BRN Discussion Ongoing

[miaeffect]

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!

Waaaat?!

 

[Diogenese]

Ooh-Ooh-Ooh!

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

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

Couch surfing and interweb surfing - there's multi-tasking for you.

https://www.bing.com/videos/search?...CEBE59C14964CC190AE9CEBE59C14964C&FORM=WRVORC

 

[Fullmoonfever]

View attachment 57159
Waaaat?!

Yeah saw that too haha

Though, there has been several proposals so suspect they will be somewhat higher

 

[Townyj]

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!

Well... ARM is from England... So kinda makes sense.

 

[Fullmoonfever]

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...

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

 

[Diogenese]

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...

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

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]

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.

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]

@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

Partners - AVID 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]

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?..

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

 

[CrabmansFriend]

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?..

Robotic surgery for Mars mission astronauts? Nice …
Feels like the movie Prometheus (Alien prequel)

 

[DingoBorat]

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

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...

 

[Esq.111]

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.

Good Morning Bravo ,

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.

 

[IloveLamp]

 

[AARONASX]

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

 

[SharesForBrekky]

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!

Whoever it is, I'm guessing they're not short of a penny.

Having a modern office in Central London isn't exactly cheap.

Just having a quick look at how much to rent office space in Central London, roughly 700 pounds for one office desk per month. This is compared to in the centre of capital cities in Australia where it's about 700 Australian dollars per desk per month.

So say minimum 10 people in the office, equates to 7000 pounds per month and therefore 84000 per year.

For anyone interested (probably not ) in reading this article about how much offices are in City of London https://rubberdesk.co.uk/research/city-of-london-office-space-price-guide

 

[Fact Finder]

@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

Partners - AVID 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

View attachment 57161

Hi FMF
It is now very tempting to add Schneider Electrical to the confirmed list. Thanks for this further link. Thick and fast they now come like a meteorite storm with hundreds to come potentially - (Rob Telson there are hundreds of companies)

My opinion only DYOR
Fact Finder

 

[Pom down under]

Whoever is buying let’s see them take out the 0.40 today.

 

[Esq.111]

Hi FMF
It is now very tempting to add Schneider Electrical to the confirmed list. Thanks for this further link. Thick and fast they now come like a meteorite storm with hundreds to come potentially - (Rob Telson there are hundreds of companies)

My opinion only DYOR
Fact Finder

Chippers ,

External links​

Schneider Electric​

From Wikipedia, the free encyclopedia

Schneider Electric SE

Schneider Electric head office in Paris
Company type	Public
Traded as	Euronext Paris: SU CAC 40 component
Industry	Electrical equipment
Predecessor	Merlin-Gerin
Founded	1836; 188 years ago (as Schneider & Cie)
Founders	Eugène Schneider Adolphe Schneider
Headquarters	Rueil-Malmaison, France
Area served	Worldwide
Key people	Peter Herweck (CEO) Jean-Pascal Tricoire (Chairman) Léo Apotheker (Director)
Products	Building automation, home automation, switches, and sockets, plant process and emergency shutdown systemss, industrial control systems, electric power distribution, electrical grid automation, smart grid, critical power & cooling for datacenters
Revenue	€34.17 billion[1] (2022)
Operating income	€4.93 billion[1] (2022)
Net income	€3.53 billion[1] (2022)
Total assets	€58.36 billion[1] (2022)
Total equity	€26.09 billion[1] (2022)
Number of employees	162,339[1] (2022)
Subsidiaries	Luminous Power Technologies Pvt Ltd., invensys, SolveIT Software, APC, Areva T&D, BEI Technologies, Cimac, Citect, Clipsal, ELAU, Federal Pioneer, Merlin Gerin, Merten, Modicon PLC, Nu-Lec Industries, PDL Group, Power Measurement, Square D, TAC, Telemecanique, Telvent, Gutor Electronic LLC, Zicom, Summit, Xantrex
Website	www.se.com
Footnotes / references [2]

Schneider Electric SE is a French multinational company that specializes in digital automation and energy management.[3][4] It addresses homes,[5] buildings,[6] data centers,[7] infrastructure and industries,[8] by combining energy technologies, real-time automation, software, and services.[9]
Schneider Electric is a Fortune Global 500 company, publicly traded on the Euronext Exchange, and is a component of the Euro Stoxx 50 stock market index.[10] In fiscal year 2022, the company posted revenues of €34.2 billion.[1]
Schneider Electric is the parent company of Square D, APC, and others. It is also a research company.[11]

History[edit]​

1836–1963[edit]​

Main article: Schneider-Creusot
In 1836, brothers Adolphe and Joseph-Eugene Schneider took over an iron foundry in Le Creusot, France.[12] Two years later, they founded Schneider-Creusot, the company that would eventually become Schneider Electric. Initially, Schneider-Creusot specialized in the production of steel, heavy machinery, and transportation equipment.[13][14] In 1871, following France's defeat in the Franco-Prussian War, the company significantly developed its capacity for weapons manufacturing.[15] Over the first half of the 20th century, Schneider-Creusot continued to grow, establishing manufacturing sites in France and abroad, including in pre-Soviet Russia and Czechoslovakia.[15][16]

1963–1999[edit]​

Main article: Empain-Schneider
In the 1960s, following the death of Charles Schneider, Schneider-Creusot was absorbed by Belgium's Empain group, which merged Schneider-Creusot with its corporate structures to form Empain-Schneider.[17][18] In 1981, the Empain family sold its controlling stake to Paribas.[18] In the 1980s and 1990s, the company, once again operating under the Schneider name, divested from steel and shipbuilding and, through strategic acquisitions, began to focus on the electricity sector.[14][19] These acquisitions included Télémécanique in 1988,[14] Square D in 1991,[20] and Merlin Gerin [fr] in 1992.[21]

1999–present[edit]​

In January 1999, Schneider acquired the Scandinavian switch-maker Lexel.[22][23] Later that year, the company renamed itself Schneider Electric, to reflect its focus on the electricity sector.[22]
In October 2006, Schneider Electric announced that it would acquire the data center equipment manufacturer American Power Conversion for $6.1 billion.[24][25] The following February, the move was finalized following its approval by the European Commission.[26] In June 2010, Schneider and the rolling stock manufacturer Alstom jointly purchased Areva's transmission and distribution businesses in a transaction totaling $2.73 billion.[27][28]
In 2016, Schneider acquired Tower Electric, a British company that manufactured fixings and fastenings for construction and electrical firms.[citation needed] In 2017, Schneider Electric became the majority shareholder of Aveva, a provider of engineering and industrial software based in the UK.[29][30] The next year, it acquired the Indian multinational Larsen & Toubro's electrical and automatic business in a cash deal for ₹140 billion (US$1.8 billion).[31]
In February 2020, Schneider made a €1.4 billion takeover bid for German company RIB Software,[32] closing the deal in July 2020.[33] Also in 2020, Schneider Electric acquired ProLeiT AG, a supplier of industrial control and MES software.[34]
In April 2021, Schneider introduced 'The Zero Carbon Project'. Since then, it has shown its commitment to minimize 'operational carbon emissions' by 2025.[35]
In January 2023, Schneider Electric's acquisition of Aveva was finalized.[36]
In November 2023, Schneider Electric finalized its acquisition of EcoAct, a company devoted to climate consulting and net-zero solutions.[37][38][39]

• 
  
• One of the first brochures for Telemecanique industrial control products. Telemecanique was acquired by Schneider Electric in 1988.

Notable acquisitions[edit]​

Acquisition date	Company	Business	Country	Source
1988	Télémécanique	Manufacturer of electrical circuit breakers, switchgear, and sensors	France	[14]
1991	Square D	Manufacturer of electrical circuit breakers, switchgear, and transformers	USA	[20]
1992	Merlin Gerin	Manufacturer of electrical circuit breakers and transformers	France	[40][41]
1997	Modicon	Manufacturer of programmable logic controllers (PLC)	USA	[42]
2001	PDL	Domestic and industrial electrical hardware	New Zealand	[43][44]
June 2003	TAC	Building automation and control	Sweden	[45]
March 2004	Kavlico	Pressure sensors	USA	[46]
2004	Clipsal	Wiring	Australia	[47]
June 2005	Juno Lighting	Lighting	USA	[48]
July 2005	BEI Technologies	Customized sensors	USA	[48]
2006	Merten	Ultra terminal	Germany	[49]
2006	Citect	SCADA system and MES automation software	Australia	[50]
February 2007	American Power Conversion	Power backup and protection and electrical distribution	USA	[24][25]
July 2009	Meher Capacitors	Power factor correction	India	[51]
March 2010	Zicom Security Systems	Security systems	India	[52]
April 2010	SCADAgroup	SCADA and control systems	Australia	[53]
December 2010	Areva T&D	Transmission & distribution	Europe	[54]
March 2011	Summit Energy	Energy management	USA	[55]
April 2011	Digilink	Network connectivity products	India	[56]
May 2011	APW President Systems	Enclosure systems	India	[57]
May 2011	Luminous	Power inverters	India	[58]
June 2011	Telvent	Real-time information management systems	Spain	[59]
December 2011	Viridity	Data center management software	USA	[60]
September 2012	SolveIT Software	Planning and scheduling software	Australia	[61]
January 2014	Invensys	Multinational engineering and information technology company	UK	[62]
January 2014	Foxboro	Control systems	USA	[63]
July 2017	ASCO	Transfer switches, power control systems, and industrial control products	USA	[64]
September 2017 (60% controlling stake)	Aveva Group	Information-technology/consulting group	UK	[65]
January 2023 (remaining shares)	[66]
November 2020	ETAP	Electrical power systems software	USA	[67]
2020	RIB Software	AEC software	USA	[68]
2020	OSISoft	Industrial data software	USA	[69]
2020	Larsen & Toubro E&A	Electrical and automation technology	India	[70]
January 2022	Zeigo	Climate-tech platform	UK	[71]
June 2022	EV Connect	Electric vehicle charging platform	USA	[72]
2022	EnergySage	Marketplace for solar panels	USA	[73]
2022	AutoGrid	Distributed energy resource management	USA	[74]

​

Another one to ponder.....

I shall refrain from adding it to the BrainChip Scroll just yet , though it certainly would slot in seamlessly .

Regards,
Eqs

 

[Colorado23]

Never ceases to amaze me how how many muppets are out there. Good humour for the morning.

 

[Fenris78]

Whoever is buying let’s see them take out the 0.40 today.

Looks like a strong opening today!!