BRN Discussion Ongoing

Rob & Todds video link of presentation below

Never heard this before "about half our engagements are with OEM's & the other half are with the semiconductor IP customers"

Enjoy :)


Thank you for recording this Tech Girl. This comment is very exciting. I also particularly liked the projected staff numbers by the end of the year. Great things ahead!
 
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Slade

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So back to this huge statement of about 1/2 our engagements are with
semiconductor IP Customers, this is mind blowing, so we already have 2 huge semiconductor IP Customers, renesas and megachips wonder who the other 5 are ……hmmm….. 🤔
Agree, it's quite mind blowing.
 
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Diogenese

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YOU KNOW ALREADY WHAT I AM GOING TO ASK. LOL. Can you do the maths for Coral and Jetson please? We all love you. FF
Well Coral stacks up remarkably well, using 735 mW to process 10000 words compared with Akida @ 300 MHz which uses about 100 mW.

Coral uses 351 microJoules per inference while Akida @ 300 uses 60 microJoules per inference.

Coral processes 2100 words per second, while Akida @ 300 processes 1693 words per second. So if we extrapolate Akida to 2100 words per second, Akida would use about 123 mW to process the 10k words, assuming a linear extrapolation.

This would make Akida @ 300 about 6 times as efficient as Coral when processing 2100 words per second.

The difference is that ARM is very slow, being basically a serial architecture CPU and thus not having a parallel architecture like GPUs. I haven't seen the Coral architecture, but, to perform at those speeds, it must be parallel.

Don't be sad. I feel bad now because you answered the question I had which was could you draw the straight line and compare both AKIDA and ARM at the same point. I am sure being 100 times more efficient than the most efficient commercial alternative from ARM will satisfy most. Some will not even care that you have not done the Jetson and Coral comparisons. Even my disappointment will pass. FF.
 
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Well Coral stacks up remarkably well, using 735 mW to process 10000 words compared with Akida @ 300 MHz which uses about 100 mW.

Coral uses 351 microJoules per inference while Akida @ 300 uses 60 microJoules per inference.

Coral processes 2100 words per second, while Akida @ 300 processes 1693 words per second. So if we extrapolate Akida to 2100 words per second, Akida would use about 123 mW to process the 10k words, assuming a linear extrapolation.

This would make Akida @ 300 about 6 times as efficient as Coral when processing 2100 words per second.

The difference is that ARM is very slow, being basically a serial architecture CPU and thus not having a parallel architecture like GPUs. I haven't seen the Coral architecture, but, to perform at those speeds, it must be parallel.
This was worthwhile, if I take you back to the Mercedes Benz statement that AKIDA technology is 6 to 10 times more efficient than other architectures, which means they could have trialled Coral OR at least an equivalent architecture.

My opinion only DYOR
FF

AKIDA BALLISTA
 
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The video is great. But what frustrates me is what Rob said around 57 minutes onwards, the question being along the lines of who are you doing things with besides Mercedes? Rob responded with it is a highly competitive environment and most companies don't want to talk about their partners because they want some differentiation from their competitors. There will be more but we have non disclosure agreements and most of them are very serious that there should be no communication about their products.

All that is well and good but the market needs more hints otherwise we continue lo languish around these levels. FF I like how you picked up the BMW logo. Maybe we continue to sleuth and take the hints as being close to facts. But we need more instos on board, or at least their buying power.
I am comfortable with NDA's as they general tend to sit around the larger deals where the stakes are higher. I am also very confident that Akida is so disruptive that in time as our profile continues to rise, the benefits it brings will drive all customers, even those currently insisting on NDA's to join the masses announcing "AKIDA Inside" given the value that such disclosure will bring to their own products.
 
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Makeme 2020

Regular
This article is a few years old and may have been replicated before, but I love to keep dwelling on Bill Gates' quote below.


Microsoft (NASDAQ:MSFT) founder Bill Gates was speaking to a group of college students in 2004.
According to The New York Times, Gates was a bit concerned about the decline in the number of computer science majors, as well as the notion that the field had matured and there weren't many breakthroughs left to achieve in the area.
One student expressed doubt that there would ever be another tech company as successful as Microsoft. Gates' reply is eye-opening:
''If you invent a breakthrough in artificial intelligence, so machines can learn, that is worth 10 Microsofts.''
He wasn't kidding...

Image Source: Getty Images.

IMAGE SOURCE: GETTY IMAGES.
Fast-forward to today, and of course someone has figured it out. This special kind of artificial intelligence is called machine learning.
If anything, Gates was too conservative in his estimates. Experts say the market opportunity is now far, fargreater than 10 Microsofts.
And Gates isn't alone in his optimism. Other top business leaders are on board as well.
  • Jeff Bezos, the founder of Amazon (NASDAQ:AMZN), said he thinks this new technology is the key to Amazon's future.
  • Sun CEO Greg Papadopoulos is calling it "a real revolution."
  • Even super investor Warren Buffett says that it will have a "hugely beneficial social effect."

When machines go to college

We've referred to machine learning before as the beginning of today's AI explosion. It's "simply" software that ingests data, learns from it, and can then form a conclusion about something in the world.

Thus, the key to understanding machine learning is that it's software that writes itself. Instead of explicitly programming software what to do, you instead provide it with large amounts of data and let it learn on its own. This allows machine learning to solve problems that earlier software with even billions of lines of code couldn't have solved.
A more powerful subset of machine learning is deep learning, which essentially simulates how neurons in the human brain strengthen connections between one another to learn.
If you're wondering how companies like Amazon, Google, Microsoft, and Facebook have grown to be among the largest in the world, part of the answer is how well they've integrated machine learning and deep learning into all aspects of their businesses.

So many examples, so little space

Amazon is a perfect example. It was among the quickest companies in the world to embrace the technology and it serves as a preview for what's to come. As CEO Jeff Bezos noted in a letter to shareholders:

"Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations."
With Amazon's success, other retailers have been forced to up their games as well. Walmart Chief Data Officer Bill Groves mentioned at a tech conference last month how his company uses NVIDIA hardware and machine learning for product forecasting, supply chain management, and understanding consumer behavior, "So when the customer comes in the product they want is sitting on the shelf."
A great non-retail example of the power of machine learning is Facebook, which uses it to determine what goes in your news feed and what advertisements you might respond to. (The company's election-related controversy also points out the extreme challenges in employing a technology this powerful.)
Facebook benefits tremendously from the network effect, which makes a service more valuable as the number of users grows. And this company has one of the largest caches of consumer data in the world. All that data wouldn't be nearly as valuable without a way of making sense of it all, and that's where machine learning comes in, as Facebook can target advertising in extremely specific and effective ways.
Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google has also been among the early leaders. It's another company with a gold mine of data that can target online ads in uncanny ways. Alphabet even offers services such as machine learning and image recognition through cloud services to other companies.
Of course, Alphabet's Waymo self-driving car division also relies heavily on AI. An autonomous car's "brains" have to constantly assess the ever-changing surroundings and make split-second life-or-death decisions. This is done so effectively that even now – in the early stages of the self-driving revolution – it's easy to see how AI can save millions of lives in the future.
There's much, much more than I have room for here. Credit card companies have used machine learning to improve fraud protection. Netflix uses it to give you movie recommendations. Advances in robotics will change our lives well beyond vacuums. There are huge implications in the field of healthcare.
To bring the story full circle, Gates' own Microsoft trails only Amazon in cloud computing and uses AI for things like object recognition and speech translation in its Azure platform.
Microsoft provides the Azure Machine Learning cloud service that allows any developer to build and train their own machine learning models, something Gates back in 2004 would have been very excited about.

Bottom line for investors

Honestly, this article is only a small first step to help you as an investor understand that AI, machine learning, and deep learning are an essential part of every industry and almost all major companies. It reminds me of a couple of decades ago when smart investors understood that eventually nearly every company would be an "Internet company." The same holds true for AI.
As processors get ever faster, storage becomes cheaper, software better, and data collection more efficient, AI will only get smarter and ever more important. It's clear to me that the great leading companies of the next decade and beyond will also be the most innovative in the development and use of AI.
BRN 10 X MICROSOFT.............$$$$$$$$$$$$$$$$$$$$$$$$$.
 
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RobjHunt

Regular
Lets have a wonderful hour of power for the end of a very nervous week.
 
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TechGirl

Founding Member
So back to this huge statement of about 1/2 our engagements are with
semiconductor IP Customers, this is mind blowing, so we already have 2 huge semiconductor IP Customers, renesas and megachips wonder who the other 5 are ……hmmm….. 🤔

My first 2 guesses are we are working with Qualcomm & NXP
 
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Dhm

Regular
My first 2 guesses are we are working with Qualcomm & NXP
May I ask 2 ignorant questions? Why are you saying 'who the other 5 are' Why 5.
And another one I have yet to figure out.....I remember seeing a graphic showing the Mercedes logo and also Pantene. What is the relevence of Pantene?
 

gex

Regular
May I ask 2 ignorant questions? Why are you saying 'who the other 5 are' Why 5.
And another one I have yet to figure out.....I remember seeing a graphic showing the Mercedes logo and also Pantene. What is the relevence of Pantene?
1645765157686.png
 
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These days whenever I read up on a potential use case that turns out not to be AKIDA I've realised that I automatically think to myself "ah its not real AI then" Is it just me, do I have a sickness? akidavirus.....

Akida the only real AI
 
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Dhm

Regular
I have seen this before, in late January, but for me anyway it is worth watching again....

 
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TechGirl

Founding Member
May I ask 2 ignorant questions? Why are you saying 'who the other 5 are' Why 5.
And another one I have yet to figure out.....I remember seeing a graphic showing the Mercedes logo and also Pantene. What is the relevence of Pantene?
Hi DHM,

Please see Gex reply to explain Pantene

Rob or Todd said half our customer engagements are Semiconductor IP Customers, we have 15 EAP's so half of that is either 7 or 8, we already know about 2 which are Renesas & Megachips so there are 5 or 6 Semiconductor IP Customers remaining that we don't know about.
 
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JK200SX

Regular
Hi DHM,

Please see Gex reply to explain Pantene

Rob or Todd said half our customer engagements are Semiconductor IP Customers, we have 15 EAP's so half of that is either 7 or 8, we already know about 2 which are Renesas & Megachips so there are 5 or 6 Semiconductor IP Customers remaining that we don't know about.


"so there are 5 or 6 Semiconductor IP Customers remaining that we don't know about."

... And that roughly corresponds with one of my assumptions in post https://thestockexchange.com.au/threads/brn-discussion-2022.1/post-18893

(BTW does anyone want to comment on the assumptions, your thoughts, etc...
 
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Mea culpa

prəmɪskjuəs
Absolutely fascinating when Dodgy Knees and the retired Sydney silk with the Massey Ferguson bounce their knowledge and intellect off each other. Wouldn't miss it for quids, or even a tenth of a cent SP increase. :)
 
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Dhm

Regular
I have also been looking at Tesla's significant inhouse investment in DOJO and it seems Tesla are very comfortable with what they have. Yet it seems extremely high in power consumption. From what I can see Tesla won't be looking in our direction anytime soon.

 
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Slade

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Rob and Todd talking about vibration and sound analysis got me thinking and searching. I think this company is very interesting and since Renesas is one of their partners I feel that there might be a connection to Akida, particularly in the area of predictive maintenance. Forgive me if previously discussed.
Company Overview

MicroAI™, an Edge AI-enablement company based in Dallas, TX., delivers Intelligent Asset Management solutions to companies within the Manufacturing, O&G, Automotive, Telecom, and Semiconductor sectors.​

MicroAI’s award-winning, proprietary, AI/ML solution—MicroAI AtomML™–enables a transformational approach to using Embedded AI to improve the performance and security of IIoT devices and machines. MicroAI is a leader in Industry 4.0 initiatives, providing Edge and Endpoint AI/ML solutions that deliver business value in the areas of Asset Optimization, Predictive Maintenance, and advanced Cyber-Security.
Making Predictive Manufacturing a Reality – MicroAI Factory™

Official Partners​

MicroAI Factory™ and the Power of Predictive Maintenance​

04 Feb 22
MicroAI Factory

Machine output and uptime are critical KPIs (key performance indicators) for any machine-intensive enterprise. Lack of real-time insights, unscheduled downtimes, and static maintenance routines can all combine to create a production ecosystem that performs well below its optimum capability.
Preventive maintenance is the legacy approach to machine optimization. For several decades that approach was sufficient to maintain decent levels (i.e. ~ 70%) of OEE (overall equipment effectiveness). To gain and maintain competitive advantage “decent” performance is no longer enough. Manufactures across every industry segment are now looking at Edge AI (artificial intelligence) and ML (machine learning) technologies to power a shift from preventive to predictive maintenance.
What is predictive maintenance? How does it work? Why is it better that the old preventive approach?

Predictive Maintenance Defined

An intelligent predictive maintenance solution utilizes Endpoint AI and Edge-native AI technologies to enable equipment operators and stakeholders to gain deeper insights into the real-time status and heath of their machine assets. Asset-specific data is analyzed to predict when that asset will require maintenance. In this method, maintenance is performed based on actual machine-generated information instead of the old time-based approach.
The primary differences between predictive and preventive maintenance are summarized below.
PredictivePreventive
AI and ML enabled?YesNo
Maintenance triggersBased on analysis of real-time and historical asset performance data. Supported by device and machine AI-enabled endpoint analytics.Based on static, manual, schedules and routines. Often hindered by lack of insight into current asset health.
Operational impactMaintenance performed when needed to maintain asset health, minimizing production impact.Maintenance often performed too early or too late, resulting in non-optimized performance and/or unnecessary downtimes.
Business impactReduced maintenance costs, increased output, better resource utilization, and optimized OEE.Higher maintenance costs, non-productive maintenance routines, shorter asset lifespans, and sub-par OEE.
Competitive impactHigher production rates + longer asset lifespans + reduced maintenance costs = lower production costs and enhanced competitive position.Non-optimized production capacity + degraded asset health + reliance on human-dependent, non-automated, processes = higher production costs, higher prices, and competitive weakness.

Predictive Maintenance at the Endpoint

MicroAI Factory is an Edge-native AI solution that provides manufacturers with predictive maintenance capabilities that produce advantages in production output, machine utilization, operator efficiency, production costs and OEE. Features of MicroAI Factory include the following:
  • Microcontroller-based intelligence: MicroAI Factory is unique in that it embeds predictive maintenance intelligence directly into the microcontroller (MCU) of the manufacturing device or machine. This approach offers distinct advantages when compared to cloud-based solutions.
    • Completely self-contained
    • Local collection and analysis of asset data
    • Reduction in the amount of data transferred to the cloud
    • More secure from cyber-attack
    • Lower cost
  • Customizable algorithms: Ability to customize AI-enabled algorithms on an asset-by-asset level to accommodate specific operational or environmental conditions for the device or machine. Predictive maintenance routines are based on real-time analytics that provide insights into current and historical performance trends.
  • Data aggregation and dashboarding: Most legacy preventive maintenance routines rely on the manual collection of asset data and subsequent siloed analysis of that data. MicroAI Factory provides automatic aggregation, analysis, and presentation of asset data. Asset owners can quickly customize asset analytics to best meet their operational needs.
  • Workflow optimization: MicroAI Factory embeds workflows that learn, train, and evolve. Predictive maintenance is supported by workflows that are automated and intelligent. This reduces or eliminates the need for human intervention in the maintenance scheduling process.

MicroAI Factory

Predictive Maintenance – Operational and Business Value

Any technological innovation is only as good as the tangible value that it provides to its adopters. Preventive maintenance powered by MicroAI Factory’s Edge-native AI technology delivers both operational and business value. Just a few examples would include:
  • Holistic and self-contained ecosystem: Non-siloed, at-a-glance, perspective of real-time performance and events. A more comprehensive and efficient predictive maintenance solution.
  • Deeper visibility and insights: Ability to fast-track issue identification and corrective action and to identify recurring problems based on historical analytics.
  • Increased machine and device uptime: Elimination of downtimes due to unnecessary maintenance activities and/or unforeseen malfunctions.
  • Optimization of production capacity: Reduction in machine downtime equates to increased output and higher OEE scores.
  • Improved product quality: Predictive maintenance helps detect those machine performance issues that can negatively impact the quality of the product(s) being produced.
  • Reduced maintenance costs: Cost is reduced via elimination of unnecessary maintenance activities, less reliance on human interaction, and automation of processes.
  • Extension of capital-intensive asset lifespans: Real-time asset health monitoring, process-driven mitigation actions, and predictive maintenance capability all combine to extend the lifespan of expensive assets.
  • More competitive operational overhead: Improved asset health and performance reduces operational costs and has a beneficial trickle effect across other business costs.

MicroAI Factory is bringing Edge-native AI predictive maintenance to companies within the manufacturing, telecom, energy, automotive, and semiconductor sectors. Factory floors are being optimized to reach new levels of operational excellence and OEE.
 
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Diogenese

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Now as for Neon it is a bit like pulling rabbits out of hats. You make it out of nothing or at least what you see when you look in the magicians top hat which as we know is actually full of air. Clearly air is equally distributed around the world so it would not be a long term shortage:


The above is science for children but it gets the idea across. Neon is one of those dirty gases that you have to be careful with in production so that is why we allowed Ukrainian workers to take the risk.

I have no doubt the tech giants if pushed would allow their employees to take the same risk in Bangladesh, Africa or India.

Did I just say that?

My opinion only DYOR
FF

AKIDA BALLISTA
50 years ago, you would have had to go to uni to learn that stuff. Now they're giving it to 5 year olds for free
 
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I have also been looking at Tesla's significant inhouse investment in DOJO and it seems Tesla are very comfortable with what they have. Yet it seems extremely high in power consumption. From what I can see Tesla won't be looking in our direction anytime soon.


15KW is 15,000 watts for one DOJO producing 400watts of heat requiring dissipation. Tesla must not have got the memo about problems with power generation, base load power or climate change. They might be processing quickly but the information/data still has to come from the vehicle to the DOJO and back to the car to take action. What happens if there is a connection problem because Russia explodes a satellite and takes out a number of Tesla's 10,000 satellites. Even a cyber attack creating mass loss of connection. I cannot see how this actually solves problems for Tesla. Maybe Dio can show me the path to righteousness.

As Tesla was speaking about its robots maybe their end game is a closed system for autonomous taxis in cities running like large robots within a defined geographic location???

My opinion only DYOR
FF

AKIDA BALLISTA
 
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D

Deleted member 118

Guest
Hi DHM,

Please see Gex reply to explain Pantene

Rob or Todd said half our customer engagements are Semiconductor IP Customers, we have 15 EAP's so half of that is either 7 or 8, we already know about 2 which are Renesas & Megachips so there are 5 or 6 Semiconductor IP Customers remaining that we don't know about.
I can’t see why they can’t just disclose to the shareholders the precise amount of NDA they have in place currently. What’s the harm in that? Does anyone want to ask @ fact finder
 
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