Pom down under
Top 20
Wonder what they mean by more hmm
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Wonder what they mean by more hmm
Sirod69
bavarian girl ;-)
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.
Yes - it's above my pay grade and very heavy going.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.
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
Top 20
Well don’t got back to bed and get up againMorning
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.
Fullmoonfever
Top 20
A project from apparently about 3 days ago on Edge Impulse.
Full details in link.
docs.edgeimpulse.com
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
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.
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.
Full details in link.
Industrial Inspection Line - Brainchip Akida | Expert Projects
An industrial inspection application that uses the Brainchip Akida Neuromorphic processor for fast and efficient quality control inferencing.
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.
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DingoBorat
Slim
It's definitely Intel right?..
The official comments coming from them on this, sound a little "too" casual?...
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.
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
Top 20
Hi Bravo,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.
Wouldn't it be awful if it started a bidding war between Softbank (ARM), Intel, and Nvidia?
If you swap the numbers on each side of the decimal point ...
TheDrooben
Pretty Pretty Pretty Pretty Good
Larry needs an accurate wake word recognition program and digital assistant!!!!!! Now where would I find one??????? (Warning strong language)
Remember to convert the rate to the correct currency
If this is not wrong; is this the opposite to trashing a company for shorting purposes by seeding positivity?
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Easytiger
Regular
The share price will go up, down or stay the same - or all of them.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.
DingoBorat
Slim
That is exactly, why I will never use that shit and disable and "dumb down" (what a contradiction) any device I have, that may have it..
Even in that supposedly "amazing" demonstration, of Groq A.I.'s natural language abilities, they just had it hum and arr a bit, while it "searched" for the answer (just like a human would
) and the CEO guy, or whoever he was, had to clearly and slowly say goodbye to it and it answered a few seconds later..I think we are still a fair way off "true" natural language interactions, with A.I.
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TasTroy77
Founding Member
Definitely the company Intel as you click and it takes to their page
DingoBorat
Slim
I tried that, but it appears to be just an image for me..
Sounds more like an excited intern?..
Would be better, if it had the actual person saying it, as it sounds more like a personal comment.
HopalongPetrovski
I'm Spartacus!
Yikes!
I better rephrase my previous admonition to "I think I have a slight grasp of the basic concept as an abstract ideation" and thank God herself I am pretty.
BrainShit
Regular
GlobalFoundries and Biden-Harris Administration Announce CHIPS and Science Act Funding for Essential Chip Manufacturing
GlobalFoundries and Biden-Harris Administration Announce CHIPS and Science Act Funding for Essential Chip Manufacturing | GlobalFoundries
$1.5 billion potential investment will support the expansion of GlobalFoundries New York facility, including construction of a new state-of-the art […]
gf.com
So, Sam + ARM + Intel + Brainchip + ..... meeting @ 21.02.24 Convention Intel Foundry right?
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