db1969oz
Regular
Interesting to see indeed, thanks for your thoughts.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Schnitzel lover
Regular
I agree wholeheartedly. To be frank, I must’ve glazed over these figures when reading the annual report earlier this year. I can completely understand how upset shareholders are at those outrageously inflated pound for pound executive salaries, compared with other high valuation companies..
No wonder BRN have the high calibre quality of executive attracted to its business.. ‘You get what you pay for’ it is said.
There is absolutely no doubt whatsoever in my mind that the remuneration report will strike again at the next AGM if there is not a swathe of multiple new licensees, and revenue through Renesas’ products or otherwise..
Schnitzel lover
Regular
“The remuneration being sought and paid our executive staff is (according to Antonio) commensurate with that demanded and paid to their contemporaries engaged by similar sized organisations in the worldwide marketplace that we compete in.”Hi Realinfo.
Whilst you continue to posit your theme that will find a lot of sympathy with share holders, allow me to make a few counter points.
As you say, the corporate culture (of entitlement) pervades the corporate community worldwide.
Yet you seem to expect us to somehow buck this trend and only offer a fraction of what is the going rate?
I don't think this is going to work in the competitive arena of either engineering talent or managerial expertise.
The remuneration being sought and paid our executive staff is (according to Antonio) commensurate with that demanded and paid to their contemporaries engaged by similar sized organisations in the worldwide marketplace that we compete in.
You won't entice and retain a world class board such as we have now by offering less than what is the going rate for people with the commensurate level of experience, expertise and valuable connections that have been garnered by careers engaged in appropriate prior employment.
We need people who are at the right stage of their career, neither too young (and so lacking seasoning, experience and relevant connections), nor too old, running out of vigour and without the drive and strength to birth and grow our concept commercially.
We are developing and growing a company with a revolutionary product line in a rapidly evolving and highly competitive cutting edge field of technical expertise, which is still in it's nascent stage of becoming.
The very field we are engaged in is still coming into existence.
We are not merely digging up rocks and shipping them overseas, nor just skimming cream off transactions, (renting at 4%, lending at 8%, and on the green by 2), we are engaged in the next big thing.
The widespread integration and adoption of AI into our lives is coming now and will radically transform societies and how we live and operate within them. And Brainchip has a stated intent to be one of the major player's in this arena.
I want and am prepared to pay for, competent, enlightened, ethical and highly motivated people running our Company whilst we achieve this not insignificant goal.
Let’s say Brainchip have the best in class tech in the AI space that is touted by its executive managers. A technology so great to be a generational disruptor. One that Bull Gates suggested would be worth 10x more than Microsoft.. One that has a 3-5 year long lead time in a market estimated to be worth several trillion dollars by 2030..
Assuming all the above is true.. Would it not also be true, that those qualified with the experience and skillset to work at said company would be competing against 100-200 other like types to get a seat at the board, to get their foot into the company, to prove their worth to be a part of its limited 100 strong employee base? Is it not fair to assume that the prestige and career making opportunity would take precedent over remuneration at the early stages of its journey?
I’d like to know name by name with examples of these other similar sizes companies with the same or similar metrics to BRN that pay the equivalent of $8million in value annually to 7 directors and 3 additional key personnel??
I highly doubt that is the norm..
chapman89
Founding Member
100% correct.Hi Realinfo.
Whilst you continue to posit your theme that will find a lot of sympathy with share holders, allow me to make a few counter points.
As you say, the corporate culture (of entitlement) pervades the corporate community worldwide.
Yet you seem to expect us to somehow buck this trend and only offer a fraction of what is the going rate?
I don't think this is going to work in the competitive arena of either engineering talent or managerial expertise.
The remuneration being sought and paid our executive staff is (according to Antonio) commensurate with that demanded and paid to their contemporaries engaged by similar sized organisations in the worldwide marketplace that we compete in.
You won't entice and retain a world class board such as we have now by offering less than what is the going rate for people with the commensurate level of experience, expertise and valuable connections that have been garnered by careers engaged in appropriate prior employment.
We need people who are at the right stage of their career, neither too young (and so lacking seasoning, experience and relevant connections), nor too old, running out of vigour and without the drive and strength to birth and grow our concept commercially.
We are developing and growing a company with a revolutionary product line in a rapidly evolving and highly competitive cutting edge field of technical expertise, which is still in it's nascent stage of becoming.
The very field we are engaged in is still coming into existence.
We are not merely digging up rocks and shipping them overseas, nor just skimming cream off transactions, (renting at 4%, lending at 8%, and on the green by 2
The widespread integration and adoption of AI into our lives is coming now and will radically transform societies and how we live and operate within them. And Brainchip has a stated intent to be one of the major player's in this arena.
I want and am prepared to pay for, competent, enlightened, ethical and highly motivated people running our Company whilst we achieve this not insignificant goal.
Let’s get rid of Antonio who was President and EVP of Commercial and Global development, Global Director of the ARM Foundry Program, and Executive VP of Worldwide Sales. Mr. Viana and looked after multi billion dollar contracts.
Let’s get rid of Duy Loan Le who worked at Texas Instruments for 35 years and was a senior fellow.
Let’s get rid of Nandan who also worked at ARM for a long time and most recently at a trillion dollar company AMAZON and was in charge of the adoption and roll out of Hey Alexa.
Let’s get rid of Rob Telson who was a Senior Director at Synopsys & also at ARM for 11 years as Global VP of Foundry Sales, also VP of Sales.
Let’s get rid of Sean Hehir who is a Silicon Valley Board Member and also at HP as VP of Strategic Allicances, VP of Microsoft Strategic Alliance, VP of Cisco Strategic Alliance, VP of Accenture Strategic Alliance.
Let’s get rid of Todd Vierra who was Director Field Sales Engineers at ARM for 14 years.
Let’s get rid of Chris Stephens who was at Syntiant as VP of Sales and sold edge devices.
Let’s get rid of everybody at Brainchip who works there, we need a complete reshuffle of our sales, board and management and let’s hire people who have just came out of uni, who have never sold any IP/chips in their life, who haven’t built up any professional relationships throughout the years, have no idea about the importance of an ecosystem , despite the fact that every semiconductor company has an ecosystem.
Let’s try hire people who have experience in AI and neuromorphic…..oh wait, they don’t exist because Brainchip is THE WORLDS FIRST.
Antonio said at the AGM that the pool of people is already so small within the AI space, so it’s hard enough to get people let alone the area where Brainchip occupy.
I am 100% confident paying the staff we have with bonuses.
Does anybody think Nanden or Rob or any others need to work at Brainchip with their experience? They can go and get a job with one of the top companies and have a cushy job, but they’re here at Brainchip because they’ve seen under the hood and are passionate and excited and motivated and see the potential, and want to be a part of something truly disruptive.
See what would happen is, people vote to not pay people what they’re worth, then let’s say Nanden and Chris stephens and Sean leave, and we adopt the notion of paying bonuses until after something material has surfaced to shareholders, then share price goes down even further because we don’t retain the amazing staff we have who are literally the best of the best, then people will complain that they haven’t achieved anything.
I fail to see why people cannot see the bigger picture. Brainchip is a global company competing in a global space, we need to keep up with the big boys.
FromBeyond
Member
Hear, hear!
Frangipani
Regular
No concrete link, but Cambridge Consultants are definitely right up our alley…
The Next Frontier is to Bring AI - and Learning - to the Edge
Prof. David Berman, head of AI Research and Technology at Cambridge Consultants, on the imperative of edge AI
Prof. David Berman
May 18, 2023
6 Min Read
UNSPLASH
Sponsored by Cambridge Consultants
At a Glance
- Not every problem can be solved by behemoth large language models such as ChatGPT
- Next-gen of edge AI will enable low-power, low-compute continuous learning, heralding a new wave of dynamic devices
This year we have seen the huge potential being realised by large language models. ChatGPT powering Microsoft 365 Copilot is just one example. These models are compute behemoths and it costs around $50m to train them using some of humanity’s largest datasets. Their performance continues to grow and use cases span areas such as coding, copywriting and much more. But not every AI problem can be solved by a large language model.
More than that, for AI to reach its full potential, the separation between the slow, compute-intensive learning stage and the quick inference stage needs to be removed to produce AI that can learn at the same time as it does inference. So, if the new challenge is to fully exploit edge AI, how do we go about it?
First, let’s see what edge AI can do well. There has been much work producing hardware relevant for edge compute with chips such as the NVIDIA Jetson and the ARM Cortex-M55both proving ideal for low-power AI inference. For example, at Cambridge Consultants we used the Cortex-M55 CPU to achieve a 7x power reduction and 1,000x speed increase for an edge AI voice detection task.
These hardware developments have been combined with the understanding of how to pack the usual AI models into ever smaller low-compute packages with various quantization and pruning methods contributing to significant technical progress. Together these methods have meant that edge AI works well implementing a pre-trained model with a strong performance in tasks such as computer vision where low latency can be crucial and edge capability is an obvious need.
A good example of the need for low latency AI is in autonomous vehicles, where visual data needs to be processed as quickly as possible. Also given that visual processing is so critical to vehicle control, one cannot rely on connected solutions even if speed were not the issue.
Now let’s look at some of the limitations of traditional edge AI. The AI that I can run on my watch or phone will be a pretrained model and any updates or training is likely to be done remotely through cloud resources. As customers become more tech savvy, they become more data aware. Sharing every piece of your data with a remote location is something people and businesses are becoming less comfortable with (and is one of the motivations behind Web3.0).
But worse is the latency issue. Do I want the delay of passing data and retraining a model to incorporate new data? Do I want the frustration of being reliant on connections with the associated security concerns and the worry of network outages? Of course, we will still want all the benefits of being cloud-connected and don’t imagine the edge device being permanently remote, but we don’t want that reliance.
Humans have always formed the benchmark for what we call intelligence. The current excitement around ChatGPT and other LLMs is really based on the fact that finally the Turing test has clearly been passed and these AIs can appear as human. But ask it to remember a simple to-do list and it can’t. For something passing as intelligent it fails straight away on the ability to learn new things. These models suffer from anterograde amnesia - a type of amnesia that affects a person's ability to create new memories after the onset of the condition.
In the case of ChatGPT, the onset is September 2021 and its knowledge after that is very uncertain. We want to do better and allow AIs to continuously learn and adapt. To do so requires more than just making models bigger. We require an algorithmic development that makes learning closer to being human, so the large compute needs aren’t required for learning as they are now. Achieving this would then have another pay-off. If algorithm development means learning will require lower compute and thus lower power, then we open the possibility of continuous learning on the edge.
Before looking at recent technical developments in the area let’s imagine a real case where edge learning would be ideal. Consider your drive to work. You have been driving for years and the experience you have built up makes you an excellent driver. This is something a current pre-trained AI could soon emulate (if not already). On your Monday morning drive, you go over a deep pothole that was barely visible. Luckily you get away with no damage. What do you do on Tuesday morning? You remember the pothole and avoid it.
This is a simple action based on your ability to continuously learn and fine-tune the model you have of your environment. Sitting in your autonomous vehicle and feeling it drive over the same pothole every day will not fill you full of confidence in the future of AI. Edge AI must do better and provide learning at the edge, not just inference, to solve such issues. The ability to adapt in real-time and learn will enhance a large set of AI-enabled devices, whether for industrial applications that adapt to a changing environment or consumer products that learn to adapt to different people and their needs.
There are now new algorithms (such as Hinton’s ‘Forward Forward’) that are inspired by human brains, in that they learn continuously and with relatively lower compute and power needs. These have yet to be industrially exploited but can provide the basis for the next generation of AI at the edge, with continuous learning at low power that can provide adaptable, dynamic solutions without the need for connectivity.
At Cambridge Consultants we are investing in this approach. We are implementing these new algorithms on low-power chips with a view to not just putting AI on the edge but bringing learning to the edge for our clients. This AI is then much more relatable to human intelligence. It has the capability, like you do, to update its view of the environment and adapt providing a more humancentric notion of AI. If you like the sound of the new edge frontier that we’re moving towards, please get in touch. I’ll be at the London AI Summit along with my colleagues Maya Dillon and Tim Ensor if you’d like to hear more.
Read more about:
AI SummitPartner Perspectives
About the Author(s)
Prof. David Berman
Head of AI Research and Technology, Cambridge Consultants
Prof. David Berman is Head of AI Research and Technology at Cambridge Consultants (CC), part of Capgemini Invent. David worked at the forefront of theoretical physics for over twenty years before moving into AI and machine learning at CC. His work focuses on leading a team to develop new AI techniques to support clients in tackling the tough, high-risk challenges that bring
Terroni2105
Founding Member
Great post ! Full stop.
AusEire
Founding Member. It's ok to say No to Dot Joining
HopalongPetrovski
I'm Spartacus!
Unfortunately some share holders have unrealistic expectations and that highly developed sense of entitlement that Realinfo has mentioned.
Everyone of us who is not a looney or a shorter in disguise wants our price going up and is in some degree of distress at where it currently sits.
Me too. However, the value of the share price from day to day is a product of many factors, some of which lay within the hands of our management and some of which do not. Continually moaning and bitching about it alters it not one jot, except perhaps by the peripheral effect of gradually grinding on the morale of existing holders. The fact that the companies success is not meeting some expectations or aligning with some personal agenda's is irrelevant. If you think the board and the management team are ripping off the holders and are failing your personal performance requirements then why would you continue to hold? Each of us have the choice whenever the market is open to Buy, sell or hold.
Feel free to exercise your prerogative.
Humble Genius
Regular
Thanks dude!
Humble Genius
Regular
HopalongPetrovski
I'm Spartacus!
Had these Dudes confused with Cambridge Analytica for a sec who had that big scandal involving Facebook 10 or so years ago.No concrete link, but Cambridge Consultants are definitely right up our alley…
The Next Frontier is to Bring AI - and Learning - to the Edge
Prof. David Berman, head of AI Research and Technology at Cambridge Consultants, on the imperative of edge AI
Prof. David Berman
May 18, 2023
6 Min Read
UNSPLASH
Sponsored by Cambridge Consultants
At a Glance
Where is the next frontier in AI? Large language models (LLMs) such as ChatGPT are everywhere, but there are still significant challenges in bringing AI to personal devices without cloud or other forms of connectedness. The emerging opportunity is to further the implementation of AI at its point of use: the ‘edge’. My sights are set on advances here that will not just put AI on the edge but bring learning to the edge – and open up new possibilities in industrial and consumer markets.
- Not every problem can be solved by behemoth large language models such as ChatGPT
- Next-gen of edge AI will enable low-power, low-compute continuous learning, heralding a new wave of dynamic devices
This year we have seen the huge potential being realised by large language models. ChatGPT powering Microsoft 365 Copilot is just one example. These models are compute behemoths and it costs around $50m to train them using some of humanity’s largest datasets. Their performance continues to grow and use cases span areas such as coding, copywriting and much more. But not every AI problem can be solved by a large language model.
More than that, for AI to reach its full potential, the separation between the slow, compute-intensive learning stage and the quick inference stage needs to be removed to produce AI that can learn at the same time as it does inference. So, if the new challenge is to fully exploit edge AI, how do we go about it?
First, let’s see what edge AI can do well. There has been much work producing hardware relevant for edge compute with chips such as the NVIDIA Jetson and the ARM Cortex-M55both proving ideal for low-power AI inference. For example, at Cambridge Consultants we used the Cortex-M55 CPU to achieve a 7x power reduction and 1,000x speed increase for an edge AI voice detection task.
These hardware developments have been combined with the understanding of how to pack the usual AI models into ever smaller low-compute packages with various quantization and pruning methods contributing to significant technical progress. Together these methods have meant that edge AI works well implementing a pre-trained model with a strong performance in tasks such as computer vision where low latency can be crucial and edge capability is an obvious need.
A good example of the need for low latency AI is in autonomous vehicles, where visual data needs to be processed as quickly as possible. Also given that visual processing is so critical to vehicle control, one cannot rely on connected solutions even if speed were not the issue.
Now let’s look at some of the limitations of traditional edge AI. The AI that I can run on my watch or phone will be a pretrained model and any updates or training is likely to be done remotely through cloud resources. As customers become more tech savvy, they become more data aware. Sharing every piece of your data with a remote location is something people and businesses are becoming less comfortable with (and is one of the motivations behind Web3.0).
But worse is the latency issue. Do I want the delay of passing data and retraining a model to incorporate new data? Do I want the frustration of being reliant on connections with the associated security concerns and the worry of network outages? Of course, we will still want all the benefits of being cloud-connected and don’t imagine the edge device being permanently remote, but we don’t want that reliance.
Humans have always formed the benchmark for what we call intelligence. The current excitement around ChatGPT and other LLMs is really based on the fact that finally the Turing test has clearly been passed and these AIs can appear as human. But ask it to remember a simple to-do list and it can’t. For something passing as intelligent it fails straight away on the ability to learn new things. These models suffer from anterograde amnesia - a type of amnesia that affects a person's ability to create new memories after the onset of the condition.
In the case of ChatGPT, the onset is September 2021 and its knowledge after that is very uncertain. We want to do better and allow AIs to continuously learn and adapt. To do so requires more than just making models bigger. We require an algorithmic development that makes learning closer to being human, so the large compute needs aren’t required for learning as they are now. Achieving this would then have another pay-off. If algorithm development means learning will require lower compute and thus lower power, then we open the possibility of continuous learning on the edge.
Before looking at recent technical developments in the area let’s imagine a real case where edge learning would be ideal. Consider your drive to work. You have been driving for years and the experience you have built up makes you an excellent driver. This is something a current pre-trained AI could soon emulate (if not already). On your Monday morning drive, you go over a deep pothole that was barely visible. Luckily you get away with no damage. What do you do on Tuesday morning? You remember the pothole and avoid it.
This is a simple action based on your ability to continuously learn and fine-tune the model you have of your environment. Sitting in your autonomous vehicle and feeling it drive over the same pothole every day will not fill you full of confidence in the future of AI. Edge AI must do better and provide learning at the edge, not just inference, to solve such issues. The ability to adapt in real-time and learn will enhance a large set of AI-enabled devices, whether for industrial applications that adapt to a changing environment or consumer products that learn to adapt to different people and their needs.
There are now new algorithms (such as Hinton’s ‘Forward Forward’) that are inspired by human brains, in that they learn continuously and with relatively lower compute and power needs. These have yet to be industrially exploited but can provide the basis for the next generation of AI at the edge, with continuous learning at low power that can provide adaptable, dynamic solutions without the need for connectivity.
At Cambridge Consultants we are investing in this approach. We are implementing these new algorithms on low-power chips with a view to not just putting AI on the edge but bringing learning to the edge for our clients. This AI is then much more relatable to human intelligence. It has the capability, like you do, to update its view of the environment and adapt providing a more humancentric notion of AI. If you like the sound of the new edge frontier that we’re moving towards, please get in touch. I’ll be at the London AI Summit along with my colleagues Maya Dillon and Tim Ensor if you’d like to hear more.
Read more about:
AI SummitPartner Perspectives
About the Author(s)
Prof. David Berman
Head of AI Research and Technology, Cambridge Consultants
Prof. David Berman is Head of AI Research and Technology at Cambridge Consultants (CC), part of Capgemini Invent. David worked at the forefront of theoretical physics for over twenty years before moving into AI and machine learning at CC. His work focuses on leading a team to develop new AI techniques to support clients in tackling the tough, high-risk challenges that bring
Space Cadet
Regular
HopalongPetrovski
I'm Spartacus!
Manny's agent off loading some, if you listen to the video kindly posted by Hop a long, Ken is very clear in his choice
of words, it wasn't a friendly process to come to an agreement, sometimes some individuals think that they are worth
more than they truly are, egos and arrogance should never be rewarded.
What I like is that the company has clearly changed direction, internal values are well and truly aligned for the
betterment of all, if strong metrics have been achieved by certain staff members, fantastic, they have earnt their
performance benefit.
Tech just airing a few opinions, nothing to overthink.....
AKD 3.0
Dhm
Regular
There is a reason why people of this calibre are at Brainchip. They are not here for a bludge (great Aussie word) They are not here to see themselves out to pasture. They ARE here because they see and recognise a quantum shift from heavy datacenter compute to the more elegant ubiquitous Edge where you have no AI help other than what Akida gives you. And Akida gives you millawattage, absolute security, instantaneous decision making and learning on chip. Independence from the cloud, accuracy thru instantaneous inference and decision making. There is no other AI play in the world that offers what Akida is doing now. That is why the Brainchip team are here now and in to the future.
Price action over the last few days demonstrates that Brainchip may well have turned the corner and the only direction now is up.
And it is thanks to the best team that computing AI has put together. Motivation. Innovation. Destination.
Disclaimer: Part of the motivation in writing this prose is a double pour of Laphroaig 10YO from Islay.
Last edited:
Frangipani
Regular
Again, not a concrete link to Brainchip either, but this recent Infineon press release is nevertheless interesting in the light of Imagimob having been a co-exhibitor at that EmbeddedWorld 2023 tinyML Foundation booth 2-238 referred to above and being an official partner of not only fellow booth exhibitors Renesas, Syntiant and Arduino, but also of Arm, STMicroelectronics as well as Texas Instruments (https://www.imagimob.com/partners).
Infineon acquires Tiny Machine Learning leader Imagimob to strengthen its offering in embedded AI solutions
May 16, 2023 | Business & Financial Press“Artificial Intelligence and Machine Learning respectively are about to largely enter every embedded application and thus enable new functionalities. With Imagimob’s outstanding platform and its expertise in developing robust machine learning solutions for edge devices, we further strengthen our ability to enable new levels of control and energy efficiency on our products while preserving privacy,” said Thomas Rosteck, President of Infineon's Connected Secure Systems division. “We enable our customers to leverage the advantages from AI/ML and bring their products to market quickly by building on our advanced sensors and IoT solutions portfolio”.
”With Infineon we can accelerate our customer’s developments, enable new applications and help them to differentiate in their markets,” said Anders Hardebring, Co-Founder and CEO of Imagimob. “Becoming an integral part of the Infineon ecosystem with their profound application expertise and an extensive product portfolio allows us energy efficient and secure implementation of advanced sensing and control in IoT contexts”.
Imagimob is a leading player in the fast-growing market for Tiny Machine Learning and Automated Machine Learning (AutoML), providing an end-to-end development platform for Machine Learning on edge devices. Imagimob’s platform enables a wide range of use cases, such as audio event detection, voice control, predictive maintenance, gesture recognition, signal classification as well as material detection, and will extend Infineon’s hardware/software ecosystem even further. Applying the combined expertise to the complete range of sensors will provide existing customers of both companies a unified user experience across products, enable rapid deployment of robust solutions and accelerate the further adoption of Tiny Machine Learning through all applications and segments.
About Imagimob
Imagimob is a fast-growing startup driving innovation at the forefront of Edge AI and Tiny Machine Learning – and enabling the intelligent products of the future. Based in Stockholm, Sweden, the company has been serving global customers within the automotive, manufacturing, healthcare, and lifestyle industries since 2013. Imagimob AI is a development platform for the swift and easy end-to-end development of Edge AI applications for devices with constrained resources. Imagimob AI guides and empowers users throughout the entire development journey, resulting in game-changing productivity and faster time-to-market.
About Infineon
Infineon Technologies AG is a global semiconductor leader in power systems and IoT. Infineon drives decarbonization and digitalization with its products and solutions. The company has around 56,200 employees worldwide and generated revenue of about €14.2 billion in the 2022 fiscal year (ending 30 September). Infineon is listed on the Frankfurt Stock Exchange (ticker symbol: IFX) and in the USA on the OTCQX International over-the-counter market (ticker symbol: IFNNY).Follow us: Twitter - Facebook - LinkedIn
Evermont
Stealth Mode
Apologies if duplicated. (I am running a couple of days behind).
Interesting notes from HP.
Cheers.
HP Inc. (HPQ) CEO Enrique Lores says consumers and businesses are poised to see big changes to their computers within 24 months due to new advances in artificial intelligence.
"What we are working on is to build AI capabilities into the PC," Lores told analysts on a late Tuesday earnings call. "So, consumers or professionals will be able to run AI applications at the edge and will not have to run them on the cloud."
"The benefit this will bring is that if you're a small company and you want to use some of your private data in an AI application, you will not have to upload it, you will be able to run it locally. And also there will be advantages in cost and advantages in latency," he said.
Lores added HP is working closely with chipmakers to integrate to new AI designs into its computers.
finance.yahoo.com
Interesting notes from HP.
Cheers.
AI is about to significantly change your computer: HP CEO
HP Inc. (HPQ) CEO Enrique Lores says consumers and businesses are poised to see big changes to their computers within 24 months due to new advances in artificial intelligence.
"What we are working on is to build AI capabilities into the PC," Lores told analysts on a late Tuesday earnings call. "So, consumers or professionals will be able to run AI applications at the edge and will not have to run them on the cloud."
"The benefit this will bring is that if you're a small company and you want to use some of your private data in an AI application, you will not have to upload it, you will be able to run it locally. And also there will be advantages in cost and advantages in latency," he said.
Lores added HP is working closely with chipmakers to integrate to new AI designs into its computers.
AI is about to significantly change your computer: HP CEO
HP Inc. is plotting an AI future as sales of PCs continue to be under pressure.
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