ZF is more than just ADAS. ZF is a global company generating 35 billion last year from basically everything automotive.. chassis, drivetrain.. the lot.. they would be very interested in what Brainchip could deliver across the spectrum all the way from ADAS to vibration analysis in the drivetrain.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Okeydokey01
Regular
Hi zeeb0t, I am starting to think with all that we have in the pipeline, pretty soon we are going to need a "mike drop" emoji added to the like list...
Nviso's product is software/datatbases, so maybe all that is required is to develop an interface between Akida and Nviso's software to get to market. With Akida doing the heavy lifting of object recognition, significant improvements in power consumption and response speed should be the result.Trolling through their recent news feeds NVISO is working with some interesting companies it seems.
European Automotive Supplier MoU for Premium OEMs - NVISO
NVISO has signed an MoU for Automotive Interior Sensing with a global supplier of consumer and automotive solutions. The partnership will provide a complete interior solution including DMS and OMS to support an engagement with a major German Automotive OEM. Connectivity, autonomy, and sharing...www.nviso.ai
NVISO and PANASONIC sign license agreement to embed Human Behaviour AI in companion robots
NVISO and PANASONIC sign license agreement to embed Human Behaviour AI in companion robotswww.nviso.ai
Another strong partnership with another premium company in my opinion. Well done Brainchip.
nViso are already working with Tobii on eye-tracking.
https://www.nviso.ai/en/news/nviso-...ate-innovation-in-interior-monitoring-systems
NVISO and Tobii to collaborate to accelerate innovation in Interior Monitoring Systems
5th August, 2021
...About: Tobii is the global leader in eye tracking. Our mission is to improve the world with technology that understands human attention and intent.
Tobii have a couple of hundred patents. They use CNN (software) installed on a server to track eye images, so Akida could also lift their game without Tobii needing to change their CNN system significantly.
US10671890B2 Training of a neural network for three dimensional (3D) gaze prediction
[0042] As illustrated in FIG. 5, a user 510 operates a computing device 520 that tracks the 3D gaze 512 of the user 510 . To do so, the computing device 520 is, in an example, in communication with a server computer 530 that hosts a deep learning system 532 . The computing device 520 sends, to the server computer 530 over a data network (not shown), a 2D image 550 showing the user eyes while the user 510 is gazing. The server computer 530 inputs this 2D image 550 to the deep learning system 532 that, in response, predicts the 3D gaze 512 . The server computer 530 sends information 560 about the 3D gaze 512 , such as the 3D eye position and 3D gaze direction back to the computing device 520 over the data network. The computing device 520 uses this information 560 to provide a 3D gaze-based computing service to the user 510 .
Fact Finder
Top 20
Some here will recall the former CEO Mr. Dinardo speaking in a webinar about a project/experiment in China using AKIDA technology to monitor and improve electricity use in a cities power grid.
The following article explains this use and how and why it is being considered in the USA:
pxFuel
America’s electrical system has long been waiting for an overhaul. With a 2021 report from the American Society of Civil Engineering indicating that 70% of T&D lines are over 25 years old, it’s no surprise that large and sustained outages are occurring with increased frequency across the country.
Last year, major blackouts in California and Texas were both triggered by extreme weather events, causing local electricity demand to exceed supply. As climate change fuels extreme weather events, factory and city officials are increasingly turning to artificial intelligence technologies to predict energy consumption levels days in advance, mitigating the potential for power outage incidents and increasing the overall reliability of the power system.
To understand the problems facing today’s power grid, which by one design is the world’s largest and most complicated machine, I contacted Steve Kwan, director of product management at Beyond Limits, which develops industrial AI for growth in a variety of industries.
GN: Can you explain some of the challenges currently facing grid operators in the United States?
Steve Kwan: The aging power grid is being tested as it expands to support a much higher volume of users than it was designed for under much more difficult circumstances. Historically, electricity has been delivered from large generators, such as power stations, to consumers. Under normal conditions, the weather does not play a big role in the decisions network managers have to make.
However, the explosion of distributed energy resources such as rooftop solar, commercial solar, and wind installations creates many other scenarios that grid managers need to consider when deciding how best to operate the grid. Now, with solar power on rooftops, energy can flow “backwards” in certain situations. This means that the cost of production can be greatly affected by weather and sunshine conditions.
These additional parameters greatly influence network manager decisions on how best to operate the network. As a result, there is a clear need for a more resilient power grid, especially as climate change experts indicate that global warming will lead to an increase in natural disasters in the years to come. The call for carbon neutrality and net-zero emissions has also bolstered efforts to transition to green energy, and as new regulations spread, industry will need to adapt to changes in standards that could significantly alter the operations.
GN: Within the network management ecosystem, where are we seeing the penetration of AI and ML technologies? What is the use case here?
Steve Kwan: AI and ML technologies are superior in their ability to learn from past behaviors and predict future needs. One of the most critical elements of grid management is the need to ensure that electricity is delivered efficiently and available at the lowest cost. As such, it is always a matter of verifying that there is sufficient power supply to meet the demand.
However, electricity supply and demand are constantly changing. Electricity demand fluctuates throughout the day due to human needs and activities. Supply and demand are influenced by weather conditions. The proliferation of electric vehicles and IoT devices is also changing energy consumption patterns. On the supply side, changes in the way we produce electricity make it much more difficult to match supply and demand. From declining fossil fuel generation to increasing renewables and climate change, grid management has become a headache both in real time and when planning for the future.
AI and ML technologies are entering the network ecosystem due to their ability to learn usage patterns and provide accurate predictions of future needs, making this technology the ideal solution for network management. AI can analyze the masses of data produced by factories and accurately predict when there will be an abundance of energy to charge the batteries versus when to drain them.
Artificially intelligent time horizon analytics will lead to dramatic improvements, allowing manufacturers to see the big picture of their operations. AI can be used to sift through all data points, including past weather conditions, current network status, and potential points of failure. As such, electricity providers use AI and ML technologies to optimize their dispatch and scheduling activities, forecast market demands, plan future business expansion, and more. Consumers use these technologies to plan and optimize their energy consumption behaviors to take advantage of excess energy and prices.
For example, consumers decide when to charge their electric vehicle based on time-of-use pricing, and in some cases can even meet grid demand by returning electricity from home batteries. to the network.
GN: When we talk about energy, we are obviously also talking about environmental concerns. What improvements can AI bring to waste and carbon emissions?
Steve Kwan: Today, the largest source of carbon emissions comes from burning fossil fuels. Fortunately, AI technologies can be harnessed to reduce these emissions in several ways. AI can reduce energy waste by helping factory and city officials predict energy consumption levels days in advance. With this foresight, producers can plan their operations based on anticipated demands and produce energy with precision. AI can also help grid managers know when to store energy or cut off in micro-grids to achieve a constant flow of energy and meet consumption demand. This not only saves energy, but also reduces stress by imposing restrictions and limitations on the network that take line capacity and congestion into account.
In addition to predicting consumption levels, AI technologies are reducing carbon emissions by helping industry switch to renewable energy sources like solar and wind. By anticipating weather conditions using historical data and impending weather patterns, these solutions can predict the availability of naturally generated energy. This allows managers to forecast the mix of renewables and traditional fossil energy needed to meet anticipated demand while maximizing the use of renewables. It also makes renewable energy sources more attractive in the energy sector, as this previously unpredictable source of energy becomes increasingly reliable.
GN: Are there any concerns about these new AI technologies or that adoption is happening too quickly?
Steve Kwan: As with all technologies, digital AI technologies need to be checked and checked from time to time, as they are often “black box” applications that are highly dependent on the implementation and data quality of the whole system. training data. When human knowledge and expertise are incorporated into digital AI technologies, they are dramatically enhanced, which can dramatically change results and generate better predictions and recommendations.
GN: What future for AI in network management?
Steve Kwan: With continued improvements in AI and ML technologies, we will be able to increase the dataset size, frequency, speed, and accuracy of model predictions. Together, this would provide better tools for network operators to optimally match supply to demand in an unprecedented way. This creates the opportunity for optimal network and generation investments in areas that would provide the most benefit. For example, AI and ML technologies can be used to optimize the placement and installation of renewable energy such as solar and wind farms. Better network modeling will help operators route energy more efficiently and allow network operators to reduce the total cost to consumers by minimizing congestion. The more accurate the models, the better network operators will be able to plan their operations and put in place mitigation measures in anticipation of impending problems”
My opinion only DYOR
FF
AKIDA BALLISTA
The following article explains this use and how and why it is being considered in the USA:
Zapped: The grid is under respiratory assistance. Can AI solve this problem?
Leave a CommentpxFuel
America’s electrical system has long been waiting for an overhaul. With a 2021 report from the American Society of Civil Engineering indicating that 70% of T&D lines are over 25 years old, it’s no surprise that large and sustained outages are occurring with increased frequency across the country.
Last year, major blackouts in California and Texas were both triggered by extreme weather events, causing local electricity demand to exceed supply. As climate change fuels extreme weather events, factory and city officials are increasingly turning to artificial intelligence technologies to predict energy consumption levels days in advance, mitigating the potential for power outage incidents and increasing the overall reliability of the power system.
To understand the problems facing today’s power grid, which by one design is the world’s largest and most complicated machine, I contacted Steve Kwan, director of product management at Beyond Limits, which develops industrial AI for growth in a variety of industries.
GN: Can you explain some of the challenges currently facing grid operators in the United States?
Steve Kwan: The aging power grid is being tested as it expands to support a much higher volume of users than it was designed for under much more difficult circumstances. Historically, electricity has been delivered from large generators, such as power stations, to consumers. Under normal conditions, the weather does not play a big role in the decisions network managers have to make.
However, the explosion of distributed energy resources such as rooftop solar, commercial solar, and wind installations creates many other scenarios that grid managers need to consider when deciding how best to operate the grid. Now, with solar power on rooftops, energy can flow “backwards” in certain situations. This means that the cost of production can be greatly affected by weather and sunshine conditions.
These additional parameters greatly influence network manager decisions on how best to operate the network. As a result, there is a clear need for a more resilient power grid, especially as climate change experts indicate that global warming will lead to an increase in natural disasters in the years to come. The call for carbon neutrality and net-zero emissions has also bolstered efforts to transition to green energy, and as new regulations spread, industry will need to adapt to changes in standards that could significantly alter the operations.
GN: Within the network management ecosystem, where are we seeing the penetration of AI and ML technologies? What is the use case here?
Steve Kwan: AI and ML technologies are superior in their ability to learn from past behaviors and predict future needs. One of the most critical elements of grid management is the need to ensure that electricity is delivered efficiently and available at the lowest cost. As such, it is always a matter of verifying that there is sufficient power supply to meet the demand.
However, electricity supply and demand are constantly changing. Electricity demand fluctuates throughout the day due to human needs and activities. Supply and demand are influenced by weather conditions. The proliferation of electric vehicles and IoT devices is also changing energy consumption patterns. On the supply side, changes in the way we produce electricity make it much more difficult to match supply and demand. From declining fossil fuel generation to increasing renewables and climate change, grid management has become a headache both in real time and when planning for the future.
AI and ML technologies are entering the network ecosystem due to their ability to learn usage patterns and provide accurate predictions of future needs, making this technology the ideal solution for network management. AI can analyze the masses of data produced by factories and accurately predict when there will be an abundance of energy to charge the batteries versus when to drain them.
Artificially intelligent time horizon analytics will lead to dramatic improvements, allowing manufacturers to see the big picture of their operations. AI can be used to sift through all data points, including past weather conditions, current network status, and potential points of failure. As such, electricity providers use AI and ML technologies to optimize their dispatch and scheduling activities, forecast market demands, plan future business expansion, and more. Consumers use these technologies to plan and optimize their energy consumption behaviors to take advantage of excess energy and prices.
For example, consumers decide when to charge their electric vehicle based on time-of-use pricing, and in some cases can even meet grid demand by returning electricity from home batteries. to the network.
GN: When we talk about energy, we are obviously also talking about environmental concerns. What improvements can AI bring to waste and carbon emissions?
Steve Kwan: Today, the largest source of carbon emissions comes from burning fossil fuels. Fortunately, AI technologies can be harnessed to reduce these emissions in several ways. AI can reduce energy waste by helping factory and city officials predict energy consumption levels days in advance. With this foresight, producers can plan their operations based on anticipated demands and produce energy with precision. AI can also help grid managers know when to store energy or cut off in micro-grids to achieve a constant flow of energy and meet consumption demand. This not only saves energy, but also reduces stress by imposing restrictions and limitations on the network that take line capacity and congestion into account.
In addition to predicting consumption levels, AI technologies are reducing carbon emissions by helping industry switch to renewable energy sources like solar and wind. By anticipating weather conditions using historical data and impending weather patterns, these solutions can predict the availability of naturally generated energy. This allows managers to forecast the mix of renewables and traditional fossil energy needed to meet anticipated demand while maximizing the use of renewables. It also makes renewable energy sources more attractive in the energy sector, as this previously unpredictable source of energy becomes increasingly reliable.
GN: Are there any concerns about these new AI technologies or that adoption is happening too quickly?
Steve Kwan: As with all technologies, digital AI technologies need to be checked and checked from time to time, as they are often “black box” applications that are highly dependent on the implementation and data quality of the whole system. training data. When human knowledge and expertise are incorporated into digital AI technologies, they are dramatically enhanced, which can dramatically change results and generate better predictions and recommendations.
GN: What future for AI in network management?
Steve Kwan: With continued improvements in AI and ML technologies, we will be able to increase the dataset size, frequency, speed, and accuracy of model predictions. Together, this would provide better tools for network operators to optimally match supply to demand in an unprecedented way. This creates the opportunity for optimal network and generation investments in areas that would provide the most benefit. For example, AI and ML technologies can be used to optimize the placement and installation of renewable energy such as solar and wind farms. Better network modeling will help operators route energy more efficiently and allow network operators to reduce the total cost to consumers by minimizing congestion. The more accurate the models, the better network operators will be able to plan their operations and put in place mitigation measures in anticipation of impending problems”
My opinion only DYOR
FF
AKIDA BALLISTA
Haven't read it yet, but it sounds important enuf to have its own thread.
Hi tls,Hopefully today’s announcement is only the tip of the NVISO iceberg - I see you Tony
View attachment 4591
View attachment 4593
View attachment 4592
PRESS RELEASE
NVISO and PANASONIC sign license agreement to embed Human Behaviour AI in companion robots
15th March, 2022
NVISO signs a multi-year license agreement with PANASONIC for deployment of its Human Behavioural Analytics AI software solution in the PANASONIC Companion Robot NICOBO
Lausanne, Switzerland – March 15, 2022 – nViso SA (NVISO), the leading Human Behavioural Analytics AI company, is pleased to announce it has signed a production license agreement with Panasonic for the deployment of NVISO’s Human Behaviour AI Apps for the Panasonic NICOBO companion robot1 . The license includes the real-time deep learning-based AI Apps for face tracking, head pose recognition, facial recognition, and emotion recognition. These AI Apps are specifically designed for low-power and low-memory companion robot hardware platforms enabling “always-on” operation without requiring an internet connection.
The global market for Companion Robots was valued at USD 1.98 billion in 2020 and according to Mordor Intelligence is expected to reach USD 11.24 billion by 2026, registering a CAGR of 34.34% during the period of 2021-20262.Companion robots are designed to interact naturally with humans, with the ability to perceive and respond to a user’s mental state, behaviours and commands. With these capabilities they can assist in combating loneliness and detecting depression along with helping in keeping people healthy at home through the remote monitoring of vital signs. This is achieved through visual comprehension and NVISO’s human behavioural analytics AI systems have the capabilities to deliver this. NVISO’s solutions do this through its range of AI Apps providing visual observation, perception and semantic reasoning capabilities, the results of which can be used in identifying issues, in decision making processes and in supporting autonomous “human like” interactions.
Examples of these AI Apps provide the analysis of core signals of human behaviour such as facial expressions, emotions, identity, head pose, gaze, gestures, activities, and the identification of objects with which users interact. These AI Apps can be optimised for typically resource constrained low power and low-cost processing
platforms demanded by battery operated consumer products. Furthermore, NVISO AI Apps can be easily configured to suit any camera system for optimal performance in terms of distance and camera angle, and thanks to NVISO’s large scale proprietary human behaviour databases NVISO’s AI Apps are robust to real-world imaging conditions as often found in consumer homes across the world. Unlike cloud-based solutions, NVISO’s solutions do not require information to be sent off-device for processing elsewhere so user privacy and safety can be protected.
"Over the last two years we have worked with Panasonic’s development team to fulfil a vision to help people live at home healthier and happier through empathic intelligent devices. Our engineering team has delivered an excellent AI solution to meet the demanding power/cost/performance needs of high-volume edge-based consumer processing platforms required by manufacturers such as Panasonic”, said Tim Llewellynn, CEO of NVISO, “For several years, we have been focusing on partnerships and projects to integrate Visual Intelligence into deep learning accelerated hardware enabling breakthrough capabilities at mass production price points. Conclusion of this production license agreement with Panasonic provides additional evidence for the strong demand we are experiencing for the integration of advanced Human Behavioural Analytics technology into edge-based systems for a wide range of applications ranging from consumer products through to medical devices and autonomous automotive systems"
About NVISO, is an Artificial Intelligence ("AI") company founded in 2009 and headquartered at the Innovation Park of the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Its mission is to help make autonomous machines safe, secure, and personalised for humans. As leader in human behavioural AI, it provides software solutions that can sense, comprehend, and act upon human behaviour in real-world environments. It achieves this through real-time perception and observation of people and objects in contextual situations combined with the reasoning and semantics of human behaviour based on trusted scientific research. NVISO’s technology is made accessible through ready-to-use AI solutions addressing Smart Mobility and Smart Health and Living applications (interior sensing, health assessments, and robot interactions) with a key focus on deployments to the deep edge. With a singular focus on how to apply the most advanced and robust technology to industry and societal problems that matter, NVISO’s solutions help advance human potential. www.nviso.ai
About PANASONIC, Panasonic Corporation is a global leader developing innovative technologies and solutions for wide-ranging applications in the consumer electronics, housing, automotive, and B2B sectors. The company, which celebrated its 100th anniversary in 2018, operates 522 subsidiaries and 69 associated companies worldwide and reported consolidated net sales of 6,698.8 billion yen for the year ended March 31, 2021. Committed to pursuing new value through collaborative innovation, the company uses its technologies to create a better life and a better world for customers. Learn more about Panasonic: https://www.panasonic.com/global
1. https://news.panasonic.com/global/stories/2021/88701.html
2. https://www.mordorintelligence.com/industry-reports/social-robots-market
Great news. A production licence - lets hope that there is an Akida IP licence in the mix.
"Our engineering team has delivered an excellent AI solution to meet the demanding power/cost/performance needs of high-volume edge-based consumer processing platforms required by manufacturers such as Panasonic”, said Tim Llewellynn, CEO of NVISO, “For several years, we have been focusing on partnerships and projects to integrate Visual Intelligence into deep learning accelerated hardware enabling breakthrough capabilities at mass production price points. Conclusion of this production license agreement with Panasonic provides additional evidence for the strong demand we are experiencing for the integration of advanced Human Behavioural Analytics technology into edge-based systems for a wide range of applications ranging from consumer products through to medical devices and autonomous automotive systems"
... and lets not forget the potential for combining Akida with Tobii's eye-tracking CNN.
Fact Finder
Top 20
They probably read one of your erudite posts explaining why analogue is for losers.
My opinion only DYOR
FF
AKIDA BALLISTA
thelittleshort
Top Bloke
I had never heard of Tobii @Diogenese. Glad to hear you think there’s potential for collaboration here
Yes - Tobii is new to me too, but as they are recently partnered with nViso and as they do eye-tracking, there is a definite synergy with Akida.
Gosh, I'm starting to sound like Manchild of fond memory.
Stable Genius
Regular
Hi TLS,
I could be wrong but I would have thought any announcement of a company taking up Brainchip at this stage would have a lot of commercial potential.
Given initially Brainchip were overwhelmed with interest they would have assessed which companies had the most potential to yield good results as they couldn’t afford to waste their staff/engineers/specialists with small companies. I thought they were targeting companies that were either Fortune 500 or suppliers for them.
Therefore although I am only learning about NVISO today I have confidence BRAINCHIP has done their due dillegence and this has a more than likely chance to yield us big results. So all the dots uncovered today looks more likely than unlikely to me!
I read that NVISO has been working with PANASONIC for two years before they signed their agreement with them. It’s likely they’ve been assessing AKIDA for a fair period also and have subsequently found a use for it which they believe will be profitable.
I’d be interested to know it’s time to market: hopefully not 2024 as well!
Regardless of their timeframe I picked up more shares today!
NVISO and PANASONIC sign license agreement to embed Human Behaviour AI in companion robots
NVISO and PANASONIC sign license agreement to embed Human Behaviour AI in companion robots
“These AI Apps are specifically designed for low-power and low-memory companion robot hardware platforms enabling “always-on” operation without requiring an internet connection.”
View attachment 2836
I knew that I had seen the video on the NVISO site before. Nice one JK. Those Linkeden likes are definitely dots to be joined.
Cool video:
thelittleshort
Top Bloke
NVISO and PANASONIC sign license agreement to embed Human Behaviour AI in companion robots
NVISO and PANASONIC sign license agreement to embed Human Behaviour AI in companion robots
“These AI Apps are specifically designed for low-power and low-memory companion robot hardware platforms enabling “always-on” operation without requiring an internet connection.”
View attachment 2836
@JK200SX
I had wondered about your Handle.. Don't happen to fly out of YSBK by any chance? I like that YAK's are identified among pilots and ATC as "The Yak" as opposed to another just another Cessna .. Yaks have their own brand.. am i right in suspecting you need a share portfolio to keep fuel up to it?
Stable Genius
Regular
I knew that I had seen the video on the NVISO site before. Nice one JK. Those Linkeden likes are definitely dots to be joined.
Cool video:
Thanks Slade,
That was a good Video. Got Akida uses cases throughout. Looked like a Volvo!
Fact Finder
Top 20
I like how it appears to be system agnostic???Thanks Slade,
That was a good Video. Got Akida uses cases throughout. Looked like a Volvo!
My opinion only DYOR
FF
AKIDA BALLISTA
Falling Knife
Regular
Yep V90Thanks Slade,
That was a good Video. Got Akida uses cases throughout. Looked like a Volvo!
Volvo V90 2022
BRN certainly has the car market wrapped up.
Cerence
LiDar
nViso
Ford EAP
Mercedes proudly advertising fact they are using Akida
BMW used in demo for voice recognition and person present
and of course future Mars cars
60 billion market here Akida comes.
Please show some SOL in the 4C
(signs of life) if you need to ask and shorters may need the defibrillator
No not Bankstown based.
Fuel is not such a great issue when compared to maintenance costs on a Yak really. 100 hourly and Annuals are not cheap anymore unless you can do your own.
yaks are still an affordable aircraft to operate.
Y.
Hoping tonight to have a good read of the AGM notes on the NEW Constitution being proposed.
looking forward to seeing what "others" have picked up as regard any changes etc.
Comparing the current Constitution to the new version management want to enact will be a key issue.
Yak52
looking forward to seeing what "others" have picked up as regard any changes etc.
Comparing the current Constitution to the new version management want to enact will be a key issue.
Yak52
Bravo
If ARM was an arm, BRN would be its biceps💪!
Thanks Slade,
That was a good Video. Got Akida uses cases throughout. Looked like a Volvo!
At the end of the video it shows "NVISO visual intelligence technology use cases for Automotive Powered by H2020 Bonseyes AI Marketplace".
So I did some snooping around and discovered that NVISO was the coordinator of the European Union’s Horizon 2020 Bonseyes project which closed on 31 January 2021.
Anyhooo, to cut a long story short, BonsAPPs is the continuation of the EU-funded 2020 Bonseyes project and it looks VERRRRY INTERESTING!
The future of AI in Europe
The problems with AI implementation in Europe are clear, but the Bonseyes Project has created a solution. Enter the new European Commission-funded project, BonsAPPs. Supporting AI4EU, BonsAPPs aims to build on the Bonseyes project results in order to put Europe on the map in the future of AI-as-a-Service.
BonsAPPs will deliver ready-to-integrate solutions that work alongside the AI4EU platform. These solutions will increase AI usage by SMEs lacking innovation capabilities internally. Users will be able to engage with data sciences, developers and integrators, creating an open innovation cycle that is easy to use and allows for seamless deployment of AI solutions.
Further pursuing the goal of European-based publicly developed AI solutions, BonsAPPs allows users to develop AI solutions to solve industry challenges. Leveraging on the Bonseyes AI Marketplace to deliver integrable solutions, AI developers and platform providers are accessible within BonsAPPS. This gives SMEs the opportunity to use advanced tools and services based on their personalized needs, reducing costs and challenges in implementation.
BonsAPPS seeks to increase SMEs’ understanding of artificial intelligence in order to reduce skill gaps at both a managerial and operational level. Offering training and workflows for AI system integrations, BonsAPPs delivers a fully automated AI procurement process. The project provides an AI-as-a-Service Platform for users across a wide range of industries such as healthcare, automotive, manufacturing and smart cities. Aiming to help SMEs fully digitalize, BonsAPPs will grant access to AI solutions that are cost-efficient and effective.
BonsAPPs: The rise of AI-as-a-Service – BonsAPPs
bonsapps.eu
BonsAPPS awarded 5M EUR grant to provide an AI-as-a-Service layer for the deep edge - NVISO
BonsAPPs received a €5M (~A$8m) grant from the European Commission to conduct a collective R&D initiative to accelerate the adoption of AI for the deep edge based on industry-driven challenges. It started on 1st January 2021, and spearheaded by Tim Llewellynn as President of the Bonseyes...
www.nviso.ai
Similar threads
