The advent of artificial intelligence (AI) has ushered in a seismic shift in the way scientific research is conducted around the globeOften referred to as AI for Science (AI4S), this innovative paradigm employs AI technologies to tackle complex scientific challenges, ultimately yielding significant discoveries and technological advancementsScholars and researchers are touting AI4S as the "fourth paradigm" of scientific inquiry, a term that emphasizes its foundational role in contemporary research alongside empirical, theoretical, and computational methodologies.

The impact of AI on various scientific fields has been profound and cannot be understatedAI4S integrates machine learning, data analytics, and high-performance computing, enabling scientists to delve deeper into their investigations and explore areas previously thought impenetrableWhile opinions among specialists regarding the definitions and implications of AI4S may vary, a common consensus emerges: AI is dramatically changing the face of scientific research.

A fascinating manifestation of AI's transformative nature was highlighted during a recent conference where it was noted that the Nobel Prizes in Physics and Chemistry for 2024 would both honor achievements in AI-related fieldsSpecifically, the Physics Prize will recognize groundbreaking foundational discoveries in machine learning based on artificial neural networksSimilarly, the Chemistry Prize will be awarded for contributions to computational protein designSuch accolades underscore the increasing prominence of AI in scientific inquiry, marking the entry of AI into the prestigious realm of Nobel recognition.

Consider, for instance, the 2024 Nobel Prize in Chemistry, which recognized the development of the AlphaFold AI modelThis innovation addresses a challenge dating back five decades: predicting the complex structures of approximately 200 million known proteinsThe implications for this advancement are staggering, propelling research and development in the biomedical arena and being utilized by over two million users worldwide

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This is a clear illustration of how AI is not just a tool but a catalyst for accelerated scientific progress.

The intertwined relationship between AI and the scientific community is becoming increasingly apparentAs pointed out by academic leaders like Academician O Wei-nan from the Chinese Academy of Sciences, contemporary scientific research can generally be classified into two paradigms: the data-driven Keplerian paradigm and the principle-driven Newtonian paradigm, both facing unique challenges in modern contextsThe solution to these challenges often converges on a singular point: the lack of effective methods to address high-dimensional mathematical problems impedes scientific progressHerein lies a fundamental opportunity for deep learning and AI to facilitate breakthroughs.

Traditionally, the focus of AI4S has been algorithm-driven, harnessing algorithmic advancements to fuel research innovationHowever, as large models continue to emerge and develop, a discernible shift from an “algorithm-driven” paradigm to a “computation-driven” approach is taking placeThis shift is particularly evident in data-intensive fields of research where vast computational, networking, and storage requirements are paramount.

Academician Wang Jian echoed similar sentiments during his address, emphasizing the crucial role the internet plays in the realm of open scienceHe argued that AI4S will foster greater inclusiveness, allowing more individuals to contribute to the pool of scientific innovationOpen science is not merely about making scientific findings accessible; it's fundamentally about rethinking how research is conducted and communicated.

In the era where data, computation, and AI exist inextricably linked to the internet, the latter has become an essential infrastructural backbone that propels various scientific inquiries forwardThe inherent scale effects of the internet amplify the potential success of AI applications, as it enables the seamless integration of data, models, and computational resources, much like the way the internet itself connects users and information across the globe.

Wang Jian also provided insights into the transformative potential of open-source platforms within the AI domain

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He discussed how projects like DeepSeek are expanding the concept of open-source and highlighting the immense value of open resources in advancing scientific and technological fieldsDeepSeek's origin under the MIT License allowed it to gain significant visibility, leading to a proliferation of scholarly articles covering the development and implications of the project shortly after its launch.

AI is increasingly being recognized as a standard-bearer for enhancing research innovation efficiencyData from Google Scholar reveals that in the past three years, the quantity of research papers utilizing AI has increased at a staggering rate exceeding threefoldThe emergence of large models has only accelerated this trend, positioning AI4S at the forefront of scientific innovation across various sectors such as chip design, biomedicine, materials science, astronomy, meteorology, and autonomous vehicles, among others, showcasing major breakthroughs along the way.

The rapid adoption of AI4S technologies is evident in the current trajectory of large modelsThe success of DeepSeek serves as a testament to the effectiveness of open-source large modelsMeta's chief scientist, Yann LeCun, noted that DeepSeek has introduced new ideas while building upon previous workIts open-source nature means that anyone can benefit from the outcomes of this research, exemplifying the power of open research and initiatives.

In essence, the characteristics of open-source large models imply that once their performance reaches excellence, supported by robust documentation, comprehensive guidelines, and an evolving toolchain, a snowball effect occurs—attracting developers and researchers to engage with its ecosystemThis leads to the production of an extensive family of derivative models that significantly enhance overall performance and quality, rivaling even the finest closed-source models.

Furthermore, the open-source model effectively mitigates the costs associated with deploying large models, overcoming previous limitations characterized by exorbitant inference costs

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By utilizing open-source large models in conjunction with public cloud and API structures, a comprehensive acceleration of innovation across multiple phases—from validating minimum viable products (MVPs) to reaching clients and refining operations—can be achieved.

For an industry perspective, private deployment of AI large models requires an investment of capital and time that can be up to tenfold compared to the public cloud and API deployment methodsPublic clouds offer a vast array of scalable, elastic, and cost-effective computational resources alongside established toolchains, sharply reducing the barrier to entry for innovationFor example, Google's cloud platform has enabled startups like Midjourney and Pika to rapidly launch new products.

From a customer engagement standpoint, public clouds boast access to an expansive pool of digitally-savvy customers, facilitating quick and economical outreachThe Mistral model, for instance, reported attracting around 1,000 quality clients immediately upon deployment on the Azure cloud platform.

This influential dynamic suggests that the combination of public clouds and APIs is set to become the mainstay for enterprises utilizing large modelsResearch institutions across China have increasingly turned to Alibaba's cloud services for conducting scientific innovations, resulting in promising advancements in areas spanning biology, agriculture, and astronomy.

Through concerted efforts in promoting powerful computational capabilities, shared data, and accessible models, Alibaba's AI for Science initiative has explored various cooperative frameworks such as infrastructure service models, specialized platform models, and joint research collaborationsFor instance, the collaboration between Alibaba AI and Sun Yat-sen University to explore "How to Use AI to Mine RNA Viruses" led to substantial discoveries, including the identification of over 510,000 virus genomes, heralded by being featured on the cover of the esteemed journal "Cell."

Additionally, long before the emergence of ChatGPT, Alibaba Cloud initiated the development of a model community—Modao Community—now hosting over 40,000 models and more than 10 million users

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