Qualcomm

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F500-2023-098-Qualcomm

The producer of processors, modems, platforms, and other connected components faced macroeconomic challenges in the semiconductor industry. It began 2023 with a hiring freeze and other cost-cutting efforts in place. 
<ul> 
 	<li>The company’s $44 billion in revenue in 2022 was a $32% increase from 2021. It rose nine places on the Fortune 500 this year.</li> 
 	<li>CEO Cristiano Amon has expressed confidence in Qualcomm’s ability to invest in areas that will deliver long-term value, such as A.I.</li> 
 	<li>In May 2023, Qualcomm announced plans to acquire Autotalks, a chipmaker that builds semiconductor and system-on-a-chip technology for vehicle safety. As part of the deal, Qualcomm plans to integrate Autotalks’ communication tech into its Snapdragon Digital Chassis portfolio, which features cloud-connected automotive platforms.</li> 
</ul>

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CTW.1122. Qualcomm

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F500-2020-126-Qualcomm

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A pioneer in wireless communications, Qualcomm makes semiconductors and software for use in vehicles, industrial technology, smartphones, laptops, and many other devices. The company uses a fabless semiconductor model, designing chips but outsourcing the manufacturing of many components. It also has a strategic investments and venture arm, which backs early-stage companies in industries like 5G, artificial intelligence, and the Internet of Things. Founded in 1985, Qualcomm developed the CDMA technology that now underpins commercial cell phone networks.&nbsp;

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Qualcomm (QCOM): Company Profile, Stock Price, News, Rankings

legacy

qualcomm

A pioneer in wireless communications, Qualcomm makes semiconductors and software for use in vehicles, industrial technology, smartphones, laptops, and many other devices. The company uses a fabless semiconductor model, designing chips but outsourcing the manufacturing of many components. It also has a strategic investments and venture arm, which backs early-stage companies in industries like 5G, artificial intelligence, and the Internet of Things. Founded in 1985, Qualcomm developed the CDMA technology that now underpins commercial cell phone networks. 

Qualcomm (QCOM): Company Profile, Stock Price, News, Rankings | Fortune

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The future of autonomous vehicles is here. From Qualcomm’s cutting-edge chips powering next-gen perception to WeRide’s real-world deployments across urban Asia, AI is transforming not just how we drive, but how mobility is imagined, regulated, and scaled. We unpack the breakthroughs and bottlenecks in hardware, data, safety, and trust that will define whether robotaxis become everyday reality or remain stuck in the slow lane.

The Reality Of Robotaxis

Conferences

Autonomous vehicles have promised a future of convenience, but trust, policy, and performance are still major factors in how mobility is regulated and scaled. Here to discuss the reality of RoboTaxis, please welcome We Ride General Manager of Singapore, Dr. Kerry Xu, Qualcomm Technologies vice president and Q Qualcomm president of Taiwan, Southeast Asia, Australia, and New Zealand, ST Liu, and Fortune Andrew Neska. How fun was that session, right? Um, OK, we're here to talk about autonomous vehicles, robotaxis, if I may, um, so let's level set as I often like to do, um, Carrie, I'll start with you. Where are robotaxis specifically in the region? Where are we seeing them in city streets and where have they not reached yet? OK, um. Uh, I think, uh, a lot of people are already familiar with, uh, like what's happening in the US. So I focused on Asia and just, um, uh, actually I think China is uh really seeing a large scale the robo taxi deployment, uh, in Beijing, Guangzhou, uh, like we ride with in this year at February, we just launched our robo taxi fire charging commercial operations in Beijing. And we now have like more than 50 of the robotaxis providing service and in Guangzhou, same thing, we already launched our commercial charging robo taxi service and most of you may also know Baidu, I think they launched the robotaxi in Wuhan with like 100 of the vehicles. And the second, uh, hot field, uh, in Asia, I think it's Middle East, um, a lot of the you maybe all the red news we are teaming up with Uber to launch our robotaxi actually in Abu Dhabi, Dubai, and in fact we already started the driverless robo taxi test in UAE right now. Wow, that's quite, I mean just thinking about all those cities, they are dramatically different. Urban landscapes and I can't even imagine the technological challenges, but that's precisely what we're going to talk about today. Um, ST, the tech is advancing rapidly. And yet there is so much we can't do. So, again, tell us what's possible, tell us what's on the cusp of changing as it pertains to autonomy. Sure. Uh, so our company comes from uh Years of innovations and creativity in coming up with a computing efficient, very efficient computing. Uh, devices and solutions and from there also with very efficient power consumption, uh, intelligence on the edge and all that. And as the automotive progresses, the need for intelligent processing, very, very high power processing at a very efficient power consumption has become more and more important. Now using that legacy of knowledge and experience over the 40. Years or so, um, we have put together a solution, what we call like Snapdragon chassis, which, which is a solution combining all the different platforms that you would need to make a AD you know automotive driving car, autonomous driving car or AAS or anything that you need to make driving or not to drive such car very comfortable infotainment, telematics and all that. Our role is to provide such a solution so that our customers can put them into products relatively faster and then as the need for autonomous driving cars become more and more important, especially in Southeast Asia or Asia where the driving condition, the weather condition is very, very different, that solution is important because, you know, ouan will bring. And encompassing that solutions, these knowledge and then on top of that allow our OEM to expand onto it, you, you know, especially with AI become available and all that, yeah, so obviously there's a lot of technology that goes into in an autonomous vehicle, right there's the power train, whether it's electric or otherwise they're almost uniformly electric, um, there's. The sensors themselves, which are relatively straightforward affair. One thing I've noticed is, as you said, you know, the kind of ever evolving intelligence of it all perfect for this conference here, you know, it has been clear in Qualcomm's earnings reports, for example, that the company is very bullish on. Artificial intelligence moving to the edge, right, moving to these out of the data centers that we love to talk about at AI conferences like this and into these other places like cars and whatever where the power, as you said, the power and the efficiency and the size, it's all a different consideration set. How is that changing the autonomous market? Well, A lot of this decision making, the decision making process and the speed of this decision making needs to happen locally. You know, latency is extremely important. So intelligence at the at the edge is a very pivotal feature. Uh it's, it's, you know, you, you imagine driving down the road, you know, you have to make decisions very, very quickly instead of going out to the car and come back in again. That's a very critical and a very typical example of intelligence at the edge. That's that's where our forte is, you know, AI at the edge, and that is, uh, that is bringing a lot of benefits from multiple perspectives, latencies one. Um, it also helped to bolster, uh, what do you call it, the protection of information data and so on and so forth. And so that's how it is changing and, and, and with AI coming on board, the, the process of learning, the, the deductions, decision making is becoming more automated and it allows a lot of developers to come up with applications that you can actually use inside the car. So autonomous driving car is not just a car that by itself, but there are lots of things you can do inside the car which bolster the drivers or the passengers' confidence sitting in the car and all that. Yeah, and to do some of that compute um locally is candidly necessary. I live in Los Angeles. There is a mountain range that cuts through the center of LA, uh, in many of the, in many places in that mountain range, there is no cellular service. I could not imagine the car just deciding, ah, we're gonna just give up, you know, mid-drive, um. I want to come back to you, Carrie, um, what are you learning as your company deploys in these different markets? As I said earlier, very different urban landscapes, very different challenges, we mentioned weather earlier, there's the landscape itself, um. There's obviously, uh, uh, uh, you know, kind of, uh, the expectations of the drivers in a given market as well. What are you learning as your company expands? Yeah, so so far we have been deployed our erasing more than 30 cities across 10 countries. We do learn a lot of uh lessons. But I want to emphasize the three lessons we learned. The first is that we, I think we have to work closely with the government and the regulators. We monitor the regulations to make sure we actually conform to the local traffic laws and the regulations. And uh in this part we actually uh hosted many delegations from different countries um like in the a few weeks ago, I think Singapore's acting Minister of Transport Jeffy, he actually led a delegate visited our HQ in Guangzhou and At our at there, he actually announced in the next 5 years, Singapore is going to see many, many AVs in the uh on the streets and they are actually planning to integrate the Aways into the uh public transport system. So I think the government support. You know, working closely with the government is one of the things when you deploy in a different country, when no matter which country you go, the first thing is that you should work closely with the government on regulation. It's an age old technology question, but we've articulated all of the technological, technical challenges of of autonomous driving, but Given what you've just said, in your opinion, do you think the greater hurdle to full deployment are the humans or the tech? Um, from my point of view, I think technology is actually mature enough. Uh, we have seen the like operations of way more in San Francisco, Los Angeles, and like uh we ride, we are operating driverless, no safety operator on board in Beijing, Guangzhou, you know, Abu Dhabi, Dubai, so technology wise. I think it's clearly mature enough to scale, but we have to work with the government on the regulation part, public acceptance, data transparency, all this. I think it takes time to for the community to completely accept the AV as like a part of their normal life. I think it will be. There, but it's just step by step taking some time to achieve it. Everybody remembers their first autonomous ride once you do it, you know, um, SD, I'm gonna, uh, come back to you. You work with many different companies and many different geographies. Uh, same question. Do you feel like the technology is what's, uh, preventing full deployment of these things, or is it in fact the humans, the policy, the. You know, I'm gonna answer this way, we always advocate that um uh that we, we always should have a cross-indust industry benchmark transparency in the training data and uh make sure that we are compliant. To safety, to, uh, to policies, etc. etc. I think those are the things that are built into our architecture. And by doing so, uh, there's you, you build trust in people, you have to build trust in passengers, in policymakers, in general public, etc. etc. So that's part of the importance of building that into the ecosystem and it's not just a technology thing, it is a private sector having to work with the government and uh you know, work with the cities and education and so and so forth, and it comes from both the Tech technological solution for us, it comes in the form of the chip set, the SOCs and the and the software stack that our OEM partners can, you know, choose and, and, and, and, and build into their cars. But at the same time, the build the confidence, I earlier mentioned the things that you can, you can do in the car, significant increase. So take care of the safety, make sure that it is transparent and then you can enjoy yourself while you are in the car, can build up that kind of Acceptability to this whole thing, but I, you know, safety is extremely important, so we have to benchmark, we have to be transparent and so on and so forth. Yeah, yeah, and I'm hearing the word trust quite a bit at this event, so I, I understand. I have one more question for you, but I just want to tell the audience, hey, we're gonna come to you for questions next, so, uh, get ready, um, all right, so. I see you, your organization works with virtually every automaker everywhere and you're of course, you know, ultimately an American company, a global company, um, and you're seeing uh massive differences across those markets in some regard. um I'm curious how you're, you know, you're supplying these, uh, automakers with this technology and working with them to integrate it in the way that it happens. Technology has been incredibly disruptive to the automotive industry. Just speaking from the US perspective, um, Detroit has built a car a certain way for many, many, many years, and it was very, very good at that. And then Silicon Valley shattered it, just totally reversed it, you know what I'm talking about, just completely just rethought the way you would put together, uh, honestly an end point is the way you might think of it in technology, right? ST as this stuff sweeps through, are you finding with your automaker partners a difference in the way they build their vehicles, the way they incorporate this technology, and by the way, is that different in, say, China or Southeast Asia versus the US? I think the big shift to the automotive is one term that you have heard a lot about is like the software defined car. I think universally I can, I can say that all the automakers. Uh, coming to the realization that this is one area that uh everybody needs to get master in. In the past, you look at power trend, you look at the car design, you look at, you know, the things that uh automotive auto automakers will look at. Now your conversation and your considerations uh Kind of get changed a little bit and say, what is your experience when you get into the car and that's where a lot of the differentiations will be brought up as well. And so if I go into the car and then I can conduct my meetings, I can use AI to ask where I am, book my restaurants and all that, just like an extension of my office or my home. I think they will make wonders and do wonders for a particular OEM or any OEM that will, you know, that's one of the ways now to differentiate the vehicles and then on top of that, no one market, no one country, no one geography is the same. So we must enable the continuous learning and with AI being available then now you do the continuous. Learning, use the smart way to factor all these things into the solutions. I think that is the way I would answer these questions. I think the opportunity is there for everyone and I can see very clearly the realization that a software defined car is is is is that is very much with us today, so I just want to correct one thing that you said. You're not going to be going into a self-driving car and booking your meetings. We have AI agents for that. Yeah, yeah, yeah, yeah, we have AI AI that's right. We solve that right here at this event. OK, let's go to the audience for questions about autonomous vehicles and robo taxis taxis. Yes, we have one right here. We just wait for the mic to reach you, and when it does, your name and who you're with. Thank you. Thanks. Hi, uh, my name is Firoz. I'm with Syngenta. Uh, my question is, how important is the infrastructure outside the car? Quality of roads, lane markings, curb, behavior of other drivers on the road, uh, for, for it to be safe. Gentlemen, which one do you want to take that, OK? Uh, yeah, I think that's a very good question. So I think earlier Andrew mentioned is the vehicle itself must be intelligent because, uh, we put the like brains in the vehicle, but Infrastructure is also important. For example, uh, network connection is like 5G, uh, a lot of this, uh, autonomous vehicles right now still have like remote monitoring in case the vehicle stuck or in certain. Conditions, the people who are monitoring remotely can still give a hint or help the vehicle. Also, in Singapore, we are already working with the government on like how to design the pickup job point to be friendly to Aways. And how you uh monitor all the different uh pickup points to uh be able to uh see where where the vehicles, all these kinds of uh infrastructure, even the how to educate the people to better interact with the AVs. I think this is something a lot of government. Actually already start looking at and I have worked with some scholars whose study topic is how to make our infrastructure friendly to uh driverless vehicles. I think this is something is going to be happening in the next uh maybe a few years. Yeah. Very good. All right. Another question. Yes, we have one over here, right here. Just wait a moment again, your name and who you're with. My name is Selvin. I'm with Alliance Asia. Just have a question. Do you think that LIDA technologies is necessary to make autonomous vehicles more safe? Is light are necessary to make it. Yeah, um, I think, uh, from my point of view, I think, uh, LEA is very necessary. Uh, in fact, is, uh, my, my, my idea is the reason some companies don't install the ladder is probably some a few years ago, ladder is still pretty expensive, uh, sensors to install which increases the cost for the vehicles. However, if you look at the edge case, so the vehicles are different from like uh some other like your cellphone. Uh, tolerance is different for vehicles. We are not looking at 99% safety. We're probably looking at 99.9999 safety. We don't, we can't afford the accident due to age case. You cannot catch it. So cameras has uh vision based has some limitations like in the very low light environment like midnight, no light, but lighter. You know, it's uh it's emitted the light, so it doesn't rely on any lights and also the lighter can accurately detect the shape of the obstacles which also helps the algorithm to more advance the control and planning algorithm to bypass or to avoid the obstacles. So with the lighter price is cheaper and cheaper, I think. Uh, like us, we are doing is we do the fusion between the camera and the ladder. So all this allows us to detect all the obstacles in like 200 m, 300 degrees, very accurately to do the planning and controlling. So yeah, that's how I believe the ladar is still very necessary. If I just add to the conversation about the infrastructure around the vehicles. I think one of the key things going into the future is. So the cellular V to X, cellular equal to any everything. And then if you think about the future of autonomous driving, uh, CV to X is something that we in Qualcomm, uh, continue to, uh, to invest a lot in. And to your point of light, when you have CV to X, obviously you can plan ahead on many, many things and and so and so forth. So I think, uh, to the questions about the road marks and things, CV. To ask the road maps and all these, just point to the fact that we need to work very closely with infrastructure partners as well as ecosystem partners and all that. Yeah, in terms of the infrastructure, I have a very good example. I keep telling my friends is that right now in the car park in certain areas we install mirror for the help drivers to look at the blind spot, but in the future for avis actually we actually can install. Those B2X equipment to help the AVs to look at the black spots. I think these are all the future probably development for the infrastructure to be AV friendly. But it is unquestionable that our our urban landscapes are going to change dramatically because of of the rise of robotaxis and autonomous vehicles. So gentlemen, thank you so much for an enlightening conversation, a round of applause please for our panelists. Thank you. Thank you.

Vinay AWASTHI, Managing Director, Greater Asia, HP Dahai LI, CEO, ModelBest  ST LIEW, Vice President, Qualcomm Moderator: Jeremy KAHN, FORTUNE

Brainstorm AI Singapore 2024: Keeping Up With AI’s Computing Demands

I think it's going to be interesting panel because all three of our panelists are in some way involved in the attempt to kind of bring A I to the edge as one potential solution for the hardware dilemma that A I poses. I'm actually going to start with, with ST I'm curious what strategies you guys have in place at Qualcomm around this. Um And, and how important it is for you to sort of integrate A I at the edge into to the solutions that that Qualcomm is working on. Well, thank you for the good questions and it's really nice to be here. Um We believe that to harness the full potential of A I and A I has been adopted in all industries in almost all walks of life increasingly so, um it needs to be intelligence everywhere. Um by having intelligence everywhere, we use the terminology of hybrid A I. So you will share the A I workloads whether at the edge or at a cloud and as such, um the full potential of A I can be gathered. And also there's a lot of uh there are all the good benefits that come from it because for instance A I that have been performed at the edge can show a lot of personal security and confidentiality, not to mention uh you know, it free up the traffic going up the cloud all the time. And on top of that, there's a huge economy to this as well because edge devices, edge devices are many, many things, cell phones, smart cars, P CS these days and thousands and millions of IOD devices. And it helps to make sure that that distributed way of processing A I will also tailor for the specific A I where it is needed. So, so we are, we are we are we are committed to that. And that's why one of the things we have done recently is we actually launched our Snapdragon X elite chip set platform for the PC. We believe 2024 is when the PC industry, the PC is being reborn A PC is a pivotal portion of the entire um edge A I and not to mention of course, smartphones and XRVR and so on and so forth. So we believe this is the right thing and the important thing to go. And there is a big need for a lot of the consumers enterprises, small medium size companies as well to upgrade a lot of the devices to be able to do these uh A is at the edge. So I think business opportunities there is also helped to, you know, um involve more people into the uh digitized economy of the future. So that's what we are well suited, you know, Qualcomm is well suited to do that. Excellent. Um Do can you tell us a little bit about model best and and about mini C PM, which is this model you develop and how this kind of represents a step in the direction of these kinds of models that can actually run on on edge devices? OK. Thanks Jeremy. Uh uh I'm very happy to be here to representing uh model best. It is a premier A I start up in Beijing model best is all about creating uh the top level big model and we really value efficiency. Uh We believe that efficient model means better performance, uh smaller uh size and low less cost. But with the same capabilities. Yeah. Um and uh even some folks in the media class Chinese Mistral because we uh because the automated pursuit with high efficiency is the thing we have in only in common. Um uh instead of just, just comparing uh the parameters and the performance, we focus on the knowledge density of the big model. Uh uh That's uh first thing we, we very, very uh very focused on. Yeah. Um uh OK. The star product, our star product is a mini C PM. It is a bunch of uh uh super light uh models that you can run on your phone. Uh um But they pack a punch with bigger ones such as GT 3.5 GT four V or even so future, maybe GT four. these models have been popular all over the world in the past half years. Yeah. Yeah. And you said we were speaking before and that he was saying you, you've released five different versions, updated version C PM just in the last six months is that we, we have, we, we move very fast. Uh We have released 45 versions of mini C PM in the past half years and uh we will uh release uh two versions uh additionally uh in fuel in risk. Oh, wow. So this is technology moving very fast and I think what you can do on devices rapidly catching up with workloads that before you could only run in a in a data center. Vinne, I want to turn to you and, and HP you're known for, for P CS, you're known for printers. Where do you see A I as you know, kind of fitting in with, with that kind of hardware? Well, I think first and foremost, great to be here and thank you for the opportunity. Um If you look at the A I story so far, I mean, I mean, it has mostly been data center and cloud centric right now. We do know that while it's a very powerful story, it is it has limitations in terms of how much it can reach um what we see is the proliferation of A I in a massive way over the next three or four years, every person in this room and everywhere in the um in the world who is using a personal computer right now will find that in order to use A I over the next 34 years, as many of the use cases emerge and perfect themselves, your current PC generations, they will be all obsolete. And for us to really take advantage of A I to every individual, every small and medium business, every large business government. Um you know, this is going to be a massive effort to provide um A IP CS because at the foundation of A I is the need for driving a very order of magnitude higher computation. So, you know, today's P CS, there is a term now we call trillions of operations for second tops, right? Today's P CS are anywhere from, you know, 6810 and we are not talking about 40 tops, 55 tops even, you know, higher than that. So our drive is to be able to provide um a huge portfolio of products P CS, printers, communication equipment that will allow people to be able to use A I models at the edge in whatever use cases they have. Now, it's interesting um when you arrive at Microsoft released this early version of an A I PC, um but it had to sort of record everything you did on the PC and some people were worried about that even though they, they were trying to sell it as more private than having to upload data to the cloud constantly. Um, there was still this concern about security. How are you thinking about this at HP? Well, I think security is going to be extremely important if you look at it, I mean, why is security important? Because as any computational power, any software, it's a tool, you can cut it both ways, right? Um If good guys are not going to use security and A I and the latest tools for the bad guys are going to do. So, so what we are doing is developing models that actually learn from the various attacks that continuously happen and those models will be A I trained. So we have a suite of security products from, you know, bios all the way up to the OS, we call it food security. And that is really based on A I. So it's just one example about how we have to be as responsible technology providers, provide technology that can keep people safe first and then allow them to do all the, you know, enjoy the benefits of the new technology that's going to come. Great. I'm going to ask for questions from the room in a minute. So please think of your questions and raise your hand and I'll get a mic to you. But first I want to, I want to talk here, you talked about how popular mini C PM has been. You also have this interesting case where some researchers at Stanford plagiarized the model or sort of built something that seemed very based on mini C PM. Can you talk a little about what happened in that case? And I'm curious whether this is sort of a risk in general as we move to smaller open source models that, you know, there will be this proliferation, it will be very hard to control what people do with them. OK. It's really interesting. Uh We released a new multimodal version of mini C PM uh that comparable to GP four V on the uh multimodal site uh in on May 20. Uh It's, it's very cool and the feedback from the open source community has been aware. But then on May 29 some people on the github homepage point out to us uh that there was a Stanford project has copied us. Uh And the team claimed to have built a better model with fewer parameters, which was 101% of GD four V, which is true. And the little training training cost only $500 which is not true. And the uh smaller as, as small as smart as the GP T four V which is true. Um After comparing uh we found these models are almost the same, the American one could even understand the Chinese Asian text, which was merely found in the public between data. So as you know, what happened next, uh The Stanford team apologized publicly after uh getting caught out of the plagiarism and they deleted the program uh the project on the github. Um This incident, uh We think it's not uh does not represent uh Stanford officially because it's just uh initiated by a few undergraduate students. Um And we forgive them and uh uh remind everyone to give credit to where it's due in the open source community. And the incident uh just make us uh appreciate the open source community even more. Uh We are thankful for the support to resolve the issue so quickly and fairly. Um And the model best has always been active in the global open source community. Um We have created created uh the open BMB community which has been the biggest open source community uh in China. Uh And we continue to work on projects, open source projects and uh cooperates with other communities to uh advance model technologies, right? I want to get for a question from the audience in a minute, but I'm going to ask a slightly awkward question, which is right now, there's a lot of concern about running these large A I workloads in data centers on the energy consumption. And a number of companies have Microsoft and Google in particular have said, you know, they've been thrown off their, their track to net zero because of the data center growth that they've had. And so some people are looking at on devices, oh, we can avoid all that. But if you, if you look carefully at where some of that additional carbon footprint has come from. It's not actually the energy used to run computations in the data center. It's all the energy that went into the production of the chips that are in the data center. And then there's also concern about, well, where are you running these models? And so if we're pushing everything onto device, my question for Qualcomm is what's happening in terms of the energy intensity of carbon intensity of the manufacture of Qualcomm chips. And then also if we're pushing everything out to consumers and the energy is being used, you know, when they charge their PC or their phone, isn't there an issue there? Because a lot of those consumers might not have access to renewable energy as their source of, of home power or office power? Well, I think first of all, I, I think that we shouldn't say that everything is pushed to the edge. There is a very close collaboration between the cloud and the edge. In fact, if you look at the entire eys I, I would say that uh you know, Qualcomm has always embraced new challenges and new technologies from three G, four G five G. And because of the Connectivity foundation that five G has brought, it has enabled us to provide the options of processing some of these A I at the edge, but you still need the cloud to do certain, you know, it's very efficient to do like massive learning development stuff on the clouds. But then there are minute little things that make sense to do it at the edge. I think the, the, the tricks and the intelligence and the innovations is what to do where. So you, you have things that will happen on the edge, on my little cell phone, my P CS in my car that are not even an option not to do it there because you need that latency, you need that short latency, like smart cars, you need to have that response immediately, right? So there are these things that are just absolutely make sense to do it there. And then as you grow it bigger, the cell get bigger and bigger, you go do the intelligence of the edge and then more edge and then to the clouds. So I think there is that intelligence that with the with the liberalization of the technology and know how and empowering so many smart people to do the right thing, there will be a good balancing point where I think the the industry and the uh the developers and companies will write the will find the right balance to where it is. That's why I think that the model that will really reap the benefits of A I is something called the hybrid A I which is going to be cloud devices and so on and so forth in a, in a network questions from the audience. I have a, have a question, if not, I've got more. So don't worry, I'm going to ask, I'm actually going to turn to Vin A also and ask on the sustainability question. Um It, I mean, you just said everybody is going to going to need a new PC uh in the next few years and maybe we're all going to need new phones too because we're going to need that. That latest elite X shift from Qualcomm. Doesn't that create also a sustainability issue? You're going to have all these people discarding these old devices, you're going to have an E waste issue. How is HP kind of looking at that? Because I know you have a, you have a firm commitment to sustainability, but does this throw you off of that? It does not actually if anything, we are accelerating of a commitment to our sustainability goals. And because you know, our sustainability uh framework is very broad, we, we look at how to make devices more efficient, which means today we are working on devices that can actually have truly 24 hours, 48 hour battery life, you make them more efficient, you know, because there is less loss of power when you are using those devices. That's one part of the framework. The second part of the framework is using sustainable materials as we build our devices. So every PC, every printer today now has recycled material, not just plastic but recycle um material that goes into the chips, recycled material that goes into the casing. We are also making our factories more efficient. So zero waste factories is our goal. Last but not the least. It's a very important part for us to also work as what we call renew supply chain, which means we can take devices back, renew them and put them back into the market, which is because there are a lot more people in this world today who need computational devices than those who actually have computational devices. So for us to work on making our products more sustainable and also our supply chains, more sustainable, making renewed products back into the market. I think that's our framework and we are very committed to that. And does A I have a role to play in doing that making your operations more efficient or find, you know, energy savings throughout your operations? Absolutely. I mean, if you look at today, we are talking about lights out factories, those factories will run because they are enabled through A I. We are talking about devices that will be optimized, whether it is, you know, eliminating the processes that are unnecessary and thereby increasing the battery life. It's all being driven by A. So it has a lot of use. Now, we are the same thing. Well, you know, the, the chip that we just introduced. And because of our heritage in in the very efficient computing and very very efficient power consumption, you are going to be able to do more things in shorter period of time. And imagine that you, you can use these A I models after you really allow the thousands and thousands of developers to think about how to solve some of the problems you just posed, you know, so I think A I with the right tools with the right liberalization and uh you know, open up of the uh of the tools will enable a lot of solutions to solve the problem. Some, some of them you just mentioned, that's fascinating. And while we have Dahi here, I want to ask it's slightly off topic. But one of the amazing things about mini C PM and some of the models you've worked on is their ability to, I guess translate these ancient Chinese calligraphy, these Tsinghua bamboo strips. I don't know if you can talk a little bit about that because that was also one of these tests for the model. And I think it's also one of the ways they caught that, as you said, the Stanford plagiarism because their model turned out could do this too. Which was very strange if it hadn't been using your training. But yeah, the the Tsinghua bamboo split is just uh Chinese Asian uh rep uh that uh uh t economy, uh a t friend uh just uh sent to Tsinghua. Uh So uh there is no uh digital information on the internet. We just take a photograph and use it as the uh fine tune um picture uh to build it into our model. So we very make sure that any other model should have not ability to identify the, the Tsinghua, the character on the Tsinghua. So that's the reason we uh quickly identify the model of the excellent. Well, we're out of time, but I want to thank uh ST and Dahi and Vin for being here. It's great to have you. Thank you so much. Thank you. Thank you.

Peggy Johnson, Chief Executive Officer, Agility Robotics Interviewer: Jason Del Rey, Fortune

Brainstorm Tech 2024: How To Build A Better ‘Bot

A lot of people want to work with robots. That's probably maybe a good start for you. Yes, I like that start. So you've been on this job an entire four months, is that right? Ok. You've had a long successful career, Qualcomm Microsoft magically most recently at CEO, I want to talk about that, but maybe that's over drinks or something. Why agility right now as your next CEO role, you know, it's a great question right now. Humanoid robots are having a moment for sure. And I've always been fascinated with robotics. My whole career, my first boss had done his phd in robotics years ago and they're finally having this moment. And when I looked across the field of players agility to me, stood far and ahead of the others. It had a working autonomous robot that was getting ready to take actual work in factories. And since that point, we actually have done that. So I think we'll get into some of that. But did you have a treat for us? I do if you'd allow me, I'd love you to let me welcome digit to the stage digit is the name of our robot. So I have one obvious question at first actually two. Why, why legs at all? Because a lot of robots maybe roll and then why those legs? Ok. So they notice they're going the other way. So we have to go where humans go. That's what we were designed for the small spaces as oh digits waving hello to everyone. The small spaces that humans operate in. So humans, if you go into some of these big warehouses, humans can only reach so high. It had to reach that high and they can reach so low. It had to reach that low. It turns out knees get in the way of of picking up heavy things from the ground. So we put the legs going the other way they don't get in the way. Do you call those reverse knees or are they just some people call them bird legs? I just like to say they're digits legs and they're what works best in that situation. I think we need audience over there to wave. Hello as well. Yeah. Digit digit don't be shy the question. Ok. Yeah, so this was um oh yeah, now he's a ham. Now we can't get him off the stage. I'm waiting for him to take one of my tasks and um so I, you know, obviously this is a brief little fun demo, but digit is actually in workplaces right now. So as of a few weeks ago, digit is at GXO Logistics. They're a third party logistics. A big one outside of Atlanta digit is taking path online and doing them in versus humans doing some of these dull repetitive tasks. Actually, Mary Daly was talking about it. If we want to augment humans, allow them to be freed up to do other things. And it will take the very repetitive things that are part of the everyday job off of the humans plate so they could be more productive in other areas. Are we going to see? I think, I think maybe we have a clip, we can show, we have a clip, we can show where we're operating right now if we can run that video. So GX actually moves spans women's wear and you know, they have these huge automated warehouses and then there's still a human moving things from a conveyor belt to a cart or from a cart to a put wall and it's those points. This actually is in an Amazon facility, a real, a real Amazon real Amazon facility. And um so amidst these islands of automation, this is sometimes the Achilles Heel because uh there's, there needs to be a human in there and they're hard to find. There's about 1.1 million open jobs in the material moving space in the US today alone and they can't be filled. Nobody wants these jobs. They're doubling it doubled in the last five years because we all want next day delivery. And you know, if you want your spans the next day, you know, you want digit in there delivering them. So we have a lot to talk about, but I'm sure the audience will have a bunch of questions. So I'm hoping to get to a few of you. So start thinking about what, what you wanna know uh from Peggy or maybe from well, goodbye digit. But um maybe from digit uh as well. So a lot of questions, you, there's a lot of money going into the space right now. A lot of noise, like you said, you're actually, you're, you have a real live commercial relationship with G XO. You're in Spank's facility. You're also in an Amazon facility. I've covered Amazon a long time. Some of the automation reduces repetitive tasks, but they've also had situations where it then speeds up the work and there's some safety concerns as well. So I'm curious like when you're going into these facilities, what the goals are and how much you prioritize safety along the way. Safety, 100% 100%. This company was built on a safety foundation. So from the software to the hardware to how it moves and how it's programmed, you are operating in an environment with other people right now, we still operate inside of a work cell. OK? But we're headed towards that means no, no humans within our able to, right. And that's powered down. They're 140 pounds. There's a lot of torque. And so you have to be careful about that. However, we're on a path to what's called functional safety. So, in the next 18 months or so, we'll be able to incorporate all of the things that are needed in order to be able to interoperate near humans. Can you talk about sort of some of the stats about what the robot can do in terms of weight, that digit can uh lift battery cycle obviously like these, this is a, this is a cost issue, right? For a lot of these employers. And so I'm curious all the metrics that go into them, figuring out whether they can save money by using digit or many digits. Yes. And right now we're looking at under a two year ry versus a human at a fully loaded 30 dollars per hour. So that's the benchmark that we're focused at. You're right about, you know, power, you need to be operating, we operate for hours, but we, we recharge ourselves right now. We're at about a 2 to 1 ratio. So what does that mean? Exactly? So two hours or units of operating time versus one unit of charging? Ok. But moving to a 4 to 1 and then moving higher up in our next gen to 8 to 10 to 1. So that's the trajectory we're on there. We can lift, you know, right now, between our current gen and next gen, we're moving from about 30 pounds to about 50 pounds. But you have this OSHA limit already in place for humans. So that's the bar we have to meet there and we'll, which is, which is the 50 uh, 50 pounds. Yeah, but, you know, if you think about 50 pounds lifting something over and over and over again, that's the problem. That's where the injuries come in. That's where the quitting comes in. The turnover. You know, there's in these warehouses, turnover is super high. So, so, um I, I will call and if, if anyone has a question, please raise your hand and I'll get to you. Um, ok, we'll, we'll go to one now and then we'll come back. Uh Please just tell us your name. Yeah, you right. Oh, you need a mic, don't you? Uh coming up, coming up right behind you. Tell us who you are and what company you're with, please. Hi, Peggy. Hi, Ken Washington. I'm the CTO at Metronic. Um, and I've known your company for a long time, um, from my days at Ford Motor Company and also at Amazon. And in both of those examples of my past, the, the digit experience has changed a lot and, but what hasn't, what I haven't seen yet is, is a inter inter, is an integration of digit with other automation platforms. Is that something that you're thinking about how to create an API so that other parts of your automated ecosystem can communicate with digit and vice versa? So that they create a system and work together in a, in an optimized way to workflow. Absolutely. So we have developed a software platform called ARC. It has all the API S to plug into the warehouse management systems that are existing in facilities today. So we've integrated already with Manhattan and several others. So we can step into the existing workflow. We don't want to have to have the operator change, the infrastructure change, anything about the workflow. We just step right in, plug into their management or their warehouse management systems. And we've also integrated with quite a few other robots, whether they're autonomous mobile robot carts or put walls or conveyor belts. And so we've got it on both ends where that human goes, digit walks in and we're connected on either side. How, how, how far along are you in that, in that journey? Like how confident are you that you can walk into a new customers facility and whatever the automation is digit can currently work within that or is that a work in progress? Very confident. The good thing is we have a team of people who started and built robotics companies on my leadership team and they brought that sensibility, the safety, the regulatory, the compliance, the fact that you have to step into an existing workflow. All of that is beyond just getting the robot to walk right. You have to enter the corporate it infrastructure and make it work for them, you don't want to make them change things. So we have a good sense of that from the leadership team that uh that the folks before I arrived had, had built, let's talk about the technology a little bit and I'll get to another question if we, if we have right after this. Um How is digit doing what it just did on stage? And then how is it learning and, or deciding how to work inside these active facilities that it's in right now, right? So how it's doing? It is company is 10 years old. It came out of Oregon State University with the Robotics Institute there. We have still a big tech force there and also a factory nearby that we have built to take advantage of the location. Um But digit has been, you know, 10 years in the making. This is highly complex, a highly complex physics project. The dynamics of locomotion are terribly complex. The team solved that part of it. Now, when, when the world changed November 30th 2022 with Chat GPT, we can start to introduce that to help with the semantic intelligence. Now, we have been using reinforcement learning for several years on the locomotion to refine the locomotion so that digit can walk over uneasy places, it can go up and down stairs and, and, and that's what makes it very different from say, a wheeled robot, it can traverse those things. So that part's been great reinforcement learning helps that. But the semantic intelligence with chat G BT is, is just amazing. It's the embodiment of A I. What, what can, can you give the audience an example of, of how that might work? Yeah. So now we have the ability to direct digit. So the other day, we threw a bunch of trash on the floor, different kinds of trash and we put all the bins behind digits. So, you know, recycle landfill and we said just gave it one command digit, clean up this trash and digit scans the room picks up the paper, puts it in the recycle, uh picks up some cardboard, does the same thing goes and picks up some bubble wrap and puts it in the, in the trash and we all went, uh you know, mistake but it turns out bubble wrap is not recyclable. So digit with the power of chat GP T underneath was able to make that decision. So now we don't have to code all of that. We can just with the, with Chat GP and other LL MS, we have the ability to just give digit a direction. So digit can do one thing in the morning for a company, a different thing in the afternoon. Um Do we have any other audience questions? Otherwise I'll keep going because I love talking. Um All right, I, I'm gonna start actually sorry, we're gonna start there. II I faked you out. Um Yeah. Right. Right. There, please tell us who you are. Yes, Sterling Audie with uh with Barclays. So the example that you showed is a single unit in a single unit use case. But I guess we can all envision armies of these being throughout the warehouse. So have you already kind of developed the the management platform and ability to kind of have a, you know, a control plane across the larger number of units? That's exactly what ARC is. So what and what we're finding is the employees who used to do, maybe all of that physical work are becoming the managers of the robots. So they're along the way getting to be upskilled with the use of our ARC platform. They're the ones who are managing the fleet and it doesn't have to be just digits, right? We're connecting to AMS and other things so they can look at the fleet of automation and manage that from there. They have a digital AM just for folks. Oh, sorry, autonomous mobile robots. So they're the carts that you'll see going back and forth in the warehouse facilities. Got it. Yeah, that answer a question for you. Ok. I'll come to you right now. Yeah. No, you can, you can go ahead a Cody Cerny with insignia Peggy. Great to see you. Is there anything that's keeping you up at night or any worries you have about the development of these robots and implementation? Well, the biggest thing keeping me up at night right now is I just got off the phone with my commercial officer and we are just getting ready to produce our next gen in the fall. It, you know, it's already developed and we have this factory up and running. We've got a line up. Why don't you tell people, I, I missed that one. You, you have your own factory. We built a factory in, out, you know, outside of Oregon State and we have the ability to ramp up to 10,000 units a year because we, we saw that demand and we didn't want to miss it as it was coming in. But right now we have handfuls of robots and we have a lot of interest from automotive companies, retail grocers across the board, anybody who's running these kind of facilities. And, and anyway, we're struggling just to figure out where they go next until the fall and we'll be able to open it up because the fall is when you'll, you'll making our next gen next gen. Um How many do you think you can produce next year? Versus that 10,000 capacity next year? We're hundreds and the year after thousands. So that's sort of the ramp that we're looking at. The good thing is, is the factory is in the US, my engineers are nearby. I don't have to put them on planes anywhere and it is, you know, it, we're capable of ramping up. It's not, and it's not Capex. Heavy. It's largely assembly test, repair, that kind of thing. So we can ramp up pretty quickly. If I need a second one, I could, I could get one up and running fairly quickly. Um, funding wise, raised hundreds of millions of dollars. Right. Um How about 180 million? Not too much about that. Ok, so sorry. Ok. Um, like how much is, is that a constraint on building out this vision that you see for many different types of workplaces? Yeah, so the next we are raising now we're in our series C right now and that will take us out the next couple of years until we hit that 10,000 unit. But the good thing is because we're deployed already. We are getting the very first robot as a service contract, multi year contract for humanoids. So we're getting a monthly revenue already on those units and every and units that we put on after that we'll have that, that incoming monthly revenue. Do you know what the discussion is that employers are that you're testing with or or putting these robots into their facilities, what they're having with their employees? Because I know what we've talked about taking tasks, not jobs, but Amazon for example, second biggest largest, second largest private sector employer in the US, 1.5 million jobs. I've talked to hundreds of these employees over the years. Some of them, a lot of them see robots and their concerns So what, what, like, what is the message there? That is believable. Well, that's believable. I think it's reality. There is 1.1 million unfilled jobs. They can't find people to do that. So, what does that do? It? Taxes the people who are there even more and they're ending up doing this repetitive work for longer parts of their day when maybe it used to be a two hour job. They're, you know, they're doing it for four or six hours. And if you see in the video, you know, it's literally moving a plastic bin from here to there, it's mind numbing. And so it's not that people are seeking those jobs. They, they just can't fill them, you know, and it's a retired workforce, it can't refill. So they have a real problem so far. Everything that we, all of our interactions have been very positive because it does give you the opportunity to upskill that worker and put them into a higher. We'll come back in five years and talk some more. I'm getting evil eye. We're out of time. Thank you so much, Peggy. Thank you. Thanks everybody.

Arvind Krishna, Chief Executive Officer, IBM 
Cristiano Amon, Chief Executive Officer, Qualcomm

CEO Initiative 2023: Navigating The New Frontiers Of Business And AI

Uncategorized

I feel like every few years A I makes a splash but this time feels a little bit different. Can you share with us more about your organization and how it currently uses A I Arvin? Happy look. A I has been making a splash for 30 years. Every 10 years. It goes up, it goes down. We've had the winter of A I, but this time is actually significantly different. You got to go to the underlying science. These technologies of large language models have fundamentally changed the cost economics of A I. You can now while it takes a lot of infrastructure to get your base model on each incremental task is an evening or a weekend of work that is fundamentally different than the 100 Ph Ds and six months of work. That's a really big difference. The multimodality is a different one and I think that the use cases of which I'm sure we will touch on. Many are across the fabric of almost everything we do, not just in tech companies but others. One tidbit in some of our hr transaction services mooring people raises moving people, employment verification. We could do 90% efficiency using these technologies. So I'll leave you with that. T Yes, look, I agree with uh I, I think A I has been with us for a while but there are a couple of things we really accelerate in the process. Um You know, the I think the A I re revolution we see right now is is basically enabled by the development in three underlying technologies. I think computing performance, especially as semiconductors are increasing dram dramatically. You have the availability of data and you have broadband both wired as well as wireless. Combining those three things to create an environment that you actually can deploy A I very pervasively. And I I think we are at a moment that we're going to see fundamental changes in actually in user behavior, both a consumer and the enterprise. You know, I like to use this example, like I want to say this could be could be as powerful as one day you have your future phone and then you saw this thing called the smartphone. And you said, wow, this is great, this is new and I think that's probably that order of magnitude change when you think about uh change in user experience. But also if you don't want to believe in that, it's almost like the underlying technology of GPS. And then all of a sudden you saw something like Uber as a very interesting application that was enabled by it. I think that's how this thing is going to develop and it's going to accelerate very fast to your question about using A I. We've been using A I for a number of years, I think uh just provide us some integrated into the integrated in how we develop our products. Uh I'll give you some examples uh when we develop our five G modem. Now, uh a significant percentage of development is using A I just processing a lot of data and helping us develop our modem. We do that for image signal processor for a camera. And even gen A I, we've been utilizing internally. We have developed our, our ability to engineers, consulting documentation, remembering documentation. We've been using GEN A I to do this with precision and I think the applications is going to continue to expand. Actually as a company, we started uh to be using uh some of the Microsoft Copilot as well and we have seen significant gains of productivity. And Arvin, you've written pieces uh and talked about how A I will affect white collar jobs. First. Can you explain this a bit more? Uh And, and how you claim that it will help workers instead of displacing them? Absolutely. Look if you look at where all of these technologies can be deployed and you look at it through automation. The first thing you can automate is a repetitive white collar job. People often confuse blue collar work. Blue collar work has had the attacks from robotics and automation but it's white collar work that will get displaced. And I'm using the word displace, but only for the lower level task. That means for the majority of people, it's a productivity augmentation. It's sitting with them. It's not really replacing the whole human because nobody has 100% of that repetitive work. Now for the 10 20% it does, there is also going to be 30% new work that is created the same way Krish, I use the example of smartphone, I'll use the internet in 1995. Nobody thought there would be 5 million web designers. There are ditto, there are going to be people who are going to come into new roles with the advent of generative A I and large language models. Second, I think that the the issue is different in the global South versus developed countries in developed countries. We have a shortage of labor, not a, not a, not a abundance of labor. So we need these technologies just in order to keep our quality of life and the GDP growth going on. Then in the other places as you begin to have more and more customer service, customer experience, the number of roles in those jobs is going to increase. Not to even touch on what the last panel was talking about in health care where the number of workers needed is going to only increase as aging goes on in all societies. So I do think that there will be 10 to 20% displacement in some white collar jobs more than made up by the increase. So we talk about our programmers getting 30% more productive. I don't intend to get rid of a single one if I'll get more because that makes the task of making things that are using more software more productive. That means you can make more of them because they can get more of a market. So that is why people mistake productivity with job displacement. It's not job displacement. It actually makes it more attractive to have those roles. Have you made any changes to departments within IBM? Because this absolutely, we have decreased in certain back office functions and we have increased in more value creating activity. Can you give an example? Yes, I began by saying in our human resources function, a few 100 people were no longer needed in the back office. That is a journey that takes 3 to 4 years. It's not a big bang on one day, but over 3 to 4 years that went away the number of people in software delivery and in client facing sales went up because we could have more attractive offerings in those areas. And by the way, the increase I think was like 8000, the decrease was like 800. So just to be clear, I mean, as you get more productive, generally people invest more. That's the fundamental issue in business. Yeah, I'll come to you guys with questions. If you have one, please raise your hand, we'll get a mic there. Um In one minute Cristiano, uh you have said that in order for generative A I to really scale and get into people's homes, it cannot all happen on the cloud, it has to happen on the device itself. Can you explain that a bit more? Absolutely. Actually, I'd like to spend a little bit of time on this because um this uh it's intuitive like when you look at, for example, especially chat C BT um made uh gen A I very popular and everybody understand it and it's intuitive. You think you go to a cloud and you do a search and, and you get results, but the reality is you should think about A I a little bit different A I is going to develop in a particular way in the cloud, but it's going to develop on devices a little bit different as a new form of computing. Think about anything that requires real time latency, you need a response right away or you need to know the context that you inserted or you have the ability uh you want to protect the data with privacy, then you're going to have A I running pervasively on the devices and I'll give an example. So I'm going to go back and talk about phones. Uh but I can talk about phones, you know, P CS and cars, but phones it. We used to have, when you look at your smartphone today, you have a computing platform, you have an OS you have applications and then you have the human and the human go and touches different apps and get data from one app to the other. Now, think about this JA I running and understanding everything that you're doing on the phone and running pervasively and try to predict your every action you take. So then all of a sudden you have the ability to have the copilot for everything you do. For example, you and I could be texting something very common with over phones. I can tell you about my vacation somewhere immediately. The phone can bring my gallery app, identify the pictures from the vacation. Ask if I want to share with you or we talk about getting together, bring the calendar app talks about availability. So think about a copilot of everything and those things are running pervasively, but then you have some interesting consumer applications as well. Well, that happens on the device like you take a picture with your phone probably multiple times a day. Take a picture. I I said I don't want, I don't want Irwin in this picture. I want to delete him. I want to change the background from this room to Venice and, and then you send it to somebody else. And I think you're gonna do this by running many models now on a PC. You think about the use of productivity and, and think about the copilot exercise and you can do a lot of those things running that on the device based on your data. The car is the one that I'm super excited about because natural language communication is perfect user interface when you are behind the wheel, especially when you think about uh navigation something as simple as I'm going from A to B. But there are so many other things that you want to do or you have done or is in your agenda and the gen A I will be able to plan your trip based on that. So the way I see it is we're seeing two parallel developments happening just because you now have the computing power data and you have broadband, you have seen the development of that in the cloud. You see that development on device and they have a point that they intersect, they intersect when you can run uh models from the cloud in the device. If a model is very well trained. And that's one of the things we have been doing, we have show running billion parameter models running on a phone. We're going to multiple tens of billion parameters and phones and P CS and cars. When you have the model well trained, you have the ability to take advantage of the distributing computer that exists in everybody's devices and, and that's a massive amount of computing power and it saves a lot of cost, especially on the cloud. So we're seeing that application as well. Sorry for the long answer, but we're very passionate about this, but there's a whole uh A I on device coming. Do we have any questions? Yes. Sorry. How are you changing? How you are recruiting um for people as you look five years down, 10 years down the road and how do you think a university should change its curriculum to meet this new A I era? I'll drop in. Plus I don't think that people there is inventing A I and there is using A I for using A I. The number of people who are skilled is massive. We just have to give them a little bit of training on how to use it and deploy it. So there, I think people with skills across the social sciences, across the hard sciences, engineering there applicable. Inventing A I is where you have a authors of skills today and that is the classic, either hard physics or computer science or math training. That is typically there. But inventing A I, you need thousands of people in the country, not hundreds of thousands, thousands using A I, we need millions and I think that a lot more attention ought to be on the using as opposed to inventing I think for universities, I will tell you that uh training people on this the same way as software engineering became a discipline. 15 years after the fact, maybe universities should jump in hard in terms of training people on how to use A I and also make them aware of what are the risks on A I. That is something which I think a lot more has to be taught on. Yeah, maybe just to add one thing to that. And, uh, when we talk about the many different benefits of A I, one thing that if you democratize A I, it has an incredible powerful um ability to accelerate training and actually to distribute knowledge to the point that you can have workers that otherwise will not be able to be qualified to do certain tasks, be able to do it with the copilot with them. So, and I think that's another uh interesting aspect of this transition. Any other questions? OK. Um Y you are both kind of very massive global companies and in a more global context. How do you see China positioning itself in the A I landscape? And, and what impact does do China's A I capabilities have on comp competition or your companies? Ivin? Well, look, um I wish that there was more peace and global trade. So I'll begin with a basic statement there. I kind of agree with what Senator Warner said about. Let's separate the politics from the, from the nation. Now, all that said China has advanced in A I, let's not make any illusions around that their ability to use large private data sets, their ability for the government to push forward on certain use cases are probably not use cases that we are going to push forward here. Now, unfortunately, as this more of a tech war there is going to be more and more development that is splintered across the two nations. In terms of these use cases, China is not going to sit still in terms of what's happening on five G and six G as well as on the intersection of A I and quantum and all those those are going to go happen. Now, I'm going to start a little bit contrarian. I actually believe when I look at it right now that at the fundamental advanced level, the US is ahead. How much ahead we can debate? Is it six months ahead or two years ahead? But the fact that charge GP D came out here got copied than six months. The fact that generative A I came out of places like Stanford and Mit and Google and Facebook is a fact that I think we should all take pride in how much longer we can maintain that lead. Is an interesting question by the one thing I want to jump in on Christian, I mentioned chip design over 3/4 of the work in the chip designs that IBM does is done by A I today using some of the same technologies that Cristiano's team does. And those chip design tool companies are also based here in the United States largely. Yeah, maybe I, I add one thing. A I it's a very pervasive technology that is going to be part of many, many industries. So as a result, I think as industries evolve, regardless where they are, uh you're going to see more and more of a role of A, I look um from a technology standpoint. Um it just because on the technology, not the politics aspect, you know, uh technology sees very uh is difficult for the technology to see boundaries, especially because the world became much smaller and we are in a very global interconnected economy. I think you have industries, there have some point of interconnection and you see A I coming different. Now there's different ways to talk about A I, you can have for example, A I that you probably already have it in your phone when you take a picture at night. And all of a sudden the picture looks very bright like in daylight is the A I figuring out this is the sky should be blue, this is a tree should be green, it's already happening. It's very different than for you who have massive ability of doing training on, on different models. So I think there's different type of applications. I think it will be very difficult, I think to be able to have a blank statement on uh where A I is going to go and how to regulate that. But the most important thing I think in this whole conversation is what I alluded to. Uh you know, the uni the United States has a significant lead in semiconductors. I think we often talk about the fact that we wish we have more semiconductor manufacturing in the country. We we don't have as much as we need on manufacturing, but on design, the majority of the global leaders on chip designs are American companies and A I, it's going to require a number of industries that the US as a leadership. So the most important thing we can do is to make sure the US remains the number one place to attract talent because there's uh some finite number of individuals that can help create A is on those technologies that we have the ability to remain competitive and we maintain our innovation engine which creates the leadership position in the first place. Perfect and note to end on. Thank you, gentlemen, so much. Thank you so much.

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