BRN Discussion Ongoing
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Food4 thought
Regular
Definitely not an easy pathAwesome!Let's do this!!!
Next list we make is Australia's richest 200...
But hopefully it will be worth the wait and stress
CHIPS
Regular
I bought some yesterday too, only 10,000 though, but nevertheless I am glad I did. They add up.
Didn’t I read somewhere that we were involved with Siemens 
. Only guessing but interesting 
blog.siemens.com
Extract :-“
30 August 2024
The primary objective of AI in this context is to make the unpredictable predictable, streamlining processes that are otherwise difficult or time-consuming for humans to manage. Moreover, AI helps safeguard against the costly consequences of equipment failure, ensuring minimal disruption in operations.
These AI systems work tirelessly behind the scenes, ensuring that facilities run smoothly with minimal interruptions. Whether it’s reducing unexpected downtime or fine-tuning the performance of machinery, AI’s potential to enhance operational efficiency is immense.
The roots of predictive maintenance can be traced back to the early days of machinery, where experienced operators would rely on their senses to detect anomalies. Today, AI enhances this process by using sensors and IoT devices to continuously monitor equipment. This data is then processed using advanced algorithms to predict potential failures long before they happen.
In industries like aerospace, predictive maintenance has been a game-changer. The ability to monitor and predict the condition of complex machines has allowed companies to offer these machines as a service, ensuring reliability and efficiency. As AI continues to evolve, other industries are beginning to adopt these practices, learning from the successes and challenges faced by early adopters.
Another significant challenge is data management. Predictive maintenance relies on vast amounts of data, which must be collected, analyzed, and interpreted accurately. Many companies already have valuable data from their existing industrial control systems, but integrating this with new AI tools can be complex. Moreover, it’s crucial to combine machine data with human insights—information from maintenance staff on past repairs and interventions—to create a complete picture of machine health.
Moreover, AI can help companies optimize their maintenance schedules, reducing the need for routine inspections and repairs. By focusing on predictive maintenance, organizations can achieve a full return on investment in as little as six months. This rapid ROI is a testament to the effectiveness of AI in reducing costs and improving operational efficiency.
It’s also essential to think big—start with a small-scale implementation but have a plan for scaling up across the entire enterprise. Success in predictive maintenance requires not just the right technology but also a cultural shift within the organization. Maintenance teams must be trained and supported as they adapt to new tools and processes.
AI-driven predictive maintenance represents a significant leap forward in industrial efficiency. By harnessing the power of AI, companies can not only prevent costly equipment failures but also optimize their entire maintenance strategy. While challenges exist, the potential benefits make it a worthwhile investment for any organization looking to improve its operations and reduce costs.
As AI continues to advance, its role in predictive maintenance will only grow, helping industries of all kinds to achieve new levels of reliability, efficiency, and profitability.”
. Only guessing but interesting
Leveraging AI for Predictive Maintenance: The Future of Industrial Efficiency | Siemens Blog | Siemens
In today's rapidly evolving industrial landscape, the integration of artificial intelligence (AI) into maintenance operations has transformed the way we…
Extract :-“
30 August 2024
Leveraging AI for Predictive Maintenance: The Future of Industrial Efficiency
In today’s rapidly evolving industrial landscape, the integration of artificial intelligence (AI) into maintenance operations has transformed the way we predict and prevent equipment failures.The primary objective of AI in this context is to make the unpredictable predictable, streamlining processes that are otherwise difficult or time-consuming for humans to manage. Moreover, AI helps safeguard against the costly consequences of equipment failure, ensuring minimal disruption in operations.
The Role of Algorithms in Predictive Maintenance
AI-driven predictive maintenance relies on sophisticated algorithms that continuously monitor, analyze, and predict the condition of machinery. These algorithms process vast amounts of data from sensors and other sources to detect patterns that might indicate an impending failure. The insights gained from this data are then turned into actionable steps that maintenance teams can take to prevent downtime and optimize performance.These AI systems work tirelessly behind the scenes, ensuring that facilities run smoothly with minimal interruptions. Whether it’s reducing unexpected downtime or fine-tuning the performance of machinery, AI’s potential to enhance operational efficiency is immense.
The Evolution of Predictive Maintenance
Predictive maintenance is not a new concept, but it has evolved significantly over time. Traditionally, maintenance was a reactive process, where issues were addressed only after they occurred. However, with the advent of AI, predictive maintenance has become more proactive and data-driven.The roots of predictive maintenance can be traced back to the early days of machinery, where experienced operators would rely on their senses to detect anomalies. Today, AI enhances this process by using sensors and IoT devices to continuously monitor equipment. This data is then processed using advanced algorithms to predict potential failures long before they happen.
In industries like aerospace, predictive maintenance has been a game-changer. The ability to monitor and predict the condition of complex machines has allowed companies to offer these machines as a service, ensuring reliability and efficiency. As AI continues to evolve, other industries are beginning to adopt these practices, learning from the successes and challenges faced by early adopters.
Challenges in Implementing Predictive Maintenance
Despite its potential, implementing AI-driven predictive maintenance comes with its own set of challenges. One of the primary hurdles is the scalability of these solutions. While many companies can implement predictive maintenance on a small scale, extending it to an entire enterprise with thousands of machines is a different story. This requires systems that are not only scalable but also vendor-agnostic and automated.Another significant challenge is data management. Predictive maintenance relies on vast amounts of data, which must be collected, analyzed, and interpreted accurately. Many companies already have valuable data from their existing industrial control systems, but integrating this with new AI tools can be complex. Moreover, it’s crucial to combine machine data with human insights—information from maintenance staff on past repairs and interventions—to create a complete picture of machine health.
The Value of AI in Predictive Maintenance
The value of AI in predictive maintenance is immense, offering both cost avoidance and cost savings. For example, in high-stakes industries like automotive manufacturing, where downtime can cost millions of dollars per hour, the ability to prevent unexpected failures is invaluable.Moreover, AI can help companies optimize their maintenance schedules, reducing the need for routine inspections and repairs. By focusing on predictive maintenance, organizations can achieve a full return on investment in as little as six months. This rapid ROI is a testament to the effectiveness of AI in reducing costs and improving operational efficiency.
Getting Started with AI-Driven Predictive Maintenance
For companies looking to embark on the journey of AI-driven predictive maintenance, the first steps involve understanding the specific needs of their operations and gathering relevant data. Engaging with experts in the field and learning from existing case studies can provide valuable insights into the potential benefits and challenges.It’s also essential to think big—start with a small-scale implementation but have a plan for scaling up across the entire enterprise. Success in predictive maintenance requires not just the right technology but also a cultural shift within the organization. Maintenance teams must be trained and supported as they adapt to new tools and processes.
AI-driven predictive maintenance represents a significant leap forward in industrial efficiency. By harnessing the power of AI, companies can not only prevent costly equipment failures but also optimize their entire maintenance strategy. While challenges exist, the potential benefits make it a worthwhile investment for any organization looking to improve its operations and reduce costs.
As AI continues to advance, its role in predictive maintenance will only grow, helping industries of all kinds to achieve new levels of reliability, efficiency, and profitability.”
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Smoothsailing
Regular
MadMayHam | 合氣道
Regular
Does anyone know what @Diogenese is talking about? where is this reference to this company taking over the tapeout?
Food4 thought
Regular
Well we can only hope so
They need it as much as we do
Justchilln
Regular
Tony Lewis said they are no longer taping out akd 2.0
DingoBorat
Slim
"Didn’t I read somewhere that we were involved with Siemens"Didn’t I read somewhere that we were involved with Siemens
Leveraging AI for Predictive Maintenance: The Future of Industrial Efficiency | Siemens Blog | Siemens
In today's rapidly evolving industrial landscape, the integration of artificial intelligence (AI) into maintenance operations has transformed the way we…
Extract :-“
30 August 2024
Leveraging AI for Predictive Maintenance: The Future of Industrial Efficiency
In today’s rapidly evolving industrial landscape, the integration of artificial intelligence (AI) into maintenance operations has transformed the way we predict and prevent equipment failures.
The primary objective of AI in this context is to make the unpredictable predictable, streamlining processes that are otherwise difficult or time-consuming for humans to manage. Moreover, AI helps safeguard against the costly consequences of equipment failure, ensuring minimal disruption in operations.
The Role of Algorithms in Predictive Maintenance
AI-driven predictive maintenance relies on sophisticated algorithms that continuously monitor, analyze, and predict the condition of machinery. These algorithms process vast amounts of data from sensors and other sources to detect patterns that might indicate an impending failure. The insights gained from this data are then turned into actionable steps that maintenance teams can take to prevent downtime and optimize performance.
These AI systems work tirelessly behind the scenes, ensuring that facilities run smoothly with minimal interruptions. Whether it’s reducing unexpected downtime or fine-tuning the performance of machinery, AI’s potential to enhance operational efficiency is immense.
The Evolution of Predictive Maintenance
Predictive maintenance is not a new concept, but it has evolved significantly over time. Traditionally, maintenance was a reactive process, where issues were addressed only after they occurred. However, with the advent of AI, predictive maintenance has become more proactive and data-driven.
The roots of predictive maintenance can be traced back to the early days of machinery, where experienced operators would rely on their senses to detect anomalies. Today, AI enhances this process by using sensors and IoT devices to continuously monitor equipment. This data is then processed using advanced algorithms to predict potential failures long before they happen.
In industries like aerospace, predictive maintenance has been a game-changer. The ability to monitor and predict the condition of complex machines has allowed companies to offer these machines as a service, ensuring reliability and efficiency. As AI continues to evolve, other industries are beginning to adopt these practices, learning from the successes and challenges faced by early adopters.
Challenges in Implementing Predictive Maintenance
Despite its potential, implementing AI-driven predictive maintenance comes with its own set of challenges. One of the primary hurdles is the scalability of these solutions. While many companies can implement predictive maintenance on a small scale, extending it to an entire enterprise with thousands of machines is a different story. This requires systems that are not only scalable but also vendor-agnostic and automated.
Another significant challenge is data management. Predictive maintenance relies on vast amounts of data, which must be collected, analyzed, and interpreted accurately. Many companies already have valuable data from their existing industrial control systems, but integrating this with new AI tools can be complex. Moreover, it’s crucial to combine machine data with human insights—information from maintenance staff on past repairs and interventions—to create a complete picture of machine health.
The Value of AI in Predictive Maintenance
The value of AI in predictive maintenance is immense, offering both cost avoidance and cost savings. For example, in high-stakes industries like automotive manufacturing, where downtime can cost millions of dollars per hour, the ability to prevent unexpected failures is invaluable.
Moreover, AI can help companies optimize their maintenance schedules, reducing the need for routine inspections and repairs. By focusing on predictive maintenance, organizations can achieve a full return on investment in as little as six months. This rapid ROI is a testament to the effectiveness of AI in reducing costs and improving operational efficiency.
Getting Started with AI-Driven Predictive Maintenance
For companies looking to embark on the journey of AI-driven predictive maintenance, the first steps involve understanding the specific needs of their operations and gathering relevant data. Engaging with experts in the field and learning from existing case studies can provide valuable insights into the potential benefits and challenges.
It’s also essential to think big—start with a small-scale implementation but have a plan for scaling up across the entire enterprise. Success in predictive maintenance requires not just the right technology but also a cultural shift within the organization. Maintenance teams must be trained and supported as they adapt to new tools and processes.
AI-driven predictive maintenance represents a significant leap forward in industrial efficiency. By harnessing the power of AI, companies can not only prevent costly equipment failures but also optimize their entire maintenance strategy. While challenges exist, the potential benefits make it a worthwhile investment for any organization looking to improve its operations and reduce costs.
As AI continues to advance, its role in predictive maintenance will only grow, helping industries of all kinds to achieve new levels of reliability, efficiency, and profitability.”
It's a really "loose" link and heresay to boot, but I believe it was @Realinfo, who was at some posh arse restaurant and overheard some Siemens guys talking about BrainChip.
At least he heard them talking about it and later asked the waiter, who they were.
That's a pretty loose dot, but the only one I'm aware of..
You obviously have good recall from way back
DingoBorat
Slim
Found a more solid link, with a simple Google search "Siemens BrainChip" from 2022, courtesy of TasTroy, from the "other place"..
Hyderabad, is basically our "software hub" and ties in with the Siemens article about algorithms for predictive maintenance.
Add a bit of TENNs sauce and you have a delicious dish, to be savored.
That "was" 2 years ago though..
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Not sure if already shared https://www.tataelxsi.com/news-and-...-healthcare-multimodal-ai-and-neuromorphic-ai
Solid DD in here. But most of it is pure speculation. "Neuromophic AI" is such a large area of different companies. Most likely little to none of it is related to brainchip. I still got hope but most of you cult fanboys who seem to be in love with this stock, gotta wake up to the fact that stock has been declined for got damn minute.. And no, it is not shorters fault. It is based on the company performance, which stinks..
Still holding tho. More than half of my investment is gone atm.
Still holding tho. More than half of my investment is gone atm.
DingoBorat
Slim
My problem is fanboys like you who can't take/handle reasonable critic about a company you seem to be in love with.
rgupta
Regular
There is no doubt SNN is a very new technology and TENNs is altogether new for the whole world. But to beat us, do we think that our patents and company is big enough to protect our business?
DingoBorat
Slim
Would you prefer if I was a sad censored like you?
I'm pretty damn sure my "investment" (not sure you can call it that, for a Company with close to zero revenue) dwarfs yours.
I am far more underwater than 50% and yet I still try to keep my posts balanced.
What exactly are you worried that the "fanboys" might do?
TasTroy77
Founding Member
God I had forgotten about that
The only Siemens I can remember is that pirate seaman stains from captain pugwash in my childhood
DingoBorat
Slim
This conversation, only goes downhill from here...God I had forgotten about that
The only Siemens I can remember is that pirate seaman stains from captain pugwash in my childhood
When you use personally insulting words like fanboy and cult you should expect a bit of flack from posters.
You are perfectly entitled to your opinion critical or not but it would have been far better received without the derogatory remark.
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