Microsoft in its early days took on Teriary Educators as partners to ensure graduate awareness.
Manipulation....hold tight... there's a rocket launching. We should all have learned over the last few years in clown world, that when we truly believe something/someone is 'right' and others are telling us different, trust your instincts! You are generally right!!Hi, @Esq.111
Could you or someone else give a explanation as to why BrainChip shares
are going down. With all the Companies that now seem to be using AKIDA and
all the Great reviews they seem to be getting, and still the price is going down.
I just cannot understand it. And please do not tell me that people are waiting for REVENUE.
People invest in the Stock Market for the reason of what the Future brings and not what the
present is, and everybody who have done their research, now know what the future will bring.
Yet the price keeps going down. (puzzled )
starts.
Magnus Östberg on LinkedIn: #ai #neuromorphiccomputing #leadincarsoftware | 19 comments
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few. How does a… | 19 comments on LinkedInwww.linkedin.com
View attachment 58928
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few.
How does a more powerful processor increase energy efficiency?
#AI is already used in advanced driving assistance systems (ADAS) and infotainment, and the complex calculations are currently performed on traditional CPUs, GPUs and NPUs, which are not energy efficient. #Neuromorphiccomputing requires for the same tasks less energy. As the number of AI functions continue to increase, the increased computing efficiency of neuromorphic hardware will require less energy in comparison to legacy hardware. Reduced energy usage will also increase vehicle range and improve sustainability.
When can I experience neuromorphic computing?
Widespread use of neuromorphic computing will depend on many factors. The technology requires new programming and algorithms, so it will not immediately replace traditional processors. One key factor for us is that automotive-grade chips must meet extremely strict reliability requirements. However, we are already actively working to drive development and we are committed to being the first to use this technology in the automotive industry.
If you haven’t read the article yet, check it out here https://lnkd.in/epnUc5Sy. Be sure to ask more questions so we can keep the conversation going.
Neuromorphic computing? We’ve got that.
Because it’s still nascent technology, I am frequently asked to describe #neuromorphic computing. It is a paradigm shift for how we perform computations in machine learning (#ML) and artificial intelligence (AI), which process massive amounts of data requiring tons of fast memory.
Currently available processor architecture separates data calculations from system memory, which is inefficient. The biological inspiration for neural networks is the human brain, where computing and memory are combined, and data processing uses neurons to communicate through electrical signals and chemical processes known as neurotransmitters.
In neuromorphic computing, those human neurons and synapses are modelled in circuits and communication is event-driven, with information coded in spikes, mimicking the processing fundamentals of the brain. Those spikes propagate through a Spiking Neural Network of artificial neurons and synapses to predict results. Information processing is measured by spike rate or spike time instead of the number of calculations. Thus, neuromorphic chips are more energy efficient and have lower latency than conventional CPUs and GPUs. That means much faster computation using considerably less power.
However, this change in data processing also requires new software algorithms specifically designed to work with neuromorphic hardware. Existing algorithms can only partially leverage the many benefits of neural technology. Thanks to Valerij, Alexander, Christina in the Innovations & Future Technology area and the rest of our team for tackling this huge project!
𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
Neuromorphic computing reduces the power required for advanced AI computation, which is useful in applications where energy is limited, like electric vehicles. However, we still need automotive-grade chips with neuromorphic technology before this technology becomes common in cars.
We at Mercedes-Benz AG are currently working on novel algorithms that take advantage of neuromorphic computing to improve the energy efficiency and performance of our cars. Our primary goals are to extend vehicle range, make safety systems react faster, and increase the number of #AI functions possible. In 2020, we already joined the #Intel Neuromorphic Research Community and since then we are continuously expanding our collaborations with other research partners and universities to ensure our software and hardware solutions continue to lead the industry.
It's an exciting time to be in the world of automotive technology. Please share any questions and comments below.
Hey @TECH aka Texta.Excellent post S G
This explanation below is first-class !!
Despite not specifically mentioning us, I'd politely suggest if you still don't think that a world leader in Neuromorphic Computing, with two proof of concept chips AKD 1000 and AKD 1500 and proof of concept in IP only at this stage in AKD 2000 are still wrapped up with MB, well then
stick with your Nickel, Iron Ore and Lithium stocks....Technology is the future, I know it, you know it and we are donkey deep at the cutting-edge !
Regards...Texta
In neuromorphic computing, those human neurons and synapses are modelled in circuits and communication is event-driven, with information coded in spikes, mimicking the processing fundamentals of the brain. Those spikes propagate through a Spiking Neural Network of artificial neurons and synapses to predict results. Information processing is measured by spike rate or spike time instead of the number of calculations. Thus, neuromorphic chips are more energy efficient and have lower latency than conventional CPUs and GPUs. That means much faster computation using considerably less power.
this isn't the case - you can search for past comments and they are there.
Any ties to BRN does anyone know ?
I/ONX Neuromorphic
https://www.linkedin.com/company/i-onx/?miniCompanyUrn=urn:li:fs_miniCompany:96327740
Computational processes using traditional GPUs and CPUs in large data centers running complex data processing tasks significantly lack energy efficiency.
We are essentially at the fundamental limit of Artificial intelligence and machine Learning using traditional data processing technologies.
Enter neuromorphic computing. It has the potential to achieve High Performance Computing and yet consumes 1/1000th of the energy! Sound interesting?
Get in contact with one of our team members today to find out how we can transform your data center into an energy saving force by mimicking the brain's parallel processing capabilities!
View attachment 58939
Magnus Östberg on LinkedIn: #ai #neuromorphiccomputing #leadincarsoftware | 19 comments
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few. How does a… | 19 comments on LinkedIn
View attachment 58928
I received many great questions from the community in response to my recent post on neuromorphic computing, so I’ll jump right in and answer a few.
How does a more powerful processor increase energy efficiency?
#AI is already used in advanced driving assistance systems (ADAS) and infotainment, and the complex calculations are currently performed on traditional CPUs, GPUs and NPUs, which are not energy efficient. #Neuromorphiccomputing requires for the same tasks less energy. As the number of AI functions continue to increase, the increased computing efficiency of neuromorphic hardware will require less energy in comparison to legacy hardware. Reduced energy usage will also increase vehicle range and improve sustainability.
When can I experience neuromorphic computing?
Widespread use of neuromorphic computing will depend on many factors. The technology requires new programming and algorithms, so it will not immediately replace traditional processors. One key factor for us is that automotive-grade chips must meet extremely strict reliability requirements. However, we are already actively working to drive development and we are committed to being the first to use this technology in the automotive industry.
If you haven’t read the article yet, check it out here https://lnkd.in/epnUc5Sy. Be sure to ask more questions so we can keep the conversation going.
Neuromorphic computing? We’ve got that.
Because it’s still nascent technology, I am frequently asked to describe #neuromorphic computing. It is a paradigm shift for how we perform computations in machine learning (#ML) and artificial intelligence (AI), which process massive amounts of data requiring tons of fast memory.
Currently available processor architecture separates data calculations from system memory, which is inefficient. The biological inspiration for neural networks is the human brain, where computing and memory are combined, and data processing uses neurons to communicate through electrical signals and chemical processes known as neurotransmitters.
In neuromorphic computing, those human neurons and synapses are modelled in circuits and communication is event-driven, with information coded in spikes, mimicking the processing fundamentals of the brain. Those spikes propagate through a Spiking Neural Network of artificial neurons and synapses to predict results. Information processing is measured by spike rate or spike time instead of the number of calculations. Thus, neuromorphic chips are more energy efficient and have lower latency than conventional CPUs and GPUs. That means much faster computation using considerably less power.
However, this change in data processing also requires new software algorithms specifically designed to work with neuromorphic hardware. Existing algorithms can only partially leverage the many benefits of neural technology. Thanks to Valerij, Alexander, Christina in the Innovations & Future Technology area and the rest of our team for tackling this huge project!
𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
Neuromorphic computing reduces the power required for advanced AI computation, which is useful in applications where energy is limited, like electric vehicles. However, we still need automotive-grade chips with neuromorphic technology before this technology becomes common in cars.
We at Mercedes-Benz AG are currently working on novel algorithms that take advantage of neuromorphic computing to improve the energy efficiency and performance of our cars. Our primary goals are to extend vehicle range, make safety systems react faster, and increase the number of #AI functions possible. In 2020, we already joined the #Intel Neuromorphic Research Community and since then we are continuously expanding our collaborations with other research partners and universities to ensure our software and hardware solutions continue to lead the industry.
It's an exciting time to be in the world of automotive technology. Please share any questions and comments below.
Hmmm but Rob don’t like it 🫡
