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Development trends and application scenarios of generative artificial intelligence

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The Vincent video model Sora released in February 2024 attracted attention. The advent of this technology is considered a major breakthrough in the field of video generation. Compared with ChatGPT, which also caused a sensation, the two are related but different in terms of technical routes and product positioning. On the one hand, Sora combines the model architecture adopted by ChatGPT, which can combine several still images to generate continuous videos, and can also automatically repair incomplete video segments. On the other hand, both Sora and ChatGPT have good natural language understanding capabilities. They can generate and adjust video content based on user descriptions, and can also summarize and supplement the materials provided by users. The emergence of Sora is the result of OpenAI's accumulation of innovations in model architecture, data management and other directions. Behind it is the integration of technology transformation and new technology innovation of the GPT series. ChatGPT focuses on the understanding and generation of text content. Users can have conversations and questions with it, and can also instruct it to write articles, code, etc. Its basic functions are mainly realized by text interaction. The most common application scenario is mainly the processing of text content, including text interpretation, reorganization, expansion, sorting, etc. Sora focuses on the creation of video content. Its core function is to generate video content based on text prompts. On the premise of understanding and simulating the physical world, it constructs a virtual world and displays the interactive rules of this world.
Vincent video models are powerful in many aspects, but they are not perfect in themselves. Similar generative models still have some problems, such as not fully reflecting all physical laws, mainly because some situations that violate common sense in life and scientific understanding still occur. The above-mentioned deficiencies of the Vincent video model have been around for a long time and are difficult to be solved in the short term. Overcoming these deficiencies while maintaining and expanding the advantages of the model will become the focus of the next stage of the artificial intelligence industry.
First of all, the primary flaw of the Vincent video model is its huge consumption of computing resources.Compared with the mainstream large language model computing power, image parameters take up more computing power resources. In order to pursue better model performance, the computing power demand of the global artificial intelligence industry will further increase, and countries and regions lacking computing power will be at a technological disadvantage.
Secondly, the phenomenon of model hallucination is still serious.Similar to text generation models, video generation models are also affected by the hallucination effect. When the training data is distorted during processing such as compression, when user prompts are too vague, or when security policies fail to respond, the model is forced to fill in the gaps. This filling may cause the model to fall into hallucinations and output content that is inconsistent with the facts or that the user did not request.
Finally, there are common sense errors in the details of generating videos.The Vincent video model's understanding of physical laws is still at the primary stage. It can correctly reflect the macroscopic interaction between people and objects, but it cannot accurately grasp the physical laws involving the change of the shape of objects. Generative AI may generate erroneous content due to a lack of cognition, or it may generate erroneous content due to improper infusion of cognition. For example, when a character takes a bite of a biscuit, the biscuit remains intact. Although this type of common sense error is usually controlled on a small scale, it still shows that the model's understanding of real physical laws is still at a shallow level.
Sora's technical roadmap and performance rely on the powerful natural language understanding capabilities provided by the large language model base. In the future, it will become easier for users to interact with generative artificial intelligence, and models will also receive further feedback training as they are used by a large number of users. The development of generative artificial intelligence that can understand, reproduce and even simulate physical interactions will become a new industry development direction.
First, the media industry can use such tools to improve content production efficiency, including TV series, movies, self-media and other industries. With the continuous improvement of performance indicators such as generation time, scene accuracy, and prompt word compliance, generative artificial intelligence will effectively reduce the production cost and employment threshold of the media industry, and change the content ecology of the media industry. Generative artificial intelligence that integrates various model architectures will also be capable of specific tasks with different content in the future, such as script writing, casting assistance, shot planning, and editing assistance for movies. Before the emergence of general artificial intelligence in a complete sense, quasi-general artificial intelligence that can undertake all tasks in a certain field or industry in parallel may enter social production first.
Second, the creative industry ecology will change due to the continuous development of generative artificial intelligence.The virtual videos generated by Vincent Video Model have a sense of imagination and design, and can generate relevant content based on keywords, pictures or videos. Creators can hand over their designs, ideas and semi-finished products to artificial intelligence to generate complete creative works. ; Or look for improvements in existing works. Generative artificial intelligence at this stage is mostly equipped with the function of connecting multiple different media forms, and can integrate text, sound, images, videos and other forms of materials to create extremely rich content. The generative artificial intelligence industry will continue to strengthen the model's ability to present human thoughts and significantly lower the threshold for content creators. Ordinary people will also have the opportunity to depict the art world in their own minds, the content and form of creative works will become richer, and the creative industry is expected to usher in new development.
Third, the game and simulation industry will gain new development directions with the help of generative artificial intelligence.The digital simulation capabilities demonstrated by the new generation of generative artificial intelligence will undoubtedly further lower the threshold for game production, allowing small teams to independently complete the development of large productions; this breakthrough also brings a new technical route to digital simulation-if Generative artificial intelligence can correctly and accurately understand physical laws, so it will be possible to use model calculations to predict the direction of complex events. In the future, generative artificial intelligence will get closer and closer to a complete virtual world engine.
Fourth, generative artificial intelligence is expected to become the basis of the metaverse world.Their performance combines virtuality and reality to a certain extent. Once combined with front-end technologies such as the Internet of Things and brain-computer interfaces, they will bring a new way of information interaction to society. After large-scale training, not only can the visual world be understood through images, but the real world can also be simulated. Although there is still a lot of room for improvement in related technologies, the most advanced generative artificial intelligence can already simulate some physical interactions. Vincent Video is just one manifestation of a new generation of generative artificial intelligence. The essential role of the physical simulation model is to further integrate virtuality and reality and create content from virtuality that is infinitely close to reality. It can be seen that generative artificial intelligence is expected to become another starting point for the construction of the metaverse.
Just as a baby can gradually understand the physical laws of the world after seeing its mother disappear and reappear countless times, generative artificial intelligence has begun to learn physical common sense such as 3D consistency and object coherence by observing dynamic videos. It may only take a few years for algorithms to go from understanding the real physical relationships of this world to simulating a realistic physical world. During this period, advanced generative artificial intelligence can empower specialized industrial software, expand its functional boundaries and improve problem-solving efficiency. More models aimed at simulating the world will come out, accurately simulating scenarios that were difficult to achieve in the past, and play a role in autonomous driving research and development, product design, film production and other businesses. With the assistance of artificial intelligence, more people can master the ability to complete most work tasks in a shorter learning cycle, and the social labor force will be further liberated. The generative artificial intelligence that appears in front of us is by no means just a simple The video generation model is the beginning of the interaction between AI and the real world.
Original source:Study Times
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