Speaker 1: For as long as humans have used computers, we have
Speaker 1: sought to make them faster, smarter, and more capable. But
Speaker 1: what happens when we harness the power of artificial intelligence
Speaker 1: with new PC hardware and software? And what if every
Speaker 1: computer could use AI technology to unleash new capabilities that
Speaker 1: will benefit anyone and everyone who uses a computer.
Speaker 2: What can I help you with?
Speaker 1: This is not a distant dream. Each day it becomes
Speaker 1: more and more of a reality. AI is no longer
Speaker 1: exclusively running in the cloud. It's increasingly finding its way
Speaker 1: into all computers, enabling business users to do more and
Speaker 1: be more with PC technology from Intel. In this episode,
Speaker 1: we'll be focusing on defining the AIPC and what represents
Speaker 1: for the future of this transformative technology. We'll also explore
Speaker 1: what the growth of AI running directly on the PC
Speaker 1: versus the cloud means and how this AIPC revolution changes
Speaker 1: how we work, live and create. Join us as we
Speaker 1: take a journey into the future of AI computing, where
Speaker 1: machines are not just tools but partners in our endeavors.
Speaker 1: Welcome to Technically Speaking, an Intel podcast produced by iHeartMedia's
Speaker 1: Ruby Studio in partnership with Intel. Hey the I'm gram class.
Speaker 1: Joining us today is Robert Hallock, the VP and General
Speaker 1: manager of Client AI and Technical Marketing at Intel. Here
Speaker 1: has a long history with product development and PCs. Welcome
Speaker 1: to the show, Robert.
Speaker 2: Thanks for having me.
Speaker 1: Good to be here. Yeah, I just want to the
Speaker 1: start off by saying, there seems to be a lot
Speaker 1: of talk around AI, and it seems to always revolve
Speaker 1: around cloud based or software as a service type business models.
Speaker 1: How Intel is taking a different route. Well, but can
Speaker 1: you give us an overview of what an AIPC means
Speaker 1: at Intel and can you help us understand some of
Speaker 1: the benefits of running AI directly on a PC versus
Speaker 1: in the cloud.
Speaker 2: Yeah, a lot of people have been exposed to AI
Speaker 2: through the cloud, and these are services like chat, GPT
Speaker 2: or Dolly three where you can create a piece of
Speaker 2: content from a text description. And that's one kind of
Speaker 2: AI that's called generative AI is a category pros and
Speaker 2: cons to that doing it in the cloud. The pro
Speaker 2: is the models are huge, right. They essentially have the
Speaker 2: Internet's collective knowledge of data to produce an answer or
Speaker 2: a picture, and that's highly detailed. But one of the
Speaker 2: cons is that they're not very specific to you what
Speaker 2: you are doing on your PC, the information on your computer,
Speaker 2: or the images that you care about. So that's sort
Speaker 2: of motivating to bring AI capabilities locally to the PC
Speaker 2: where you don't need an Internet connection to use these capabilities.
Speaker 2: And that's sort of the tip of the iceberg, because
Speaker 2: when you move AI to the PC without a cloud connection,
Speaker 2: you can also do new types of AI workloads that
Speaker 2: would be too big or simply can't run in the cloud.
Speaker 2: So a good example of that is in teleconferencing, lots
Speaker 2: of people use background blurring or background pictures, and we
Speaker 2: can actually make that a lot more energy efficient. We
Speaker 2: can actually give you hours of battery life back by
Speaker 2: using AI on that use case. So the whole point
Speaker 2: of AI is that there's a wave of software coming
Speaker 2: that uses AI to improve performance or to reduce power consumption.
Speaker 2: So you'll get better performance from your system and longer
Speaker 2: battery life as well. And this will continue to grow
Speaker 2: over the next five years.
Speaker 1: And Robert I was wondering if you could please describe
Speaker 1: to the audience what the current state of the art
Speaker 1: is with PC architecture. You know, we've got CPUs, which
Speaker 1: are central processing units that make computing possible, and you've
Speaker 1: got GPUs, which are graphical processing units that are instrumental
Speaker 1: for machine learning, gaming applications, video editing. So how does
Speaker 1: that actually differ from the concept of an AIPC.
Speaker 2: That's a great question. So, yeah, users may have heard
Speaker 2: about an AIPC at this point, and at the basic level,
Speaker 2: this is just a new generation computer with hardware inside
Speaker 2: that is capable of accelerating an AI based workload and
Speaker 2: previous hardware, let's say early twenty twenty three computers, you
Speaker 2: can run AI workloads, but they will fall back to
Speaker 2: the CPU cores, which are not as fast and not
Speaker 2: as energy efficient as running that same workload on the
Speaker 2: new accelerators in an AIPC. And the way we're doing
Speaker 2: it at Intel is actually the CPU cores themselves. We've
Speaker 2: added AI accelerating capabilities to those cores. The GPU that's
Speaker 2: built into our processor also has accelerators, and then we've
Speaker 2: also added an entirely new component called the NPU or
Speaker 2: neural processing unit, which sits next to the CPU and
Speaker 2: the GPU and is now its own third category of
Speaker 2: acceleration on the device. And different workloads in AI, or
Speaker 2: different features in AI run best on one of those
Speaker 2: three engines, so you kind of need all three to
Speaker 2: do this well. And our new product, Intel Core Ultra
Speaker 2: is our first AIPC processor, and so if you see
Speaker 2: that name Core Ultra, you know that it has the
Speaker 2: AI accelerators to run these features well. But at its route,
Speaker 2: it is a PC that has AI specific hardware.
Speaker 1: And I'm quite interested in I guess the thinking behind
Speaker 1: at Intel of this shift towards this new architecture. Was
Speaker 1: it something that was internally driven or did you see
Speaker 1: some conversations and discussions with your key customers and clients
Speaker 1: driving that sort of shift.
Speaker 2: This is very much a software industry driven transition, and
Speaker 2: CPU vendors like Intel were innovating to keep pace with
Speaker 2: what's going on in the software environment. And that's kind
Speaker 2: of a key thing I'd want to stress. You know,
Speaker 2: maybe you're on the fence about AIPC or you wonder,
Speaker 2: you know, how long is this thing going to stick around.
Speaker 2: This is one of those cases where it's both the
Speaker 2: hardware industry and the software industry agreeing that this is
Speaker 2: the right thing to do for performance and features and power.
Speaker 2: Because I truly believe that over the next three to
Speaker 2: five years we will reach this point of general acceptance
Speaker 2: in AI, where it's whether you know it or not,
Speaker 2: widely diffused. In most of the applications you're working on
Speaker 2: or working with, many of the features that you value
Speaker 2: will use AI again transparently in the background. But this
Speaker 2: is very much a collaborative, industry wide effort from all
Speaker 2: the hardware makers, all the big software vendors. This is
Speaker 2: here to stay for sure. Let's pause for second here
Speaker 2: to reiterate Robert's point. Right of adoption is always a
Speaker 2: crucial aspect of any new technology, and that's certainly true
Speaker 2: of what we're discussing today with AIPCS. It's important to
Speaker 2: understand that the leaders in the field of computing, both
Speaker 2: in the hardware and software domain, have already begun down
Speaker 2: this revolutionary path and it doesn't seem like there's any
Speaker 2: roadblocks in site. With that in mind, I asked Robert
Speaker 2: about the benefits of moving AI workloads from the cloud
Speaker 2: to the PC, especially when it comes to issues of
Speaker 2: data privacy, latency, and connectivity. You just touched couple that
Speaker 2: are really important to local AI. It's that data privacy
Speaker 2: or data security. We've all read about information going up
Speaker 2: to a cloud resource of some kind, and you don't
Speaker 2: really know what's going to happen with your data or
Speaker 2: your request after that. And that's not to say there's
Speaker 2: anything malicious implied. You just don't know. So I'm sure
Speaker 2: people in corporation, certainly at Intel, when you go to
Speaker 2: a generative AI website, it says, hey, be careful what
Speaker 2: you enter into this textbox. And so moving this stuff
Speaker 2: offline gives you a couple of things. Yes, you get
Speaker 2: chain of custody over the data. It's private, right, it's
Speaker 2: working on your information offline, right, so it doesn't have
Speaker 2: to go to the cloud. That's a big one. Cost
Speaker 2: is another component. A lot of the most powerful AI
Speaker 2: services online, you know, ten to twenty bucks a month,
Speaker 2: and it's not cheap to have several of those. The
Speaker 2: last that is interesting is cloud servers are intrinsically pricey.
Speaker 2: I'm not saying they're expensive, but for AI as a
Speaker 2: genre of software to truly take off and thrive in
Speaker 2: all the way the software vendors want it to, it
Speaker 2: has to reach the local PC. It has to reach
Speaker 2: you know, tens of millions of users, hundreds of millions
Speaker 2: of users on a local device and that's sort of
Speaker 2: that critical mass install base that takes this effort to
Speaker 2: the next level. Cloud was sort of one point zero
Speaker 2: of AI, and now we're trying to, you know, for
Speaker 2: one of a better term, create the two point zero
Speaker 2: where lots of people have access to this and it's
Speaker 2: widely available and in a couple of years Intel alone
Speaker 2: we want to get one hundred million accelerators for AI
Speaker 2: into the hands of people.
Speaker 1: And do you have any examples of at this early
Speaker 1: stage of actually pushing the boundaries of using this sort
Speaker 1: of power in a local PC to do some really
Speaker 1: interesting work.
Speaker 2: One for an enterprise that we've been tinkering with is
Speaker 2: a technology called rag rag, and it's the idea where
Speaker 2: you have a language model running offline on the user's PC.
Speaker 2: But this RAG component can scan your documents, your corporate information,
Speaker 2: and then specialize the LM to be for you, your work,
Speaker 2: your knowledge. So just as an example, we scanned one
Speaker 2: state's DMV manual, which hundreds of pages long for the
Speaker 2: Department of Motor Vehicle manual and now you can ask
Speaker 2: very specific procedural and legal questions about the subject of
Speaker 2: that manual and it'll spit back highly accurate answers for you.
Speaker 2: And if you extend out out words, protecting and promoting
Speaker 2: institutional knowledge is hard. You might have that employ that's
Speaker 2: been there for twenty years and has all that institutional
Speaker 2: knowledge in their head and if they leave, it goes
Speaker 2: with them. But a RAG model could synthesize that knowledge
Speaker 2: for people, so a new employee could just ask a
Speaker 2: question in a text box and get an accurate answer
Speaker 2: about what that company's working on or what this feature does.
Speaker 2: And that's just huge for business.
Speaker 1: Yeah, you know, I'm from a small business sort of background.
Speaker 1: My dad has a small business. And the fact that
Speaker 1: you can bring this power to the small and micro
Speaker 1: businesses as well without having to pay these cloud based
Speaker 1: prices and also in conjunction with using some of the
Speaker 1: open source type software that I know Intel is very
Speaker 1: supportive of. I'm just really excited to see the little guys,
Speaker 1: you know, be able to compete with some of the
Speaker 1: technology that the big boys have.
Speaker 2: Absolutely, AI is, at the end of the day, a
Speaker 2: force multiplier for a person. Right like at the root,
Speaker 2: AI is designed to save time writing meeting minutes or
Speaker 2: email summaries or drafting in outline. These are all just
Speaker 2: like time consuming tasks for people, and they don't require
Speaker 2: skill per se, but it's it's time consuming, and so
Speaker 2: being able to offload that to a digital assistant that
Speaker 2: can just sort of ninety percent or ninety five percent
Speaker 2: do that for you allows you to refocus your efforts
Speaker 2: back to something else that is more productive and more
Speaker 2: worthy of your time. And especially for a small business,
Speaker 2: administrative overhead, bureaucratic overhead is hard, it's time consuming. I
Speaker 2: myself own a single member LLC and administrative stuff takes
Speaker 2: up a ton of my time. Like I'd love to
Speaker 2: outsource that to AI.
Speaker 1: That's right. In terms of Intel's history with past, you know,
Speaker 1: technological revolutions, I'm reminded of Intel's initiative to get Wi
Speaker 1: Fi into every your laptop and that was code named Centrino,
Speaker 1: and it's interesting to hear that again. Intel are trying
Speaker 1: to push new technology so that it's ubiquitous, and that's
Speaker 1: exactly what they're doing with these aipcs. And in ten
Speaker 1: twenty years time, I think that aipowered PCs will be
Speaker 1: so ubiquitous that we won't think anything of it. I'd
Speaker 1: like Robert your thoughts on that and more insights into
Speaker 1: the way Intel is evolving their strategy and Intel's role
Speaker 1: in this. Yeah.
Speaker 2: Actually, Centrino is a really nice analogy because that's sort
Speaker 2: of what we're trying to do with these AI accelerators.
Speaker 2: It's not a huge tweak to the configuration of a
Speaker 2: system design because most of the work happens inside the CPU,
Speaker 2: and there are very few external requirements that would change
Speaker 2: a system designed to make this possible. Right, So it's
Speaker 2: a system vendor could theoretically update last year's chassis to
Speaker 2: have a new CPU and that would confer the benefits
Speaker 2: from Core Ultra and an AI workloads and Centrino not
Speaker 2: much different, right. You're adding a Wi Fi chip and
Speaker 2: an antenna to the system.
Speaker 1: Yes, But for those.
Speaker 2: Of you who weren't around during the Centrino days, it
Speaker 2: used to be very common that a laptop would not
Speaker 2: have Wi Fi, which seems sort of unimaginable now, but
Speaker 2: that's because Intel made this massive effort to make it
Speaker 2: common and we all sort of take it for granted now.
Speaker 2: Another analogous moment for me is the addition of graphics
Speaker 2: in the processor. I was a hardware reviewer when that
Speaker 2: started happening, and I remember the conversations back then like
Speaker 2: why are we doing this? You can't even play a
Speaker 2: game on it, what's the point? This is just going
Speaker 2: to make CPUs more expensive, YadA, YadA, YadA. Today web
Speaker 2: pages are rendered by your graphics card orphics accelerator in
Speaker 2: the processor, your browser uses it. It's everywhere. So both of
Speaker 2: those are very foundational examples for me, because I see
Speaker 2: AI as being quite analogous in both respects. But I
Speaker 2: think history will bear out that this was a pivotal
Speaker 2: moment and AI will be very, very widespread, just like
Speaker 2: graphics and just like Wi Fi.
Speaker 1: Coming up next on Technically Speaking and Intel podcast.
Speaker 2: Being familiar with how prompts work, it's going to be
Speaker 2: a key business skill, and I think there'll be a
Speaker 2: real advantage for employees who know how to engineer a
Speaker 2: good prompt to get a great result quickly.
Speaker 1: We'll be right back after a brief message from our
Speaker 1: partners that Intel welcome back to Technically Speaking. I'm here
Speaker 1: now with Robert Hallett getting back to I guess more
Speaker 1: on the business side of things. Maybe if you can
Speaker 1: talk a little bit about our friends in the IT
Speaker 1: department having to manage the security and all these sort
Speaker 1: of things. Yeah, what's some of the benefits they could
Speaker 1: look forward to in terms of managing these types of
Speaker 1: aipcs in their enterprise.
Speaker 2: I think it'll make every ITDM happy that at the
Speaker 2: end of the day, these AI applications are nothing more
Speaker 2: than endpoint applications. You download them, you install them, and
Speaker 2: they're from software vendors, and I'm sure they'll be breakout
Speaker 2: ISVs that come under the scene as a result of
Speaker 2: this AI transformation, but largely speaking, trusted vendors doing new work.
Speaker 2: And because it's entirely offline, your user has custody of
Speaker 2: the data and the information, which is no different from
Speaker 2: any other application today. So it's not like this transformation
Speaker 2: comes part and parcel with like a radical transformation and
Speaker 2: endpoint management, which would make it way harder. From a
Speaker 2: security point of view, AI has some very interesting benefits
Speaker 2: for security models. So now this is the ITDM point
Speaker 2: of view. We recently with Dell and CrowdStrike, and if
Speaker 2: people don't know what CrowdStrike is, it's, amongst many things,
Speaker 2: an endpoint security solution which is specifically designed to help
Speaker 2: prevent threats that don't attack files. Many of the tax
Speaker 2: that go into a system now aren't like a virus
Speaker 2: that infects a file, it's actually resident in memory. They're
Speaker 2: fileless attacks, and these are way harder to detect and prevent.
Speaker 2: So CrowdStrike uses convolutional neural networks, which is a simpler
Speaker 2: form of neural network or AI, to monitor the real
Speaker 2: time conditions of the system and see if something unusual
Speaker 2: is happening. And this experiment moves these convolutional neural network
Speaker 2: models or CNNs to that neural processing unit or the
Speaker 2: NPU in coraltrum. It did a couple things when we
Speaker 2: did that. First, it gave the processor cores twenty percent
Speaker 2: performance back. The second thing it did it made the
Speaker 2: security model slightly smaller, so it had a smaller memory
Speaker 2: footprint now so user gets some RAM back. And it
Speaker 2: also improved the accuracy of the model because they can
Speaker 2: make the model computationally bigger, right because it has its
Speaker 2: own dedicated accelerator now to run on. And so security
Speaker 2: got better and crowdstreg is a very popular solution, and
Speaker 2: there are other solutions in the pipe for phishing detection,
Speaker 2: which is notoriously hard pattern matching problem. And that's just
Speaker 2: two examples of the way security can be enhanced by
Speaker 2: offloading to an AI specific accelerator.
Speaker 1: Yeah, and we talked a little bit about you know,
Speaker 1: businesses and enterprises adopting these new aipcs. Is anything special
Speaker 1: that IT departments and organizations need to do to prepare
Speaker 1: for this.
Speaker 2: I'll say there's probably three things that an ITDM would
Speaker 2: want to think about if they intend to use large
Speaker 2: language models or just generative AI. Those workloads are pretty
Speaker 2: sensitive to memory bandwidth, so you wouldn't normally think about
Speaker 2: memory bandwidth in a system purchase, but making sure that
Speaker 2: it has two memory sticks over one. For example, right,
Speaker 2: if you want to get sixteen gigs a RAM, make
Speaker 2: sure that's two by eight instead of one by sixteen,
Speaker 2: Just as an example, that will dramatically improve the performance
Speaker 2: of the LLM, because these language models are fundamentally limited
Speaker 2: or enhanced by the performance of the memory subsystem. Outside
Speaker 2: of that, for other forms of AI in the year ahead,
Speaker 2: there is a calculation called TOPS or terra operations per second,
Speaker 2: which is sort of a ballpark for how much AI
Speaker 2: performance a device can give you. Software makes or breaks
Speaker 2: the acquisition of that TOPS figure. So I can give
Speaker 2: you a billion TOPS, and if I had a very
Speaker 2: poor software stack under that you would never see the
Speaker 2: billion tops. So there's a new level of knowledge the
Speaker 2: ITDM needs to develop on sort of software stack robustness
Speaker 2: underneath that rating. Okay, so you have to be familiar
Speaker 2: with what Intel's doing in the software space, what its
Speaker 2: competitors are doing in the software space to really understand
Speaker 2: whether or not you're going to get a good experience
Speaker 2: out of the device you're purchasing. So that's number two,
Speaker 2: and number three I think would be to say that
Speaker 2: itdms will probably be faced with a lot of advocacy
Speaker 2: for the NPU as an AIX, but it's important to
Speaker 2: understand that the software industry broadly also wants to use
Speaker 2: GPU and CPU as well. So if you make an
Speaker 2: upgrade decision that is all in on NPU performance and
Speaker 2: you didn't check on the GPU or the CPU, you
Speaker 2: may be out in the cold on performance or power
Speaker 2: efficiency for these Frankly a large number of workloads that
Speaker 2: use graphics and CPU for AI acceleration. Those are the
Speaker 2: three things that I would say are new or different
Speaker 2: in this era, But overall it is something that you
Speaker 2: can integrate into your upgrade cycle piecemeal. YEP, Newer devices
Speaker 2: will be faster. I mean, they always are, but it's
Speaker 2: not like you're missing out on a new feature, right,
Speaker 2: We're going to make them faster, But the features are
Speaker 2: delivered by the software, are not the hardware. You can
Speaker 2: kind of get in at any point and get the
Speaker 2: goodness of AI, which is pretty cool as well.
Speaker 1: Yeah, yeah, And are you helping the software vendors, I
Speaker 1: guess compile their code so that it will help them
Speaker 1: really utilize that hardware underneath.
Speaker 2: Yeah, that's the secret of AI. And I'll start with
Speaker 2: an analogy of PC gaming, which I think is a
Speaker 2: lot more familiar to people. So at the bottom, you
Speaker 2: have a piece of hardware, a graphics card, and they
Speaker 2: tell you, you know, it's certain terra flops or gigaflops of performance,
Speaker 2: but everybody knows that really depends on how well optimized
Speaker 2: the game engine is, how well optimized the game you're
Speaker 2: running is, how good the graphics drivers are. AI is
Speaker 2: no different. AI accelerators are actually exposed in direct X
Speaker 2: in Windows as a GPU without display outputs, so the
Speaker 2: system sees them as essentially graphics cards, and instead of
Speaker 2: game engines you have AI models will have features and
Speaker 2: apps just like games you even have run times or
Speaker 2: an environment where the code is running in there's a
Speaker 2: DirectX run time for graphics, there is equivalent run times
Speaker 2: for AI. So in many respects, the AI software stack
Speaker 2: looks and works a lot like the gaming software stack.
Speaker 2: And so if people think about all the times that
Speaker 2: a GPU that's supposed to be faster on paper didn't
Speaker 2: live up to that number because of one software reason
Speaker 2: or another, that is a possible reality for AI as well.
Speaker 2: And that's why it's so important to make your decision
Speaker 2: not just on tops or what the accelerator is, but
Speaker 2: on the robustness of the software underneath, because that's where
Speaker 2: it really happens.
Speaker 1: Regular listeners to the show Mike remember back in season one,
Speaker 1: we divided a whole episode on the skills that workers
Speaker 1: will need in order to take advantage of the kinds
Speaker 1: of AI tools we discussed today. AI technology can only
Speaker 1: expand our expectations of what's possible if we understand the
Speaker 1: most effective ways to use it. So I asked Robert
Speaker 1: for his thoughts and what workers should focus on to
Speaker 1: take advantage of this new wave of technology, and he
Speaker 1: began his answer with something just about everyone does on
Speaker 1: the Internet, every day.
Speaker 2: Ooh, that's a good one. I actually want to start
Speaker 2: briefly at web searching. You know, web searching, a good,
Speaker 2: well composed query is a learned skill. We have all
Speaker 2: encountered people that haven't quite learned that skill, and they
Speaker 2: get bad search results and they're frustrated with a search engine.
Speaker 2: And not a lot of people teach that skill because
Speaker 2: it's its own language of sorts. You know, you want
Speaker 2: to freeze things to a search box differently than how
Speaker 2: I would ask it out loud. So that takes me
Speaker 2: to AI, where you are still engineering a search of sorts.
Speaker 2: It's called a prompt now, but it's a search and
Speaker 2: the skill is in like, how do you craft that
Speaker 2: prompt to get the result that you're looking for? You
Speaker 2: have to be somewhat specific, and you have to understand
Speaker 2: the limitations of the AI model you're working with. So
Speaker 2: let's take an image creation model. Some of them only
Speaker 2: support what's called positive prompts. You have to describe the
Speaker 2: things that you want and it will kind of omit
Speaker 2: anything else that you're not asking for explicitly. Others support
Speaker 2: negative prompts, and if you're working with a positive only
Speaker 2: image service, you could easily have users saying I want this,
Speaker 2: this and this, but not that, that and that. Yeah,
Speaker 2: the not operator the AI engine's not going to understand it,
Speaker 2: so it's going to give you the things in the
Speaker 2: not category. In the picture.
Speaker 1: Yeah, it's like saying I want a picture of a zoo,
Speaker 1: but no elephants.
Speaker 2: Right, and it doesn't nderstand the word no, So in.
Speaker 1: A positive one, it'll just have elephants.
Speaker 2: Yeah, you get all elephants exactly right. So understanding this
Speaker 2: is a real skill. Understanding like what the AI model
Speaker 2: or by distant extension, what the game engine will let
Speaker 2: you do is really important, and how to be specific
Speaker 2: and understanding how to provide follow up commentary to tweak
Speaker 2: the result to get what you're looking for.
Speaker 1: This is a.
Speaker 2: Whole category of skill that is not taught, not widely
Speaker 2: known in business or even amongst users because this is
Speaker 2: so new. But being familiar with how prompts work is
Speaker 2: going to be a key business skill, and I think
Speaker 2: there'll be a real advantage for employees who know how
Speaker 2: to engineer a good prompt to get a great result quickly.
Speaker 1: So going to take a bit of a step back
Speaker 1: and look far in the horizon in the sort of
Speaker 1: the three to five seven year time range. What's Intel's
Speaker 1: plan for this journey into AI.
Speaker 2: I think at top level, i'd want to acknowledge that
Speaker 2: I understand the skepticism or the I don't understand of AI.
Speaker 2: But where we're going is like a complete industry transformation.
Speaker 2: Intel believes by twenty twenty seven or twenty twenty eight
Speaker 2: that eighty percent of all the computer sold will have
Speaker 2: AI accelerators inside. And by the end of this year,
Speaker 2: we want at least one hundred different software developers partnered
Speaker 2: with Intel. We want to deliver three hundred or so
Speaker 2: different AI features into the marketplace through all of those companies,
Speaker 2: help them optimize it, deliver it, market it. We want
Speaker 2: to bring one hundred million accelerators into the space by
Speaker 2: the end of twenty twenty five, and so just between
Speaker 2: now and twenty twenty seven twenty eight, we're talking about
Speaker 2: zero to eighty percent of the market in under four years,
Speaker 2: which is an extraordinary velocity. And as of right now,
Speaker 2: Intel has the largest number of accelerators, the most number
Speaker 2: of applications in the market, and sort of below the scenes,
Speaker 2: all the enabling tools and softwares and frameworks, we also
Speaker 2: have the most of those as well, So we want
Speaker 2: to be the scale provider for AI, you know, the
Speaker 2: biggest install based and we want to help to succeed
Speaker 2: because that's what software vendors are asking us to do.
Speaker 2: So that's the longest short of it.
Speaker 1: That's great. I think I'll leave it there. Thanks Robert.
Speaker 1: Thanks my deepest thanks to Robert Hallock for his invaluable
Speaker 1: contributions to today's episode of Technically Speaking. Robert's passion and
Speaker 1: enthusiasm has convinced me that we are on the brink
Speaker 1: of a revolutionary technology that could transform humanity. We've all
Speaker 1: used PCs both in our personal and professional lives, but
Speaker 1: the leap to aipowered processes represents a once in a
Speaker 1: generation advancement. Imagine having your own personal AIS system that
Speaker 1: continually adapts to your needs and priorities. Admittedly, this is
Speaker 1: a prospect I'm still trying to fully grasp, but I
Speaker 1: especially appreciate that this technology will operate locally on my PC,
Speaker 1: keeping my data private and secure. I'm also particularly interested
Speaker 1: in how technology can empower the underdog to do amazing things.
Speaker 1: The garage based tech entrepreneur building the next world beating
Speaker 1: app the corner cafe owner getting that extra hour per
Speaker 1: day to spend with their family, and the first year
Speaker 1: design student creating beautiful and innovative art. With AI enhanced
Speaker 1: pieces readily accessible, I believe we can greatly advance human prosperity.
Speaker 1: The future looks bright. Ground. Next episode will continue to
Speaker 1: unpact the involvement of AI to create a more accessible
Speaker 1: and livable city for everyone. Join us again on Tuesday,
Speaker 1: April twenty third for another enlightening discussion here on Technically
Speaker 1: Speaking and Intel podcast. Technically Speaking was produced by Ruby
Speaker 1: Studio from iHeartRadio in partnership with Intel and hosted by
Speaker 1: me Graham Class. Our executive producer is Molly Sosher, our
Speaker 1: EP of Post Production is James Foster, and our supervising
Speaker 1: producer is Nikia Swinton. This episode was edited by Sierra
Speaker 1: Spreen and was written by Molly Sosher and Nick Ferschall.