One day two years ago, OpenAI employees received an unexpected task: quickly release a chatbot .
At the time, an executive announced that the chatbot would be called "GPT-3.5," and that it was supposed to be free to the public in two weeks before changing its mind.
It is understood that other members of OpenAI believe that using old models to quickly launch new products can help them gather feedback to improve new models. So they decided to start over, using an enhanced version of the GPT-3 language model introduced in 2020 to update an unreleased chatbot.
13 days later, ChatGPT was born .
ChatGPT has become a global phenomenon in the months since its debut, with millions of people using it to write poems, develop apps and perform temporary therapy.
Today, ChatGPT is crowned with praise from most news outlets, marketing firms, and business leaders, and ChatGPT has logically sparked a frenzy among investors trying to capitalize on the next wave of the AI boom. Take a share.
With the explosion of ChatGPT, the artificial intelligence automatic content generation technology (AIGC) based on large models, big data, and high computing power has been pushed into the "hot search", thereby opening up the imagination of the market's demand for computing power .
In recent years, computing power has gradually penetrated into all aspects of people's daily life, and various integrated applications have emerged.
So, what is the connection between cloud computing and artificial intelligence technology? Can cloud computing as a computing power infrastructure also take advantage of the wind of artificial intelligence, unleash the potential of big data, and promote the development of digital industries?
The combination of AI and cloud computing
Generally speaking, artificial intelligence is the process of programming computers to make decisions for themselves, a technology that creates intelligent applications that reason, learn and act independently.
At present, the content of artificial intelligence research is concentrated in the six major directions of machine learning, natural language processing, computer vision, robotics, automatic reasoning and knowledge representation. At present, the application range of machine learning is still relatively wide, such as automatic driving, smart medical care and other fields have a wide range of applications.
Compared with traditional data analysis techniques, artificial intelligence is based on neural networks. Just like the human brain, artificial intelligence can develop divergent neural networks during evolution to perform comprehensive machine learning.
Compared with the traditional algorithm, the artificial intelligence algorithm has no redundant assumptions, but completely uses the input data to simulate and build the corresponding model structure. This algorithm characteristic determines that it is more flexible and can be based on different training data. The ability to self-optimize also brings a significant increase in the amount of computation.
Before a breakthrough in computing power, the advantages of big data are almost useless, let alone the use of artificial intelligence.
Nowadays, people are entering an era of rapidly expanding data volume, and a small number of servers can no longer solve the problem, so people begin to aggregate the power of multiple servers and use the cloud platform to realize the analysis and integration of massive data.
With the development of cloud computing, high-speed parallel computing, massive data, and more optimized algorithms have jointly contributed to breakthroughs in technological development, and have also provided more possibilities for the development and application of artificial intelligence in cloud computing .
Cloud computing provides computing services through the Internet, including servers, storage, databases, networks, software, analysis and intelligence, to provide faster innovation, flexible resources and economies of scale, while artificial intelligence can help cloud providers provide customers with smarter and personalized service.
For example, artificial intelligence can automatically provision and manage cloud resources, optimize cloud infrastructure, and protect data and applications in the cloud. We can also use AI in cloud computing to develop new cloud services, such as voice-activated assistants and predictive analytics.
Cloud computing is becoming increasingly popular for businesses of all sizes . It offers many benefits, including the ability to scale quickly and easily, offering pay-as-you-go pricing, and increased flexibility and agility.
As a huge high-tech collection, "artificial intelligence + cloud computing" is beginning to sprout as a new economic format. More and more cloud computing companies are beginning to embrace artificial intelligence, using "artificial intelligence + cloud computing" to help technology and further development of the industry.
Artificial intelligence born on the fertile soil of cloud computing and big data is the chosen one. With the advent of the new technology era, people's lives are more closely linked with new technologies such as artificial intelligence and cloud computing.
What can Web3 cloud bring to the development of AI?
As we mentioned above, the continuous iteration of massive data, models, and computing power brought about by the mobile Internet era, as well as commercialization attempts in various application scenarios, have laid a solid foundation for the commercialization of artificial intelligence, and it is expected to accelerate the release of artificial intelligence in the future. Smart industry momentum.
According to the forecast of the famous consulting firm Deloitte, the scale of the global artificial intelligence industry is expected to grow from US$690 billion in 2017 to US$6.4 trillion in 2025, with a compound growth rate of 32.10%.
The cloud computing industry has also gone through the road from hype to widespread adoption. Today's cloud computing is like a power unit, providing a continuous source of energy for artificial intelligence.
However, the cloud computing we introduced above only includes the traditional centralized cloud. As the Web3 infrastructure has ushered in a critical development stage in the past two years, during this period, the Web3 cloud is capturing higher industry value before the application .
In this track with great potential, Phala Network can be regarded as one of the representatives with comprehensive strength.
The core of Phala Network is a cloud computing network. Compared with the current cloud services, it can not only provide huge decentralized computing power, but also provide privacy protection for regulated programs, and maintain the security and trustless properties of the blockchain .
In addition, Phala Network can also freely combine with other decentralized smart contracts, storage protocols, and data indexing services to connect the cloud computing power of many distributed devices on the Internet, so as to ensure low-cost and efficient operation Realize true decentralization.
So, compared with the traditional centralized cloud, what can the Web3 cloud computing network built by Phala Network provide for the development of artificial intelligence? Let's break it down one by one:
- Lower cost and wider computing power
The most attractive factor of using AI with cloud computing via blockchain is its significant reduction in cost.
Compared with traditional cloud computing that provides users with centralized machines and services, Phala Network connects distributed computing power from all over the world. The investment and use of idle resources makes the computing power cost of artificial intelligence more significantly reduced.
In addition, Phala adopts the model of "on-chain consensus, off-chain computing".
Among them, off-chain computing nodes are not constrained by the consensus algorithm. Through concurrent programming, the computing power of multiple nodes can be combined. Even in the face of heavy computing tasks for artificial intelligence, Phala can provide it with a steady stream of computing power services .
- Trustless Artificial Intelligence Ecosystem
Finding the balance between privacy protection, policy regulation, and business demands in the era of artificial intelligence has become an urgent problem to be solved.
Phala Network is built based on the Secure Enclave trusted execution environment, which means that even malicious nodes cannot steal artificial intelligence data or manipulate the execution of its automated programs to provide false results.
Through the trustless computing environment provided by Phala, people can solve various problems such as privacy protection that may exist in the application process of artificial intelligence through the underlying technical framework without worrying about centralized control, thereby establishing a reliable and trustless computing environment. Artificial intelligence ecology.
- Super Internet for easy access
When accessing the Internet, artificial intelligence plays an important role in data processing, management and structuring .
Artificial intelligence tools simplify the absorption, modification and management of data, thus effectively providing more comprehensive, intelligent and practical services for the Internet and users.
Phat Contract, the core product of Phala Network, can access and access any Web2 and Web3 data and services through the built-in Internet. Moreover, Phala's cross-chain bridge SubBridge connects multiple blockchain ecologies, even data and assets on different chains can communicate with each other, allowing artificial intelligence to complete easily accessible Internet services and respond to a wider range of network requests.
- Low Latency AI Interaction
Theoretically, for developers, what artificial intelligence pursues is speed, which includes the training speed of the model, the reasoning speed of the model application, etc., eliminating the undifferentiated heavy tasks in deep learning applications, and continuously Iterate quickly.
The interaction speed of artificial intelligence depends on the performance of the computing node device itself. Phala Network uses the model executed off-chain to achieve millisecond-level request response. Therefore, through continuous iteration and training of Phala technology, low-latency interaction can be achieved.
All in all, the decentralized cloud computing network built by Phala not only has the functions of traditional smart contracts, but the important thing is that the "separation of consensus and computing" makes large-scale off-chain computing, off-chain data requests and real-time responses a reality, and can carry large-scale Application scenarios such as high density, low latency, real-time interaction, and off-chain interconnection provide a solid infrastructure base for the development of artificial intelligence technology.
It can be said that it has become an inevitable trend to integrate the advantages of Phala Network and other Web3 decentralized clouds with artificial intelligence technology. So, going back to Deloitte's previous predictions, will people's predictions for artificial intelligence applications be conservative ?
Where is the ceiling of AI?
As the AIGC field of generative artificial intelligence is heating up, the business competition on artificial intelligence with ChatGPT as the core has quietly begun, and how to dig out the dark horses in the industry is a point worthy of attention today.
With a series of new releases recently, technology giants are in full swing to launch more powerful chat robot services and more AI functions for their products or developers, in order to take the lead in the new wave of artificial intelligence set off by ChatGPT.
For example, Microsoft expanded its partnership with OpenAI and introduced its own search engine Bing and cloud service Azure into ChatGPT; or Google recently invested 400 million US dollars in AI start-up Anthropi, whose core product Claude is known as ChatGPT's strongest Competing products, Anthropi will also use Google cloud services in the future.
It can be seen that in the AIGC competition, traditional large technology companies such as Microsoft, Google, and Amazon are not only cloud service providers, but also competitors in the field of artificial intelligence .
From the perspective of the development of the cloud industry, the continuous explosion of ChatGPT has brought new growth to AIGC, and at the same time, it has put forward higher requirements for the computing power support required for artificial intelligence model training.
As the foundation of computing power, cloud computing infrastructure has become more and more important under the impetus of AI development. According to the report, benefiting from the emergence of diverse computing power demand scenarios such as AI and industrial digital transformation, the estimated computing power demand will grow at a rate of more than 20% every year, and the data center will benefit from computing power infrastructure for a long time.
Although the traditional centralized cloud is maturing day by day, the Web3 cloud is also accumulating strength. If the Web3 cloud can achieve greater development, its industry's requirements for computing power will bring about a large increase in cloud infrastructure, helping the cloud service industry to quickly get out of the circle.
With emerging technologies, application requirements, and industrial evolution never stopping and rushing forward, how far will cloud technology advance, and how high will the ceiling of artificial intelligence develop with the support of cloud computing? Time will tell us the answer.
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