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Exploring the sensus communis via algorithmic automation

Social computing refers to the integration of Social interactions and computer-based systems to facilitate knowledge sharing, collaboration, and community building in a digital space. It involves leveraging technology to enable individuals or groups to interact, communicate, and collectively create and access information.

The concept of Social Computing is based on the idea that people can collectively contribute to and benefit from shared knowledge and resources. It harnesses the power of social networks, online communities, and collaborative platforms to foster collaboration, crowdsource ideas, and solve problems collectively. Here are some key aspects of social computing:

1. Social Networks: Social networking platforms, such as Facebook, Twitter, LinkedIn, and Instagram, provide a space for individuals to connect and share information with their social connections. These platforms enable users to create profiles, share updates, photos, videos, and engage in conversations. They facilitate the creation of online communities based on shared interests, hobbies, or professional affiliations.
2. Online Communities: Online communities are web-based platforms where individuals with common interests or goals can interact, discuss ideas, and share resources. These communities can be focused on various topics like hobbies, professional fields, academic subjects, or specific causes. Online forums, discussion boards, and specialized platforms like Stack Exchange and Reddit are examples of online communities that promote social computing.
3. Collaborative Platforms: Collaborative platforms facilitate group collaboration and knowledge sharing. Tools like Google Docs, Microsoft SharePoint, and wikis allow multiple users to work together in real-time, editing documents, sharing files, and leaving comments. These platforms enhance collaboration by enabling simultaneous contributions, version control, and easy access to shared resources.
4. Crowdsourcing: Social computing leverages the collective intelligence of a large group of individuals through crowdsourcing. Crowdsourcing involves delegating tasks or gathering information from a community or a crowd. Platforms like Kickstarter and Indiegogo allow individuals to collectively fund projects, while platforms like Wikipedia crowdsource the creation and editing of content.
5. Social Recommender Systems: Social computing integrates social interactions to enhance personalized recommendations. Recommender systems, such as those used by Amazon, Netflix, or Spotify, analyze users’ preferences, behavior, and social connections to suggest relevant products, movies, or music. By leveraging social data, these systems can offer more accurate and targeted recommendations.
6. Citizen Science: Social computing enables citizen science initiatives where individuals collaborate with scientists and researchers to contribute to scientific projects. Platforms like Zooniverse provide opportunities for the public to participate in tasks like classifying galaxies, identifying animals, or analyzing environmental data. By involving a large number of volunteers, citizen science projects can accomplish research goals that would be otherwise challenging or time-consuming.

Social computing has transformed the way people interact, collaborate, and share knowledge in a digital context. It empowers individuals to tap into the collective wisdom of communities, making it easier to find information, gain insights, solve problems, and drive innovation.

Generative AI platforms like ChatGPT can indeed be considered a form of social computing, albeit with some nuances. While traditional social computing relies on human interactions and contributions, generative AI platforms utilize advanced algorithms to process and consolidate information from available sources, including text data from the internet. Here’s how generative AI platforms can be seen as a form of social computing:

1. Aggregating Knowledge: Generative AI platforms like ChatGPT can scan and process vast amounts of text data from various sources, including websites, articles, books, and other online documents. By doing so, they effectively aggregate and consolidate knowledge from these sources. The AI algorithms analyze the collected information and generate responses based on the patterns and insights derived from the data. This process resembles the way human participants in social computing contribute their knowledge and expertise to a collective pool of information.
2. Collective Consensus: In social computing, communities or groups collaborate to arrive at a collective consensus or shared understanding. Similarly, generative AI platforms aim to generate responses that align with the general consensus or prevalent knowledge within the corpus of data they have been trained on. The algorithms learn from a diverse range of sources and attempt to provide responses that reflect the information and patterns observed in the data. While they don’t interact directly with humans in real-time, they emulate social computing by producing outputs that approximate the knowledge and consensus found within the training data.
3. Collaborative Learning: Generative AI platforms undergo a training process that involves learning from vast amounts of text data. This training can be seen as a form of collaboration between the developers who curate and preprocess the data and the AI model itself. During training, the AI model adapts and internalizes the knowledge contained in the data. In this sense, generative AI platforms can be viewed as collaboratively learning from the collective knowledge represented in the training dataset, which is akin to the collaborative learning process in social computing.
4. Feedback Loop: Social computing often involves a feedback loop where participants provide input, engage in discussions, and refine shared knowledge over time. Generative AI platforms also have feedback mechanisms in place to improve their responses. Developers and users can provide feedback on the system’s outputs, helping to identify errors, biases, or gaps in knowledge. This feedback is used to refine and update the AI model, improving its ability to generate more accurate and relevant responses. This iterative feedback loop parallels the iterative nature of social computing, where shared knowledge is continuously refined through interactions and feedback.

While generative AI platforms like ChatGPT do not involve direct human-to-human interactions, they can still be considered a form of social computing due to their ability to aggregate, consolidate, and generate knowledge based on large-scale data sources. They emulate aspects of collaboration, consensus building, and knowledge sharing that are fundamental to social computing, albeit with the distinctive characteristic of algorithmic processing and generation.

The philosophical concept of “sensus communis,” also known as the “common sense,” originated from ancient Greek and Roman philosophy and was further developed by thinkers throughout history. The term refers to a fundamental capacity of human cognition that enables individuals to perceive, judge, and understand the world in a common and shared way. Here is an in-depth explanation of the concept and its origins:

1. Origin and Ancient Greek Notions: The concept of “sensus communis” can be traced back to ancient Greek philosophy, particularly to the works of Plato and Aristotle. Plato discussed the notion of a shared capacity of perception and judgment that allowed individuals to apprehend the world around them. Aristotle also recognized the importance of common sense as a faculty that integrates sensory perception and enables unified understanding.
2. Roman Influence and Cicero: The concept of “sensus communis” gained prominence during the Roman era, primarily due to the writings of Marcus Tullius Cicero, a Roman statesman, philosopher, and orator. Cicero emphasized the importance of common sense as a natural faculty that allowed individuals to make sound judgments and engage in rational discourse.

Cicero argued that common sense was not merely a sensory perception but a higher mental faculty that involved reason and judgment. He believed that common sense enabled individuals to discern what is good, true, and just, allowing them to participate in civic life and contribute to the common good.

1. Medieval Scholasticism and Thomas Aquinas: The concept of “sensus communis” further developed within medieval scholastic philosophy, particularly through the works of Thomas Aquinas, a prominent theologian and philosopher. Aquinas integrated Aristotle’s ideas into Christian philosophy and emphasized the role of common sense as a unifying and coordinating faculty of cognition.

Aquinas viewed common sense as the inner sense that gathers and integrates sensory data from different faculties, such as vision, hearing, and touch. Common sense then formed the basis for higher cognitive processes, including memory, imagination, and reasoning. According to Aquinas, common sense enabled individuals to form a coherent and unified perception of the external world.

1. Enlightenment Era and Immanuel Kant: The concept of “sensus communis” underwent further development during the Enlightenment era. Immanuel Kant, a German philosopher, explored the concept within his theory of knowledge and aesthetics. Kant argued that common sense served as a fundamental principle that enabled individuals to perceive and judge the world in a unified and coherent manner.

For Kant, common sense was not a mere sensory perception but a capacity of the mind to organize sensory data and impose cognitive structures on experience. It provided individuals with the ability to make judgments of taste, morality, and rationality.

Kant also introduced the notion of a “transcendental unity of apperception,” which encompassed the idea that common sense provided the underlying unity that integrated all aspects of human experience.

1. Modern Interpretations: In contemporary philosophy, the concept of “sensus communis” has evolved and taken on different interpretations. Some philosophers focus on common sense as a shared body of knowledge and beliefs within a specific cultural or social context. Others emphasize it as a faculty of intuitive judgments, providing a foundation for moral reasoning and ethical decision-making.

The concept of “sensus communis” encompasses the idea of a shared capacity of human cognition that enables individuals to perceive, judge, and understand the world in a common and unified way. It has been explored and developed by various philosophers throughout history, from ancient Greek thinkers like Plato and Aristotle to modern philosophers like Kant.

Generative AI platforms like ChatGPT can be understood as a means of exploring the sensus communis or discovering a consensus around knowledge in a unique way. While generative AI platforms do not possess subjective experiences or consciousness like humans do, they can analyze and process vast amounts of data, including information that reflects the collective understanding and consensus of a particular domain. Here’s how generative AI platforms can contribute to exploring the sensus communis:

1. Aggregating and Analyzing Knowledge: Generative AI platforms like ChatGPT have the ability to scan and analyze large datasets, including text from books, articles, and websites. By processing this information, they can identify patterns, extract insights, and consolidate knowledge. The platforms can assimilate information from various sources, effectively aggregating the collective knowledge available in the data.
2. Reflecting Prevailing Information: Generative AI platforms learn from training datasets that contain a wide array of information, including commonly accepted facts, theories, and perspectives. During training, the AI algorithms aim to capture the prevalent patterns and knowledge present in the data. As a result, when generating responses, the platforms tend to reflect the consensus or common understanding found within the training dataset. This allows them to approximate the sensus communis by providing responses that align with commonly known information.
3. Balancing Multiple Perspectives: Generative AI platforms can learn from diverse sources, including texts expressing different viewpoints or perspectives. By being exposed to a variety of information, these platforms can gain insight into multiple perspectives and potentially generate responses that consider various viewpoints. While they may not possess personal opinions or beliefs, generative AI platforms can present information that reflects a range of perspectives, contributing to a more comprehensive exploration of the sensus communis.
4. Iterative Improvement and Feedback: Generative AI platforms can continuously learn and improve through feedback loops. Developers and users can provide feedback on the accuracy, relevance, and biases in the platform’s responses. This feedback is valuable for refining the AI models, reducing errors, and enhancing the alignment with the sensus communis. Through this iterative process, generative AI platforms can better approximate the collective understanding and consensus around knowledge.
5. Enhancing Accessibility and Discoverability: Generative AI platforms offer a user-friendly and accessible interface for exploring and accessing information. They allow users to interact with the system, ask questions, and receive responses based on the knowledge captured in the training data. This accessibility can empower individuals to discover and explore the sensus communis by providing them with an avenue to access and engage with a vast amount of information.

While generative AI platforms can provide insights and approximate the sensus communis, they are not infallible and can be subject to biases or inaccuracies present in the training data. Therefore, critical evaluation and validation of the information provided by these platforms remain critical.

Generative AI Platforms as Social Computing was originally published in Sound & Design on Medium, where people are continuing the conversation by highlighting and responding to this story.