BRN Discussion Ongoing

[Foxdog]

The link above shows this wearable in action ,

I see our resident verification engineer @chapman has asked the pertinent question re: neural networks. There seems to be a sudden proliferation of researchers/companies claiming to be using neural networks. Either AKIDA is everywhere or BRN's successes in this field have sparked others to succeed similarly - bit like the 4 minute mile. Time for some big IP announcements please to keep us ahead of the pack.

 

[Getupthere]

Only Seven weeks to go for the 2.0

 

[Fullmoonfever]

Only Seven weeks to go for the 2.0

Hopefully moved us to the pad in readiness for something decent via official channels

 

[Cartagena]

View attachment 41690

So many single figure trades today, laughable!

If you don’t realise that there’s manipulation going on with the share price I’m not sure what will make it more obvious?

I want a big announcement, also tattslotto numbers and to be younger and better looking. Looks like I need BRN to shine

Good to see someone also noticing the funny business occuring. I think ball will drop soon. I loved picking up100K more shares at 36 cents. Mission accomplished
Was watching the last 10 minutes of trade and it reminded me of a video game, the big block of sell orders magically got thinner and thinner and then buyers started to bid up then it went back down like it was tug o war. Definitely to collect the cheapest possible shares, and make a profit at the end.

 

[cosors]

University of Virginia Joins the BrainChip University AI Accelerator Program

University of Virginia Joins the BrainChip University AI Accelerator Program

www.businesswire.com

University of Virginia Joins the BrainChip University AI Accelerator Program​

August 01, 2023 02:00 PM Eastern Daylight Time
LAGUNA HILLS, Calif.--(BUSINESS WIRE)--BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, today announced that the University of Virginia has joined the BrainChip University AI Accelerator Program, ensuring that UVA students have the tools and resources needed to establish the development of leading-edge technologies that will continue to usher in an era of intelligent AI solutions.



UVA’s computer engineering program gives students an opportunity to collaborate with top researchers in the country and participate in new research initiatives. The program is jointly administered by the Charles L. Brown Department of Electrical and Computer Engineering and Computer Science in the School of Engineering and Applied Science. The BrainChip University AI Accelerator Program equips UVA to incorporate neuromorphic technology – simulation of the brain’s neural network – into the department’s leading-edge curriculum.

BrainChip’s University AI Accelerator Program provides platforms, and guidance to students at higher education institutions with AI engineering programs training. Students participating in the program will have access to real-world, event-based technologies offering unparalleled performance and efficiency to advance their learning through graduation and beyond.
“As technology evolves, we are constantly adding areas of research to allow our students every opportunity to experience cutting-edge technology firsthand,” said Mircea Stan, director of the Virginia Microelectronics Consortium (VMEC) and professor of computer engineering at UVA. “As one of the leading institutions at the forefront of AI, we are excited to partner with BrainChip to share their approach to neuromorphic computing with the next generation of computer scientists by providing them an opportunity to learn and apply practical applications in the world of intelligent computing.”

BrainChip’s neural processor, Akida™ IP is an event-based technology that is inherently lower power when compared to conventional neural network accelerators. Lower power affords greater scalability and lower operational costs. BrainChip’s Akida supports incremental learning and high-speed inference in a wide variety of use cases. Among the markets that BrainChip’s Essential AI technology will impact are the next generation of smart cars, smart homes of today and tomorrow, and industrial IoT.

“The University of Virginia has developed a computer engineering program that focuses on several areas of research that give students a deep and meaningful learning experience across fields that are revolutionizing computing,” said Rob Telson, VP Ecosystems and Partnerships at BrainChip. “Our University AI Accelerator Program continues to provide top educational and research institutions with real-world opportunities to learn more about neuromorphic computing and its applications. We are pleased to work with the University of Virginia on advancing their mission to provide cutting-edge tools and resources that help them achieve their objectives.”

The University of Virginia joins current participants Arizona State University, Carnegie Mellon University, Rochester Institute of Technology, and the University of Oklahoma in the accelerator program. Other institutions of higher education interested in how they can become members of BrainChip’s University AI Accelerator Program can find more details at https://brainchip.com/brainchip-university-ai-accelerator/.

About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY) BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company’s first-to-market, fully digital, event-based AI processor, AkidaTM, uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Akida uniquely enables edge learning local to the chip, independent of the cloud, dramatically reducing latency while improving privacy and data security. Akida Neural processor IP, which can be integrated into SoCs on any process technology, has shown substantial benefits on today’s workloads and networks, and offers a platform for developers to create, tune and run their models using standard AI workflows like Tensorflow/Keras. In enabling effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers’ products, as well as the planet. Explore the benefits of Essential AI at www.brainchip.com.
Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc
Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006

About UVA Engineering
As part of the top-ranked, comprehensive University of Virginia, UVA Engineering is one of the nation’s oldest and most respected engineering schools. Our mission is to make the world a better place by creating and disseminating knowledge and by preparing future engineering leaders. Outstanding students and faculty from around the world choose UVA Engineering because of our growing and internationally recognized education and research programs. UVA is among the top engineering schools in the United States for the four-year graduation rate of undergraduate students and among the top-growing public engineering schools in the country for the rate of Ph.D. enrollment growth. Learn more at https://engineering.virginia.edu/.

Contacts​

Media Contact:
Mark Smith
JPR Communications
818-398-1424
Investor Contact:
Tony Dawe
BrainChip
tdawe@brainchip.com

BRAINCHIP HOLDINGS LTD​

"UVA Joins BrainChip AI Accelerator Program
​

Charlottesville, Va. — BrainChip Holdings, a California-based producer of artificial intelligence IP that simulates the brain’s neural network, said the University of Virginia has joined its BrainChip University AI Accelerator Program. The company said the move will allow UVA’s engineering department to incorporate neuromorphic technology — simulation of the brain’s neural network — into its curriculum."
https://www.potomactechwire.com/p/potomac-tech-wire-aug-10

UVA’s engineering department
=> https://engineering.virginia.edu/

e. g. =>

"On-Chip Neuromorphic Hardware Will Optimize Size, Power and Speed of Computing Devices​

By Karen Walker
mkw3a@virginia.edu

The Virginia NanO-computing (ViNO) group simulates and designs the hardware underlying the internet of things, from exploring the fundamental physics of emerging materials to quantum transport of electrons, to projecting overall device, circuit and system-level performance for memory, logic and sensing applications. Led by Avik Ghosh, professor of electrical and computer engineering and physics, the team develops state-of-the-art computational models and collaborates with experimentalists to understand the limits of various low-power electronic computing paradigms.

At the lowest atomistic level of modeling, the ViNO group specializes in exploring fundamental physical properties of a wide range of nanomaterials for emerging device technologies. Examples include nanomagnetic alloys that can store non-volatile data at high-bit density, 2-D materials such as graphene and topological insulators that capitalize on unconventional electron flow at very high mobility, compositionally graded thermal interface materials that minimize heat loss, and digitally grown III-V alloys and polycrystalline lead salts for high-sensitivity, single-photon detectors.

At the opposite, higher level of systems modeling, the ViNO group is looking at “on-chip” neuromorphic hardware to optimize the trade-off between a device’s size and power requirements and a software algorithm’s processing speed. The group's simulations show that a noisy low-barrier magnet* may enable the design of a scalable low-power hardware unit that behaves like an analog neuron, which can be used in turn to build large-scale hardware neural networks for real-time learning and prediction.

Neural networks are computing systems that learn to perform tasks through training sequences, without being pre-programmed with task-specific rules. This resulting artificial analog neural net could potentially be attached directly on chip with an image sensor to identify and track moving objects on video in real time, and for deployment in self-driving automobiles, robots and unmanned autonomous vehicles.
Maybe that's why they joined the fabulous BRN Ai AP?

A self-contained, low-power hardware neural chip might also be trained to recognize an individual’s medical signals, similar to how an electrocardiogram monitors heartbeats, or to identify atypical events and quickly classify the type of anomaly for real-time personalized medicine. Judicious on-chip processing of sensor data can greatly reduce size, weight and power of such networks, enabling the chip to operate “off line”—in the absence of a reliable wifi signal, and to protect against cyber-hacking.

The ViNO research leverages funding from NASA, the Defense Advanced Research Projects Agency, the Semiconductor Research Corporation’s Joint University Microelectronics Program, and a multi-university National Science Foundation Industry-University Cooperative Research Center on Multifunctional Integrated Systems Technology, for which Ghosh is a site-leader."
Or did NASA give the hint that UVA should join Brainchip's programme?
https://engineering.virginia.edu/ne...s-nano-materials-emerging-device-technologies

* @Diogenese and/or others
What do you think could be meant with 'noisy low-barrier magnet'? Do they mean NC with NVM (non-volatile memory) or something else entirely?

___________
Here in Germany, the universities of Bonn and Bochum should take their feet in their hands and join as well.

 

[cosors]

Hopefully moved us to the pad in readiness for something decent via official channels

View attachment 41747

That's exactly the right calibre! That's what I want to see! Let's rocket!

 

[TECH]

"UVA Joins BrainChip AI Accelerator Program​

​

Charlottesville, Va. — BrainChip Holdings, a California-based producer of artificial intelligence IP that simulates the brain’s neural network, said the University of Virginia has joined its BrainChip University AI Accelerator Program. The company said the move will allow UVA’s engineering department to incorporate neuromorphic technology — simulation of the brain’s neural network — into its curriculum."
https://www.potomactechwire.com/p/potomac-tech-wire-aug-10

UVA’s engineering department
=> https://engineering.virginia.edu/

e. g. =>

"On-Chip Neuromorphic Hardware Will Optimize Size, Power and Speed of Computing Devices​

By Karen Walker
mkw3a@virginia.edu

The Virginia NanO-computing (ViNO) group simulates and designs the hardware underlying the internet of things, from exploring the fundamental physics of emerging materials to quantum transport of electrons, to projecting overall device, circuit and system-level performance for memory, logic and sensing applications. Led by Avik Ghosh, professor of electrical and computer engineering and physics, the team develops state-of-the-art computational models and collaborates with experimentalists to understand the limits of various low-power electronic computing paradigms.

At the lowest atomistic level of modeling, the ViNO group specializes in exploring fundamental physical properties of a wide range of nanomaterials for emerging device technologies. Examples include nanomagnetic alloys that can store non-volatile data at high-bit density, 2-D materials such as graphene and topological insulators that capitalize on unconventional electron flow at very high mobility, compositionally graded thermal interface materials that minimize heat loss, and digitally grown III-V alloys and polycrystalline lead salts for high-sensitivity, single-photon detectors.

At the opposite, higher level of systems modeling, the ViNO group is looking at “on-chip” neuromorphic hardware to optimize the trade-off between a device’s size and power requirements and a software algorithm’s processing speed. The group's simulations show that a noisy low-barrier magnet* may enable the design of a scalable low-power hardware unit that behaves like an analog neuron, which can be used in turn to build large-scale hardware neural networks for real-time learning and prediction.

Neural networks are computing systems that learn to perform tasks through training sequences, without being pre-programmed with task-specific rules. This resulting artificial analog neural net could potentially be attached directly on chip with an image sensor to identify and track moving objects on video in real time, and for deployment in self-driving automobiles, robots and unmanned autonomous vehicles.
Maybe that's why they joined the fabulous BRN Ai AP?

A self-contained, low-power hardware neural chip might also be trained to recognize an individual’s medical signals, similar to how an electrocardiogram monitors heartbeats, or to identify atypical events and quickly classify the type of anomaly for real-time personalized medicine. Judicious on-chip processing of sensor data can greatly reduce size, weight and power of such networks, enabling the chip to operate “off line”—in the absence of a reliable wifi signal, and to protect against cyber-hacking.

The ViNO research leverages funding from NASA, the Defense Advanced Research Projects Agency, the Semiconductor Research Corporation’s Joint University Microelectronics Program, and a multi-university National Science Foundation Industry-University Cooperative Research Center on Multifunctional Integrated Systems Technology, for which Ghosh is a site-leader."
Or did NASA give the hint that UVA should join Brainchip's programme?
https://engineering.virginia.edu/ne...s-nano-materials-emerging-device-technologies

* @Diogenese and/or others
What do you think could be meant with 'noisy low-barrier magnet'? Do they mean NC with NVM (non-volatile memory) or something else entirely?

___________
Here in Germany, the universities of Bonn and Bochum should take their feet in their hands and join as well.

Good morning,

Couple of things.

PyTorch is available via onnx...info for the engineer/s among us.

How many Australian Universities have the facilities to offer the Brainchip AI Accelerator Program ?...I personally believe many
top Universities worldwide will continue to join this brilliant program if agreements can be reached with the company.

My crystal ball is thinking plural here in the Brainchip Spiritual HQ's...Australia

Tech.

 

[wilzy123]

Only Seven weeks to go for the 2.0

Or thereabouts. It's an "expected" date, not set in stone.

 

[equanimous]

View attachment 41739

Tata Consultancy Services - Research on LinkedIn: #smartwatch #wearables #researchpaper #tcsresearch #inventingforimpact… | 11 comments

Your #smartwatch is about to get smarter! Existing #wearables have motion sensing capabilities that are limited to mostly activity tracking. At the… | 11 comments on LinkedIn

www.linkedin.com

I didnt think of that, Using your smart watch as a remote via hand gesture controls for everything.

Deaf people could also translate in real time to people who can not read sign language via Siri translation.

SNN really needs to be incorporated into smart watches for battery life, performance and efficiency.

 

[Damo4]

I didnt think of that, Using your smart watch as a remote via hand gesture controls for everything.

Deaf people could also translate in real time to people who can not read sign language via Siri translation.

SNN really needs to be incorporated into smart watches for battery life, performance and efficiency.

It's quite clever.
A friend of mine has the Garmin's that can be utilised for Golf.
Whilst coming pre-loaded with the course, it can also register every hit, and then update it's path based on your next hit.
It can also somehow exclude practise swings, fresh air shots and mishits into the ground.
Whilst already "activity" based, this shows they are already thinking beyond a glorified pedometer/heart rate monitor.

Imagine using Akida to learn velocity, strike zones and swing arcs allowing for real time shot data before you line up your next shot.
Might even give me advice on how not to slice my driver??

 

[Frangipani]

Edge AI in Medical Applications - Digica | AI powered software

A practical use for object detection based on Convolutional Neural Networks is in devices which can support people with impaired vision. An embedded

www.digica.com

View attachment 41613

Incidentally, Chris Stevens also liked this blog post by Digica’s Head of Talent.
Two likes by senior Brainchip staff for an eight month old (!) post suggests more than mere coincidence to me.

Could Digica possibly be a company that only very recently revealed to Brainchip what they had used Akida for?
Otherwise why like their blog post only now, eight months after it got initially posted?

I checked out Digica’s website:

“Digica is an international software solutions company working at the forefront of the Artificial Intelligence revolution making a real difference to our clients and the world.”

AI solutions tailored for the defence industry​

Reduce AI innovation risks and exceed product development goals

• Intelligence, surveillance and reconnaissance - target acquisition and battlefield simulations optimised with deep learning
• Autonomous systems - computer vision-based automatic mission support services
• Chemical analysis - ML trained models for contaminant detection and spectroscopic analysis
• Fault prediction & prevention - cutting edge modelling at both device and system level

​

Delivering value and transforming performance in healthcare through AI​

Driving cost out of medical process

• Surgical equipment - instrument tray imaging
• Digital pathology - microscopic tissue feature detection
• Telemedicine - remote life signs measurement
• Digital stethoscope - heart murmur detection

Improving AI performance with hardware acceleration​

• Hardware Acceleration - boosting AI performance with GPUs (NVIDIA, MALI), ARM Neon, and other accelerators
• Algorithm Implementation - optimising computer vision and AI algorithms using compute APIs such as OpenCL, CUDA, ArmNN
• Edge AI Solutions - AI application design for System-on-Chip (SoC) and Microcontroller Unit (MCU) based devices: ST Microelectronics, NXP and NVIDIA-powered edge computing
• Computer Vision - seamless integration of computer vision algorithms for industry-specific applications
• AI-Enhanced Performance - leveraging cutting-edge hardware accelerators to enhance processing speed and efficiency
• Customizable Solutions - tailored AI and hardware acceleration strategies for unique semiconductor challenges

Could Akida be hidden behind the cutting-edge hardware accelerators?



This is from the blog post liked by both Todd Vierra and Chris Stevens:

So my guess is, Digica is where one of the Raspberry Pi 4 models sold by Brainchip may have ended up?

 

[toasty]

Brainchip mentioned in this article about the same deal with Prophesee.
View attachment 41722

There seems to be a clear inference (sic) in this article that Prophesee are achieving their goals as a result of their collaboration with Brainchip. If that is, in fact, the case then there is the often talked about dot joining us to Qualcomm, albeit indirectly. How many mobile phones are sold worldwide with Snapdragon on board? The mind boggles at what this would mean for Brainchip if Prophesee is incorporating our IP into this deal..........Hard to imagine they wouldn't be after what they have previously said about us closing the circle (my term) for them and their stated goal of a commercial relationship with us. Definitely one to watch closely!!!!!

 

[Diogenese]

"UVA Joins BrainChip AI Accelerator Program​

​

Charlottesville, Va. — BrainChip Holdings, a California-based producer of artificial intelligence IP that simulates the brain’s neural network, said the University of Virginia has joined its BrainChip University AI Accelerator Program. The company said the move will allow UVA’s engineering department to incorporate neuromorphic technology — simulation of the brain’s neural network — into its curriculum."
https://www.potomactechwire.com/p/potomac-tech-wire-aug-10

UVA’s engineering department
=> https://engineering.virginia.edu/

e. g. =>

"On-Chip Neuromorphic Hardware Will Optimize Size, Power and Speed of Computing Devices​

By Karen Walker
mkw3a@virginia.edu

The Virginia NanO-computing (ViNO) group simulates and designs the hardware underlying the internet of things, from exploring the fundamental physics of emerging materials to quantum transport of electrons, to projecting overall device, circuit and system-level performance for memory, logic and sensing applications. Led by Avik Ghosh, professor of electrical and computer engineering and physics, the team develops state-of-the-art computational models and collaborates with experimentalists to understand the limits of various low-power electronic computing paradigms.

At the lowest atomistic level of modeling, the ViNO group specializes in exploring fundamental physical properties of a wide range of nanomaterials for emerging device technologies. Examples include nanomagnetic alloys that can store non-volatile data at high-bit density, 2-D materials such as graphene and topological insulators that capitalize on unconventional electron flow at very high mobility, compositionally graded thermal interface materials that minimize heat loss, and digitally grown III-V alloys and polycrystalline lead salts for high-sensitivity, single-photon detectors.

At the opposite, higher level of systems modeling, the ViNO group is looking at “on-chip” neuromorphic hardware to optimize the trade-off between a device’s size and power requirements and a software algorithm’s processing speed. The group's simulations show that a noisy low-barrier magnet* may enable the design of a scalable low-power hardware unit that behaves like an analog neuron, which can be used in turn to build large-scale hardware neural networks for real-time learning and prediction.

Neural networks are computing systems that learn to perform tasks through training sequences, without being pre-programmed with task-specific rules. This resulting artificial analog neural net could potentially be attached directly on chip with an image sensor to identify and track moving objects on video in real time, and for deployment in self-driving automobiles, robots and unmanned autonomous vehicles.
Maybe that's why they joined the fabulous BRN Ai AP?

A self-contained, low-power hardware neural chip might also be trained to recognize an individual’s medical signals, similar to how an electrocardiogram monitors heartbeats, or to identify atypical events and quickly classify the type of anomaly for real-time personalized medicine. Judicious on-chip processing of sensor data can greatly reduce size, weight and power of such networks, enabling the chip to operate “off line”—in the absence of a reliable wifi signal, and to protect against cyber-hacking.

The ViNO research leverages funding from NASA, the Defense Advanced Research Projects Agency, the Semiconductor Research Corporation’s Joint University Microelectronics Program, and a multi-university National Science Foundation Industry-University Cooperative Research Center on Multifunctional Integrated Systems Technology, for which Ghosh is a site-leader."
Or did NASA give the hint that UVA should join Brainchip's programme?
https://engineering.virginia.edu/ne...s-nano-materials-emerging-device-technologies

* @Diogenese and/or others
What do you think could be meant with 'noisy low-barrier magnet'? Do they mean NC with NVM (non-volatile memory) or something else entirely?

___________
Here in Germany, the universities of Bonn and Bochum should take their feet in their hands and join as well.

Hi Cosors,

Here's my explanation:

Binary stochastic neurons (BSN’s) form an integral part of many machine learning algorithms, motivating the development of hardware accelerators for this complex function. It has been recognized that hardware BSN’s can be implemented using low barrier magnets (LBM’s) by minimally modifying presentday magnetoresistive random access memory (MRAM) devices. A crucial parameter that determines the response of these LBM based BSN designs is the correlation time of magnetization, τc. In this letter, we show that for magnets with low energy barriers (∆ ≈ kBT and below), circular disk magnets with in-plane magnetic anisotropy (IMA) lead to τc values that are two orders of magnitude smaller compared to τc for magnets having perpendicular magnetic anisotropy (PMA) and provide analytical descriptions. We show that this striking difference in τc is due to a precession-like fluctuation mechanism that is enabled by the large demagnetization field in IMA magnets. We provide a detailed energy-delay performance evaluation of previously proposed BSN designs based on Spin-Transfer-Torque (STT) MRAM and Spin-Orbit-Torque (SOT) MRAM employing low barrier circular IMA magnets by SPICE simulations. The designs exhibit sub-ns response times leading to energy requirements of ∼a few fJ to evaluate the BSN function, orders of magnitude lower than digital CMOS implementations with a much larger footprint. While modern MRAM technology is based on PMA magnets, results in this paper suggest that low barrier circular IMA magnets may be more suitable for this application.
...
In PMA, the thermal noise makes the magnetization fluctuate randomly anywhere on the Bloch sphere, while in IMA the large demagnetization field restricts the fluctuations to an in-plane precession-like fluctuations making it much faster.
...
In low barrier IMA magnets when thermal noise kicks the magnetization out-of-plane, due to the presence of large orthogonal demagnetization field HD the in-plane magnetization starts precessing. If we consider an ensemble of such magnets each with a different precession frequency due to thermal noise, the average magnetization vector would quickly dissipate.

Well actually I pinched it from:
https://arxiv.org/pdf/1902.03650v1.pdf

I'm guessing that it is to do with the alignment of the magnetic dipoles, but this technology is outside my experience.

However, it is interesting that the wheel has turned full circle s far as magnet memories are concerned. Before silicon, early computer memory bits were stored in ferrite rings with two control wires and a sensing wire.

PS: I would like to join the ViNO group.
PPS: Is taking your feet in your hands the German equivalent of bootstrapping?

 

[Vladsblood]

Over on Sputnik news a good story about TSMC building a plant in Dresden Germany. Interesting to see who the main shareholders are too! Vlad

 

[Iseki]

There seems to be a clear inference (sic) in this article that Prophesee are achieving their goals as a result of their collaboration with Brainchip. If that is, in fact, the case then there is the often talked about dot joining us to Qualcomm, albeit indirectly. How many mobile phones are sold worldwide with Snapdragon on board? The mind boggles at what this would mean for Brainchip if Prophesee is incorporating our IP into this deal..........Hard to imaging they wouldn't be after what they have previously said about us closing the circle (my term) for them and their stated goal of a commercial relationship with us. Definitely one to watch closely!!!!!

The trouble is that Qualcomm and Sony manufacture their own chips. So one of them would need to license Akida, and this would require an ASX announcement.

 

[Straw]

So are you saying Prophesee can't licence it or someone has to have a completed deal now or it can't happen? Not sure I'm following your logic.

 

[AARONASX]

The trouble is that Qualcomm and Sony manufacture their own chips. So one of them would need to license Akida, and this would require an ASX announcement.

Maybe in a hypothetical senario, to give Sony and Qualcomm the ledging edge 'they' in the interim go through a 3rd party until everything on their end is sorted out this avoids showing their cards and going public.

I would suspect if Akida is truely groundbreaking as the tell us there is going to be a lot of backroom deals done like we have never seen before. IMO

 

[supersonic001]

Teksun Inc on LinkedIn: #mlinnovation #predictiveanalytics #cuttingedgeexpertise #teksuntech…

Teksun's Machine Learning Solutions drive innovation and enhance business outcomes through intelligent data analysis and predictive capabilities. Unlock the…

www.linkedin.com

 

[Boab]

I know my Westpac trading account is normally a bit behind the times but a very slow trading day in progress and most of the exchanges are in the 2 and 3 figure range.
I have found myself becoming immune to these types of days with a shrug of the shoulders and a ho hum.
Happy days around the corner.

 

[SharesForBrekky]

I know my Westpac trading account is normally a bit behind the times but a very slow trading day in progress and most of the exchanges are in the 2 and 3 figure range.
I have found myself becoming immune to these types of days with a shrug of the shoulders and a ho hum.
Happy days around the corner.

Yeah, had a look through the course of sales, and it's incredible the number of 2 figure trades there have been today. Not sure the %, but has to be pretty high.

We're now half way through the day and the volume sits at 510,100. The lowest volume for a day this year was 2,686,383 back on 4th May. The way things are going, today could beat that low easily.