KMuzza
Mad Scientist
2024 Neo QLED AI TV - More Wow than ever | Samsung Australia
Explore the latest Samsung Neo QLED AI TV with cutting-edge technologies. Compare the specs and features of various Samsung's Neo QLED TV models.
BRN Discussion Ongoing
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Samsung does a lot of work on analog ReRAM.2024 Neo QLED AI TV - More Wow than ever | Samsung Australia
Explore the latest Samsung Neo QLED AI TV with cutting-edge technologies. Compare the specs and features of various Samsung's Neo QLED TV models.www.samsung.com
US2023386601A1 NEUROMORPHIC DEVICE 20220531
A neuromorphic device includes a memory cell array including first resistive memory cells connected to word lines, bit lines and source lines, second resistive memory cells connected to the word lines, at least one redundancy bit line and at least one redundancy source line, third resistive memory cells connected to at least one redundancy word line, the bit lines and the source lines. The memory cell array stores data corresponding to a weight of a neural network in the first resistive memory cells, and is configured to generate a plurality of read currents based on input signals and the data. The neuromorphic device further includes an analog to digital converter (ADC) circuit configured to convert the plurality of read currents into a plurality of digital signals.
Fullmoonfever
Top 20
Wouldn't mind being in the cognitive AI part of this solution.
www.tcs.com
TCS Neural Manufacturing™: Enabling Manufacturing Innovation & Growth
TCS’ solution aids transformation in the manufacturing industry & enables manufacturers to become future-ready by building connected & collaborative ecosystems.
cosors
👀
I thought so too, but there is a counterexample:
...Anson is not a straight line to 0.23
DingoBorat
Slim
Reporting short positions isn't "enforced" by either the ASX or ASIC.
So whatever figures are or aren't given to indicate "no increase" "a steady increase" "a strong drop" or whatever, are just further tools for them to manipulate market perception, really...
Hard to figure out how the game is being played, when the participants don't "need" to follow any rules and the the so called "umpires" are on a perpetual coffee break, stuffing donuts and other "objects" down their throats.
cosors
👀
This is one of the reasons why I advise young or new investors here not to buy ASX shares if they do not have experience.
To be honest, if I'd had my experience back then and had known apart from this issue above that the pensions of an entire state was attached to it and that it was a completely different playing field to what we're used to here in Europe, then I would never have seriously invested at the ASX.
I was warned to invest in South Korea
I can only smile. They have a handle on the economic damage of shorting.
I don't even want to know how much the Chinese control your stock market in the background.
I don't care about that now. I have made my choice for StartUps and will finish reading my novels.
The ASX seems to me to be the opposite of a transparent stock exchange where I personally would want to see my pension.
But I don't want to be provocative and have little idea what it's really like. Just a naive view from the other side of the world.
___
Essentially I have four ASX start-ups. Three have a tec or resource that stinks China.
The fourth has fallen to 0.23% shorts (ASN). Not only are they largely owned by a Chinese investor, but one of their tecs is Chinese IP and this in the heart of the USA.)
Without ulterior motives... LoL
It's just that as a foreigner I can't see how your policies are protecting your stock market or pensions.
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cosors
👀
And yes, spelling mistakes and that again and again are not good and cannot be debated away.
For me, the last one wasn't so dramatic because I like the abbreviation. BRD stands for Federal Republic of Germany (Bundesrepublik Deutschland) and is a loved term for me after our reunification. BRD says it all.
I know that doesn't help - LoL
For me, the last one wasn't so dramatic because I like the abbreviation. BRD stands for Federal Republic of Germany (Bundesrepublik Deutschland) and is a loved term for me after our reunification. BRD says it all.
I know that doesn't help - LoL
Investig8ed
Regular
Has anyone participated in the CR through AusSuper, if so how did you do it. All I received was an email with no links and can't find anything on the app.
rgupta
Regular
Management is not responsible for their remarks unless they come out and provide us a forward looking statement. Without the same everything else is smoke.
Directors are judged in board meetings depending what they committed to BODs and if BOD get revenue update and they feel satisfy they will release the same for retail holders as well.
I am not expecting anything for at least next one year. I was thinking like you before last CR but now it looks like the experiment phase is still not finishing.
Dyor
Good morning,
In this episode of “This is Our Mission” podcast, Sean Hehir and guest, Dr. Eric Gallo, Technology R&D Senior Principal at Accenture Labs discuss the benefits of neuromorphic technology and the current state of edge computing plus advancements in SpaceTech. Dr. Gallo is at the forefront of next-generation computing technology and sustainable smart electronics. With a PhD in semiconductor devices and electronic materials, he has extensive experience in designing and prototyping advanced situational awareness systems for military and aerospace applications.
This podcast was giving us a very strong signal of whom we are dealing with, I believe indirectly that Sean and Rob (for that matter) have
sent out cryptic messages through the guests on our podcasts, as in EAP's or guests are actually the conduit between us and their client, working on design briefs, future potential products etc.
SELF-LEARNING NEUROMORPHIC ACOUSTIC MODEL FOR SPEECH RECOGNITION
US2024096313A1 • 2024-03-21 •
ACCENTURE GLOBAL SOLUTIONS LTD
Earliest priority: 2022-09-16 • Earliest publication: 2024-03-21
The above patent as with 4 others currently listed have us directly mentioned in the artwork, the above patent names Dr, Eric Gallo as one of
the inventors....I have mentioned ACCENTURE numerous times, in my opinion, they, like Digimarc Corp. act as conduits to the big whales currently
circling our technology, hidden at all times in a very thick "pea souper".
In this episode of “This is Our Mission” podcast, Sean Hehir and guest, Dr. Eric Gallo, Technology R&D Senior Principal at Accenture Labs discuss the benefits of neuromorphic technology and the current state of edge computing plus advancements in SpaceTech. Dr. Gallo is at the forefront of next-generation computing technology and sustainable smart electronics. With a PhD in semiconductor devices and electronic materials, he has extensive experience in designing and prototyping advanced situational awareness systems for military and aerospace applications.
This podcast was giving us a very strong signal of whom we are dealing with, I believe indirectly that Sean and Rob (for that matter) have
sent out cryptic messages through the guests on our podcasts, as in EAP's or guests are actually the conduit between us and their client, working on design briefs, future potential products etc.
SELF-LEARNING NEUROMORPHIC ACOUSTIC MODEL FOR SPEECH RECOGNITION
US2024096313A1 • 2024-03-21 •
ACCENTURE GLOBAL SOLUTIONS LTD
Earliest priority: 2022-09-16 • Earliest publication: 2024-03-21
The above patent as with 4 others currently listed have us directly mentioned in the artwork, the above patent names Dr, Eric Gallo as one of
the inventors....I have mentioned ACCENTURE numerous times, in my opinion, they, like Digimarc Corp. act as conduits to the big whales currently
circling our technology, hidden at all times in a very thick "pea souper".
I don’t know about you guys but I’m looking forward to a huge short squeeze. Just the imagination about how they will panic infront of their computer or phone, is already satisfying! Then I will turn to “the dean” and his friends and say “AHHHHH what a nice day to delete all your accounts and search for the next stock to bash ha?”
No but honestly….. it would be satisfying
No but honestly….. it would be satisfying
Smoothsailing
Regular
Now we just need some ham in that soup, a bit of meat on the bone.
rgupta
Regular
Hi Tech
Are you still looking forward to 1st January 2025 or you want to move your target date. The due date is approaching very fast and nothing is seeing on the horizon yet.
DingoBorat
Slim
"but now it looks like the experiment phase is still not finishing"
Keep in mind, that the Research and Development component, of any Company deeply rooted in "High Technology" will always be a very strong part of that Company.
AKIDA and TENNS tech, will never be "finished" they will always be improving.
They are commercially available and commercially viable now though.
It's not easy to get other Companies to commit, to anything inparticular, when everything is developing so quickly, but it's something that can't just be stopped either.
As the curve of Human Technological achievement steepens, it will be increasingly difficult, to have arrived at any particular point of development, that is "it".
There is also the old "If it ain't broke, don't fix it" thing, with moving to new technology and the desire to protect the status quo.
Thing is though, the "current" direction of old, isn't sustainable.
A.I. Technology, HAS to move to the Edge, otherwise it's just not going to work to it's full potential and will always be glitchy (through things like latency, connection and just the sheer amount of "things" plugged into an increasingly inadequate resource/infrastructure).
The Time we start seeing serious commercial traction though is hard to judge...
It could be a year (or even more) like you've said, but not for the reason you've stated.
I do bloody hope it's soon though!
Yes..when I originally chose that 2 year timeframe I thought at the time it was a "very fair" guide to make a personal call on the conpanies progress..we have made excellent progress so far..BUT currently I'd be lying if I said I was happy with our business model focused on IP...many holders are also well aware that things can flip very rapidly once an announcement linked to potentially huge revenue streams is released..so, yes I'm currently a little flat like the rest of us long termers.
Have a good day....Texta
Nice find Tech,
I like this bit ...
[0109] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array), an ASIC (application specific integrated circuit) a neuromorphic research chip, such as Intel's Loihi chip, or a neural network processor, such as BrainChip's Akida™ chip.
Getupthere
Regular
The shorters always have an exit strategy.
They won’t get burnt unless approx 30,000 registered BRN shareholders let them continue doing what they have been doing.
GameStop is the prefect example.
They had to stop everything to help the shorters that were shorting GameStop get out because all the GameStop shareholders bought on market and sent the share price north very quickly.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Inside Amazon’s new ‘Just Walk Out’: AI transformers meets edge computing
James Thomason@jathomasonJuly 31, 2024 6:07 PM
Amazon Just Walk Out
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On the first floor of an industrial modern office building, we are among a select group of journalists invited into a secretive lab at Amazon to see the latest Just Walk Out (JWO) technology.
Now used in more than 170 retail locations worldwide, JWO lets customers enter a store, select items, and leave without stopping to pay at a cashier, streamlining the shopping experience.
Harnessing the Power of Generative AI: How AI Is Changing Work and Beyond
We’re about to see the new AI-based system Amazon has developed, which uses multi-modal foundation models and transformer-based machine learning to simultaneously analyze data from various sensors in stores. Yes, this is the same fundamental technique used in large language models like GPT, only instead of generating text, these models generate receipts. This upgrade improves accuracy in complex shopping scenarios and makes the technology easier to deploy for retailers.
Our host is Jon Jenkins (JJ), Vice President of JWO at Amazon, who leads us past the small groups of Amazon employees sipping coffee in the lobby, through the glass security gates, and down a short dark hallway to a nondescript door. Inside we find ourselves standing in a full replica of your local bodega, complete with shelves of chips and candy, refrigerators of Coca Cola, Vitamin Water, Orbit Gum, and various odds and ends.
Aside from the electronic gates, and a latticework of Amazon’s specialized 4-in-1 camera devices above us, the lab store otherwise appears to be a perfectly ordinary retail shopping experience – minus the cashier.
Photo: We couldn’t take photos in the lab, but here’s the real deal JWO store across the square
How JWO works
JWO (they say “jay-woh” at Amazon) uses a combination of computer vision, sensor fusion, and machine learning to track what shoppers take from or return to shelves in a store. The process of building a store begins by creating a 3D map of the physical space using an ordinary iPhone or iPad.The store is divided into product areas, which are discrete spaces that correlate with the inventory of products. Then, RGB cameras are installed on a rail system hanging from the ceiling, and weight sensors are installed at the front and back of each shelf.
Photo: In the real JWO store cameras and sensors are suspended above the shopping area
JWO tracks the orientation of the head, left hand, and right hand to detect when a user interacts with a polygon. By fusing the inputs of multiple cameras and weight sensors, together with object recognition, the models predict with great accuracy whether a specific item was retained by the shopper.
JJ explains the system previously used multiple models in a chain to process different aspects of a shopping trip. “We used to run these models in a chain. Did he interact with a product space? Yes. Does the item match what we thought he did? Yes. Did he take one or did he take two? Did he end up putting that thing back or not? Doing that in a chain was slower, less accurate, and more costly.”
Now, all of this information is now processed by a single transformer model. “Our model generates a receipt instead of text, and it does it by taking all of these inputs and acting on them simultaneously, spitting out the receipt in one fell swoop. Just like GPT, where one model has language, it has images all in one model, we can do the same thing. Instead of generating text, we generate receipts.”
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Image: JWO Architecture courtesy Amazon
The improved AI model can now handle complex scenarios, such as multiple shoppers interacting with products simultaneously or obstructed camera views, by processing data from various sources including weight sensors. This enhancement minimizes receipt delays and simplifies deployment for retailers.
The system’s self-learning capabilities reduce the need for manual retraining in unfamiliar situations. Trained on 3D store maps and product catalogs, the AI can adapt to store layout changes and accurately identify items even when misplaced. This advancement marks a significant step forward in making frictionless shopping experiences more reliable and widely accessible.
JWO is powered by edge computing
One of the interesting things we saw was Amazon’s productization of edge computing. Amazon confirmed that all model inference is performed on computing hardware installed on-premise. Like all AWS services, this hardware is fully managed by Amazon and priced into the total cost of the solution. In this respect, to the customer the service is still fully cloud-like.advertisement
“We built our own edge computing devices that we deploy to these stores to do the vast majority of the reasoning on site. The reason for that is, first of all, it’s just faster if you can do it on site. It also means you need less bandwidth in and out of the store,” said JJ.
VentureBeat got a close up look at the new edge computing hardware. Each edge node is an approximately 8x5x3 rail-mounted enclosure featuring a conspicuously large air intake, which is itself installed inside a wall-mounted enclosure with networking and other gear.
Of course, Amazon would not comment on what exactly was inside these edge computing nodes just yet. However, since these are used for AI inference, we speculate they may include Amazon GPUs such as Trainium and Inferentia2, which AWS has positioned as a more affordable and accessible alternative to Nvidia’s GPUs.
JWO’s requirement to process and fuse information from multiple sensors in real-time shows why edge computing is emerging as a critical layer for real world AI inference use cases. The data is simply too large to stream back to inference models hosted in the cloud.
Scaling up with RFID
Our next stop, down another long dark corridor, and behind another nondescript door, we found ourselves in another mock retail lab. This time we are inside something more like a retail clothier. Long racks with sweatshirts, hoodies, and sports apparel line the walls — each item with its own unique RFID tag.In this lab, Amazon is rapidly integrating RFID technology into JWO. The AI architecture is still the same, featuring a multi-modal transformer fusing sensor inputs, but without the complexity of multiple cameras and weight sensors. All that is required for a retailer to implement this flavor of JWO is the RFID gate and RFID tags on the merchandise. Many retail clothing items already come with RFID tags from the manufacturer, making it all the easier to get up and running quickly.
The minimal infrastructure requirements here are a key advantage both in terms of cost and complexity. This flavor of JWO could also potentially be used for temporary retail inside of fairgrounds, festivals, and similar locations.
What it took Amazon to build JWO
The JWO project was announced publicly in 2018, but the project R&D likely goes back a few years earlier. JJ politely declined to comment on exactly how large the JWO product team is or its total investment in the technology, though it did say over 90% of the JWO team is scientists, software engineers, and other technical staff.However, a quick check of LinkedIn suggests the JWO team is at least 250 full time employees and could even be as high as 1000. According to job transparency site Comparably, the median compensation at Amazon is $180k per year.
Speculatively, then, assuming the cost breakdown of JWO development resembles other software and hardware companies, and further assuming Amazon started with its famous “two pizza team” of 10 full time staff back around 2015, that would put the cumulative R&D between $250M-$800M. (What’s a few hundred million between friends?)
The point is not to get a precise figure, but rather to put a ballpark on the cost of R&D for any enterprise thinking about building their JWO-like system from scratch. Our takeaway is: come prepared to spend several years and tens of million dollars to get there using the latest techniques and hardware. But why build if you can have it now?
The build-vs-buy dilemma in AI
The estimated (speculative) cost of building a system like JWO illustrates the high-risk nature of R&D when it comes to enterprise AI, IoT, and complex technology integration. It also echoes what we heard from many enterprise decision makers a couple of weeks ago at VB Transform in San Francisco: Large dollar hard-tech AI investments only make sense for companies like Amazon, which can leverage platform effects to create economies of scale. It’s just too risky to invest in the infrastructure and R&D at this stage and face rapid obsolescence.This dynamic is part of why we see hyperscale cloud providers winning in the AI space over in-house development. The complexity and cost associated with AI development are substantial barriers for most retailers. These businesses are focused on increasing efficiency and ROI, making them more likely to opt for pre-integrated, immediately deployable systems like JWO, leaving the technological heavy lifting to Amazon.
When it comes to customization, if AWS history is indicative, we’ll likely see components of JWO increasingly showing up as standalone cloud services. In fact, JJ revealed this has already happened with AWS Kinesis Video Streams, which originated in the JWO project. When asked if JWO models would be made available on AWS Bedrock for enterprises to innovate on their own, JJ responded, “We’re actually not, but it’s an interesting question.”
Toward widespread adoption of AI
The advances in JWO AI models show the continuing impact of the transformer architecture across the AI landscape. This breakthrough in machine learning is not just revolutionizing natural language processing, but also complex, multi-modal tasks like those required in frictionless retail experiences. The ability of transformer models to efficiently process and fuse data from multiple sensors in real-time is pushing the boundaries of what’s possible in AI-driven retail (and other IoT solutions).Strategically, Amazon is tapping into an immense new source of potential revenue growth: third-party retailers. This move plays to Amazon’s core strength of productizing its expertise and relentlessly pushing into adjacent markets. By offering JWO through Amazon Web Services (AWS) as a service, Amazon is not only solving a pain point for retailers but also expanding its dominance in the retail sector.
The integration of RFID technology into JWO, first announced back in the fall of 2023, remains an exciting development that could truly bring the system to the mass market. With millions of retail locations worldwide, it’s hard to overstate the size of the total addressable market – if the price is right. This RFID-based version of JWO, with its minimal infrastructure requirements and potential for use in temporary retail settings, could be a key to widespread adoption.
As AI and edge computing continue to evolve, Amazon’s JWO technology stands as a prime example of how hyperscalers are shaping the future of retail and beyond. By offering complex AI solutions as easily deployable services, the success of JWO’s and similar business models may well determine broader adoption of AI in everyday businesses.
Inside Amazon’s new ‘Just Walk Out’: AI transformers meets edge computing
We’re about to see the new AI-based system Amazon has developed, which uses multi-modal foundation models and transformer-based ML.
rgupta
Regular
Thanks for an honest opinion Tech.Yes..when I originally chose that 2 year timeframe I thought at the time it was a "very fair" guide to make a personal call on the conpanies progress..we have made excellent progress so far..BUT currently I'd be lying if I said I was happy with our business model focused on IP...many holders are also well aware that things can flip very rapidly once an announcement linked to potentially huge revenue streams is released..so, yes I'm currently a little flat like the rest of us long termers.
Have a good day....Texta
Regarding business model I still feel we are on the right path. Making own chip or product require much more resources than what brainchip is applying right now.
Ip model is a slow process but once we start getting in roads ( if we can) company can spend more on R&D and business growth than we are allowed to do now.
Regarding the progress we are cruising through the space, but other applications cannot be far behind.
Not sure about profitability in near future but definitely we may start touching our legs on ground next year or so
Dyor
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