how to continuously meet the computing power needs of the ai era? huawei's rotating chairman xu zhijun gives the answer

2024-09-19

huawei vice chairman and rotating chairman xu zhijun delivered a keynote speech

phoenix technology news: on september 19, at the 2024 huawei connect conference, huawei vice chairman and rotating chairman xu zhijun delivered a keynote speech entitled "challenges and opportunities in the era of comprehensive intelligence", sharing huawei's observations, thinking, strategies and practices in the field of artificial intelligence (ai).

xu zhijun first reviewed the rapid development of ai technology and its profound impact on various industries. he pointed out that ai has become a key technology to promote the intelligence of various industries, and its commercial applications are mainly concentrated in product development, marketing and business operations. corporate executives have a positive attitude towards ai, and the continuous progress of ai technology is leading the wave of comprehensive intelligence.

xu zhijun proposed the "six a" characteristics that enterprises should have in the intelligent era, including adaptive user experience, auto-evolving products, autonomous operation, augmented workforce, all-connected resources, and ai-native infrastructure.

these characteristics not only represent the effects of intelligence, but also lay the foundation for intelligence, providing a clear direction for enterprises in the era of intelligence.

intelligence will inevitably be a long process, and computing power is the key foundation of intelligence. the sustainability of intelligence depends first on the sustainability of computing power. computing power depends on semiconductor technology, but we must face a reality, that is, the us sanctions on china in the field of ai chips will not be lifted for a long time, and china's semiconductor manufacturing technology will lag behind for a long time due to us sanctions, which means that the advancement of the chips we can manufacture will be restricted. this is the challenge we must face in creating computing power solutions.

artificial intelligence is becoming the dominant computing power demand, prompting structural changes in computing systems, which require system computing power rather than just the computing power of a single processor. these structural changes provide us with opportunities to create an independent and sustainable computing industry development path through architectural innovation.

the core of our strategy is to fully seize the opportunities brought by the ai revolution, based on the actual available chip manufacturing processes, collaborative innovation in computing, storage and network technologies, create a computing architecture, and build a "super node + cluster" system computing power solution to continuously meet computing power needs in the long term.

in response to the current ai computing power construction boom, xu zhijun reminded enterprises not to blindly follow the trend. he pointed out that not every enterprise needs to build large-scale ai computing power or train its own basic large models. instead, enterprises should choose the most appropriate model according to their own business scenario needs, and solve problems and create value through a combination of multiple models.

as an important part of huawei's comprehensive intelligent strategy, huawei cloud has also carried out a full-stack upgrade for ai. xu zhijun introduced that huawei cloud continues to build ascend cloud services, allowing enterprises to obtain powerful ai computing power with one click, and provides modelarts services to support the industry's mainstream basic large models out of the box. at the same time, huawei cloud is also working hard to build pangu 5.0, which supports a full range of models and provides enterprises with more choices.

in the terminal field, huawei has deeply integrated ai technology with the hongmeng operating system based on the architecture of device, chip and cloud collaboration, and rebuilt the ai-centered hongmeng native intelligence. xu zhijun said that huawei will upgrade the "xiaoyi" intelligent body based on hongmeng native intelligence to provide users with full-scenario intelligent and personalized services, and work with hongmeng ecosystem partners to jointly build intelligent capabilities for future products.

in addition, xu zhijun also introduced huawei's progress in the intelligent fields of network and automotive autonomous driving. huawei is committed to reshaping network experience and operation and maintenance through autonomous driving networks, and creating autonomous driving solutions centered on safety and experience, and ultimately realizing unmanned driving.

in terms of ecological development, xu zhijun emphasized that huawei will make strong strategic investments in ecological development, and guide, promote, and drive the development of the computing industry and the terminal industry through ecological development. at the same time, huawei insists on advocating and practicing ai for good, and is committed to improving people's work efficiency and quality of life, creating a wider range of welfare for society, and applying it to ecological environmental protection and sustainable development.

finally, xu zhijun said that the era of comprehensive intelligence has arrived, bringing new opportunities and challenges to everyone and every company. he called on the industry to work together to promote comprehensive intelligence, so that everyone can have their own exclusive smart assistant, so that every company can become an intelligent company, and so that every car can be driverless.

the following is the full text of xu zhijun's speech:

ladies and gentlemen, old and new friends, good morning! welcome to huawei connect 2024. i hope you have a pleasant journey in shanghai. at huawei connect 2018, i released huawei's artificial intelligence development strategy and full-stack, full-scenario ai solutions, and positioned ai as a general-purpose technology. in 2021, i talked about pangu big model enabling intelligence in all walks of life at huawei connect. from 2018 to now, the development of ai has been changing with each passing day, and has attracted great attention from the global investment community, industry, and government. since 2018, huawei has steadily promoted the ai development strategy, and at last year's huawei connect, it further clarified the company's comprehensive intelligence strategy. for intelligence, every industry and every enterprise has its own exploration. i have heard that many achievements have been made, but i have also noticed that there are still many confusions. today, i would like to take this opportunity to share our observations, thoughts, strategies, and practices.

ai has become the technology with the greatest impact on the industry

first, let's look at the commercial progress of ai. from the perspective of business applications, no technological advancement has ever had such a great impact in such a short period of time as ai. research by mckinsey and stanford university shows that ai applications in various industries are currently mainly concentrated in three aspects: product development, marketing, and business operations. secondly, from the perspective of corporate executives, gartner's survey results show that ceos have a very positive view of ai. so in summary, the continuous advancement of ai technology is driving the continuous deepening of intelligence in all walks of life and is moving towards comprehensive intelligence.

companies that look forward to the intelligent era

standing at the beginning of the era of comprehensive intelligence, each of our companies not only hopes to use ai to create value as soon as possible today, but also hopes to achieve leadership in the future intelligent competition. this is also a question we have been thinking about. i think it is very important to first think clearly about the future direction of enterprises in the intelligent era, and then think about today's strategies and actions from the end to the beginning. based on this consideration, combined with huawei's own intelligent practice and huawei's many years of support for the exploration of intelligence in various industries, i would like to take this opportunity to share our vision for enterprises in the intelligent era, that is, what enterprises in the intelligent era will look like and what characteristics they will have.

we believe that enterprises in the era of intelligence should have "six a" characteristics. the first four a's represent the effects of intelligence, including:

the first a is about how companies need to serve their customers in the future. we think it is adaptive user experience, which means that intelligent companies should be able to perceive and understand user behavior, needs, interests, tastes, and environmental changes, and actively adjust to provide services that best meet user needs. products that can meet a large number of personalized and unique needs in a timely and simultaneous manner need to be specially designed from the beginning, not just tailored. for example: ai learning machines automatically adjust teaching content and difficulty based on students' age, learning progress, comprehension ability, and test feedback, so that each student can get a learning experience that suits them at different times. providing customers with a preset, certain experience to an adaptive experience is a leap, and every company needs to provide a customer experience that adapts to the intelligent era.

the second a answers what kind of products companies will need to create. we believe that they are auto-evolving products, which means that products in the intelligent era will have the ability to learn autonomously, continuously iterate, adapt to changes, and be able to self-optimize and self-evolve. for example, self-driving cars will become easier to drive. the transition from product digitization to product intelligence is a leap that will greatly change competition. every company needs to think about integrating intelligent capabilities into its products.

the third a answers the future of daily business operations, namely autonomous operation, which means achieving highly autonomous business operations, from perception, planning, decision-making to execution, and end-to-end autonomous closed loop. for example, ports can automatically generate operation plans through intelligent planning platforms, and automatically complete horizontal container transportation through self-driving container trucks. enterprise operation automation has been pursued by many companies for many years. the autonomy of operations is a leap forward in improving operational efficiency. every company needs to think about using ai to empower and change business operations in a wider and deeper range.

the fourth a answers the future of employee work experience and work style, namely augmented workforce, which means that every employee should have an intelligent assistant that "understands me" to complete every task efficiently and with high quality. for example, the on-site maintenance personnel of the operator's base station can quickly obtain information such as the fault location, root cause of the fault, and handling suggestions through the maintenance assistant app. the meaning of ai's existence is to make ai benefit mankind, and to give employees a better work experience is the key foundation for the competitiveness of every enterprise in the era of intelligence.

the next two a's represent the foundation of intelligence. the fifth a, all-connected resources, means to achieve full interconnection of the company's assets, employees, customers, partners, and ecosystems, and to digitize all business objects, processes, and rules. it is necessary not only to increase the quantity of information, but also to improve the quality of information, so that the company has the data and information foundation necessary for intelligence, that is, in-depth and comprehensive digitization.

the sixth a stands for ai-native infrastructure. it means that, on the one hand, ict infrastructure must be constructed in a systematic manner to adapt to the needs of intelligent applications, namely ict for intelligence; on the other hand, the operation and maintenance management and experience assurance of the infrastructure itself must be fully intelligent, namely intelligence for ict.

the characteristics of these 6 a’s are our initial thoughts and summary based on our own practice and understanding. we hope that they will be helpful for everyone to think about how to make good use of ai. they are provided for your reference. we hope that every company can become a winner in the era of intelligence.

promoting comprehensive intelligent strategy

to adapt to the arrival of the era of comprehensive intelligence, huawei proposed a comprehensive intelligence strategy at the hc conference in 2023. the comprehensive intelligence strategy covers a wide range of aspects. today i will mainly share our thoughts from seven aspects.

1. providing sustainable computing power solutions through architectural innovation

first, let's talk about computing power. intelligence will inevitably be a long process, and computing power is the key foundation of intelligence, both in the past and in the future. therefore, the sustainability of intelligence depends first on the sustainability of computing power. computing power depends on semiconductor technology, but we must face a reality, that is, the us sanctions on china in the field of ai chips will not be lifted for a long time, and china's semiconductor manufacturing technology will lag behind for a long time due to us sanctions, which means that the advancement of the chips we can manufacture will be restricted. this is the challenge we must face in creating computing power solutions.

based in china, only computing power built on the actual available chip manufacturing process is sustainable in the long term, otherwise it is unsustainable. huawei sees challenges, opportunities and possibilities, which further inspires our passion for innovation. because artificial intelligence is becoming the dominant computing power demand, computing systems are undergoing structural changes, requiring system computing power, not just the computing power of a single processor. these structural changes provide us with opportunities to create an independent and sustainable computing industry development path through architectural innovation.

the technological breakthrough of big models has greatly accelerated the process of intelligence. for a period of time, almost all walks of life have mentioned big models, and have built ai computing power and trained big models. this is undoubtedly a major benefit for computing power providers like huawei. however, from a long-term development perspective, we always believe that only the continued success of our customers can lead to the continued development of huawei. today, i would like to share some thoughts on a few issues.

first, not every enterprise needs to build large-scale ai computing power. we all know that ai servers, especially ai computing power clusters, are different from general-purpose x86 servers. they have extremely high requirements for data center computer room environment such as power supply and heat dissipation. as large models become larger and larger, ai computing power will also move towards a larger scale and change at a fast pace. ai servers are rapidly upgraded, and data center computer rooms are faced with the dilemma of either wasting or not being able to meet demand.

secondly, the industry now launches new ai hardware products every one to two years on average, with a fast iteration speed. compared with public clouds, enterprises are limited by the small scale of computing power. faced with rapidly changing large models, it is difficult for each generation of computing hardware to complete the work independently. instead, they hope to use multiple generations of products in combination for model training. this leads to high complexity in resource scheduling. in addition, due to the "short board" effect of historical generational products, the performance of the new generation of products is dragged down and the ability to train large models is affected.

finally, there are challenges brought by operation and maintenance. ai technology is still in its growth stage, with rapid technological changes, coexistence of multiple generations of products, and high skill requirements, which makes operation and maintenance difficult. this is a major challenge for many companies that only have traditional it maintenance capabilities. since these challenges will continue to exist for some time, i believe that every company must think about the way to obtain ai computing power that suits them, rather than just building their own ai computing power.

second, not every company needs to train its own basic big model. the key to training a basic big model is data, and preparing enough high-quality data is a big challenge. the amount of pre-training data for basic big models reaches 10 trillion tokens, which not only means high costs for companies, but also whether they can obtain enough data is also a challenge.

secondly, model training is difficult. the number of parameters in the basic large model continues to increase, and model iteration and optimization are difficult. it usually takes months to years to complete model iteration training. each company should focus on its core business. training the basic large model by itself will affect ai's ability to empower core business as soon as possible.

finally, it is difficult to acquire talent. the relevant technologies involved in basic large models are updated every day, and there are few technical experts with practical experience. for enterprises, it is also a challenge to establish sufficient technical talent resources.

third, not all applications need to pursue "big" models. from huawei pangu's practice in the industry, the billion-parameter model can meet the needs of business scenarios such as scientific computing, prediction and decision-making, such as rainfall prediction, drug molecule optimization, and process parameter prediction. the billion-parameter model is also widely used on end-side devices such as pcs and mobile phones. the 10-billion-parameter model can meet the needs of a large number of specific field scenarios such as nlp, cv, and multimodality, such as knowledge question and answer, code generation, agent assistant, and security testing. complex tasks for nlp and multimodality can be completed with a hundred-billion-parameter model.

therefore, we believe that what enterprises need is to choose the most appropriate model based on the needs of their own different business scenarios, solve problems and create value through a combination of multiple models.

2. huawei cloud upgrades to full-stack ai to empower intelligence in all industries

based on the ideas i just mentioned, i think that for many companies that do not have the ability to build their own ai computing power and train their own basic large models, choosing cloud services is a more reasonable and sustainable choice. huawei cloud has also upgraded its full stack for ai to address these challenges, and is committed to enabling every company to train models and apply model reasoning on demand and efficiently.

first, huawei cloud continues to build ascend cloud services, allowing enterprises to obtain powerful ai computing power with one click, without the need to renovate or build their own computer rooms, or operate and maintain ai computing infrastructure. at the same time, through end-to-end collaboration of computing, storage, and network, it has achieved 40 days of uninterrupted cloud training of a model with hundreds of billions of parameters.

secondly, huawei cloud upgraded the modelarts service to support the industry's mainstream basic large models out of the box, including pangu, open source, and third-party large models, so that enterprises do not need to prepare large amounts of data and iterative training for basic large models, and provide one-stop model tuning, deployment, evaluation and other tool chain support, lowering the technical threshold for enterprise model fine-tuning and incremental training.

at the same time, huawei cloud is working hard to build pangu 5.0, which supports a full range of models, including billion-level, 10 billion-level, and 100 billion-level models, to best adapt to the needs of different scenarios of enterprises, and provides more than 100 large models through the 100-model-1000 community, providing enterprises with more choices. in summary, i think cloud services are the best choice for many companies to promote intelligence. through huawei cloud ascend cloud service and model cloud service, we hope to enable every enterprise to obtain ai computing power on demand in real time, as well as efficiently train models and apply model reasoning.

huawei cloud provides systematic security capabilities to ensure the security of large model training and reasoning

training and reasoning of large models on the cloud brings new security challenges. to address these new security challenges, huawei cloud has greatly enhanced its security capabilities to ensure the security of large model training and reasoning, mainly including:

in terms of security concept, huawei cloud carries out security design based on the concept of "defending against extreme attacks". based on zero trust, it has built seven layers of defense including physical, identity, network, application, host, data, and operation and maintenance, and a security operation center. it successfully resists up to 1.2 billion attacks every day, ensuring that the business "is not paralyzed by attacks, data is not lost, and regulatory compliance is achieved."

in terms of security mechanisms, huawei cloud provides hierarchical cloud to build a secure digital space for customers, supporting physical isolation or logical isolation. the operations of the cloud platform are transparent and auditable, ensuring that customers can use the cloud with peace of mind.

in terms of security technology, huawei cloud provides an end-to-end full-stack data security protection solution, which provides all-round security protection for the entire life cycle of data, including data flow, large model training, and inference data, from hardware, software, and applications. at the same time, it ensures the end-to-end security and compliance of training data and generated content.

in terms of intellectual property rights, if the content generated by customers using huawei cloud big model service infringes the intellectual property rights of a third party, huawei will defend the customer at its own expense and compensate you for the losses, costs and expenses caused by the final court judgment or settlement with the third party. the specific content is subject to the contract agreement.

3. build hongmeng native intelligence and create a full-scenario smart experience

in the era of intelligence, terminals are an indispensable part. in the field of terminals, huawei was the first to introduce ai into smartphones. as early as 2017, huawei launched the mate10, which had a built-in ai chip and applied ai smart imaging, ai translation and other capabilities to mobile phones for the first time, ushering in the era of mobile ai. today, as ai enters the era of big models, we have deeply integrated ai technology with the hongmeng operating system based on the architecture of terminal, chip and cloud collaboration, and rebuilt the ai-centered hongmeng native intelligence, achieving comprehensive intelligence from the kernel to system applications, while achieving more open ecological collaboration and more reliable privacy and security protection.

based on hongmeng native intelligence, huawei will upgrade "xiaoyi" to an intelligent entity, achieve more natural multi-modal interaction, more comprehensive integrated perception, accurately understand users, the digital world and the physical world, and provide users with full-scenario intelligent and personalized services. at the same time, we will work with hongmeng ecosystem partners to build intelligent capabilities for future products based on consumers' full-scenario needs in work, study, life, entertainment, etc., and realize full openness from ai model capabilities to ai control layers, enable third-party applications, and prosper the hongmeng native application ecosystem.

terminal ai is experience-centric, not computing-centric

we have also noticed that it has become a general trend to introduce ai capabilities into various terminals, such as building ai phones and ai pcs. as a result, there are various opinions in the industry on how to define smart terminals in the ai era. we always believe that the consumer experience is the first priority. consumers find it difficult to understand what chip technology, computing power tflops, model parameter quantity... actually mean, but they pay more attention to their personal experience. therefore, we advocate that terminal ai should be experience-centered rather than computing-centered.

based on this concept, in order to allow consumers to have a clearer and more intuitive understanding of the capabilities of ai terminals, and at the same time to allow the industry to reach a unified consensus on the evolution of ai terminal capabilities and coordinate the orderly development of the industry, we and the tsinghua university artificial intelligence industry research institute jointly proposed the ai terminal intelligence l1 to l5 classification standard, guided by consumer experience, quantifying the user's intelligent experience, and providing users with a better experience by continuously improving the intelligence level. we look forward to colleagues in the industry working together to improve and optimize the classification standard and jointly promote the orderly development of terminal ai.

4. reshaping network experience and o&m with autonomous driving networks

in the network field, huawei first proposed the use of ai in telecommunications networks and proposed an autonomous driving network architecture in 2018. we are currently introducing the communication big model and network digital twins, and working with industry partners such as tm forum and china mobile to promote high autonomy based on value scenarios, enabling the network to gradually achieve l4 high autonomy and achieve full autonomy in the future. among them, through operator network autonomous driving, we are committed to achieving the ultimate user experience of zero waiting, zero interruption, and zero contact, as well as minimalist network operation and maintenance with self-configuration, self-repair, and self-optimization.

at the same time, we have also introduced the concept of autonomous driving networks into enterprise networks, because enterprise networks also face challenges in operation and maintenance. first of all, with fully wireless office, cloud-based and video-based applications, it is difficult to fully guarantee the office experience of employees. at the same time, the scale of enterprise offices, production, data centers, branches, and multi-cloud networks is getting larger and larger, with more and more types of equipment, and the scope and complexity of daily maintenance continue to increase. today, we propose that through autonomous driving in enterprise networks, our goal is to achieve zero lag in enterprise business, zero network interruption, zero waiting for activation, and zero security risk.

5. create autonomous driving solutions, centered on safety and experience, and ultimately achieve driverless driving

the car autonomous driving solution is also an important area of ai that huawei invested in at the beginning, because the goal of autonomous driving is unmanned driving, which is one of the most challenging scenarios for ai application. the ads 3.0 version we launched can make autonomous driving decisions more accurate, traffic more efficient, the experience more human-like, and driving safer. it also realizes "one-click" arrival from parking space to parking space, and connects all scenarios from public roads to campus roads to underground parking spaces. and further upgrade the omnidirectional collision avoidance system to cover more speed ranges and achieve omnidirectional obstacle avoidance.

these advances have allowed consumers to truly feel the safety and experience improvements brought by intelligent driving. now, chinese consumers are very familiar with intelligent driving of cars. the proportion of new cars equipped with advanced intelligent driving versions is very high, and the intelligent driving capabilities of cars have become a key consideration for chinese consumers when buying new cars. next, we will continue to evolve autonomous driving solutions based on fusion perception, and gradually achieve: on highways, you can rest as soon as you get in the car, and sleep peacefully on long journeys; on urban and suburban roads, it is easy to drive everywhere, safe and stable like an experienced driver; in rural and mountainous roads: go up the mountains and go down the countryside, and drive with confidence in all terrains and all weather conditions. in parking scenarios: achieve leaving the car and going, zero scratches, and zero stuck; in terms of safety, we must achieve all-round and all-directional active safety, mainly to clear the main responsibility collision and reduce secondary responsibility. on the basis of achieving these key scenario goals, unmanned driving will eventually be realized in the future.

6. build an ecosystem together, create a unified developer platform, and achieve win-win development

developing an ecosystem has always been an important part of huawei's strategy. we have always worked hard to build an ecosystem with our partners, create a unified developer platform, and achieve win-win development. from 2017 to 2019, huawei has successively started building the huawei cloud, ascend, kunpeng, and hongmeng ecosystems. in 2024 and the next five years, huawei will make strong strategic investments in the development of the ecosystem, and through the development of the ecosystem, it will guide, promote, and drive the development of the computing and terminal industries, providing the world with a second choice in the computing field, and at the same time providing the world with a third mobile operating system.

7. advocate and practice ai for good and enhance the well-being of humans, society and the environment

finally, the applications of ai will be endless, but in the final analysis, it is to serve people. we insist on advocating and practicing ai for good. we believe that:

ai should serve people and improve their work efficiency and quality of life. it should enable industry digitalization through ai, change the industry's production methods, and become the core engine for various industries to enter the intelligent world. it should lower the threshold of ai technology so that everyone, every family, and every organization have equal opportunities to access and use ai technology.

ai should be used for benign purposes such as creating greater benefits for society. in the process of designing, developing, and using ai, we will carefully evaluate the long-term and potential impact of ai technology on society and avoid the abuse of ai technology.

ai should be used in ecological environmental protection and sustainable development, and should be actively used to study and solve issues of global concern, such as the united nations sustainable development goals.

the era of comprehensive intelligence has arrived, bringing new opportunities and challenges to everyone and every company. let us work together to promote comprehensive intelligence, so that everyone has their own exclusive smart assistant, every company can become an intelligent company, and every car can be driverless. thank you!

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