Jumpchooks
Regular
Legends in Their Time (Sky) thank you for sharing
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Legends in Their Time (Sky) thank you for sharing
thelittleshort
Top Bloke
What about XMOS?
Article below by their CEO Mark Lippett in Forbes. In my opinion Mark is basically describing the problems he knows he can fix using Akida IP - due to the partnership between XMOS and Plumerai
BrainChip smart homes and cybersecurity
INNOVATION
Privacy, Intelligence, Agency: Security In The Smart Home
Mark Lippett
Forbes Councils Member
Forbes Technology CouncilCOUNCIL POST| Membership (fee-based)
May 5, 2022,06:30am EDT
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’ experience in startup, scale-up and blue-chip companies.
While the dawn of the smart home has been heralded for years, the establishment of smart speaker technology means we’re finally actually entering the era of intelligent, responsive homes.
Home automation is projected to expand its value to $75.3 billion by 2025, with a compound annual growth rate (CAGR) of just under 16%. Smart speakers are the key driver of growth here. Data from Statista suggests that 320 million smart speakers were deployed as of 2020, projected to double by 2024.
With pandemic-related factors accelerating this growth as people spend more time indoors, concerns over security and privacy come to the fore. Many IoT environments are failing to properly protect their users, given their over-reliance on cloud infrastructures and internet connectivity.
Balancing Innovation With Privacy
Having Amazon Alexa or Google Assistant may be convenient when turning off the lights or ordering in, but it also offers an open window into your home with the possibility of thousands looking in, listening from anywhere in the world.
Cloud connectivity and the “always-on” power model raise significant ethical concerns, with consumers understandably wary of their every word being recorded and transferred online.
Despite the short-term financial incentive, this model may be self-defeating. Bringing smart home architectures to market will be harder if privacy concerns aren’t addressed—and that’s true not only for today’s devices but future models, too.
Manufacturers need to strike a technical balance between innovation and privacy for our smart homes to evolve. Devices need to interpret user activity and context and respond—that’s their purpose. They’re not “smart” if they can’t perform this function.
However, residents must also feel secure in their own homes: recognized, not watched. Striking this balance will be crucial for device engineers going forward.
Process Your Data Locally
The key to providing secure, private and responsive homes is reducing dependency on the cloud, instead embedding intelligence within the home environment itself.
At present, our devices can’t process user inputs alone. Sensor data must be transferred to the cloud for interpretation and contextualization before instructions can be carried out. This causes latency issues where smart homes take longer to respond. At best, this is a pain; at worst, tasks or value-adding use cases are rendered useless due to resulting health and safety concerns.
Thankfully, we have an alternative to the cloud: the artificial intelligence of things (AIoT). This model embeds intelligence and processing power directly into the smart home device, processing commands locally. I’ve written about it before for Forbes, examining the convergence of AI and IoT devices.
However, there’s a challenge in bringing this edge intelligence to market. AI chips are expensive, and it’s difficult to configure them for compatibility with home devices.
Making The AIoT Work
The chips that drive such home devices must deliver a combination of AI, DSP, control and communications. Economics demand that these are delivered in a single device, affording designers greater control over how these four attributes are balanced.
Manufacturers will also need to deliver this in smaller packages with low overall BOM costs. Creating these programmable, efficient devices with AI capabilities won’t be easy, but it’s an essential piece of the data protection puzzle.
Rather than rely on the cloud for context, AIoT-enabled chips would enable more sophisticated sensor processing—delivering face and image identification, presence detection and even life sign monitoring to capture rich, contextualized information and react independently.
Simply put, you won’t need to risk or surrender personal information to make the smart home work.
Security And Privacy Aren’t Negotiable
Realizing edge AI for smart homes would mark significant progress for not only consumer privacy but overall home security. Improvements to latency, processing and more sophisticated multimodal sensors will all contribute to a better-protected home.
For example, edge intelligence will allow smart home devices to distinguish between household occupants with ease. Devices will be able to identify and ignore potentially dangerous commands from young children, contact emergency services in case of medical emergencies and even alert household members if an unrecognized individual has entered the building.
In the longer term, a symbiotic relationship would allow devices to “borrow” intelligence from one another to build sophisticated room-to-room continuity. Alarm clocks can initiate coffee machines; gas sensors could disperse dangerous gases by opening windows; and security lights in the back garden could lock the front door. It all depends on the sensors and intelligence available.
However, with consumer concerns around privacy a problem for right now, such devices need to balance perceptiveness with some tact—and that means the edge. In removing the reliance on data transference to the cloud, edge devices pose very little risk to consumers’ privacy. On-device processing allows smart home tech to dispense with data once it’s used. In many cases, little-to-no data would need to be stored in any format.
All of this drastically reduces the amount of data leaving the house, as well as the number of attack surfaces the smart home is exposed to. It means a more cybersecure home, one that’s better protected against data exploitation by private corporations.
We can only make that a reality by addressing the growing concerns over consumer privacy, with 63% of consumers considering connected devices “creepy“ in terms of data collection and behavior, while 50% of U.K. consumers were “fairly concerned” about the use of their data, according to a Deloitte study from 2020.
Choosing to make devices independently intelligent, rather than relying on the cloud with its associated data transfer, is a critical enabler for an exciting new smart age—free from invasion of privacy.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Follow me on LinkedIn. Check out my website.
Mark Lippett
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’ experien…
Read More
Jumpchooks
Regular
I was at Melb Uni in 1975 when we were allowed to stop using Slide Rules and use electric Calculators in the exams. Texas Instruments were the only option. I am a big fan. Can AKIDA use a Slide Rule?
Fact Finder
Top 20
Great analysis TLS. It is very logical.What about XMOS?
View attachment 7273
Article below by their CEO Mark Lippett in Forbes. In my opinion Mark is basically describing the problems he knows he can fix using Akida IP - due to the partnership between XMOS and Plumerai
BrainChip smart homes and cybersecurity
INNOVATION
Privacy, Intelligence, Agency: Security In The Smart Home
Mark Lippett
Forbes Councils Member
Forbes Technology CouncilCOUNCIL POST| Membership (fee-based)
May 5, 2022,06:30am EDT
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’ experience in startup, scale-up and blue-chip companies.
While the dawn of the smart home has been heralded for years, the establishment of smart speaker technology means we’re finally actually entering the era of intelligent, responsive homes.
Home automation is projected to expand its value to $75.3 billion by 2025, with a compound annual growth rate (CAGR) of just under 16%. Smart speakers are the key driver of growth here. Data from Statista suggests that 320 million smart speakers were deployed as of 2020, projected to double by 2024.
With pandemic-related factors accelerating this growth as people spend more time indoors, concerns over security and privacy come to the fore. Many IoT environments are failing to properly protect their users, given their over-reliance on cloud infrastructures and internet connectivity.
Balancing Innovation With Privacy
Having Amazon Alexa or Google Assistant may be convenient when turning off the lights or ordering in, but it also offers an open window into your home with the possibility of thousands looking in, listening from anywhere in the world.
Cloud connectivity and the “always-on” power model raise significant ethical concerns, with consumers understandably wary of their every word being recorded and transferred online.
Despite the short-term financial incentive, this model may be self-defeating. Bringing smart home architectures to market will be harder if privacy concerns aren’t addressed—and that’s true not only for today’s devices but future models, too.
Manufacturers need to strike a technical balance between innovation and privacy for our smart homes to evolve. Devices need to interpret user activity and context and respond—that’s their purpose. They’re not “smart” if they can’t perform this function.
However, residents must also feel secure in their own homes: recognized, not watched. Striking this balance will be crucial for device engineers going forward.
Process Your Data Locally
The key to providing secure, private and responsive homes is reducing dependency on the cloud, instead embedding intelligence within the home environment itself.
At present, our devices can’t process user inputs alone. Sensor data must be transferred to the cloud for interpretation and contextualization before instructions can be carried out. This causes latency issues where smart homes take longer to respond. At best, this is a pain; at worst, tasks or value-adding use cases are rendered useless due to resulting health and safety concerns.
Thankfully, we have an alternative to the cloud: the artificial intelligence of things (AIoT). This model embeds intelligence and processing power directly into the smart home device, processing commands locally. I’ve written about it before for Forbes, examining the convergence of AI and IoT devices.
However, there’s a challenge in bringing this edge intelligence to market. AI chips are expensive, and it’s difficult to configure them for compatibility with home devices.
Making The AIoT Work
The chips that drive such home devices must deliver a combination of AI, DSP, control and communications. Economics demand that these are delivered in a single device, affording designers greater control over how these four attributes are balanced.
Manufacturers will also need to deliver this in smaller packages with low overall BOM costs. Creating these programmable, efficient devices with AI capabilities won’t be easy, but it’s an essential piece of the data protection puzzle.
Rather than rely on the cloud for context, AIoT-enabled chips would enable more sophisticated sensor processing—delivering face and image identification, presence detection and even life sign monitoring to capture rich, contextualized information and react independently.
Simply put, you won’t need to risk or surrender personal information to make the smart home work.
Security And Privacy Aren’t Negotiable
Realizing edge AI for smart homes would mark significant progress for not only consumer privacy but overall home security. Improvements to latency, processing and more sophisticated multimodal sensors will all contribute to a better-protected home.
For example, edge intelligence will allow smart home devices to distinguish between household occupants with ease. Devices will be able to identify and ignore potentially dangerous commands from young children, contact emergency services in case of medical emergencies and even alert household members if an unrecognized individual has entered the building.
In the longer term, a symbiotic relationship would allow devices to “borrow” intelligence from one another to build sophisticated room-to-room continuity. Alarm clocks can initiate coffee machines; gas sensors could disperse dangerous gases by opening windows; and security lights in the back garden could lock the front door. It all depends on the sensors and intelligence available.
However, with consumer concerns around privacy a problem for right now, such devices need to balance perceptiveness with some tact—and that means the edge. In removing the reliance on data transference to the cloud, edge devices pose very little risk to consumers’ privacy. On-device processing allows smart home tech to dispense with data once it’s used. In many cases, little-to-no data would need to be stored in any format.
All of this drastically reduces the amount of data leaving the house, as well as the number of attack surfaces the smart home is exposed to. It means a more cybersecure home, one that’s better protected against data exploitation by private corporations.
We can only make that a reality by addressing the growing concerns over consumer privacy, with 63% of consumers considering connected devices “creepy“ in terms of data collection and behavior, while 50% of U.K. consumers were “fairly concerned” about the use of their data, according to a Deloitte study from 2020.
Choosing to make devices independently intelligent, rather than relying on the cloud with its associated data transfer, is a critical enabler for an exciting new smart age—free from invasion of privacy.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Follow me on LinkedIn. Check out my website.
Mark Lippett
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’ experien…
Read More
View attachment 7282
View attachment 7283
My opinion only DYOR
FF
AKIDA BALLISTA
PS: @BaconLover another nominee XMOS
thelittleshort
Top Bloke
What about XMOS?
View attachment 7273
Article below by their CEO Mark Lippett in Forbes. In my opinion Mark is basically describing the problems he knows he can fix using Akida IP - due to the partnership between XMOS and Plumerai
BrainChip smart homes and cybersecurity
INNOVATION
Privacy, Intelligence, Agency: Security In The Smart Home
Mark Lippett
Forbes Councils Member
Forbes Technology CouncilCOUNCIL POST| Membership (fee-based)
May 5, 2022,06:30am EDT
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’ experience in startup, scale-up and blue-chip companies.
While the dawn of the smart home has been heralded for years, the establishment of smart speaker technology means we’re finally actually entering the era of intelligent, responsive homes.
Home automation is projected to expand its value to $75.3 billion by 2025, with a compound annual growth rate (CAGR) of just under 16%. Smart speakers are the key driver of growth here. Data from Statista suggests that 320 million smart speakers were deployed as of 2020, projected to double by 2024.
With pandemic-related factors accelerating this growth as people spend more time indoors, concerns over security and privacy come to the fore. Many IoT environments are failing to properly protect their users, given their over-reliance on cloud infrastructures and internet connectivity.
Balancing Innovation With Privacy
Having Amazon Alexa or Google Assistant may be convenient when turning off the lights or ordering in, but it also offers an open window into your home with the possibility of thousands looking in, listening from anywhere in the world.
Cloud connectivity and the “always-on” power model raise significant ethical concerns, with consumers understandably wary of their every word being recorded and transferred online.
Despite the short-term financial incentive, this model may be self-defeating. Bringing smart home architectures to market will be harder if privacy concerns aren’t addressed—and that’s true not only for today’s devices but future models, too.
Manufacturers need to strike a technical balance between innovation and privacy for our smart homes to evolve. Devices need to interpret user activity and context and respond—that’s their purpose. They’re not “smart” if they can’t perform this function.
However, residents must also feel secure in their own homes: recognized, not watched. Striking this balance will be crucial for device engineers going forward.
Process Your Data Locally
The key to providing secure, private and responsive homes is reducing dependency on the cloud, instead embedding intelligence within the home environment itself.
At present, our devices can’t process user inputs alone. Sensor data must be transferred to the cloud for interpretation and contextualization before instructions can be carried out. This causes latency issues where smart homes take longer to respond. At best, this is a pain; at worst, tasks or value-adding use cases are rendered useless due to resulting health and safety concerns.
Thankfully, we have an alternative to the cloud: the artificial intelligence of things (AIoT). This model embeds intelligence and processing power directly into the smart home device, processing commands locally. I’ve written about it before for Forbes, examining the convergence of AI and IoT devices.
However, there’s a challenge in bringing this edge intelligence to market. AI chips are expensive, and it’s difficult to configure them for compatibility with home devices.
Making The AIoT Work
The chips that drive such home devices must deliver a combination of AI, DSP, control and communications. Economics demand that these are delivered in a single device, affording designers greater control over how these four attributes are balanced.
Manufacturers will also need to deliver this in smaller packages with low overall BOM costs. Creating these programmable, efficient devices with AI capabilities won’t be easy, but it’s an essential piece of the data protection puzzle.
Rather than rely on the cloud for context, AIoT-enabled chips would enable more sophisticated sensor processing—delivering face and image identification, presence detection and even life sign monitoring to capture rich, contextualized information and react independently.
Simply put, you won’t need to risk or surrender personal information to make the smart home work.
Security And Privacy Aren’t Negotiable
Realizing edge AI for smart homes would mark significant progress for not only consumer privacy but overall home security. Improvements to latency, processing and more sophisticated multimodal sensors will all contribute to a better-protected home.
For example, edge intelligence will allow smart home devices to distinguish between household occupants with ease. Devices will be able to identify and ignore potentially dangerous commands from young children, contact emergency services in case of medical emergencies and even alert household members if an unrecognized individual has entered the building.
In the longer term, a symbiotic relationship would allow devices to “borrow” intelligence from one another to build sophisticated room-to-room continuity. Alarm clocks can initiate coffee machines; gas sensors could disperse dangerous gases by opening windows; and security lights in the back garden could lock the front door. It all depends on the sensors and intelligence available.
However, with consumer concerns around privacy a problem for right now, such devices need to balance perceptiveness with some tact—and that means the edge. In removing the reliance on data transference to the cloud, edge devices pose very little risk to consumers’ privacy. On-device processing allows smart home tech to dispense with data once it’s used. In many cases, little-to-no data would need to be stored in any format.
All of this drastically reduces the amount of data leaving the house, as well as the number of attack surfaces the smart home is exposed to. It means a more cybersecure home, one that’s better protected against data exploitation by private corporations.
We can only make that a reality by addressing the growing concerns over consumer privacy, with 63% of consumers considering connected devices “creepy“ in terms of data collection and behavior, while 50% of U.K. consumers were “fairly concerned” about the use of their data, according to a Deloitte study from 2020.
Choosing to make devices independently intelligent, rather than relying on the cloud with its associated data transfer, is a critical enabler for an exciting new smart age—free from invasion of privacy.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Follow me on LinkedIn. Check out my website.
Mark Lippett
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’ experien…
Read More
View attachment 7282
View attachment 7283
More from Mark
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INNOVATION
How The Pandemic Has Shifted Attitudes To The 'Artificial Intelligence Of Things' And The Smart Home
Mark Lippett
Forbes Councils Member
Forbes Technology CouncilCOUNCIL POST| Membership (fee-based)
Aug 11, 2021,08:00am EDT
The last 12 months have undoubtedly been incredibly tough. The pandemic has wreaked havoc across the world, and everyone is now realizing that, unfortunately, Covid-19 is here to stay.
As we begin to accept this fact, we are now looking to adapt the way we live and interact with the world around us. Industries, businesses and governments are all adjusting the status quo in an attempt to keep people safe, productive and able to live their lives.
How — and where — we work is just one of the ways most people have had to change. Working from home has been the norm for so many over the past year, and even as we progress out of this pandemic, we may see businesses continue to allow employees to work from home. As a result, this way of working has placed a renewed focus on the importance of our homes, and discussions around the tech-enabled "smart" home have never been timelier.
Just as the pandemic was taking off, a new kind of technology ecosystem called the "artificial intelligence of things" (AIoT) was taking a foothold. The AIoT represents a convergence of connected things (the IoT) and artificial intelligence (the AI) deployed within those things. I've previously written about what the AIoT is and how it's set to transform all kinds of industries, from healthcare to transport, but there's no bigger opportunity for it than the smart home.
It's still a relatively nascent industry, and so last year, we conducted research into the barriers holding back the AIoT. Within that research, electronics engineers highlighted significant market-level and device-level concerns. We then conducted the same research a year later to see how things had changed. The headline? Necessity is the mother of all invention, and the pandemic has accelerated the development and adoption of the AIoT for the smart home and beyond.
Security, connectivity and scalability are all becoming easier to address.
In our original report in 2020, engineers cited security, connectivity and scalability as the biggest market issues facing the AIoT. Over the last year, however, opinions have softened, and many believe the barriers are more surmountable.
With security, AI raises privacy concerns because of its reliance on data. The "smarter" the device, the more information it requires. However, in the last 12 months, engineers have realized that processing data locally instead of in the cloud can solve the privacy issue. Homes can keep their data within their four walls without the need to send it to third parties in the cloud, reducing the risk of leaks.
By keeping data within a home, a remote cybercriminal would have to turn into a common burglar to steal that data. While that's unlikely to happen, it's still important that device manufacturers ensure the processing that happens on their devices is secure. A gamut of device-level security features — including secure key storage, accelerated encryption and true random number generators — can provide a foundation for significantly improved safety for both data and decision-making.
Security aside, engineers also felt connectivity posed a large barrier to AI deployment, with 38% voicing concerns about the technology's ability to overcome latency problems. In-home healthcare monitoring, for example, cannot afford to be burdened by unreliable connectivity issues when they need to make decisions based on potentially life-changing situations like heart attacks. Now, however, on-device processing lessens the need for networks, making network latency a moot point; only 27% of industry experts consider connectivity as a major barrier to the technology.
The industry should move to on-device processing if it wants to create applications that aren't held back by latency. Certain AIoT chips are now lightning-fast and predictable, with execution determinism measured in single-digit nanoseconds, which enables products to think and make decisions at speed.
Finally, last year, engineers highlighted the issue of scalability. Engineers are aware that the number of connected devices is rising, placing more and more strain on cloud infrastructure. In 2020, about a quarter of engineers believed scalability to be a major barrier to the success of edge technology. However, experts are now beginning to see the benefits of the AIoT's deep-rooted scalability. Processing at the edge removes the dependence on the cloud, negating any potential growth and scaling issues. Now, fewer than one-fifth of engineers believe that cloud infrastructure could hold back edge AI.
The good news is that the electronics industry doesn't need to do anything specifically to maintain the AIoT's scalability, as one of the major technical barriers to the AIoT's expansion — the need for the cloud to process billions more devices and petabytes of data in the future — has been extinguished.
Power capability up, power consumption down.
As the AIoT market has matured over the past year, it's also progressed on a technical front. On-device processing capabilities have increased while reducing the amount of power and expenditure it takes to enable AI. Now, chips are flexible enough to meet the diverse needs of the AIoT at a much more affordable price point than ever before.
As AIoT chips become a more realistic option for product manufacturers, how can engineers transition toward using them?
One of the key considerations is the development environment. Too often, new chip architectures mean new and immature proprietary programming platforms, which engineers need time to learn and get familiar with. Instead, engineers should look for flexible platforms that are accessible using industry-standard techniques that they are already familiar with — full programmability in C, runtime environments like FreeRTOS and AI tool flows like TensorFlow Lite. Working with familiar platforms means engineers can program chips quickly without having to learn new languages, tools or techniques.
A single programming environment for all of an AIoT system's compute needs is a fundamental enabler to the design speed that is crucial to ushering in the new era of fast and secure AI in the home.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Follow me on LinkedIn. Check out my website.
Mark Lippett
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’
... Read More
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Jumpchooks
Regular
I would hope that collisions like Melbourne / Voyager 1964 may be avoided by the use of AKIDA?
thelittleshort
Top Bloke
More from Mark
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INNOVATION
How The Pandemic Has Shifted Attitudes To The 'Artificial Intelligence Of Things' And The Smart Home
Mark Lippett
Forbes Councils Member
Forbes Technology CouncilCOUNCIL POST| Membership (fee-based)
Aug 11, 2021,08:00am EDT
The last 12 months have undoubtedly been incredibly tough. The pandemic has wreaked havoc across the world, and everyone is now realizing that, unfortunately, Covid-19 is here to stay.
As we begin to accept this fact, we are now looking to adapt the way we live and interact with the world around us. Industries, businesses and governments are all adjusting the status quo in an attempt to keep people safe, productive and able to live their lives.
How — and where — we work is just one of the ways most people have had to change. Working from home has been the norm for so many over the past year, and even as we progress out of this pandemic, we may see businesses continue to allow employees to work from home. As a result, this way of working has placed a renewed focus on the importance of our homes, and discussions around the tech-enabled "smart" home have never been timelier.
Just as the pandemic was taking off, a new kind of technology ecosystem called the "artificial intelligence of things" (AIoT) was taking a foothold. The AIoT represents a convergence of connected things (the IoT) and artificial intelligence (the AI) deployed within those things. I've previously written about what the AIoT is and how it's set to transform all kinds of industries, from healthcare to transport, but there's no bigger opportunity for it than the smart home.
It's still a relatively nascent industry, and so last year, we conducted research into the barriers holding back the AIoT. Within that research, electronics engineers highlighted significant market-level and device-level concerns. We then conducted the same research a year later to see how things had changed. The headline? Necessity is the mother of all invention, and the pandemic has accelerated the development and adoption of the AIoT for the smart home and beyond.
Security, connectivity and scalability are all becoming easier to address.
In our original report in 2020, engineers cited security, connectivity and scalability as the biggest market issues facing the AIoT. Over the last year, however, opinions have softened, and many believe the barriers are more surmountable.
With security, AI raises privacy concerns because of its reliance on data. The "smarter" the device, the more information it requires. However, in the last 12 months, engineers have realized that processing data locally instead of in the cloud can solve the privacy issue. Homes can keep their data within their four walls without the need to send it to third parties in the cloud, reducing the risk of leaks.
By keeping data within a home, a remote cybercriminal would have to turn into a common burglar to steal that data. While that's unlikely to happen, it's still important that device manufacturers ensure the processing that happens on their devices is secure. A gamut of device-level security features — including secure key storage, accelerated encryption and true random number generators — can provide a foundation for significantly improved safety for both data and decision-making.
Security aside, engineers also felt connectivity posed a large barrier to AI deployment, with 38% voicing concerns about the technology's ability to overcome latency problems. In-home healthcare monitoring, for example, cannot afford to be burdened by unreliable connectivity issues when they need to make decisions based on potentially life-changing situations like heart attacks. Now, however, on-device processing lessens the need for networks, making network latency a moot point; only 27% of industry experts consider connectivity as a major barrier to the technology.
The industry should move to on-device processing if it wants to create applications that aren't held back by latency. Certain AIoT chips are now lightning-fast and predictable, with execution determinism measured in single-digit nanoseconds, which enables products to think and make decisions at speed.
Finally, last year, engineers highlighted the issue of scalability. Engineers are aware that the number of connected devices is rising, placing more and more strain on cloud infrastructure. In 2020, about a quarter of engineers believed scalability to be a major barrier to the success of edge technology. However, experts are now beginning to see the benefits of the AIoT's deep-rooted scalability. Processing at the edge removes the dependence on the cloud, negating any potential growth and scaling issues. Now, fewer than one-fifth of engineers believe that cloud infrastructure could hold back edge AI.
The good news is that the electronics industry doesn't need to do anything specifically to maintain the AIoT's scalability, as one of the major technical barriers to the AIoT's expansion — the need for the cloud to process billions more devices and petabytes of data in the future — has been extinguished.
Power capability up, power consumption down.
As the AIoT market has matured over the past year, it's also progressed on a technical front. On-device processing capabilities have increased while reducing the amount of power and expenditure it takes to enable AI. Now, chips are flexible enough to meet the diverse needs of the AIoT at a much more affordable price point than ever before.
As AIoT chips become a more realistic option for product manufacturers, how can engineers transition toward using them?
One of the key considerations is the development environment. Too often, new chip architectures mean new and immature proprietary programming platforms, which engineers need time to learn and get familiar with. Instead, engineers should look for flexible platforms that are accessible using industry-standard techniques that they are already familiar with — full programmability in C, runtime environments like FreeRTOS and AI tool flows like TensorFlow Lite. Working with familiar platforms means engineers can program chips quickly without having to learn new languages, tools or techniques.
A single programming environment for all of an AIoT system's compute needs is a fundamental enabler to the design speed that is crucial to ushering in the new era of fast and secure AI in the home.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Follow me on LinkedIn. Check out my website.
Mark Lippett
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’
... Read More
@Diogenese hopefully you’ll know if this looks like we’re involved. Links to the XMOS website and processors below
Website
XCORE.AI
XCORE-200
thelittleshort
Top Bloke
Jumpchooks
Regular
I found my Fabre Castell Slide Rule was superior doing Trigonometric Calcs. The Abacus was handy but a bitch to carry on the tram.
MDhere
Regular
same. i thought i still had my original somewhere but may have thrown it out
ok not 1975 it was in the 80s
MDhere
Regular
thelittleshort
Top Bloke
Hi @dippY22
Fair response and concerns
Personally I do not believe XMOS are a competitor
Below is the 2022 tinyML presentation posters by XMOS. Their representative was on after BrainChip
XMOS seem to only have the license plate identification tech available at the moment - as per their tinyML Summit presentation, where you would assume they would showcase their A-game technology
The previous year they were involved directly with Plumerai
www.linkedin.com
Fair response and concerns
Personally I do not believe XMOS are a competitor
Below is the 2022 tinyML presentation posters by XMOS. Their representative was on after BrainChip
XMOS seem to only have the license plate identification tech available at the moment - as per their tinyML Summit presentation, where you would assume they would showcase their A-game technology
The previous year they were involved directly with Plumerai
Sign Up | LinkedIn
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Tuliptrader
Regular
What about XMOS?
View attachment 7273
Article below by their CEO Mark Lippett in Forbes. In my opinion Mark is basically describing the problems he knows he can fix using Akida IP - due to the partnership between XMOS and Plumerai
BrainChip smart homes and cybersecurity
INNOVATION
Privacy, Intelligence, Agency: Security In The Smart Home
Mark Lippett
Forbes Councils Member
Forbes Technology CouncilCOUNCIL POST| Membership (fee-based)
May 5, 2022,06:30am EDT
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’ experience in startup, scale-up and blue-chip companies.
While the dawn of the smart home has been heralded for years, the establishment of smart speaker technology means we’re finally actually entering the era of intelligent, responsive homes.
Home automation is projected to expand its value to $75.3 billion by 2025, with a compound annual growth rate (CAGR) of just under 16%. Smart speakers are the key driver of growth here. Data from Statista suggests that 320 million smart speakers were deployed as of 2020, projected to double by 2024.
With pandemic-related factors accelerating this growth as people spend more time indoors, concerns over security and privacy come to the fore. Many IoT environments are failing to properly protect their users, given their over-reliance on cloud infrastructures and internet connectivity.
Balancing Innovation With Privacy
Having Amazon Alexa or Google Assistant may be convenient when turning off the lights or ordering in, but it also offers an open window into your home with the possibility of thousands looking in, listening from anywhere in the world.
Cloud connectivity and the “always-on” power model raise significant ethical concerns, with consumers understandably wary of their every word being recorded and transferred online.
Despite the short-term financial incentive, this model may be self-defeating. Bringing smart home architectures to market will be harder if privacy concerns aren’t addressed—and that’s true not only for today’s devices but future models, too.
Manufacturers need to strike a technical balance between innovation and privacy for our smart homes to evolve. Devices need to interpret user activity and context and respond—that’s their purpose. They’re not “smart” if they can’t perform this function.
However, residents must also feel secure in their own homes: recognized, not watched. Striking this balance will be crucial for device engineers going forward.
Process Your Data Locally
The key to providing secure, private and responsive homes is reducing dependency on the cloud, instead embedding intelligence within the home environment itself.
At present, our devices can’t process user inputs alone. Sensor data must be transferred to the cloud for interpretation and contextualization before instructions can be carried out. This causes latency issues where smart homes take longer to respond. At best, this is a pain; at worst, tasks or value-adding use cases are rendered useless due to resulting health and safety concerns.
Thankfully, we have an alternative to the cloud: the artificial intelligence of things (AIoT). This model embeds intelligence and processing power directly into the smart home device, processing commands locally. I’ve written about it before for Forbes, examining the convergence of AI and IoT devices.
However, there’s a challenge in bringing this edge intelligence to market. AI chips are expensive, and it’s difficult to configure them for compatibility with home devices.
Making The AIoT Work
The chips that drive such home devices must deliver a combination of AI, DSP, control and communications. Economics demand that these are delivered in a single device, affording designers greater control over how these four attributes are balanced.
Manufacturers will also need to deliver this in smaller packages with low overall BOM costs. Creating these programmable, efficient devices with AI capabilities won’t be easy, but it’s an essential piece of the data protection puzzle.
Rather than rely on the cloud for context, AIoT-enabled chips would enable more sophisticated sensor processing—delivering face and image identification, presence detection and even life sign monitoring to capture rich, contextualized information and react independently.
Simply put, you won’t need to risk or surrender personal information to make the smart home work.
Security And Privacy Aren’t Negotiable
Realizing edge AI for smart homes would mark significant progress for not only consumer privacy but overall home security. Improvements to latency, processing and more sophisticated multimodal sensors will all contribute to a better-protected home.
For example, edge intelligence will allow smart home devices to distinguish between household occupants with ease. Devices will be able to identify and ignore potentially dangerous commands from young children, contact emergency services in case of medical emergencies and even alert household members if an unrecognized individual has entered the building.
In the longer term, a symbiotic relationship would allow devices to “borrow” intelligence from one another to build sophisticated room-to-room continuity. Alarm clocks can initiate coffee machines; gas sensors could disperse dangerous gases by opening windows; and security lights in the back garden could lock the front door. It all depends on the sensors and intelligence available.
However, with consumer concerns around privacy a problem for right now, such devices need to balance perceptiveness with some tact—and that means the edge. In removing the reliance on data transference to the cloud, edge devices pose very little risk to consumers’ privacy. On-device processing allows smart home tech to dispense with data once it’s used. In many cases, little-to-no data would need to be stored in any format.
All of this drastically reduces the amount of data leaving the house, as well as the number of attack surfaces the smart home is exposed to. It means a more cybersecure home, one that’s better protected against data exploitation by private corporations.
We can only make that a reality by addressing the growing concerns over consumer privacy, with 63% of consumers considering connected devices “creepy“ in terms of data collection and behavior, while 50% of U.K. consumers were “fairly concerned” about the use of their data, according to a Deloitte study from 2020.
Choosing to make devices independently intelligent, rather than relying on the cloud with its associated data transfer, is a critical enabler for an exciting new smart age—free from invasion of privacy.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
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Mark Lippett
CEO of AIoT chip company XMOS, Mark Lippett is a technology leader with 25 years’ experien…
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Another member of the Forbes technology Council we should be familiar with. It wouldn't be unreasonable to think they would have crossed paths.
https://www.globenewswire.com/news-...-accepted-into-Forbes-Technology-Council.html
BrainChip CEO Louis DiNardo accepted into Forbes Technology Council
Forbes Technology Council Is an Invitation-Only Community for World-Class CIOs, CTOs, and Technology Executives.
September 24, 2019 08:00 ET| Source: BrainChip Holdings LtdSAN FRANCISCO, Sept. 24, 2019 (GLOBE NEWSWIRE) --
Louis DiNardo, CEO of BrainChip, a leading provider of ultra-low power, high performance edge AI technology has been accepted into Forbes Technology Council, an invitation-only community for world-class CIOs, CTOs, and technology executives.
DiNardo was vetted and selected by a review committee based on the depth and diversity of his experience. Criteria for acceptance include a track record of successfully impacting business growth metrics, as well as personal and professional achievements and honors.Louis DiNardo, CEO of BrainChip, a leading provider of ultra-low power, high performance edge AI technology has been accepted into Forbes Technology Council, an invitation-only community for world-class CIOs, CTOs, and technology executives.
"I look forward to providing insight on the game changing AI technology that we have created at BrainChip to global companies such as those represented in Forbes Councils," said DiNardo. "The technology community is embracing AI and I am excited to share information on why implementing these critical edge attributes for applications in surveillance, advanced driver assistance systems (ADAS), autonomous vehicles (AV), vision guided robotics, drones, augmented and virtual reality (AR/VR), acoustic analysis and Industrial Internet-of-Things (IoT) are so important."
“We are honored to welcome Louis DiNardo into the community,” said Scott Gerber, founder of Forbes Councils, the collective that includes Forbes Technology Council. “Our mission with Forbes Councils is to bring together proven leaders from every industry, creating a curated, social capital-driven network that helps every member grow professionally and make an even greater impact on the business world.”
As an accepted member of the Council, DiNardo has access to a variety of exclusive opportunities designed to help him reach peak professional influence for BrainChip. He will connect and collaborate with other respected local leaders in a private forum. DiNardo will also be invited to work with a professional editorial team to share his expert insights in original business articles on Forbes.com, and to contribute to published Q&A panels alongside other experts.
TT
MDhere
Regular
I agree Tt . One thing that continually sticks in my mind is a speech that LDN did, he appeared to really emphasis the significance of Forbes. So i wouldn't be surprised that many connecting dots are within this network.Another member of the Forbes technology Council we should be familiar with. It wouldn't be unreasonable to think they would have crossed paths.
https://www.globenewswire.com/news-...-accepted-into-Forbes-Technology-Council.html
BrainChip CEO Louis DiNardo accepted into Forbes Technology Council
Forbes Technology Council Is an Invitation-Only Community for World-Class CIOs, CTOs, and Technology Executives.
September 24, 2019 08:00 ET| Source: BrainChip Holdings Ltd
SAN FRANCISCO, Sept. 24, 2019 (GLOBE NEWSWIRE) --DiNardo was vetted and selected by a review committee based on the depth and diversity of his experience. Criteria for acceptance include a track record of successfully impacting business growth metrics, as well as personal and professional achievements and honors.
Louis DiNardo, CEO of BrainChip, a leading provider of ultra-low power, high performance edge AI technology has been accepted into Forbes Technology Council, an invitation-only community for world-class CIOs, CTOs, and technology executives.
"I look forward to providing insight on the game changing AI technology that we have created at BrainChip to global companies such as those represented in Forbes Councils," said DiNardo. "The technology community is embracing AI and I am excited to share information on why implementing these critical edge attributes for applications in surveillance, advanced driver assistance systems (ADAS), autonomous vehicles (AV), vision guided robotics, drones, augmented and virtual reality (AR/VR), acoustic analysis and Industrial Internet-of-Things (IoT) are so important."
“We are honored to welcome Louis DiNardo into the community,” said Scott Gerber, founder of Forbes Councils, the collective that includes Forbes Technology Council. “Our mission with Forbes Councils is to bring together proven leaders from every industry, creating a curated, social capital-driven network that helps every member grow professionally and make an even greater impact on the business world.”
As an accepted member of the Council, DiNardo has access to a variety of exclusive opportunities designed to help him reach peak professional influence for BrainChip. He will connect and collaborate with other respected local leaders in a private forum. DiNardo will also be invited to work with a professional editorial team to share his expert insights in original business articles on Forbes.com, and to contribute to published Q&A panels alongside other experts.
TT
Darn, you got to read my post before I realised I made a mistake. I deleted my post because it didn't make sense, on hindsight. Sorry for my confusion. Onward.....Hi @dippY22
Fair response and concerns
Personally I do not believe XMOS are a competitor
Below is the 2022 tinyML presentation posters by XMOS. Their representative was on after BrainChip
XMOS seem to only have the license plate identification tech available at the moment - as per their tinyML Summit presentation, where you would assume they would showcase their A-game technology
The previous year they were involved directly with Plumerai
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Posted by the Technion Institute a week ago and shared by Professor Haick recently...........he's been a little more vocal lately.......perhaps something is close to being commercialised............
eurotech-universities.eu
Faces behind EuroTech: Hossam Haick, Technion
Hossam Haick is an expert in the field of nanotechnology and non-invasive disease diagnosis. Learn more about him in our interview!
eurotech-universities.eu
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My brain ain’t working but I came across this article that uses Akida hardware I think.
Fact Finder
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PRESS RELEASE
NVISO advances its Human Behaviour AI SDK for neuromorphic computing using the BrainChip Akida platform
11th May, 2022
Lausanne, Switzerland – May 11, 2022 – nViso SA (NVISO), the leading Human Behavioural Analytics AI company, today announced at the AI Expo Japan new capabilities and milestones for its Human Behaviour AI SDK in support for neuromorphic computing. These accomplishments build upon the partnership recently announced in April 2022 by NVISO and BrainChip, where it will be showing the world’s first neuromorphic SDK for Human Behavioural AI designed for ultra-low power mass market consumer products at AI Expo 2022 Tokyo from the 11th to the 13th of May.New features and achievements facilitated by the upgrade to neuromorphic computing include:
- The world’s first commercial grade emotion recognition AI App designed for neuromorphic processing, with peak speeds of more than 250fps achieved using the BrainChip Akida processor. Using NVISO’s advanced edge deployment methodology and BrainChip MetaTF framework, the neuromorphic optimized Emotion Recognition AI App currently delivers an accuracy of 90.28% with speeds up to 250fps using 2-bit quantization. By comparison the same AI App implemented on a Raspberry Pi 4 single core ARM A53 CPU processor using 8-bit compression shows similar accuracy but only running at 12fps, resulting in 20x improvement in performance.
- Fully integrated AI Solutions for Smart Living and Smart Mobility use cases requiring ultra-low power consumption leveraging true heterogeneous digital to analog AI processing technologies ranging from MPU, CPU, GPU, and NPU combined with neuromorphic computing technology. These AI Apps are specifically designed to leverage resource constrained low-power and low-memory hardware platforms deployed at the extreme edge, where human observations are made and processed locally without an internet connection both addressing privacy concerns and removing the latency and power consumption costs associated with moving data for processing in the cloud”
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