wilzy123
Founding Member
Alwaysmisleading. Alwaysvictim. Always.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
rgupta
Regular
We can understand your frustrations but if you think you are old and cannot wait in that case you should have chosen a less volatile stock.
Getting on to your question of I want answer, I think we all know that is distant dream. No management listen but they have obligation to work in best interests of share holders long term interest.
So I can only say only patience will prevail here.
DerAktienDude
Regular
Stop blaming the short sellers. They were right with their decisions and we were wrong. Whilst the long term holders were influenced by statements like the explosion of sales etc. did the short sellers all earn money while we lost any gains we’ve had. And yes even if we’re fans of the company… we want to earn money. That’s the point of investing. And we’ve lost. There is not one sign whatsoever that this will change in the following years.
Tothemoon24
Top 20
Tata !!!
SUKRITI JALALI
Principal Consultant, BFSI, TCS
Neuromorphic computing: Ushering in AI innovation in BFSI
HIGHLIGHTS
- Firms in the banking, financial services, and insurance (BFSI) industry have embraced artificial intelligence (AI) and machine learning (ML).
- These technologies use deep learning (DL) models that require massive sets of training data, consume enormous amounts of power, and fall short in adapting to the changing business environment.
- Neuromorphic computing (NC) can help firms leverage the latest innovations in AI to address some of the existing challenges while paving the way for next-generation use cases.
ABSTRACT
The banking, financial services, and insurance (BFSI) industry has been at the forefront of embracing disruptive technologies.
Firms have adopted artificial intelligence (AI) and machine learning (ML) to recast customer experience, improve business operations, and develop futuristic products and services. Existing AI and ML technologies utilize deep learning (DL) models that run on compute-intensive data centers, require massive sets of training data, consume a large amount of power to train, and fall short in adapting to the changing business environment.
In our view, the BFSI industry can overcome these challenges by exploring neuromorphic computing (NC) for certain kinds of use cases. Spiking neural networks (SNNs), which take inspiration from the functioning of biological neural networks in the human brain, when run on NC hardware, perform on par with DL models but consume significantly lesser power. They are purpose-built for AI and ML and offer advantages such as speed of learning and faster parallel processing. We highlight how NC can help firms overcome inefficiencies in the existing AI and ML deployments in the BFSI industry and examine new use cases.
INTRODUCTION
The proliferation of connected devices in the BFSI industry has generated enormous amounts of data.
This data needs to be analyzed and insights delivered in real time to enable instant action. Firms have been making use of data derived from images, videos, text, audio, and IoT devices. In the insurance industry, the use cases span property damage analysis, driver sleep detection, elderly care, and predictive asset maintenance. Robo-advisory for investment and wealth management, customer sentiment analysis, and fraud detection are other critical areas in the BFSI sphere that benefit from AI and ML.
However, much of the data analysis is post-facto or after-the-event, which means firms do not receive a timely warning and cannot take action to avert adverse events or minimize their impact. In addition, the existing models use DL networks that consume massive amounts of energy, both for training and inference. Firms need voluminous data sets to train the models while processing data sequentially. Enhancing the models or modifying their parameters are complex and cumbersome tasks. All this has resulted in several negative impacts for BFSI firms: higher carbon footprint, increased time and effort to train models, processing delays, and high manual effort across the AI and ML lifecycle whenever there is a change in input parameters or training data.
NC TO THE RESCUE
In our view, the BFSI industry should explore third-generation AI systems powered by neuromorphic computing(NC) platforms and spiking neural networks(SNNs).
This will help them address the aforementioned shortcomings and improve the response time, while significantly lowering the carbon footprint. NC closely replicates how the human brain responds to complex external events and learns unsupervised while using minimal energy. We believe that these systems will facilitate a natural progression toward developing ultra-low energy adaptive AI applications by mimicking human cognitive capabilities. NC will also reduce cloud dependence, which means that edge applications can be enabled without compromising privacy and security.
Key features of NC include:
- Sparsity – allows models to be trained with a lesser amount of data compared to the existing DL models. This dramatically reduces memory and input training data requirements. For instance, in touchless banking kiosks, cameras underpinned by NC can recognize and learn individual gestures much faster, enabling personalized customer experience.
- Event-based processing – allows firms to detect and respond to events in real time. For instance, for parametric insurance policies, instant detection of a threshold breach is essential for immediate, frictionless pay-outs, which is key to superior customer experience.
- Colocation of memory and compute – enables faster parallel processing of multiple data streams. An insurance use case in focus is the prevention of work-related injuries in hazardous environments, resulting in reduced accidents and workers’ compensation claims. Given the low energy use of NC, some of the models can easily run on handheld devices without the need for cloud connectivity.
PUTTING THEORY TO ACTION
The insurance industry is moving from a protection to a prevention and preservation paradigm.
And embracing NC will help insurers accelerate this shift. Currently, data from IoT devices – wearables, connected vehicles, or drones – is sent over a network to cloud servers, where pre-trained algorithms process, analyze, and respond to each event. The response needs to travel back to the edge, based on which action is taken. This causes delays, consumes significant processing power on the server, and requires all scenarios to be pre-trained. This is not the best approach where a real-time response is critical to prevent the occurrence of adverse events or minimize their impact.
With its in-situ processing capabilities and ability to offer real-time inferences, NC offers a superior alternative. In our view, there is tremendous scope for NC technology to improve edge AI applications (see Figure 1). For example, real-time driver sleep detection is imperative to prevent an accident and the consequent insurance claims. Similarly, in home care, NC can prove to be a game changer for the remote monitoring of elderly patients. A fall or a sudden heart attack can be detected in real time. The connected ecosystem of family, doctors, ambulance, caregivers, and insurance providers can be alerted without delay. Insurance applications that need analytical insights at the edge span a wide range. They include usage-based vehicle insurance, real-time tracking of perishable cargo, predictive maintenance of critical equipment, elder care, early detection of anomalies in home insurance, and video- based claims processing. NC can also aid in faster detection of natural disasters such as floods, fires, or other calamities. This information can be fed to the insureds in advance. Parametric insurance products that offer pre-specified payouts based upon a trigger are gaining traction in recent times. We believe that a combination of blockchain- and NC-based real-time event detection is superior to existing parametric claims processing mechanisms.
Figure 1: BFSI use cases that can benefit from neuromorphic computing
Time series data analysis is crucial for capital market firms for functions such as stock prices prediction, asset value fluctuation, derivative pricing, asset allocation, fraud detection, and high frequency trading. It requires learning and predicting patterns over a time period, where early experiments have found SNNs to be better than existing alternatives, especially for predicting future data points. NC can benefit each of these scenarios, but the actual gain will have to be evaluated on a case-by-case basis, depending on the number of model parameters, input datasets, the need for real-time predictions, and lower latency.
The most important benefit of NC will be in reducing the carbon footprint, especially as sustainability has become a boardroom agenda for BFSI firms, with the industry making net-zero commitments following the Paris agreement. With its key characteristic of lower power consumption, NC adoption will emerge as a priority for BFSI organizations given their reliance on IT infrastructure and ML applications, which contribute to higher emissions. As the integration of speech, video, images, generative AI, and facial recognition technologies into BFSI applications increases, reimagining the entire ML lifecycle from a sustainability perspective will become imperative. In early trials, NC has proved to be significantly more energy efficient while achieving accuracy that is comparable with DL models on a standard CPU or GPU. The limitations of existing models such as the need for multiple training cycles, hundreds of training examples, massive number crunching, and retraining due to information changes make the learning and inference process energy- and effort-intensive. NC can help overcome these challenges and accelerate green IT efforts.
In addition to reducing the carbon footprint, protecting property and communities from damage induced by climate change is also high on the regulatory agenda. For instance, to address wildfire risk intensified by climate change, the California Department of Insurance has issued ‘Safer From Wildfires’, a new insurance framework, which recommends actions that insurers should consider to mitigate their impact on communities. In our view, NC can help insurers enable the real-time audit of a slew of mitigation actions and features like Class-A fire rated roof, ember- and fire-resistant vents, and defensible space compliance.
Digital ecosystems are slowly but surely gaining traction in the BFSI industry as banks and insurers look for innovative business models to pursue new value streams and steal a march over the competition. Initiatives such as embedded lending, embedded investing, connected wellness, KYC automation, and parametric insurance will continue to push the boundaries of security and privacy. Existing techniques rely on pre-trained data sets and perform post-facto analysis to detect security breaches. NC can improve monitoring by detecting a new threat seconds before it evolves into a security ‘event.’ NC can enhance the in-situ processing of biometrics data in know your customer (KYC) verification and ensure that data from wearables is encrypted before it is sent over a network. Digital banking transactions on smartphones can be monitored in real time and instant action can be taken to prevent a breach when anomalous patterns are detected.
WHAT LIES AHEAD
In our view, BFSI firms should adopt a use case-centric approach to NC adoption to understand the advantages it can bring to existing AI and ML deployments.
And the advantages span a wide spectrum – from providing real-time insights in a connected insurance ecosystem to instantly detecting anomalous user behavior in digital banking transactions or running specific time-sensitive calculations in capital markets. We believe that it will be advantageous for BFSI firms to identify specific use cases that can significantly benefit from NC and run early proofs of concepts to evaluate its transformational potential.
However, a word of caution: not all BFSI AI and ML use cases will gain from NC, and a careful analysis of the nature of the use case, latency, and the expected outcomes is key. We envisage the co-existence of traditional CPUs and/or GPUs, neural hardware and TPUs, as well as neuromorphic platforms. Having said that, we expect NC – with its ability to enhance customer experience, facilitate early risk detection, deliver inferences in real time, and lower carbon footprint – to emerge as the natural choice for the BFSI industry. We believe that BFSI firms must stay abreast of the evolution of NC and its potential applications in the industry—once the technology matures, quick action will be necessary to gain a lead.
Sukriti Jalali
Sukriti Jalali is a principal consultant and thought leader in TCS’ Banking, Financial Services, and Insurance (BFSI) business unit. She is passionate about technology-enabled business transformation and helping customers achieve their growth and transformation objectives. Sukriti has presented at various industry forums and regularly publishes thought papers on digital transformation, IoT, a
rgupta
Regular
Sentiments keep on changing. So if you are a holder just hold for your time.
Shorters or day traders they work as fuel to the fire. If the sentiments are up, day traders ignite it and make short terms gain similarly when sentiments are down short sellers ignite the fire and let it further down. But patience is the key.
alwaysgreen
Top 20
They're all coming out of the woodwork now! 
Dr E Brown
Regular
I love me some TATA! Brilliant workTata !!!
SUKRITI JALALI
Principal Consultant, BFSI, TCS
Neuromorphic computing: Ushering in AI innovation in BFSI
HIGHLIGHTS
- Firms in the banking, financial services, and insurance (BFSI) industry have embraced artificial intelligence (AI) and machine learning (ML).
- These technologies use deep learning (DL) models that require massive sets of training data, consume enormous amounts of power, and fall short in adapting to the changing business environment.
- Neuromorphic computing (NC) can help firms leverage the latest innovations in AI to address some of the existing challenges while paving the way for next-generation use cases.
ABSTRACT
The banking, financial services, and insurance (BFSI) industry has been at the forefront of embracing disruptive technologies.
Firms have adopted artificial intelligence (AI) and machine learning (ML) to recast customer experience, improve business operations, and develop futuristic products and services. Existing AI and ML technologies utilize deep learning (DL) models that run on compute-intensive data centers, require massive sets of training data, consume a large amount of power to train, and fall short in adapting to the changing business environment.
In our view, the BFSI industry can overcome these challenges by exploring neuromorphic computing (NC) for certain kinds of use cases. Spiking neural networks (SNNs), which take inspiration from the functioning of biological neural networks in the human brain, when run on NC hardware, perform on par with DL models but consume significantly lesser power. They are purpose-built for AI and ML and offer advantages such as speed of learning and faster parallel processing. We highlight how NC can help firms overcome inefficiencies in the existing AI and ML deployments in the BFSI industry and examine new use cases.
INTRODUCTION
The proliferation of connected devices in the BFSI industry has generated enormous amounts of data.
This data needs to be analyzed and insights delivered in real time to enable instant action. Firms have been making use of data derived from images, videos, text, audio, and IoT devices. In the insurance industry, the use cases span property damage analysis, driver sleep detection, elderly care, and predictive asset maintenance. Robo-advisory for investment and wealth management, customer sentiment analysis, and fraud detection are other critical areas in the BFSI sphere that benefit from AI and ML.
However, much of the data analysis is post-facto or after-the-event, which means firms do not receive a timely warning and cannot take action to avert adverse events or minimize their impact. In addition, the existing models use DL networks that consume massive amounts of energy, both for training and inference. Firms need voluminous data sets to train the models while processing data sequentially. Enhancing the models or modifying their parameters are complex and cumbersome tasks. All this has resulted in several negative impacts for BFSI firms: higher carbon footprint, increased time and effort to train models, processing delays, and high manual effort across the AI and ML lifecycle whenever there is a change in input parameters or training data.
NC TO THE RESCUE
In our view, the BFSI industry should explore third-generation AI systems powered by neuromorphic computing(NC) platforms and spiking neural networks(SNNs).
This will help them address the aforementioned shortcomings and improve the response time, while significantly lowering the carbon footprint. NC closely replicates how the human brain responds to complex external events and learns unsupervised while using minimal energy. We believe that these systems will facilitate a natural progression toward developing ultra-low energy adaptive AI applications by mimicking human cognitive capabilities. NC will also reduce cloud dependence, which means that edge applications can be enabled without compromising privacy and security.
Key features of NC include:
These factors make NC a natural choice for BFSI use cases that require real-time insights and are time-sensitive in nature.
- Sparsity – allows models to be trained with a lesser amount of data compared to the existing DL models. This dramatically reduces memory and input training data requirements. For instance, in touchless banking kiosks, cameras underpinned by NC can recognize and learn individual gestures much faster, enabling personalized customer experience.
- Event-based processing – allows firms to detect and respond to events in real time. For instance, for parametric insurance policies, instant detection of a threshold breach is essential for immediate, frictionless pay-outs, which is key to superior customer experience.
- Colocation of memory and compute – enables faster parallel processing of multiple data streams. An insurance use case in focus is the prevention of work-related injuries in hazardous environments, resulting in reduced accidents and workers’ compensation claims. Given the low energy use of NC, some of the models can easily run on handheld devices without the need for cloud connectivity.
PUTTING THEORY TO ACTION
The insurance industry is moving from a protection to a prevention and preservation paradigm.
And embracing NC will help insurers accelerate this shift. Currently, data from IoT devices – wearables, connected vehicles, or drones – is sent over a network to cloud servers, where pre-trained algorithms process, analyze, and respond to each event. The response needs to travel back to the edge, based on which action is taken. This causes delays, consumes significant processing power on the server, and requires all scenarios to be pre-trained. This is not the best approach where a real-time response is critical to prevent the occurrence of adverse events or minimize their impact.
With its in-situ processing capabilities and ability to offer real-time inferences, NC offers a superior alternative. In our view, there is tremendous scope for NC technology to improve edge AI applications (see Figure 1). For example, real-time driver sleep detection is imperative to prevent an accident and the consequent insurance claims. Similarly, in home care, NC can prove to be a game changer for the remote monitoring of elderly patients. A fall or a sudden heart attack can be detected in real time. The connected ecosystem of family, doctors, ambulance, caregivers, and insurance providers can be alerted without delay. Insurance applications that need analytical insights at the edge span a wide range. They include usage-based vehicle insurance, real-time tracking of perishable cargo, predictive maintenance of critical equipment, elder care, early detection of anomalies in home insurance, and video- based claims processing. NC can also aid in faster detection of natural disasters such as floods, fires, or other calamities. This information can be fed to the insureds in advance. Parametric insurance products that offer pre-specified payouts based upon a trigger are gaining traction in recent times. We believe that a combination of blockchain- and NC-based real-time event detection is superior to existing parametric claims processing mechanisms.
Figure 1: BFSI use cases that can benefit from neuromorphic computing
Time series data analysis is crucial for capital market firms for functions such as stock prices prediction, asset value fluctuation, derivative pricing, asset allocation, fraud detection, and high frequency trading. It requires learning and predicting patterns over a time period, where early experiments have found SNNs to be better than existing alternatives, especially for predicting future data points. NC can benefit each of these scenarios, but the actual gain will have to be evaluated on a case-by-case basis, depending on the number of model parameters, input datasets, the need for real-time predictions, and lower latency.
The most important benefit of NC will be in reducing the carbon footprint, especially as sustainability has become a boardroom agenda for BFSI firms, with the industry making net-zero commitments following the Paris agreement. With its key characteristic of lower power consumption, NC adoption will emerge as a priority for BFSI organizations given their reliance on IT infrastructure and ML applications, which contribute to higher emissions. As the integration of speech, video, images, generative AI, and facial recognition technologies into BFSI applications increases, reimagining the entire ML lifecycle from a sustainability perspective will become imperative. In early trials, NC has proved to be significantly more energy efficient while achieving accuracy that is comparable with DL models on a standard CPU or GPU. The limitations of existing models such as the need for multiple training cycles, hundreds of training examples, massive number crunching, and retraining due to information changes make the learning and inference process energy- and effort-intensive. NC can help overcome these challenges and accelerate green IT efforts.
In addition to reducing the carbon footprint, protecting property and communities from damage induced by climate change is also high on the regulatory agenda. For instance, to address wildfire risk intensified by climate change, the California Department of Insurance has issued ‘Safer From Wildfires’, a new insurance framework, which recommends actions that insurers should consider to mitigate their impact on communities. In our view, NC can help insurers enable the real-time audit of a slew of mitigation actions and features like Class-A fire rated roof, ember- and fire-resistant vents, and defensible space compliance.
Digital ecosystems are slowly but surely gaining traction in the BFSI industry as banks and insurers look for innovative business models to pursue new value streams and steal a march over the competition. Initiatives such as embedded lending, embedded investing, connected wellness, KYC automation, and parametric insurance will continue to push the boundaries of security and privacy. Existing techniques rely on pre-trained data sets and perform post-facto analysis to detect security breaches. NC can improve monitoring by detecting a new threat seconds before it evolves into a security ‘event.’ NC can enhance the in-situ processing of biometrics data in know your customer (KYC) verification and ensure that data from wearables is encrypted before it is sent over a network. Digital banking transactions on smartphones can be monitored in real time and instant action can be taken to prevent a breach when anomalous patterns are detected.
WHAT LIES AHEAD
In our view, BFSI firms should adopt a use case-centric approach to NC adoption to understand the advantages it can bring to existing AI and ML deployments.
And the advantages span a wide spectrum – from providing real-time insights in a connected insurance ecosystem to instantly detecting anomalous user behavior in digital banking transactions or running specific time-sensitive calculations in capital markets. We believe that it will be advantageous for BFSI firms to identify specific use cases that can significantly benefit from NC and run early proofs of concepts to evaluate its transformational potential.
However, a word of caution: not all BFSI AI and ML use cases will gain from NC, and a careful analysis of the nature of the use case, latency, and the expected outcomes is key. We envisage the co-existence of traditional CPUs and/or GPUs, neural hardware and TPUs, as well as neuromorphic platforms. Having said that, we expect NC – with its ability to enhance customer experience, facilitate early risk detection, deliver inferences in real time, and lower carbon footprint – to emerge as the natural choice for the BFSI industry. We believe that BFSI firms must stay abreast of the evolution of NC and its potential applications in the industry—once the technology matures, quick action will be necessary to gain a lead.
Sukriti Jalali
Sukriti Jalali is a principal consultant and thought leader in TCS’ Banking, Financial Services, and Insurance (BFSI) business unit. She is passionate about technology-enabled business transformation and helping customers achieve their growth and transformation objectives. Sukriti has presented at various industry forums and regularly publishes thought papers on digital transformation, IoT, a
IndepthDiver
Regular
Interesting read from Andrew Ng, a pioneer in ML. Talks about how there has been a lot of excitement at this year's CVPR conference, with one of the key items being vision transformers.
www.deeplearning.ai
I spent Sunday through Tuesday at the CVPR computer vision conference in Vancouver, Canada, along with over 4,000 other attendees. With the easing of the pandemic, it’s fantastic that large conferences are being held in person again!
There’s a lot of energy in computer vision right now. As I recall, the natural language processing community was buzzing about transformers a couple of years before ChatGPT revolutionized the field more publicly. At CVPR, I sensed similar excitement in the air with respect to computer vision. It feels like major breakthroughs are coming.
It is impossible to summarize hundreds of papers into a single letter, but I want to share some trends that I’m excited about:
Vision transformers: The Batch has covered vision transformers extensively, and it feels like they’re still gaining momentum. The vision transformer paper was published in 2020, and already this architecture has become a solid alternative to the convolutional neural network. There are complexities still to be worked out, however. For example, whereas turning a piece of text into a sequence of tokens is relatively straightforward, many decisions need to be made (such as splitting an image into patches, masking, and so on) to turn an image processing problem into a token prediction problem. Many researchers are exploring different alternatives.
Innovations in Computer Vision Stole the Spotlight at This Year's CVPR Conference
I spent Sunday through Tuesday at the CVPR computer vision conference in Vancouver, Canada, along with over 4,000 other attendees. With the easing...
I spent Sunday through Tuesday at the CVPR computer vision conference in Vancouver, Canada, along with over 4,000 other attendees. With the easing of the pandemic, it’s fantastic that large conferences are being held in person again!
There’s a lot of energy in computer vision right now. As I recall, the natural language processing community was buzzing about transformers a couple of years before ChatGPT revolutionized the field more publicly. At CVPR, I sensed similar excitement in the air with respect to computer vision. It feels like major breakthroughs are coming.
It is impossible to summarize hundreds of papers into a single letter, but I want to share some trends that I’m excited about:
Vision transformers: The Batch has covered vision transformers extensively, and it feels like they’re still gaining momentum. The vision transformer paper was published in 2020, and already this architecture has become a solid alternative to the convolutional neural network. There are complexities still to be worked out, however. For example, whereas turning a piece of text into a sequence of tokens is relatively straightforward, many decisions need to be made (such as splitting an image into patches, masking, and so on) to turn an image processing problem into a token prediction problem. Many researchers are exploring different alternatives.
Boab
I wish I could paint like Vincent
alwaysKaren
JoMo68
Regular
Let’s hope our friend ‘Littlepenis’ is reading this.
cosors
👀
I had understood the case to be a kind of pawn sacrifice and not to hit those who are causing us so much worry.
It is one thing to push the SP and then sell at a tidy profit.
Another is to work with shorts and then hand in hand in a system of press and forums with false claims, lies and downramping on top of the shorts and bot trading to push the SP and stoke fears.
It is very easy for me to suspect that said person is part of this system.
The whole also has another serious drawback you also know from Talga. Yesterday we got a main permit and the SP actually ended up in the negative. We both and the others here on TSE can assess the situation well which most can't as they don't take that much time to look behind the what and figure out the why. Yesterday a small financial advisor posted on Youtube his recommendations. This included as a high risk Talga only buy very little and look. Reason was that the SP was very low for a long time and just yesterday and that alone shows how risky this stock is. He does not know the circumstances as well as we do. Now imagine how that looks with BRN for newcomers when they look at the chart. Does it look like a good opportunity or a dead end. Then they may google the reasons and read in the financial press what this person writes about us, seemingly serious since big thing. Many do not know what all has been achieved.
The shorters and others, I summarize them for me recently to the makers, have done a great job here and there.
You grew up with the machinations on the ASX but not the rest for example from Europe or as in my case from Germany.
We don't have a startup culture here that retailers can participate in, consequence of the .com bubble.
So many look to the ASX because many of your stocks are also listed here.
Very few know that TMH runs the forum HC or that they even run their own fund with contracts. Very few understand that many avatars on HC are not private individuals in the sense and what kind of agenda they run and wich different roles they play in their community. The fewest know how excessive bot trading is taking place and smallest trades are used to move the SP to where they want to have. Very few know that the ASX distributes tickets according to the agenda when they are asked by a shorter to follow up when the SP rises against their will or that the ASX does not like to intervene when the SP is systematically pushed down. I could go on long with my observations of the last few years but you all know that, as well as that AISIC is a paper tiger. I could put it even more drastically.
If I would have had my knowledge years ago I probably would have looked for my startups somewhere else in a more transparent system that is not so one-sidedly interlocked. You don't have the choice because of your super or pension system like the rest of us. I suspect that this is the main reason why the dark side can act freely. What politics would be interested in changing laws and thus admit that they are responsible for it and that when they themselves profit from it? I think none and therefore nothing changes.
The ASX is a very special trading place and a very unusual ecosystem and playground for those who belong to the financial club. Everyone else who knows the patterns can only run with it.
So I don't find the pushers so questionable who drive up the SP with a lot of work and influencers since no one has to believe them when they turn on the brain and question critically.
But against the other side of a positive investment allowing a company to go its way to develop with the money of the shareholders, against this other dark purely driven by greed side is each one of us powerless. In one case, I simply don't need to trade or follow. In the other case a covered system of media, forum, hired avatars, bots, favoritism on the ASX on multiple levels takes a position that is unassailable.
Back to the topic.
Where do you think Weebit would be today if Mickle and co and the makers ran the same as they did/do with us. Each of us can only hope that his beloved stock will not be the victim on the ASX by this side. Then everything doesn't matter no matter how much research or no matter how good a job the company does.
The dangerous thing is the dark influencers pick out crumbs and make the whole picture out of it and they are covered by this system on the ASX.
Whether I have aluminum on my head for you or not I do not care because I know that I have a clear opinion and the mostly neutral in the middle.
I wait that not a private person is sued but the other side the dark system. Nevertheless I know that this will never happen.
More often I am approached by young investors or beginners, even though I actually still count myself among them. But I have made my experience on the ASX. And the first thing I say: Let's put your company that you're interested in aside for now. I'll tell you what you're getting into and then we'll look at your company on the ASX. I also mention that the retailer has nothing under control no matter how much research, in the case when the company becomes the victim of the dark side. There is nothing to be done. One side is covered and protected, the other side that is to be milked is not.
I still stay because I have now gone along too long and want to follow my little ones and their story. That was always my desire to look back someday and to be able to say to myself that I was there for this important thing. On the way through the novel I also got to know the system against which every retailer is powerless. That's all.
... too much written again.
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D
Deleted member 118
Guest
That’s the most I’ve read in years
cosors
👀
Wrote only so much since I was so pissed with the action yesterday with Talga. The Fools would write:That’s the most I’ve read in years
Talga droped. Here are the reasons: Talga got a main permit. The market doesn't seem to trust the company's skill.
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