Peirce’s Trichotomy of Inferences Informs the Curriculum of Deep Learning Training
As a fan of John Vervaeke’s work on Recursive Relevance Realization (R3) and its role in human cognition, I am excited to share my own proposal for a similar framework for artificial intelligence: Incremental Inductive Inference (I3). While current AI systems have not yet achieved the level of sophistication necessary for R3, I believe that I3 offers a promising alternative that could help us to better understand the processes underlying human cognition and ultimately lead to more advanced AI. I invite you to hear me out as I delve into the details of I3 and its potential applications.
Peirce’s trichotomy of inference is a classification system for the three major types of reasoning that are used in logic and philosophy. These three types of reasoning are deduction, induction, and abduction.
Deduction is a type of reasoning that involves starting with a general rule or principle and applying it to a specific case or situation. This allows us to draw conclusions that are logically certain, based on the rules and principles that we start with.
Induction is a type of reasoning that involves making inferences based on observed patterns and relationships within the data. This allows us to make predictions and generalizations about the underlying principles and regularities that govern a given system.
Abduction is a type of reasoning that involves making inferences based on incomplete or imperfect information. This involves making educated guesses or hypotheses, based on the available data, in order to explain or understand a given phenomenon.
Overall, Peirce’s trichotomy of inference is a useful classification system for understanding the three major types of reasoning that are used in logic and philosophy. These three types of reasoning, deduction, induction, and abduction, are all essential for making logical and well-supported arguments and conclusions.
Self-supervised learning is a powerful approach to training machine learning models without the need for human-generated labels. In this method, the supervision signal is extracted from the data in a deductive manner, using an algorithm to separate the label from the rest of the data. The model is then trained to predict the label from the remaining data.
One key aspect of self-supervised learning is the use of an algorithm to extract the supervision signal in a deductive manner. This algorithm is designed to identify the label within the data and separate it from the rest of the information. By doing so, it allows the model to focus on learning from the remaining data, which can be more complex and diverse than the label itself.
Once the supervision signal has been extracted, the next step is to train a network to predict the label from the remaining data. This involves feeding the data into the network and using a supervised learning algorithm to train the model to make accurate predictions. The goal is to teach the network to understand the underlying patterns and relationships within the data, so that it can make predictions about the label with a high degree of accuracy.
Overall, self-supervised learning offers a powerful approach to training machine learning models without the need for human-generated labels. By extracting the supervision signal in a deductive manner and training a network to predict the label from the remaining data, we can teach the model to understand the underlying patterns and relationships within the data, leading to more accurate and reliable predictions.
In the context of machine learning, we often use a combination of deductive and inductive methods to train models and make predictions. In the case of self-supervised learning, for example, we first use a deductive algorithm to extract the supervision signal from the data. This allows us to separate the label from the rest of the information and focus on training the model on the remaining data.
Once the supervision signal has been extracted, we then train a network to use an inductive method to make predictions about the label. This involves feeding the data into the network and using a supervised learning algorithm to train the model to make accurate predictions. The result is a network that can use an inductive method to guess the label, based on the patterns and relationships within the data.
When trained on sequences, such as language, this approach can allow the network to generate correct sentences. By learning the underlying patterns and relationships within the data, the network can make educated guesses about the correct sequence of words and generate coherent sentences.
Overall, the combination of deductive and inductive methods in self-supervised learning allows us to train networks that can make accurate predictions and generate coherent outputs. By extracting the supervision signal in a deductive manner and training the network to use an inductive method to make predictions, we can create models that can effectively learn from data and generate meaningful outputs.
The recent discovery that self-supervised learning can be used to transform mechanical transformation methods into inductive ones has been demonstrated in image generators like Dall-E and StableDiffiusion. These models, which are based on self-supervised learning, are able to generate images from noise, creating remarkable and often surreal results.
Self-supervised learning is a powerful approach to training machine learning models without the need for human-generated labels. In this method, the supervision signal is extracted from the data in a deductive manner, using an algorithm to separate the label from the rest of the information. The model is then trained to predict the label from the remaining data.
When applied to the noising of images, self-supervised learning can create diffusion models that can effectively recreate the original images from the noise. By training the network on the noised images and the corresponding labels, we can teach the model to understand the underlying patterns and relationships within the data. This allows the network to effectively reverse the noise and generate the original image.
This discovery has been demonstrated in image generators like Dall-E and StableDiffiusion, which are able to generate stunning and sometimes surreal images from noise. By using self-supervised learning to transform mechanical transformation methods into inductive ones, these models are able to learn from the data and generate creative and unexpected outputs. This has exciting implications for the field of artificial intelligence and could lead to many new and exciting applications in the future.
The flywheel effect is a phenomenon that can be observed when a system becomes self-sustaining and gains momentum over time. This effect is often seen in systems that involve feedback loops, where the output of one process becomes the input of another, creating a reinforcing cycle.
In the context of self-supervised learning, the flywheel effect can be observed when the model becomes increasingly accurate and reliable over time. This is because self-supervised learning involves iterative inductive inference (I3), where the model makes predictions and then uses those predictions to refine its own learning. This creates a feedback loop, where the output of the model’s predictions becomes the input for its own learning, allowing it to continually improve and gain momentum.
The notion of prompting is also important in this process, as it allows the model to incrementally refine its learning. By using the model’s outputs to guide and refine its learning, we can create a flywheel effect, where the model gains momentum and becomes increasingly accurate and reliable over time.
The key to the effective use of this feedback loop is to take incremental steps, rather than making large jumps. If we take too wide a step when feeding the generative results back into the model, it can cause the learning process to become unstable and less effective. This is because the model may become over-confident in its predictions, leading to errors and inaccuracies. By taking incremental steps and carefully refining the model’s learning, we can avoid this problem and create a flywheel effect that leads to more effective and reliable results.
Overall, the flywheel effect is an important aspect of self-supervised learning and the development of advanced AI systems. By creating feedback loops and using prompting to incrementally refine the learning process, we can create systems that gain momentum and become increasingly effective over time. This has exciting implications for the future of artificial intelligence and could lead to many new and exciting applications.
One of the most exciting developments in the field of artificial intelligence is the emergence of platforms like MidJourney, which showcase the astounding creativity that is possible when using iterative learning methods. MidJourney is a platform that allows users to manipulate and tweak images, but restricts them from performing iterative actions such as variations or upscaling. Despite this restriction, the results that users are able to achieve are often incredibly creative and impressive.
This creativity is a consequence of the iterative nature of self-supervised learning, which involves training a model to make predictions and then using those predictions to refine its own learning. This creates a feedback loop, where the output of the model’s predictions becomes the input for its own learning, allowing it to continually improve and gain momentum.
The platform MidJourney takes advantage of this iterative learning process by restricting users from performing certain actions, such as variations or upscaling, which could disrupt the feedback loop and undermine the model’s learning. This allows the model to focus on learning from the data and generating creative and surprising results.
The creativity that emerges from an I3 system, such as the one used on the platform MidJourney, is fundamentally the same as the creativity that emerges from Vervaeke’s concept of Recursive Relevance Realization (R3). This is because, in an I3 system, it is specifically the R3 systems, such as human users, that drive the evolutionary selection process.
There are several reasons why abduction could be seen as similar to Vervaeke’s concept of Recursive Relevance Realization (R3). These reasons include:
- Both abduction and R3 involve making inferences and guesses based on incomplete or imperfect information.
- Both abduction and R3 involve making connections and associations between different pieces of information, in order to explain or understand a given phenomenon.
- Both abduction and R3 involve the idea of recursion, where the process of making inferences and connections is repeated and refined over time.
- Both abduction and R3 are concerned with the process of making sense of the world, and understanding the patterns and relationships that govern the behavior of different systems.
In an I3 system, the feedback loop between the model and its predictions allows for the continual refinement of the model’s learning. This leads to more accurate and reliable predictions, as well as the emergence of creative and surprising outputs. However, it is the R3 systems, such as human users, that provide the guidance and direction for this learning process.
In the context of a hivemind, where multiple I3 systems are working together, it is the R3 systems, such as humans, that drive the evolutionary selection process. They provide the feedback and guidance that allows the I3 systems to learn and improve, leading to the emergence of creative and novel outputs.
Overall, the creativity that emerges from an I3 system is fundamentally the same as the creativity that emerges from Vervaeke’s concept of R3. This is because it is specifically the R3 systems, such as humans, that drive the evolutionary selection process and provide the guidance and direction for the I3 systems’ learning.
If you train an I3 system on the hivemind’s R3 process, you could potentially create a more advanced and sophisticated learning system. The I3 system would be able to learn from the feedback and guidance provided by the R3 systems, such as human users, and use that information to continually improve and refine its own learning.
This could lead to more accurate and reliable predictions, as well as the emergence of creative and surprising outputs. The I3 system would be able to learn from the R3 systems and adapt to their feedback, allowing it to continually evolve and improve.
Disclaimer: Text and Images here are generated by AI and guided by the author.
