BRN Discussion Ongoing

[Esq.111]

What’s the total shorts outstanding ?

Afternoon Pom down under ,

From Shortman data today, which gives 13thfeb , shares shorted stood at 4.13% & BRN was the 44th most shorted.

Would appear to be dropping out of favour , from their perspective.

Regards,
Esq.

 

[buena suerte :-)]

What’s the total shorts outstanding ?

 

[Pom down under]

Afternoon Pom down under ,

From Shortman data today, which gives 13thfeb , shares shorted stood at 4.13% & BRN was the 44th most shorted.

Would appear to be dropping out of favour , from their perspective.

Regards,
Esq.

Well they not got out in the last 2 weeks which is

 

[wilzy123]

I was more baiting the parrots in Panama.

Looks like they haven't been seen this afternoon. Naturally makes sense to flush them out.

 

[cosors]

You may be right.
I notice that the rightmost line shows the 'spread', the difference between bid/ask. If the supply is poor here and there is very little trading, then it is normal for this to be well above 5% (I think 5 is ~normal vor ASX stocks here). If there is a lot of trading it is below 5%. I would like to exclude a bit Lang & Schwarz from this, they are private.
In any case, I have just compared your price at the ASX with ours here. Ours is about 10% higher than yours, including currency conversion. So if you deduct that, we would be quite close to you again.
The last time I saw such a big difference, I think it was also 10% off but at that time in our favor here, i.e. a kind of discount, was the evening before the first short attack that I noticed. The next day there were transferred I mean someone said 25M shares to you at the ASX and were dumped into the market. And the difference between the markets was evened out again. I remember it quite well because I found it exciting and scary at the same time. I still don't know whether there was a connection to the short attack.
But these two moments are linked by this rare 10% difference in value. Only the other way around back then. Let's see what happens on Monday. Perhaps a few million shares will be withdrawn from the ASX and thrown into the market here? Who knows. Sometimes I would love to have a look behind the scenes at how things like this work.

The 10% difference was equalised. AUD0.36 corresponds to €0.22. And as you can see, the ratio including currency conversion is balanced again.

Of course, I have no idea how the market makers work. Perhaps they were supplied with fresh shares from the ASX?

 

[Getupthere]

Crackdowns on data centre power use threaten AI expansion — Proactive

Governments worldwide are upping regulations on the development of data centres amid concerns over energy usage and the potential repercussions on power infrastructure and national climate targets. The Financial Times reports that nations including China, Singapore and Ireland have imposed...

apple.news

Crackdowns on data centre power use threaten AI expansion


Governments worldwide are upping regulations on the development of data centres amid concerns over energy usage and the potential repercussions on power infrastructure and national climate targets.


The Financial Times reports that nations including China, Singapore and Ireland have imposed restrictions on new data centres to align with more stringent environmental standards.


Ireland — recognised as a pivotal location for cloud computing firms due to its favourable tax regime and strategic access to global internet connections — is facing the most acute challenges.


The country's energy and water regulator's 2021 decision to restrict new data connections has led to the rejection of permits for new projects in Dublin by data centre operators Vantage, EdgeConneX and Equinix.


Germany and Loudoun County in Virginia, United States, have also adopted strategies to mitigate the environmental impact of data centres. These include limiting permits in residential zones and mandating contributions to renewable energy and waste heat reuse.


Exacerbating the situation are the burgeoning energy demands of artificial intelligence (AI) with the United States hosting a significant portion of the world's data centres.


AI encourages growth of renewables


Escalating power consumption from the rise of AI has led to increased scrutiny of tech giants Microsoft, Alphabet and Amazon to engage more actively in renewable energy generation and improve efficiency. Investments in wind and solar energy are underway, and Microsoft is also exploring nuclear options to power its facilities.


Barclays analysts warn that the implications of rising internet usage on power grids are yet to be fully accounted for by governments, suggesting a trend towards global restrictions. This could impact the $220 billion data centre and cloud industry, which is anticipated to be worth $418 billion by the decade's end amid soaring data demands.

 

[macro]

Hi macro

I would back Cambridge Consultants because of their ties with ARM and previous indications they have some understanding of AKIDA technology.

I would dismiss Imperial College London as they are deeply engaged with Intel and publish papers regarding Loihi fairly regularly.

I have not that I can remember ever come across any research by the other three that raised a suspicion they had links to Brainchip.

Maybe FMF has picked up something I missed.

My opinion only DYOR
Fact Finder

Hi FF

Dr Aidong Xu Head of Semiconductor Capability Cambridge Consultants

https://www.cambridgeconsultants.com/us/insights/opinion/what-is-neuromorphic-computing

What is neuromorphic computing and why do businesses and society need it?


Cambridge Consultants is a technology development firm that has worked with NETRI (Neuro Engineering Technologies Research Institute) on a breakthrough in precision imaging of brain-on-a-chip technology

https://www.cambridgeconsultants.co...hrough-unlocks-progress-brain-chip-technology

https://www.cambridgeconsultants.com/case-studies

Works/ed with ARM, Ford, NASA, Caterpillar, Phillips, Hitachi, SpaceX, Nvidia, Iridium, Uber and others

 

[Pom down under]

Maybe an even finish. More than happy with the last few trading days closes though. @Bravo what sort of state did the spa end up after the 30c part-ay? Probably recommend the flux capacitor have another flush out though as the 50c spa gathering may not be too far away

 

[Sirod69]

Machine Learning Engineer - Neuromorphic Computing (Akida 2) - London​

We are at the forefront of advancing neuromorphic computing technology. We are dedicated to developing cutting-edge solutions that transform how machines learn and interact with the world. Our team is growing, and we are seeking a talented Machine Learning Engineer to join our London office, focusing on developing applications using the Akida 2 neuromorphic computing platform.

Job Description:
As a Machine Learning Engineer, you will play a crucial role in our dynamic team, focusing on the development and implementation of machine learning algorithms tailored for the Akida 2 neuromorphic computing platform. Your expertise will contribute to optimizing AI models for energy efficiency and performance, aligning with the unique capabilities of neuromorphic computing.

https://www.upwork.com/freelance-jobs/apply/Machine-Learning-Engineer-Neuromorphic-Computing-Akida-London_~01e0cd3804f2f5bf56/

 

[Pom down under]

Machine Learning Engineer - Neuromorphic Computing (Akida 2) - London​

We are at the forefront of advancing neuromorphic computing technology. We are dedicated to developing cutting-edge solutions that transform how machines learn and interact with the world. Our team is growing, and we are seeking a talented Machine Learning Engineer to join our London office, focusing on developing applications using the Akida 2 neuromorphic computing platform.

Job Description:
As a Machine Learning Engineer, you will play a crucial role in our dynamic team, focusing on the development and implementation of machine learning algorithms tailored for the Akida 2 neuromorphic computing platform. Your expertise will contribute to optimizing AI models for energy efficiency and performance, aligning with the unique capabilities of neuromorphic computing.

https://www.upwork.com/freelance-jobs/apply/Machine-Learning-Engineer-Neuromorphic-Computing-Akida-London_~01e0cd3804f2f5bf56/

Morning

 

[Pom down under]

Wonder what they mean by more hmm

 

[Sirod69]

Morning

Morning

our price was red when I got up, now it was green and now it is at 0%. At the moment he doesn't know where he wants to go.

 

[Diogenese]

Thank you.
I understood that.
A score of 8000 previous words for context is amazing to me.
Had no idea LLMs were that powerful.
No wonder they use a lot of juice.

Yes - it's above my pay grade and very heavy going.

In speech recognition, in some cases, the speech must first be converted to text although, it is also possible to work with phonemes.

The processor needs to understand the nature of the words:
Nouns
Verbs
Adjectives
Adverbs
Prepositions
Conjunctions
Articles

... then to comprehend the context.

One problem is discovering how far back you need to go to find the correct context.

In written language, a lot of context would be found in a single sentence. A paragraph would capture a lot more context. But the context. In a book, it may be necessary to recall a chapter to descry the context.

In normal speech, the context should be close at hand (or ear), unless it is a familiar term.

LSTM, Transformers, and Attention, not to mention LLMs, have come along in quick succession.

This 2021 paper gives an inkling of the complexity:

Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition
Priyabrata Karmakar, Shyh Wei Teng, Guojun Lu

https://arxiv.org/pdf/2102.07259.pdf

There have been several attempts to find the optimal Attention system:

TABLE I

DIFFERENT TYPES OF ATTENTION MECHANISM FOR ASR

Name Short description

Global/Soft [10] At each decoder time step, all encoder hidden states are attended.

Local/Hard [23] At each decoder time step, a set of encoder hidden states (within a window) are attended.

Content-based [24] Attention calculated only using the content information of the encoder hidden states.

Location-based [25] Attention calculation depends only on the decoder states and not on the encoder hidden states.

Hybrid [11] Attention calculated using both content and location information.

Self [20] Attention calculated over different positions(or tokens) of a sequence itself.

2D [26] Attention calculated over both time-and frequency-domains.

Hard monotonic [27] At each decoder time step, only one encoder hidden state is attended.

Monotonic chunkwise [28] At each decoder time step, a chunk of encoder states (prior to and including the hidden state identified by the hard monotonic attention) are attended.

Adaptive monotonic chunkwise [29] At each decoder time step, the chunk of encoder hidden states to be attended is computed adaptively. models



This is a diagram of the configuration of a transformer-based encoder/decoder:

Unfortunately, the paper does not cover TeNNs.

It's getting to where there's only one chair left. Who will find a seat when the music stops?

 

[Pom down under]

Morning

our price was red when I got up, now it was green and now it is at 0%. At the moment he doesn't know where he wants to go.

Well don’t got back to bed and get up again

 

[Fullmoonfever]

A project from apparently about 3 days ago on Edge Impulse.

Full details in link.

Edge Impulse Documentation - Edge Impulse Documentation

docs.edgeimpulse.com

Industrial Inspection Line - Brainchip Akida Neuromorphic Processor​

An industrial inspection application that uses the Brainchip Akida Neuromorphic processor for fast and efficient quality control inferencing.
Created By: Peter Ing
Public Project Links:
Object Detection - https://studio.edgeimpulse.com/studio/349843 Classification - https://studio.edgeimpulse.com/studio/349858
GitHub Repo: https://github.com/peteing/brainchip_edgeimpulse_inspectionsystem.git

Introduction​

In the ever-evolving landscape of modern manufacturing, the efficiency and accuracy of production lines are paramount. The meticulous inspection of products at various stages ensures not only the adherence to quality standards but also the optimization of resources. In this dynamic scenario, the integration of cutting-edge technologies such as computer vision and artificial intelligence has emerged as a game-changer.
Initially, machine vision systems relied on basic image processing techniques and rule-based algorithms. These early systems were capable of performing relatively simple tasks, such as inspecting products for basic defects or checking for the presence of specific features. These systems required cameras with high-cost Industrial PC's to perform CPU based processing that was expensive and power hungry while offering limited performance.
Today the trend has shifted towards using Deep Learning specifically Convolutional Neural Networks on Graphics Processing Units and specialized CNN hardware accelerators. The solutions on the market are still relatively costly and power hungry. Camera and IPC's are available with integrated acceleration built-in for industrial use cases, but are very expensive.
Neuromorphic processing, inspired by the human brain, diverges from traditional computing with its parallel, adaptive features like Spiking Neural Networks, parallel processing, event-driven computation, and synaptic plasticity. This disruptive technology holds promise for energy-efficient, brain-like information processing, particularly in tasks like pattern recognition and sensory processing. This makes Neuromorphic computing ideal for use in Industrial Inspection systems where it can provide real-time insights into inspections. The benefits include reduced costs and improved performance and being able to adapt the system at the edge to new use cases.
Brainchip Akida represents the state of the art in production-ready Neuromorphic computing ideally suited to edge use-cases. We will be demonstrating the power of the Brainchip Akida in an industrial setting in this guide as part of a standalone inspection system that can be setup along a production line.
The Akida processor is available on a PCI-E card form-factor for integration into your own hardware, or ships as either an Intel or Arm-based developer kit. For the purpose of this project our focus is on the Arm-based developer kit, which consists of a Raspberry Pi Compute Module 4 mounted on a Raspberry PI Compute Module 4 IO board, which is what we are using for this application.
Many users coming from an Industrial environment have limited experience when it comes to AI and Deep Learning and this can seem daunting. There are very expensive platforms and solutions that help simplify the process, but none can match the ease of use and rapid performance of using Edge Impulse for the AI component of your project.

Industrial Inspection Use Case​

A typical scenario in an industrial manufacturing plant is defect detection. This can be applied to a range of different product types but essentially the requirement is always to determine which products to reject, out of a set of products that are often in motion using some kind of materials handling equipment such as a conveyor.
To achieve this, classic machine vision techniques using old camera systems running CPU algorithms often included detecting a Region of Interest (ROI) and then focusing on that area, and using tools such as edge and blob detection to find anomalies.

Deep learning solves this approach by making use of learning algorithms to simply teach the system what is correct and what isn't. This results in a 2 stage pipeline that first does Object Detection, then cascades the results to a classifier.

The Object Detector functions as the Region of Interest segmenter, while the classifier then determines if a product is defective or damaged, or passes the quality check. We will proceed to implement such a pipeline together with a custom GUI based app.
Akida Neuralmorphic technology is unrivaled in terms of power usage at a given performance level. Neuromorphic also provides unique features not found in other technologies such as in device edge learning made possible by the Spiking Neural Network architecture.

 

[DingoBorat]

View attachment 57211


Wonder what they mean by more hmm

It's definitely Intel right?..

The official comments coming from them on this, sound a little "too" casual?...

 

[Diogenese]

In the 70s, there was a British TV show called The Wheel Tappers and Shunters Club.

The wheel tappers used to tap train wheels to detect a clear ring from a sound wheel, or a clunk from a cracked wheel.

Imagine how this could be used with Akida to detect faulty welds or casts.

 

[Pom down under]

It's definitely Intel right?..

The official comments coming from them on this, sound a little "too" casual?...

Last time I looked it was

 

[Diogenese]

Ooh-Ooh-Ooh!

Hey Eskie, there maybe 4 entities as Softbank‘s Masayoshi Son is looking to raise up to $100 billion for a chip venture that will rival Nvidia. This project is apparently set to focus on semiconductors essential for artificial intelligence.

Ps: Bravo reporting for duty, currently couch surfing at my Mums armed with an iPad and not much else after exiting my town that is still without power or interwebs.

Hi Bravo,

Wouldn't it be awful if it started a bidding war between Softbank (ARM), Intel, and Nvidia?

 

[Diogenese]

Good morning all, take it as a grain of salt, as always been undervalued

View attachment 57166

If you swap the numbers on each side of the decimal point ...