This text argues that countries and large corporations need to establish their own Strategic Fact Reserves to ensure they have access to reliable training data for AI development. As AI becomes increasingly essential, akin to infrastructure like GPS, the possession of AI resources becomes a national security and corporate competition issue. Concerns about the biases in AI models based on their creators’ ideologies highlight the need for independent and uncontaminated data. The author emphasizes the importance of preserving digital data now, suggesting a national archival program managed by librarians and archivists, to safeguard this vital resource for future AI training.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| Strategic Fact Reserve Development | Countries are urged to create independent AI data reserves for future use. | From reliance on external data sources to independent national AI reserves. | Nation-states will possess their own data reserves to support and train AI systems. | The desire for national security and independence in AI technology. | 5 |
| AI as Fundamental Infrastructure | AI is increasingly recognized as vital as traditional infrastructures like energy and food. | From AI seen as optional to being classified as critical infrastructure. | AI will be a core part of national infrastructure, akin to energy or transportation. | The integration of AI into everyday productivity and governance will necessitate this change. | 4 |
| Evolving AI Ideologies | Large language models exhibit ideological biases from their creators. | From neutral algorithms to ideologically driven AI outputs. | AI outputs may more explicitly reflect national or cultural ideologies, influencing public opinion. | The political and cultural backgrounds of AI developers will shape AI technologies. | 4 |
| National AI Capability Independence | Countries may need independent AI training capabilities to ensure reliable outcomes. | From international dependency to self-sufficiency in AI training methods. | Nations will maintain their own resources for AI training to ensure data integrity and availability. | Concerns about monopolies and national security drive the need for independent AI capabilities. | 5 |
| Archiving Digital Data | A need to archive digital data safely for future AI training. | From unstructured digital data access to structured, secured digital archives. | National archives of trusted data will be critical resources for AI development. | The realization that data integrity is paramount for future AI evolution. | 5 |
| Dependency on Clean Training Data | Future AI training may rely on uncontaminated, high-quality datasets. | From diverse data sources to focused, clean data reserves. | The training of AI may be limited by access to quality data, affecting AI performance. | The necessity for accurate AI outputs in governance and industry dictates this need. | 4 |
| name | description | relevancy |
|---|---|---|
| Dependence on AI as Cognitive Infrastructure | Society’s increasing reliance on AI for decision-making may lead to diminished critical thinking skills and unevaluated reliance on technology. | 4 |
| Ideological Bias in AI Training Data | AI models reflect the ideological views of their creators, leading to biased outcomes based on geographic and cultural contexts. | 5 |
| Data Availability and Accessibility | Future availability of trusted training data for AI development is uncertain, which may hinder innovation and access. | 5 |
| National Security Risks of AI Dependence | Countries lacking independent AI capabilities may face vulnerabilities, particularly regarding AI-generated information and strategic decisions. | 4 |
| Market Control by Corporates | The competition between large corporates for dominant AI technologies could lead to monopolistic behaviors and hinder equitable access. | 4 |
| Degradation of Public Trust | Increased use of AI in critical decision-making might erode public trust in institutions if perceived biases or errors arise. | 4 |
| Loss of Control over Data | Shifts in copyright or data-sharing policies could prevent access to essential training data, limiting AI development. | 5 |
| Unpredictable Future of AI Research | The evolution of AI technology might lead to reliance on self-augmenting models that could collapse under specific conditions. | 3 |
| Challenges in Preservation of Digital Knowledge | Failure to establish reliable repositories for safe, uncontaminated digital data could limit future AI training opportunities. | 4 |
| name | description | relevancy |
|---|---|---|
| Strategic Fact Reserve Development | Countries and corporations should start creating their own strategic reserves of factual data to ensure future AI development and training capacities. | 5 |
| AI as Cognitive Infrastructure | Growing dependence on AI to augment cognitive tasks, transforming it into an essential tool for productive work. | 5 |
| Autonomous AI Creation | The need for countries to independently develop AI systems ensures they are not reliant on foreign technologies and control. | 4 |
| Valuation of Training Data | Recognition of the increasing importance and scarcity of clean, trusted training data for AI models. | 5 |
| Archival Expertise for Digital Data | Emergence of a need for librarians and archivists to manage the long-term archiving of digital data for AI training. | 4 |
| Geopolitical AI Dynamics | AI systems reflecting the ideologies and perspectives of their creators, influencing global political landscapes. | 5 |
| Commercial and National Security Intersections | AI advancements becoming crucial for both corporate competitiveness and national security considerations. | 4 |
| Critical Analysis of AI Outputs | Growing awareness and scrutiny of AI-generated content to prevent biased or ideologically driven outputs. | 4 |
| description | relevancy | src |
|---|---|---|
| AI is becoming an essential infrastructure comparable to energy and education systems, facilitating automation in various sectors. | 5 | 05e89c4773a48ddeceedf5e1e0e1d4fe |
| The concept of preserving high-quality training data for AI models to ensure future accessibility and reliability, much like archival methods for cultural data. | 4 | 05e89c4773a48ddeceedf5e1e0e1d4fe |
| Countries need to develop their own AI capabilities and infrastructures to avoid dependency on external systems and ensure national security. | 5 | 05e89c4773a48ddeceedf5e1e0e1d4fe |
| Utilizing contributor-based systems like OpenStreetMap to maintain an accurate and reliable data environment, preventing monopolization. | 4 | 05e89c4773a48ddeceedf5e1e0e1d4fe |
| Establishing read-only archives for digital data to ensure its integrity and accessibility for future AI training. | 5 | 05e89c4773a48ddeceedf5e1e0e1d4fe |
| Understanding the ideological implications and biases inherent in large language models based on their training data and creators. | 3 | 05e89c4773a48ddeceedf5e1e0e1d4fe |
| name | description | relevancy |
|---|---|---|
| Strategic Fact Reserves | Countries and corporations should develop their own data reserves to safeguard AI training data against geopolitical risks. | 4 |
| AI as Cognitive Infrastructure | AI to be viewed as essential infrastructure, potentially on par with GPS, transforming decision-making and productivity. | 5 |
| Dependency on Training Data Availability | Concerns about future availability and accessibility of trusted training data for AI development pose risks to national security. | 5 |
| Ideological Bias in AI | Large Language Models inherit and reflect the ideologies of their creators, raising concerns about fairness and inclusivity in AI applications. | 4 |
| The Role of Archivists and Librarians | Future AI developments require careful management of data curation by professionals to ensure the integrity and accessibility of training data. | 4 |
| Corporate vs. National AI Strategy | The competition for AI capabilities between corporations and nations may drive new strategies in data accumulation and technological development. | 4 |
| Risks of AI-Generated Content Quality | Potential collapse of AI performance due to reliance on recursively generated training data and the quality of AI-generated scientific research. | 4 |