computer simulations/models only. This is still a speculative stock at this stage (regardless of how confident current holders may be) - DYOR, fair disclosure etc

    Their website also http://brainchipinc.com/

  15. #15
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    S/P taken a dive ; pretty well down to the 1 for 26 non renounceable offer @ 15c in April.When will they start making sales?Running low on cash hence the re $4mill raise;$1.75v mill underwritten for a 6% fee. Too early ,too risky for me.
    Prospectus - Non Renounceable Rights Issue-BRN.AX
    Investor Presentation-BRN.AX
    Annual Report to shareholders-BRN.AX Appendix 4C - quarterly-BRN.AX
    Company Update-BRN.AX
    Virtual Roadshow Presentation-BRN.AX
    RIGHTS ISSUE UNDERWRITTEN FOR $1.75M AND SHAREHOLDER SUPPORT-BRN.AX
    Last edited by Joshuatree; 10-05-2016 at 08:38 PM.

  16. #16
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    There is this little University in the USA that has developed their own "Brain Chip", going the other way, capable of doing deep learning. I can't help but feel that there is more hype than reality around Brainchip, many buzz words, promises, not much in the way of progress (reminds me of In***net to be honest).

    Personally, I'd go with that little University winning this race, you might of heard of them... MIT. http://news.mit.edu/2016/neural-chip...e-devices-0203

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    Many thanks Knot; even this technophobe can understand it; have posted it
    Larry hardesty MIT News office

    In recent years, some of the most exciting advances in artificial intelligence have come courtesy of convolutional neural networks, large virtual networks of simple information-processing units, which are loosely modeled on the anatomy of the human brain.

    Neural networks are typically implemented using graphics processing units (GPUs), special-purpose graphics chips found in all computing devices with screens. A mobile GPU, of the type found in a cell phone, might have almost 200 cores, or processing units, making it well suited to simulating a network of distributed processors.
    At the International Solid State Circuits Conference in San Francisco this week, MIT researchers presented a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing.
    Neural nets were widely studied in the early days of artificial-intelligence research, but by the 1970s, they’d fallen out of favor. In the past decade, however, they’ve enjoyed a revival, under the name “deep learning.”
    “Deep learning is useful for many applications, such as object recognition, speech, face detection,” says Vivienne Sze, the Emanuel E. Landsman Career Development Assistant Professor in MIT's Department of Electrical Engineering and Computer Science whose group developed the new chip. “Right now, the networks are pretty complex and are mostly run on high-power GPUs. You can imagine that if you can bring that functionality to your cell phone or embedded devices, you could still operate even if you don’t have a Wi-Fi connection. You might also want to process locally for privacy reasons. Processing it on your phone also avoids any transmission latency, so that you can react much faster for certain applications.”
    The new chip, which the researchers dubbed “Eyeriss,” could also help usher in the “Internet of things” — the idea that vehicles, appliances, civil-engineering structures, manufacturing equipment, and even livestock would have sensors that report information directly to networked servers, aiding with maintenance and task coordination. With powerful artificial-intelligence algorithms on board, networked devices could make important decisions locally, entrusting only their conclusions, rather than raw personal data, to the Internet. And, of course, onboard neural networks would be useful to battery-powered autonomous robots.
    Division of labor
    A neural network is typically organized into layers, and each layer contains a large number of processing nodes. Data come in and are divided up among the nodes in the bottom layer. Each node manipulates the data it receives and passes the results on to nodes in the next layer, which manipulate the data they receive and pass on the results, and so on. The output of the final layer yields the solution to some computational problem.
    In a convolutional neural net, many nodes in each layer process the same data in different ways. The networks can thus swell to enormous proportions. Although they outperform more conventional algorithms on many visual-processing tasks, they require much greater computational resources.
    The particular manipulations performed by each node in a neural net are the result of a training process, in which the network tries to find correlations between raw data and labels applied to it by human annotators. With a chip like the one developed by the MIT researchers, a trained network could simply be exported to a mobile device.
    This application imposes design constraints on the researchers. On one hand, the way to lower the chip’s power consumption and increase its efficiency is to make each processing unit as simple as possible; on the other hand, the chip has to be flexible enough to implement different types of networks tailored to different tasks.
    Sze and her colleagues — Yu-Hsin Chen, a graduate student in electrical engineering and computer science and first author on the conference paper; Joel Emer, a professor of the practice in MIT’s Department of Electrical Engineering and Computer Science, and a senior distinguished research scientist at the chip manufacturer NVidia, and, with Sze, one of the project’s two principal investigators; and Tushar Krishna, who was a postdoc with the Singapore-MIT Alliance for Research and Technology when the work was done and is now an assistant professor of computer and electrical engineering at Georgia Tech — settled on a chip with 168 cores, roughly as many as a mobile GPU has.
    Act locally
    The key to Eyeriss’s efficiency is to minimize the frequency with which cores need to exchange data with distant memory banks, an operation that consumes a good deal of time and energy. Whereas many of the cores in a GPU share a single, large memory bank, each of the Eyeriss cores has its own memory. Moreover, the chip has a circuit that compresses data before sending it to individual cores.
    Each core is also able to communicate directly with its immediate neighbors, so that if they need to share data, they don’t have to route it through main memory. This is essential in a convolutional neural network, in which so many nodes are processing the same data.
    The final key to the chip’s efficiency is special-purpose circuitry that allocates tasks across cores. In its local memory, a core needs to store not only the data manipulated by the nodes it’s simulating but data describing the nodes themselves. The allocation circuit can be reconfigured for different types of networks, automatically distributing both types of data across cores in a way that maximizes the amount of work that each of them can do before fetching more data from main memory.
    At the conference, the MIT researchers used Eyeriss to implement a neural network that performs an image-recognition task, the first time that a state-of-the-art neural network has been demonstrated on a custom chip.
    “This work is very important, showing how embedded processors for deep learning can provide power and performance optimizations that will bring these complex computations from the cloud to mobile devices,” says Mike Polley, a senior vice president at Samsung’s Mobile Processor Innovations Lab. “In addition to hardware considerations, the MIT paper also carefully considers how to make the embedded core useful to application developers by supporting industry-standard [network architectures] AlexNet and Caffe.”

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    I'm still very bullish on Brainchip.
    Deep learning is making a lot of noise, but it's old technology. The power/processing requirements are too high for small embedded and remote devices.
    Rapid Autonomous Learning (Brainchip) is in its infancy, so will take a little while to get going, but it's definitely the future. The disruption factor is just too significant for it to be ignored.
    They've kept all their promises thus far and delivered significant progress in a short time, and the advisory board alone makes me confident.

    Patience is required. The hype got a bit ahead of itself, and some people got burnt bigtime, so the sentiment is low. If you believe in the technology then it would be called a perfect time to accumulate.

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    Up 143% on good news and indications of more to come...

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    Yes - BRN up 15c today 142.9% to $0.255 on $14,775K

    BrainChip to roll out game protection technology at major Las
    Vegas casino following successful completion of trial
    Highlights
    BrainChip will roll out its innovative casino table security technology at one of
    Las Vegas’ biggest casinos following the successful completion of the phase one
    trial
    The roll out of BrainChip’s Spiking Neural Networkbased SNAPvision
    technology will initially be across all baccarat tables in the trial casino with a
    second casino to be added in the next month, followed by a roll out to more of
    the group’s casinos
    The roll out will deliver an immediate and growing revenue stream to
    BrainChip
    Second product application is currently being trialled across a range of table
    games including Blackjack, Blackjack Switch and Ultimate Poker
    Huge market opportunity with the casino management industry, which
    includes security and surveillance, expected to grow to US$4.5 billion in size by
    2018

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