Over the past year, people have begun to realize the enormous power of AI and the innovation potential it can unleash, and the buzz around AI has been high, many of which will profoundly change the course of the technology industry and the entire world. In the future, the lifeblood of Al will depend on an open ecosystem. This ecosystem provides developers with options and helps them migrate applications across domains and vendors. This means developing platforms and solutions that transform the world's physical infrastructure into seamlessly connected, ubiquitous software.
Historically, Al has been concentrated in the data center, but market research firm Gartner predicts that "by 2025, more than 50 percent of data managed by enterprises will be created and processed outside the data center or cloud." Today, enterprises are seeking more opportunities through Al based automated operations, which is accelerating these trends. Due to the explosive growth of large amounts of data at the edge, and the increasing level of intelligence driven by data generated by mobile phones, PCS or retail stores, Al everywhere is possible. As one of the largest computing workloads, Al focuses on decision-making to drive efficiencies and tangible benefits in retail, manufacturing, hospitality and other industries.
At Intel, whether it's economic development and the evolution of the physical world, or the coming wave of autonomous, context-aware and collaborative software, each factor will drive a surge in demand for edge computing, while also dramatically reducing power consumption and total cost of ownership. Companies want to automate not only to improve price competitiveness or mitigate the impact of talent shortages, but also to enhance innovation, improve efficiency and reduce time to market. On a physical level, while sending data to the cloud for processing produces desirable results, the task is expensive and time-consuming.
From an economic perspective, it is more cost-effective and efficient to generate and process data on your own on-premises equipment than to rent, maintain and transfer data to cloud servers. Moreover, in terms of data security compliance, processing data locally at the edge not only complies with the necessary laws, but also helps to protect the privacy of the generated data.
Even with the challenges, many partners are eager to deploy AI as soon as possible to reap the benefits. Some early AI adopters are already operating digitally and reaping the rewards by layering edge Al in existing everyday applications such as restaurants, factories, point-of-sale terminals, and more. By eliminating the latency and bandwidth costs associated with cloud processing, Edge Al automates decision-making, helping to address issues such as talent shortages and privacy regulations. However, some of the inherent complexity of edge technologies should not be underestimated. For example, flexibility can be affected by issues such as lack of scale, limited computing power, and power constraints, and security and heterogeneity factors need to be taken into account. So integrating new technologies into these scenarios is complicated, and so is bringing AI to the edge.
With the help of the transformation of telecommunications networks, the mobile industry has been able to address the challenges of complexity, and Al technology has exploded at the edge. As the manager of the last mile for all enterprises, communication service providers offer a huge opportunity to help enterprises slice through the network while leveraging Al technology to optimize and operate the network more efficiently. In addition, AL-based radio intelligent controllers and predictive maintenance can also provide enterprises in many vertical industries with new edge Al products and enable them to profit from it.
Understandably, most customers prefer to integrate AI technology on top of their existing infrastructure, rather than build it from scratch. However, there is a real difficulty in integrating new technologies on existing infrastructure, which is recognized by the industry as an inevitable challenge. As with any emerging technology, while there is great expectation for the rapid development of AI, making decisions before the technology is fully mature inevitably comes with certain risks. In the absence of uniform industry-wide standards and protocols, companies that are early adopters of AI deployment and implementation may need to re-evaluate and adjust previous decisions in the future.
Analysts believe that the development of edge AI will go through three stages. The first is the highly customized use cases that are now widely adopted. Second, over time, these use cases will be replaced by industry-specific solutions that have inherent challenges in interoperability and energy efficiency, as well as the complexity of operating different systems and software. Ultimately, a foundational cross-industry platform will emerge to address common challenges across industries. Faced with the inevitable interoperability challenges and development shackles of the past few years, we should actively pursue and develop innovative solutions to address the core challenges posed by the evolution of the intelligent edge.
We believe that by taking an open, modular, unified platform approach at its core, communications service providers, developers, infrastructure operators and enterprises will be able to more easily and efficiently develop, deploy, run and manage scalable edge solutions. This will open the way for the secure deployment and automated management of heterogeneous edge device clusters across geographies, which will continue to expand and adapt as business needs change. Just as open standards and software-defined networking have played a crucial role in the evolution of cloud computing, we can learn from these principles to accelerate the deployment of edge Al solutions. By integrating software, hardware, and platform solutions developed specifically for Edge Al, we are expected to build a digital ecosystem with the goal of providing Al functionality wherever it is needed.
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