Another trap is ALT ("E", "S") to "paste special" - that'll send you off the rails in TSEx ...
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
DingoBorat
Slim
I'd like for BrainChip to remain independent, but it seems like it may be increasingly unlikely..
I think we could easily manage a 9 billion market capitalisation, over the next few years, if we gain as much market penetration, as we look like getting..
But that would give us a share price of around $5.20 with current shares on issue.
Altium, have only 131.6 million SOI...
DingoBorat
Slim
Never heard of it..
Stable Genius
Regular
Thanks for posting that @Meetupsoon
A great read from a true genius.
I love this gem;
AI Time Journal would like to congratulate Brainchip for being awarded the patent on “AI Dynamic Neural Network”. Can you provide us some insights on it and the challenges you find in placing this as a market product?
This is a supporting patent from our first patent “Autonomous learning dynamic artificial neural computing device and brain inspired system”, which describes the event-based spike processing method used in all BrainChip products. This second patent describes a way that information can be shared between two or more Akida devices. For instance, when an Akida chip is used in a car and it learns something new, it would be beneficial if it could share that knowledge with all other Akida devices used in similar functions in other cars. It may learn about a new object to avoid, or a better optimization. Through a library, which may exist in the cloud, it can share this information with other devices. This is a product enhancement, rather than a stand-alone product.I wish PVDM was 20 years younger. He is a visionary who has achieved greatness with his intelligence and dedication. I hope he is one day better recognised for what he has accomplished!
I wish him good health in his well deserved retirement.
Cheers
Fact Finder
Top 20
Hi All
Those who listened to the recent podcast with the CEO of Brainchip Sean Hehir would have heard him mention VVDN AKIDA Edge Box, Unigen AKIDA Cupcake Edge Server and Teksun.
This reference reminded me of the following:
Well both Sean Hehir with his remark and Vivek Joshi with his tease of things to come have left me intrigued particularly as I have studied Teksun’s website and understand the breadth of product and industries serviced by it.
Further intrigue is generated by the completely random reference to Vivoka in the following linked article:
teksun.com
Then throw in the fact that Vivoka (a software only company) and Teksun announced they were partnering back in 2023 has my mind racing:
vivoka.com
Do these connections tie together and provide clues to the something intriguing coming in the second quarter from Teksun.
One thing is clear Brainchip is certainly not sitting idol waiting for the phone to ring.
My opinion only DYOR
Fact Finder
Those who listened to the recent podcast with the CEO of Brainchip Sean Hehir would have heard him mention VVDN AKIDA Edge Box, Unigen AKIDA Cupcake Edge Server and Teksun.
This reference reminded me of the following:
Well both Sean Hehir with his remark and Vivek Joshi with his tease of things to come have left me intrigued particularly as I have studied Teksun’s website and understand the breadth of product and industries serviced by it.
Further intrigue is generated by the completely random reference to Vivoka in the following linked article:
Teksun's Next-Gen IoT and AI Solutions Steal the Show at CES 2023
Teksun also highlighted an extensive range of IoT and AI solutions in CES 2023
teksun.com
Then throw in the fact that Vivoka (a software only company) and Teksun announced they were partnering back in 2023 has my mind racing:
Vivoka & Teksun partner to develop innovative solution – Vivoka
Vivoka and Teksun signed a partnership agreement to collaborate on innovative technology, products, and services.
vivoka.com
Do these connections tie together and provide clues to the something intriguing coming in the second quarter from Teksun.
One thing is clear Brainchip is certainly not sitting idol waiting for the phone to ring.
My opinion only DYOR
Fact Finder
Bravo
If ARM was an arm, BRN would be its biceps💪!
Ooh-Ooh-Ooh!Morning Pom down under ,
Funnily enough I read it as a positive share price too, though it is not.
On a side note , on some very very loose numbers.....
Over the last week or so three entitys have come out with rather large ambitions..
1, Sam Altman..hoping to raise 5 to 7 trillion
2, Article on Japanese gov & industry with potential to deploy 1 trillion to capture 1/3 of the tech market
3, ARM potentialy going to spend somewhere in the region of 100 billion.
The above three total equal something in the region of say 7.1 trillion.
EXPECT LOTS OF MERGER & OR AQUSITION's , as well as new ventures / construction etc.
Now for some fun ....
If ...BrainChip captures 1% of the above capitol looking for a home..
7,100,000,000,000.00÷ 100= 71,000,000,000.00 ( = 1% ).
71,000,000,000 ÷ SAY 2,000,000,000 SHARES = $35.50 PER SHARE.
Oh, and thats USD.
Note : All numbers and eventualitys outlined above have been drawn from a loose but vivid imagination.
Looking foward to tomorrow .
Regards,
Esq.
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.
Last edited:
Fullmoonfever
Top 20
Wonder who in London is playing with or wants to play with Akida Gen 2....hmmmm 
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!
Machine Learning Engineer - Neuromorphic Computing (Akida 2) - London
Posted 3 weeks agoU.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!
Wonder who in London is playing with or wants to play with Akida Gen 2....hmmmm
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!
Waaaat?!
Couch surfing and interweb surfing - there's multi-tasking for you.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.
https://www.bing.com/videos/search?...CEBE59C14964CC190AE9CEBE59C14964C&FORM=WRVORC
Fullmoonfever
Top 20
Yeah saw that too haha
Though, there has been several proposals so suspect they will be somewhat higher
Townyj
Ermahgerd
Well... ARM is from England... So kinda makes sense.Wonder who in London is playing with or wants to play with Akida Gen 2....hmmmm
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!
Fullmoonfever
Top 20
From the NASA site....wonder if we have been playing with any Blue Marbles at all
Published a couple of weeks ago...and there's that LLM again
ntrs.nasa.gov
Artificial Intelligence Medical Support for Long-Duration Space Missions
We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible.
In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations.
As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios.
Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.
Document ID
20240000754
Document Type
White Paper
Authors
Lauren Marie Sanders(Blue Marble Space Seattle, Washington, United States)
Ryan Thomas Scott(Wyle (United States) El Segundo, California, United States)
Date Acquired
January 18, 2024
Publication Date
February 2, 2024
Subject Category
Aerospace Medicine
Funding Number(s)
TASK: 10449.2.04.01.20.2418
CONTRACT_GRANT: 80NSSC18M0060
CONTRACT_GRANT: NNA14AB82C
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
Artificial Intelligence
machine learning
large language model
medical operations
Published a couple of weeks ago...and there's that LLM again
Artificial Intelligence Medical Support for Long-Duration Space Missions - NASA Technical Reports Server (NTRS)
We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth...
Artificial Intelligence Medical Support for Long-Duration Space Missions
We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible.
In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations.
As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios.
Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.
Document ID
20240000754
Document Type
White Paper
Authors
Lauren Marie Sanders(Blue Marble Space Seattle, Washington, United States)
Ryan Thomas Scott(Wyle (United States) El Segundo, California, United States)
Date Acquired
January 18, 2024
Publication Date
February 2, 2024
Subject Category
Aerospace Medicine
Funding Number(s)
TASK: 10449.2.04.01.20.2418
CONTRACT_GRANT: 80NSSC18M0060
CONTRACT_GRANT: NNA14AB82C
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
Artificial Intelligence
machine learning
large language model
medical operations
From the NASA site....wonder if we have been playing with any Blue Marbles at all
Published a couple of weeks ago...and there's that LLM again
Artificial Intelligence Medical Support for Long-Duration Space Missions - NASA Technical Reports Server (NTRS)
We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth...
Artificial Intelligence Medical Support for Long-Duration Space Missions
We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible.
In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations.
As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios.
Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.
Document ID
20240000754
Document Type
White Paper
Authors
Lauren Marie Sanders(Blue Marble Space Seattle, Washington, United States)
Ryan Thomas Scott(Wyle (United States) El Segundo, California, United States)
Date Acquired
January 18, 2024
Publication Date
February 2, 2024
Subject Category
Aerospace Medicine
Funding Number(s)
TASK: 10449.2.04.01.20.2418
CONTRACT_GRANT: 80NSSC18M0060
CONTRACT_GRANT: NNA14AB82C
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
Artificial Intelligence
machine learning
large language model
medical operations
I recall Peter saying that Akida would be the standard, or words to that effect.
Now that the Akida SoC has been created and will continue to evolve, there is a great deal of work to be done in converting existing models and creating new ones compatible with Akida.
The existing models will need to be converted via CNN2SNN and standardized on Akida format.
Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios.
DingoBorat
Slim
Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight.
Seeing as this is from NASA, are they trying to tell us something here?..
Seems a bit odd, that they had to reference the planet of informational origin?..
Fullmoonfever
Top 20
@Stable Genius @Fact Finder
Just read your posts on Schneider, zero waste and edge AI.
Was just flicking through AVIDs site who are partnered with Circle 8 who we obviously know are partnered with us.
AVID has some other partners too and not saying this is connected but how strange....or maybe not
avidgroup.com.au
Just read your posts on Schneider, zero waste and edge AI.
Was just flicking through AVIDs site who are partnered with Circle 8 who we obviously know are partnered with us.
AVID has some other partners too and not saying this is connected but how strange....or maybe not
Partners - AVID EPG Group
An Australian company based in Perth W.A. providing electrical contracting that focuses on electrical design, servicing of electrical assets, assembly of low voltage switchboards (Motor control centers), assembly and wiring of high voltage switch-gear of trusted global brands
avidgroup.com.au
BrainShit
Regular
There might be two reasons...
1. An "on Earth" trained model might have imputed variances / deviations
2. If you use Akida in space it'll and have to train itself on "an Earth" trained model
Last edited:
CrabmansFriend
Regular
Robotic surgery for Mars mission astronauts? Nice …
Feels like the movie Prometheus (Alien prequel)
DingoBorat
Slim
Yeah, that makes sense, there would be physiological differences in Space, not fully counteracted by whatever artificial gravity they use, plus they may be on "Space meds" or something like that..
Not very healthy, living in those conditions...
Good Morning 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.
Great to have you back.
Regarding this post , my number three company ARM , with some $100 billion for potential aqusitions / expansion...
ARM and SoftBank ...to myself ( Though thay are diffrent entitys ) are one and the same..as SoftBank own 90% of ARM...good to see thay are going to attach some LEGS .
Hope your power issues are sorted .
There is certainly an absolute avalanche of capitol waiting & being deployed in our sector .
Regards,
Esq.
Last edited:
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