ChatGPT developed by OpenAI based on the Transformer architecture and the recent SORA fire, but many authoritative figures in the AI industry have sprayed this technical route and pushed the world model. So, who represents the future between Transformer and World Model, and who has more hope of realizing our ultimate dream - AGI?
This paper briefly reviews the development process of AI, especially the background and development of the Transformer architecture and world model.
In today's era, AI technology is developing at an unprecedented speed, among which ChatGPT launched by OpenAI and the latest SORA have sparked widespread attention and discussion. The success of these technologies not only demonstrates the enormous potential of AI to understand and generate language-like languages, but also leads us to an exciting future where artificial general intelligence (AGI) is possible. AGI, an AI system with human intelligence that can demonstrate a high degree of flexibility and adaptability in a variety of tasks, has long been a dream for scientists, engineers, and philosophers.
However, despite the remarkable achievements of the Transformer architecture, some authoritative figures in the AI community have criticized this technical route and strongly advocated an alternative approach, the world model. The world model proposes a different perspective: to enhance the decision-making and capabilities of AI systems by simulating and understanding complex environments, which is considered to be another possible path to achieve AGI.
This debate on the future direction of AI not only highlights the technology choices and challenges we face in the pursuit of AGI, but also triggers deep thinking about the future development path of AI. So, between the Transformer architecture and the world model, which is the correct solution to implement AGI?
In the development of artificial intelligence, the Transformer architecture is undoubtedly an epoch-making innovation. It was first introduced in 2017 in Attention Is All You Need and aims to solve the problem of sequence-to-sequence conversion in natural language processing tasks. The core of Transformer is the self-attention mechanism, which enables the model to assign different weights to different parts when processing sequence data, so as to effectively capture the long-distance dependencies within the sequence.
The innovation of the self-attention mechanism is that it does not rely on the traditional recurrent network structure (such as LSTM or GRU), but directly calculates the relationship between the elements in the sequence, which makes the Transformer model more efficient when processing long texts, and also reduces the computational complexity. In addition, Transformer adopts the concept of multi-head attention, which further enhances the model's ability to capture information from different contexts.
Over time, Transformer architectures have expanded from the original NLP domain to include computer vision, speech recognition, and even reinforcement learning. In computer vision, for example, Transformer is used for tasks such as image classification, object detection, and image generation, and has demonstrated performance comparable to or better than traditional convolutional neural networks (CNNs). In addition, its application in processing time series data, processing and multimodal learning tasks is becoming more and more extensive, proving its powerful generalization ability.
The widespread adoption of the Transformer architecture in a short period of time is mainly due to its remarkable capabilities in language understanding and generation. The model learns the complex dependencies in the text through the self-attention mechanism, and can generate coherent and logical text, which is especially prominent in applications such as machine translation, text summarization, and dialogue systems. At the same time, the design of the transformer supports parallel computing, which greatly improves the training efficiency, which makes it possible to process large-scale datasets.
However, the Transformer architecture also has certain limitations.
Despite its excellent performance at capturing long-distance dependencies, the computational and storage overhead when dealing with extremely long sequences is still significant. In addition, Transformer models usually require a large amount of data to train to avoid overfitting and have high requirements for data quality. These features mean that while Transformer excels in resource-rich situations, it may not be as effective in resource-constrained or data-scarce scenarios.
What's more, despite the success of Transformer architectures in several domains, their ability to understand complex concepts and common-sense reasoning is still limited. This is because models rely primarily on learning patterns from data, rather than truly understanding the logic and reasons behind those patterns. This is especially true when trying to achieve true artificial general intelligence (AGI), which requires not only human-level intelligence on specific tasks, but also the ability to learn and adapt across domains.
At the other end of the AI spectrum, the world model challenges conventional wisdom and proposes an entirely new approach to understanding and interacting with complex environments. Unlike transformer-based architectures, which focus on pattern recognition and sequence processing of data, the world model attempts to understand the dynamics of the environment through internal simulations, so as to make more rational decisions.
The basic idea of the world model is derived from the observation of how humans and animals understand the world. Our brains are able to construct internal representations, simulate possible future scenarios, and make decisions based on those simulations. Drawing on this mechanism, the world model aims to provide an AI system with a simulation of the internal environment, so that it can make adaptive decisions in different scenarios by changing the state of the external world.
In the field of reinforcement learning, the world model has shown its strong potential. By simulating the environment in the model, AI can not only "imagine" the consequences of performing actions in the virtual environment, but also be able to evaluate the effects of different action plans before actually executing them, which greatly improves learning efficiency and decision-making quality. In addition, in autonomous decision-making systems, such as driverless cars and automated robots, the world model can help the system better respond to possible changes, improving safety and reliability.
The biggest advantage of the world model is its ability to simulate the environment and **, which allows the AI system to evaluate the consequences of different actions through internal simulations before moving on to actual operation, which is especially important in resource-limited or high-risk scenarios. The world model also supports the improvement of decision support and planning capabilities, as it allows the system to "see" and choose the optimal path among multiple possible futures.
However, the construction and application of the world model also faces significant challenges. First, the accuracy of the environment simulation is highly dependent on the complexity of the model and the quality of the data it has. To accurately measure dynamic changes in complex environments requires large amounts of data and powerful computing resources, which can be a limitation for resource-constrained projects. Second, it is extremely challenging to build a model of the world that can generalize to many different environments, because the complexity and impossibility of the real world is far beyond the processing power of any existing model.
Despite the great potential of the world model in theory, there are still many unknowns in practical applications. For example, how to ensure the accuracy of the model, how to deal with the possible deviation of the model, and how to adjust the model parameters to suit specific needs in different application scenarios all require further research and exploration.
On the path of exploring AGI, the Transformer architecture and the world model represent two very different design philosophies and goals in AI research. Both approaches have advantages and disadvantages in understanding complex systems, dealing with unknown environments, and learning efficiency, leading to a lively debate about which one is closer to achieving AGI.
A very different design philosophy
The Transformer architecture, with its self-attention mechanism at its core, aims to optimize the information processing process by analyzing patterns in large amounts of data. Its design philosophy is based on a deep understanding of the relationships between data and is particularly well-suited to working with serialized information such as text and language. This makes Transformer shine in areas such as natural language processing, NLP, etc.
In contrast, the design philosophy of the world model focuses more on simulation and the dynamics of the environment. It attempts to make adaptive decisions in a variety of contexts by building internal models to understand the external world. This approach is similar to how humans and animals use internal representations to ** and plan behaviors, and is therefore considered to have potential advantages in achieving AGIs.
Understanding complex systems is not the same as the ability to deal with unknown environments
Transformer architectures have the advantage of capturing deep patterns and relationships by analyzing large-scale datasets to understand complex systems. However, its performance may be limited when faced with an unknown environment or a situation where data is scarce, as the Transformer relies on patterns in existing data to learn.
The world model understands complex systems by simulating possible environmental states, especially when dealing with unknown environments. Through internal simulations, it is able to "imagine" different future scenarios, even those that have never been directly experienced. This capability gives the world model significant potential for strategic planning and decision support.
There are significant differences in learning efficiency
In terms of learning efficiency, the Transformer architecture can quickly learn from large amounts of data, especially when sufficient computing resources are available. However, this approach can lead to inefficient use of resources, especially when you need to work with very large datasets.
The advantage of the world model in terms of learning efficiency is that it can learn effectively with a small number of real-world interactions. By "experimenting" with different action strategies in an internal model, the world model is able to optimize decision-making without directly interacting with the environment, reducing the reliance on actual data in the learning process.
Is it possible to combine the Transformer architecture and the world model?
Exploring the possibility of combining the Transformer architecture with the world model may open up a new path for AGI implementation. For example, the powerful language processing capabilities of the Transformer architecture can be used to enhance the environment simulation capabilities inside the world model, or the Transformer module can be integrated under the framework of the world model to improve the depth of the model's understanding of environmental changes. This convergence may present new challenges, such as how to balance the computational demands of the two architectures and how to integrate their respective learning mechanisms.
Of course, in addition to combining existing architectures, implementing AGI also requires the exploration of new technologies and theories. This could include developing new neural network architectures, delving into brain and cognitive science for inspiration, or developing algorithms that can learn and adapt across domains. These new explorations will require the AI research community to integrate knowledge from neuroscience, psychology, computer science, and other fields across disciplinary boundaries.
On the road to the pursuit of AGI, the Transformer architecture and the world model have their own strengths, representing two different paths for the development of AI technology. While each approach has its own unique advantages and limitations, future AGI implementations may not rely solely on a single technology or approach. Conversely, combining the strengths of both architectures, and even exploring new technologies and theories, could be the key to achieving truly intelligent, flexible, and adaptable AGI systems.
With the advancement of technology and the deepening of interdisciplinary collaboration, we are getting closer to realizing the dream of AGI.