BRN Discussion Ongoing

[macro]

Wonder who in London is playing with or wants to play with Akida Gen 2....hmmmm

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

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

Posted 3 weeks ago

U.K. located freelancers only
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.

Key Responsibilities:

Develop and optimize machine learning models for the Akida 2 platform.
Collaborate with cross-functional teams to integrate AI solutions into products.
Conduct research and stay updated with the latest trends in neuromorphic computing.
Provide technical guidance and mentorship to junior team members.
Participate in code reviews and maintain high standards in development practices.

Qualifications:

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.
Proven experience in machine learning and neural network development.
Familiarity with neuromorphic computing, particularly Akida 2, is highly desirable.
Strong programming skills in Python and experience with machine learning frameworks.
Excellent problem-solving abilities and a collaborative team player.
Strong communication skills, both written and verbal.

What We Offer:

Competitive salary and benefits package.
Opportunity to work on groundbreaking technology in a fast-paced environment.
Professional development opportunities and a collaborative team culture.
Central London location with modern office facilities.

Application Process:
To apply, please submit your CV and a cover letter outlining your suitability for the role. Shortlisted candidates will be invited for an interview process, which may include technical assessments.

We are an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Join us in shaping the future of AI and neuromorphic computing. Apply today!

Who is at the forefront of advancing neuromorphic computing technology in London?

Perplexity AI says:

• Cambridge Consultants, which has a head of semiconductor capability involved in neuromorphic computing
• Silicon Storage Technology, which is a leading patent filer in neuromorphic computing
• UCL, which has researchers such as Professor Tony Kenyon and Dr. Adnan Mehonic working on neuromorphic computing
• Imperial College London, which has researchers like Dr. Oscar Lee, Dr. Jack Gartside, and Professor Will Branford involved in neuromorphic computing
• University of West London, which is involved in the development of methods and computational tools for neuromorphic AI

 

[mrgds]

No thanks to @Bravo .............................
GREY BABY YEAH ................

 

[Fact Finder]

Who is at the forefront of advancing neuromorphic computing technology in London?

Perplexity AI says:

• Cambridge Consultants, which has a head of semiconductor capability involved in neuromorphic computing
• Silicon Storage Technology, which is a leading patent filer in neuromorphic computing
• UCL, which has researchers such as Professor Tony Kenyon and Dr. Adnan Mehonic working on neuromorphic computing
• Imperial College London, which has researchers like Dr. Oscar Lee, Dr. Jack Gartside, and Professor Will Branford involved in neuromorphic computing
• University of West London, which is involved in the development of methods and computational tools for neuromorphic AI

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

 

[Dr E Brown]

Many thanks to those who replied, especially FMF who gave me a better understanding. I appreciate your help. My thoughts are a continuing uptrend, which hopefully is gentle until Sean gives us a presentation around release of half yearly figures. Thanks again.

 

[TheDrooben]

Looks like the buyers were back at the finish to soak up shares from the day traders and weak hands. Great finish and set up for the next leg up IMO. Gee a surprise positive pre-open anouncement in the morning would really provide the fuel............

HAPPY AS LARRY

 

[Diogenese]

OK Mia! I’ll go outside and shovel some mulch for my Mum and we can see if that does anything. Back later!

We've got some second hand mulch that we've cleaned as best as we can.

 

[Fact Finder]

We've got some second hand mulch that we've cleaned as best as we can.

Is that the same stuff you tried to sell me with the warning about not to be used for Smoking Ceremonies?

 

[Fact Finder]

Hi All
I found this a useful guide to understanding LLMs and that they are all one thing it is just that to use them for a particular purpose such as running at the Edge you would likely want to optimise them to remove those parts which are not needed for your intended purpose.

An LLM for handling Macca’s drive through orders would likely not need the ability to discuss quantum mechanics:

“Optimizing LLMs for Your Use Cases: A Developer’s Guide​

Pankaj Pandey
·
Follow
4 min read
·
Jan 13, 2024

Listen
Share

Optimizing LLMs for specific use cases can significantly improve their efficiency and performance.

Photo by 1981 Digital on Unsplash
Large Language Models (LLMs) hold immense potential to revolutionize diverse workflows, but unlocking their full potential for specific use cases requires thoughtful optimization.
Below are some guides for developers looking to optimize LLMs for their own use cases:

1. Analyze Your Use Case:​

• Define the task: Identify the specific task you want the LLM to perform (e.g., code generation, text summarization, question answering).
• Data analysis: Assess the type and size of data available for training and fine-tuning. Is it curated, labeled and relevant to your specific domain?
• Evaluation metrics:Determine how you’ll measure success. Are you aiming for accuracy, speed, creativity or something else?

For Example, imagine you are developing a chatbot for a customer support application. The use case is to generate helpful responses to user queries, providing accurate and relevant information.

2. LLM Selection and Fine-tuning:​

• Model selection: Choose an LLM with capabilities aligning with your task. Consider pre-trained models for your domain may help you do this task easily.
  For Example, choose GPT-3 as it excels in natural language understanding and generation, which aligns well with the conversational nature of the customer support chatbot.
• Fine-tuning: Adapt the LLM to your specific data using transfer learning. Popular frameworks like Hugging Face offer tools and tutorials for fine-tuning.
  Fine-tune GPT-3 using a dataset of customer support interactions. Provide examples of user queries and corresponding responses to help the model understand the specific context and language used in the customer support domain.
• Hyperparameter optimization: Adjust settings like learning rate, batch size and optimizer to maximize performance on your data. Consider using automated Hyperparameter Optimization (HPO) tools.
  Experiment with smaller variants of GPT-3 or adjust hyperparameters to find the right balance between model size and performance. For a latency-sensitive application like customer support, a smaller model might be preferred.

3. Data Wrangling and Augmentation:​

• Data quality: Ensure data cleanliness and relevance. Label inconsistencies, biases and irrelevant examples can negatively impact performance.
  Apply quantization to reduce model precision, making it more efficient. Prune unnecessary connections to create a more compact model without compromising performance.
• Data augmentation: Artificially expand your data with techniques like synonym substitution, back-translation or paraphrasing to improve model generalization.
• Active learning: Interactively query the LLM to identify informative data points for further labeling, focusing resources on areas where the model needs improvement.

4. Integration and Deployment:​

• API integration: Connect the LLM to your application or workflow through APIs offered by platforms like OpenAI or Google Cloud AI.
• Latency optimization: Optimize resource allocation and inference techniques to minimize response time and improve user experience.
• Monitoring and feedback: Continuously monitor model performance and gather feedback from users. Use this data to further refine the LLM and iterate on your solution.

5. Caching and Memorization:​

• Implement caching and memorization strategies to store and reuse intermediate results during inference. This can significantly reduce redundant computations and improve response times.
• Implement caching of frequently used responses. For commonly asked questions, store and reuse the model’s previous outputs to reduce redundant computations and improve response times.

6. User Feedback Loop:​

• Establish a feedback loop with end-users to understand their experience and gather insights for further optimization. User feedback can help refine the model and identify areas for improvement.
  For Example, gather feedback from users regarding the effectiveness of the chatbot’s responses. Use this feedback to identify areas for improvement, update the model accordingly and enhance the overall user experience.

Additional Tips:​

• Consider interpretability: Choose LLMs with built-in explainability features to understand their reasoning and build trust with users.
• Utilize transfer learning techniques: Leverage pre-trained knowledge from similar tasks to accelerate development and improve performance.
• Collaborate with the LLM community: Stay informed about advances in LLM research and best practices, participate in forums and contribute your findings.

By following these steps and continuously iterating, you can significantly improve the efficiency and efficacy of LLMs for your specific use cases. Remember, optimizing LLMs is an ongoing process and dedication to the data, model and integration aspects will ultimately unlock their full potential in your workflows.

Helpful Resources:​

Fine-tuning — OpenAI API
Customize a model with Azure OpenAI Service — Azure OpenAI | Microsoft Learn
Please note: This guide provides a general framework. Specific steps and tools may vary depending on your chosen LLM, framework and use case.”

This is what Brainchip’s engineers and scientists have been doing that Dr. Lewis described as achieving SOTA performance.

My opinion only DYOR
Fact Finder

 

[Diogenese]

Is that the same stuff you tried to sell me with the warning about not to be used for Smoking Ceremonies?

It's ok as long as you don't inhale.

 

[Teach22]

Do we have any (good) chartists that could give their interpretation of today’s trading please?

 

[Diogenese]

Hi All
I found this a useful guide to understanding LLMs and that they are all one thing it is just that to use them for a particular purpose such as running at the Edge you would likely want to optimise them to remove those parts which are not needed for your intended purpose.

An LLM for handling Macca’s drive through orders would likely not need the ability to discuss quantum mechanics:

“Optimizing LLMs for Your Use Cases: A Developer’s Guide​

Pankaj Pandey
·
Follow
4 min read
·
Jan 13, 2024

Listen
Share

Photo by 1981 Digital on Unsplash
Large Language Models (LLMs) hold immense potential to revolutionize diverse workflows, but unlocking their full potential for specific use cases requires thoughtful optimization.
Below are some guides for developers looking to optimize LLMs for their own use cases:

1. Analyze Your Use Case:​

• Define the task: Identify the specific task you want the LLM to perform (e.g., code generation, text summarization, question answering).
• Data analysis: Assess the type and size of data available for training and fine-tuning. Is it curated, labeled and relevant to your specific domain?
• Evaluation metrics:Determine how you’ll measure success. Are you aiming for accuracy, speed, creativity or something else?

For Example, imagine you are developing a chatbot for a customer support application. The use case is to generate helpful responses to user queries, providing accurate and relevant information.

2. LLM Selection and Fine-tuning:​

• Model selection: Choose an LLM with capabilities aligning with your task. Consider pre-trained models for your domain may help you do this task easily.
  For Example, choose GPT-3 as it excels in natural language understanding and generation, which aligns well with the conversational nature of the customer support chatbot.
• Fine-tuning: Adapt the LLM to your specific data using transfer learning. Popular frameworks like Hugging Face offer tools and tutorials for fine-tuning.
  Fine-tune GPT-3 using a dataset of customer support interactions. Provide examples of user queries and corresponding responses to help the model understand the specific context and language used in the customer support domain.
• Hyperparameter optimization: Adjust settings like learning rate, batch size and optimizer to maximize performance on your data. Consider using automated Hyperparameter Optimization (HPO) tools.
  Experiment with smaller variants of GPT-3 or adjust hyperparameters to find the right balance between model size and performance. For a latency-sensitive application like customer support, a smaller model might be preferred.

3. Data Wrangling and Augmentation:​

• Data quality: Ensure data cleanliness and relevance. Label inconsistencies, biases and irrelevant examples can negatively impact performance.
  Apply quantization to reduce model precision, making it more efficient. Prune unnecessary connections to create a more compact model without compromising performance.
• Data augmentation: Artificially expand your data with techniques like synonym substitution, back-translation or paraphrasing to improve model generalization.
• Active learning: Interactively query the LLM to identify informative data points for further labeling, focusing resources on areas where the model needs improvement.

4. Integration and Deployment:​

• API integration: Connect the LLM to your application or workflow through APIs offered by platforms like OpenAI or Google Cloud AI.
• Latency optimization: Optimize resource allocation and inference techniques to minimize response time and improve user experience.
• Monitoring and feedback: Continuously monitor model performance and gather feedback from users. Use this data to further refine the LLM and iterate on your solution.

5. Caching and Memorization:​

• Implement caching and memorization strategies to store and reuse intermediate results during inference. This can significantly reduce redundant computations and improve response times.
• Implement caching of frequently used responses. For commonly asked questions, store and reuse the model’s previous outputs to reduce redundant computations and improve response times.

6. User Feedback Loop:​

• Establish a feedback loop with end-users to understand their experience and gather insights for further optimization. User feedback can help refine the model and identify areas for improvement.
  For Example, gather feedback from users regarding the effectiveness of the chatbot’s responses. Use this feedback to identify areas for improvement, update the model accordingly and enhance the overall user experience.

Additional Tips:​

• Consider interpretability: Choose LLMs with built-in explainability features to understand their reasoning and build trust with users.
• Utilize transfer learning techniques: Leverage pre-trained knowledge from similar tasks to accelerate development and improve performance.
• Collaborate with the LLM community: Stay informed about advances in LLM research and best practices, participate in forums and contribute your findings.

By following these steps and continuously iterating, you can significantly improve the efficiency and efficacy of LLMs for your specific use cases. Remember, optimizing LLMs is an ongoing process and dedication to the data, model and integration aspects will ultimately unlock their full potential in your workflows.

Helpful Resources:​

Fine-tuning — OpenAI API
Customize a model with Azure OpenAI Service — Azure OpenAI | Microsoft Learn
Please note: This guide provides a general framework. Specific steps and tools may vary depending on your chosen LLM, framework and use case.”

This is what Brainchip’s engineers and scientists have been doing that Dr. Lewis described as achieving SOTA performance.

My opinion only DYOR
Fact Finder

Fine tuning: Too much shiraz has flowed under the bridgework, an article was just discussed where Peter was asked about a patent and he said it was an auxiliary invention for transfer learning via the cloud.

Model selection: Really CNN2SNN gives Akida an unfair advantage in adapting existing models.

 

[Deadpool]

Do we have any (good) chartists that could give their interpretation of today’s trading please?

It went up it went down it went flat

 

[wilzy123]

View attachment 57193

For the inept forum chooks?

 

[HopalongPetrovski]

Do we have any (good) chartists that could give their interpretation of today’s trading please?

 

[Esq.111]

Afternoon Chippers ,

19/2/2024
Trading Report..

Days Trading Range : $0.33 to $0.39 AU
Finnished : $0.36 AU
Volume : Getting rather Exiting.

SHOOTING STAR RESEARCH
Date : 19 Feb 2024
☆☆☆☆☆ STAR RATING.
Fair Value :
$5.22 AU
3.01081 CHF. Swiss Franks
3 & seven eighths SCS ( Standard bags of Cowrie Shells.
* Note : Above dose not even remotely include a buyout premium.
* Rockerrothsgettyfellerchild Inc .LLC
Corporate Office : Post Box 1A , Little Cayman Island
Offices : Canairy Islands , Liechtenstein & Switzerland
Trading desk : Wilds of Sotherm Panama.

* This Investment House operates on the highest standards of integrity , with the ultimate goal to maximise investment return by way of shits & giggles too clientele, whilst also delivering a fair & equitable shareprice guestimate to both sides of the investment community.

* Average return over 10 years is in excess of 25% shifts & giggles margin above nearest competitors

Not Financial Advice. One should always insult a financial planer if in doubt.



Regards,
Esq.

 

[Diogenese]

Hi Hoppy,

The follow-on explains the difference between NLP and LLM ... no really>

 

[Esq.111]

For the inept forum chooks?

I was more baiting the parrots in Panama.

 

[TheDrooben]

Afternoon Chippers ,

19/2/2024
Trading Report..

Days Trading Range : $0.33 to $0.39 AU
Finnished : $0.36 AU
Volume : Getting rather Exiting.

SHOOTING STAR RESEARCH
Date : 19 Feb 2024
☆☆☆☆☆ STAR RATING.
Fair Value :
$5.22 AU
3.01081 CHF. Swiss Franks
3 & seven eighths SCS ( Standard bags of Cowrie Shells.
* Note : Above dose not even remotely include a buyout premium.
* Rockerrothsgettyfellerchild Inc .LLC
Corporate Office : Post Box 1A , Little Cayman Island
Offices : Canairy Islands , Liechtenstein & Switzerland
Trading desk : Wilds of Sotherm Panama.

* This Investment House operates on the highest standards of integrity , with the ultimate goal to maximise investment return by way of shits & giggles too clientele, whilst also delivering a fair & equitable shareprice guestimate to both sides of the investment community.

* Average return over 10 years is in excess of 25% shifts & giggles margin above nearest competitors

Not Financial Advice. One should always insult a financial planer if in doubt.



Regards,
Esq.

 

[HopalongPetrovski]

Hi Hoppy,

The follow-on explains the difference between NLP and LLM ... no really>

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.

 

[Pom down under]

Very small amount taken out Friday

View attachment 57175

View attachment 57174

Back to 38c soooooon

What’s the total shorts outstanding ?