Preface
In recent years, the global IoT industry has continued to advance, and its related application scenarios have expanded from basic hardware to comprehensive interactive systems such as software, services and platforms. It has also rapidly enabled the digitalization and intelligent innovation and upgrading of the traditional economy in multiple fields such as industry, agriculture and commerce.
As the digital engine and core driving force of a new round of industrial transformation, AI is further releasing the enormous energy stored in previous scientific and technological revolutions and industrial innovations, reconstructing key market activities such as production, distribution, and interaction in multiple dimensions, and gradually stimulating and giving birth to new technologies, new products, new business forms, and new models.
At present, AI is accelerating its integration with all walks of life. Disruptive innovation helps traditional industries transform and upgrade, improve quality and efficiency, and lead the global future industrial technology wave. However, according to data from the analysis company Similarweb, as of August 2023, ChatGPT's global desktop and mobile website visits fell by 3.2% to 1.43 billion. In addition, investment in the AI field in the primary market has gradually cooled down. According to CB Insights data, in the second quarter of 2023, the total investment in the global AI field plummeted by 38% month-on-month. It can be said that the market cooling-off period has arrived. How can AI move from virtual to real and start a new chapter?
A look at the current state of AI around the world
Macro-upward Interactive integration
Technology co-creation
The development and evolution of AI technology is no longer a single upgrade and vertical exploration, but has gradually begun to interact, integrate and develop with emerging digital technologies such as blockchain, 5G, and XR, achieving breakthroughs in multiple areas.
As of today, AI has made milestone progress in many fields such as big data processing, image processing, speech recognition, and machine translation (both accuracy and efficiency have been significantly improved), and through in-depth integration with diversified digital technologies Integrate, continuously improve the density of technology and product innovation, and enrich ecological experience.
Multi-dimensional applications
First, the relationship between humans and AI is not a simple substitution relationship. Second, the interaction and cooperation between humans and AI can further promote the efficiency of market operations, build a more complete social ecology, and continue to provide key growth momentum for the digital and intelligent transformation and upgrading of enterprises. At present, the actual application scenarios of AI are very extensive - covering entertainment, finance, medical care, education, transportation and many other fields.
· Industrial chain collaboration
The AI industry chain mainly includes technology providers, application developers, service providers and other sectors. In depth, technology providers mainly provide AI algorithms and technology platforms; application developers develop specific applications based on the technology providers' platforms; service providers focus on providing a series of products and services based on AI technology. At present, the major links in the global AI industry chain are collaborating with each other, innovating together, and are jointly committed to promoting the healthy and sustainable innovative development of the AI industry.
Investment boom
The global AI industry market investment boom is continuing to rise, and the relevant investment amount and the number of investment institutions have maintained a sustained growth trend in the long term. The investment areas involved cover all major tracks of AI technology innovation and application - including speech recognition, natural language processing, image processing, human-computer interaction, etc. It is foreseeable that the investment boom in the AI industry will continue to remain high in the future, and the global market will show a wide-ranging breakthrough and a multi-point blossoming situation.
· Policy Support
At present, governments around the world are deeply planning the construction of AI digital innovation ecosystems and continuously strengthening policy support for the AI industry, including tax incentives, financing and investment, talent introduction and many other aspects. For example, the US government has proposed an "AI national strategy" and strengthened continuous investment and market policy support in the field of AI; the Chinese government has also launched an integrated development plan and policy support measures for the AI industry, aiming to promote AI technology research and development, product development and scenario applications. These policy supports provide strong guarantees for the development of the AI industry.
Competition for talent
People who understand emerging industries such as AI always have a consensus that the competition for core talents is extremely fierce. In order to attract and cultivate outstanding talents and maintain the sustainability of the talent ecosystem, governments, enterprises and institutions have been continuously strengthening the cultivation, introduction and reserve of core industry talents, and constantly strengthening and improving strategic planning and financial investment in talent training, support and development.
Social ethics
AI is constantly rewriting the operating model of human society, and its rapid development trend has also brought many problems to the entire human society - the impact of AI on human society and ethics, the bias and discrimination of AI algorithms, the high energy consumption of AI large model training, AI's manipulation of false information and social public opinion, and the role of generative AI in human innovation and creation... Today, as the AI industry is accelerating, these issues are worth our careful consideration, and all sectors of the world are also continuously strengthening research and discussion on AI ethical issues, and promoting the formulation of corresponding industry ethical norms and ecological standards.
Concrete cultivation in six major areas
Information processing
The rapid development of AI theory has provided new ideas and methods for information processing technology, and promoted the development and application of intelligent information processing. AI is rapidly penetrating the computer and other information processing terminal markets, focusing on exploring issues such as intelligent processing, communication and control of text, images, voice and other information.
Content interaction
With the iterative development of a series of application-level artificial intelligence content generation (AIGC) algorithms such as text generation, image generation, and 3D model generation, AI has initially acquired the ability to produce and interact with digital content, and continues to break through many barriers such as logical reasoning and common sense cognition. At present, a large number of AI engineers around the world are deeply engaged in technologies that aim to accurately identify objects, sounds, texts, and other content, aiming to provide infrastructure support for the multi-dimensional ecological expansion of [AI+].
· Calculation and regulation reform
AI and machine learning models are directly related to the quality of the data they use. If the data is biased, erroneous, or noisy, the output of the model may also be inaccurate or biased. Therefore, it is very important to make corresponding planning reforms in data collection, information storage, and algorithm processing, which is a necessary condition for expanding the scope of AI applications.
Hardware development
As generative artificial intelligence (AI) continues to develop, hardware innovation is becoming a new business opportunity, and AI-driven hardware innovation is also accelerating. AI has been constantly changing the development of software, and the new generation of hardware is also actively supporting the development of machine learning to complete larger-scale computing tasks.
Embodied Intelligence
Under the wave of artificial intelligence, the robot embodied intelligence (Embodied Intelligence) supported by it has attracted widespread attention from all over the world and has undoubtedly become the next hot spot in the robotics industry. From the perspective of the industrial chain, machine vision, edge computing, etc. are important basic components of the embodied intelligence industry. Driven by policy support and market demand, it is expected to drive the exploration and development of related industries. Among them, helping intelligent robots achieve precise control of any action is an important research field in the AI industry.
General AI
There are currently two main understandings of general AI: General Artificial Intelligence (GAI) refers to artificial intelligence that can handle a variety of tasks; Artificial General Intelligence (AGI), also known as strong artificial intelligence, super artificial intelligence, etc., refers to an intelligent system that reaches and exceeds the human level in all aspects.
General AI is the ultimate goal of AI development. In recent years, large models have continued to expand in scale, and have shown an "emergence" of capabilities from quantitative change to qualitative change, showing unexpected capabilities in language understanding generation, logical reasoning, etc., opening the prelude to general AI and becoming an important driving force for a new round of scientific and technological revolution and industrial transformation.
AI achieves breakthrough in key bottlenecks
Data and Algorithms
Data privacy and security issues
Large-scale data collection and processing often lead to violations of personal privacy and data security, and some AI applications require large amounts of personal data to train models, which leads to a series of potential privacy violations and other issues.
Algorithmic fairness and transparency
Due to bias in training data and the design and implementation rules of the algorithm, some AI systems may have problems such as bias and discrimination, which is a serious challenge for users and other stakeholders.
Ethics and Regulation
AI Ethical Issues
As the AI industry continues to develop, a series of social ethical issues have begun to emerge, such as: Will AI threaten the existence of humans as subjects and dignity? Will humans be marginalized or replaced? These issues involve the relationship between humans and machines, as well as the values and meaning of humans themselves.
Standards and regulation
Overall, there is currently a lack of unified standards and regulatory frameworks for the AI industry worldwide, which has directly led to differences in the development of AI ecosystems around the world and a large number of cases of using AI to seek improper benefits.
Talent and implementation
Urgent need for talent
In addition to the extreme need for highly specialized talents in the AI industry (including machine learning experts, data scientists and engineers), the AI industry also needs talents with interdisciplinary skills such as professional knowledge, business insight and teamwork leadership in various traditional fields. As the industry continues to expand, the demand for professional talents in various fields has also surged, which has directly led to fierce market competition for talents.
· Landing difficulties
The main issues include uncertainty in business applications and ROI (return on investment). First, AI technology is very powerful in theory, but success in actual business applications is not always easy to achieve. Companies may face the problem of considering return on investment and how to integrate AI technology into existing business processes. It may take some time to overcome the difficulties and promote the application of "AI +" products and services in the market.
In addition to the three major areas discussed above, if AI wants to achieve exponential growth and innovation, it must also solve many subtle problems such as high costs and low interaction rates.
It can be said that the world is currently in a cooling-off period for AI. After half a year of "stabilization", modeled AI is no longer a panacea that can save all walks of life. The market's return to calm is not necessarily a bad thing. On the contrary, thinking in silence, "AI+" industries/scenarios will create more new narratives and a new future for us.
From virtual to real - multi-dimensional cultivation and ecological co-construction
AI itself has great development potential. With the continuous advancement of related technologies and the continuous maturity of solutions, the market may become active again in the near future. In this regard, we who are experiencing a cooling-off period in the global AI market should open up our minds, conduct in-depth research and multi-dimensional analysis from multiple dimensions such as the market and ecology, and pay close attention to the latest developments in the field.
AI is gradually moving from pure theoretical concepts and laboratory research to actual market applications, penetrating into various industries in real society and our daily life scenes. In order to truly move from virtual to real, AI must further complete the expansion of diversified scenarios and co-construction of the industry. In this process, it involves the collaborative assistance of technology, ecology, policy, talent and implementation.
Technology Innovation
Model optimization and lightweighting
In order to adapt to diversified ecological scenarios, it is necessary to continuously optimize and lightweight AI models to better adapt to different hardware and environments with the advantages of low threshold and low cost.
Adaptive learning and transfer learning
AI systems need to have adaptive learning capabilities to ensure that they can quickly learn and adapt to new scenarios, while transfer learning can help models quickly and efficiently complete information interaction and knowledge sharing between different tasks.
Industry collaboration
Industry-University-Research
The cooperation between industry, academia and research institutions is of great significance to the stable and sustainable development of the AI industry. Through multi-party knowledge sharing and resource interaction, the application of AI technology in practical scenarios can be further accelerated.
Linkage platform
It requires multiple parties to work together to establish an open innovation linkage platform to organize companies and institutions from different fields to jointly solve many problems that are difficult for individuals to face, thereby promoting cross-industry collaborative applications of AI.
Policy Specifications
Industry norms
Formulate reliable data privacy laws and ethical standards to protect users' personal privacy and data security, thereby ensuring the stable and efficient operation of AI applications within the legal and ethical framework.
· Incentives
By formulating corresponding planning and supporting policies and measures, such as tax incentives and financial support, we can stimulate the overall positive development of the AI industry and encourage traditional enterprises to invest moderately and apply AI in multiple scenarios to achieve their own digital innovation and upgrading.
Talent Education
Cross-training
Promote interdisciplinary guidance education, cultivate talents with professional knowledge in different fields, and reserve comprehensive talents for AI to empower traditional industries.
· life-long learning
Cultivate the concept of lifelong learning for all people, and support industry stakeholders to continuously improve their knowledge reserves and skill proficiency to adapt to the rapid development of the industry and the needs of multiple scenarios.
Let’s talk about the future development trend of AI
Needless to say, the future development of AI will certainly cover many fields and various tracks, whether it is the iterative innovation of technology, the reform and development of products and services, or the continuous expansion of application fields, etc., it would be too messy to discuss them one by one. This article mainly integrates and predicts the following three directions:
Multi-technology integration
Future AI may focus more on innovations in autonomous learning capabilities, including self-supervised learning and enhanced supervised learning, which will make AI systems more flexible and efficient, allowing them to better adapt to new environments and complete larger-scale complex tasks.
As for future models, they will face more complex and diverse interactive scenarios, and pay more attention to the integration of various forms of information such as text, images, and videos. In this regard, future AI models may become larger and more complex, and will involve larger-scale deep learning models, faster computing hardware, and more efficient model architectures.
Multimodal models can process diversified data such as visual information, text information, and auditory information, and can integrate and understand information in different forms, which is an important step for AI to fully understand the real world. In addition, with the continuous development of hardware technology, integrating AI algorithms into edge devices, reducing dependence on cloud computing, and improving response speed is an important direction for future development.
Vertical industry implementation
In the future, AI will more extensively and deeply empower various vertical industries, including healthcare, finance, manufacturing, retail, energy, etc. This will inevitably involve more systematic and customized solutions, thus giving rise to special needs of specific industries.
In addition, as technology develops, AI will further penetrate into emerging fields such as quantum computing, biomedicine, renewable energy, etc., and continue to drive innovation and progress in these fields.
Ecological construction specifications
As AI technology continues to develop, society is paying more attention to ethical and legal issues. In the future, there may be more emphasis on the ethical use of AI, privacy protection, and fairness, and countries may develop more complete legal frameworks to regulate the development and application of AI. A series of ethical and regulatory issues, including privacy, security, and restrictions, are beginning to be resolved, and the formulation of corresponding laws and policies has been completed, but it may take time to form a global consensus, and the public's awareness and acceptance of AI technology will also increase significantly.
The foreseeable trends are only some possible directions. Actual development depends on the combined influence of multiple factors such as technological progress, social needs and policy changes. As a highly dynamic, fast-paced iterative innovation process, the field of artificial intelligence can give rise to new development trends and directions at any time due to sudden changes in demand and market turmoil.
end
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Against the backdrop of the economic construction of new industries, new formats, and new business models, companies' demand for AI is gradually increasing, and the growth rate of AI output value is remarkable. As AI is being implemented at an accelerated pace in thousands of industries, relevant companies must focus deeply on and continue to delve into the "data + AI" track, seize the opportunities brought about by AI technological changes, create new development momentum with scientific and technological innovation, actively explore the application value of multiple scenarios, and promote the healthy and high-quality development of the industry.
