The increasing influence of artificial intelligence (AI) in various sectors has drawn significant attention to the frameworks that support its developmentThese frameworks, often referred to as the "operating systems" of the AI age, play a crucial role in interconnecting computational power, data, and algorithmsRecent predictions by the Chinese Academy of Sciences highlight that by 2024, MindSpore, an AI framework developed by Huawei, is expected to capture a 30% market share in China's emerging AI framework landscape, placing it among the top contenders globally.
Such advancements represent a pivotal shift for Chinese AI frameworks within the smart technology era and underscore the country's commitment to self-driven innovationHowever, as the pace of AI development accelerates with the emergence of massive model technologies, substantial challenges and opportunities ariseWith parameter scales reaching trillions and rapid advancements in model structure evolving towards multimodality, the AI framework ecosystem is undergoing a transformation, requiring support for multiple models and tasks
The integration of deep learning and reinforcement learning further complicates the landscape.
At the recent MindSpore Artificial Intelligence Framework Summit, Huawei's President of ICT Strategy and Business Development, Peng Honghua, stated that AI frameworks must embody efficiency, agility, and openness—qualities essential for the software backbone in this new eraHe emphasized that Huawei will continue to invest in MindSpore, driven by an open-source approach that nurtures a healthy and rapid development of China's AI ecosystem.
The significance of AI frameworks cannot be overstatedThey serve as the bridge between diverse computational capabilities and the fertile ground for algorithmic innovationIn the era of large models, the predominant concerns revolved around hardware capabilities—resources such as graphical processing units (GPUs) were paramountNotably, during discussions last year, a well-known academic quipped, “Discussing GPUs invites feelings; avoiding the topic excludes them entirely.” This reflects the industry's past preoccupation with hardware alone.
However, as hardware availability improves, the question arises: how can these resources be optimally utilized? This is where AI frameworks come into play, essential for maximizing training performance
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Zhang Peng, CEO of Zhipu AI, articulated the immense costs associated with hardware for startups and startups' reliance on AI frameworks to unlock the full potential of these investmentsOptimizing training performance is, therefore, not only a necessity but a priority, achievable through continuous improvements of the framework.
Chinese telecommunications giant China Mobile has experienced firsthand the importance of AI frameworksThis year, the company unveiled the world’s largest single-computer cluster, built in Harbin, but extracting maximum performance from it relies significantly on a robust AI framework.
Wang Bin, Senior Director at China Mobile’s Artificial Intelligence and Smart Operations Center, noted that due to the phased construction of their cluster, they often faced complexities related to migration and parallel processing strategiesWang's team collaborated with the MindSpore team to develop simulation model compilation technologies, drastically reducing the time required for implementing parallel strategies from weeks to days—an significant advancement in operational efficiency
The synergy of MindSpore with NPU (Neural Processing Unit) technology has proven to rival the best combinations available in the industry, delivering enhanced stability and capability.
Wang further acknowledged that while China faces constraints in semiconductor technology, innovation at the algorithm and operator level on the MindSpore framework presents a viable pathway forward.
Research trends emphasize that for models to progress, they must evolve alongside software stacks and hardwareHan Xu, an Assistant Researcher at Tsinghua University and Chief Researcher at Mianbi Intelligent Technology, highlighted their team's collaboration with MindSpore, which has enhanced resource efficiency in running models on lower-resource devices while also enabling larger models to be deployed effectively in more constrained hardware environments.
The next few years promise rapid advancements in frameworks like MindSpore, potentially leading to smarter, more interactive devices that leverage embodied intelligence
Han envisions a future brimming with innovative human-machine interaction models.
The rise of Chinese AI frameworks marks a significant chapter in the evolution of artificial intelligenceHistorically dominated by tech giants like Google's TensorFlow and Meta's PyTorch, the landscape is being radically redefinedEmerging frameworks like MindSpore, PaddlePaddle, and BytePS have quickly asserted themselves, disrupting the established order and presenting robust competition.
Among these, MindSpore stands out as one of the fastest-growing frameworksIn just four years of open-source development, the MindSpore community has garnered over 37,000 developers, with downloads surpassing 11 millionAs stated by He You, Vice President of the Chinese Academy of Artificial Intelligence, the influence of the MindSpore community has skyrocketed, positioning it firmly among the leading global AI frameworks
The framework not only simplifies operational interfaces but also optimizes computational resources, akin to a transmission mechanism that enhances system performance.
Recent market analysis from Omdia reveals that both MindSpore and PaddlePaddle have achieved higher recognition among domestic developers than many well-established foreign alternativesAs technology continues to evolve alongside an array of application scenarios, the outlook for domestic AI frameworks appears promisingThe global surge in AI investment is reflective of a burgeoning market, with projections suggesting that China's AI framework landscape could surpass several billion yuan by 2025.
Additionally, the MindSpore community has made significant strides in supporting over 50 mainstream large modelsPartnership initiatives with entities such as the Commercial Aircraft Corporation of China and State Grid have led to the creation of large-scale simulation models for practical applications
The vision for the future includes collaborating with more enterprises and research organizations to expand the framework's capabilities across various sectors and scientific frontiers.
Open-source innovation is a hotly debated topic in the realm of large modelsOpinions on the merits of open versus closed-source models vary widelySenior Algorithm Director at China Telecom, Song Shuangyong, argues that open source significantly lowers entry barriers for researchers, accelerating progress by allowing access to shared knowledge and resources for developing new models.
From a societal perspective, the willingness of powerful entities to share their extensive investments in model training and research exemplifies a commitment to pushing the industry forwardThe open-sourcing of AI models fosters a standardization of parameters and interfaces, making advanced technology more accessible and user-friendly for all.
The open-source movement has proven to be essential for the flourishing of AI frameworks
Since its inception in 2020, the MindSpore project has continuously driven innovation, resulting in a thriving ecosystemThe statistics speak for themselves: over 11 million downloads globally, substantial contributions from over 37,000 developers, and collaborations with more than 360 academic institutions showcase the framework's impact and reach in the international AI community.
Liu Haibo, Deputy Director at the Chinese Academy of Sciences' Science and Technology Strategy Consulting Institute, asserted that a collaborative approach involving universities, enterprises, and foundations is crucial for fostering open-source innovationHe emphasized that initiatives led by universities like Tsinghua and Peking are foundational to this evolution, while contributions from tech enterprises are equally indispensable in driving advancements in AI frameworks and foundational models.
However, Liu cautions that the landscape demands an effective governance framework to foster ongoing cooperation and innovation