Futures

The Dawn of Mediocre Computing: AI Achieves Parity with Mediocre Human Performance, (from page 20221204.)

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Summary

The article discusses the significant achievement of AI, particularly OpenAI’s ChatGPT, in reaching mediocre-human parity in writing. This development is paralleled with prior AI successes in games like chess and Go. The author argues that the appropriate standard for evaluating AI in writing is not excellence but the ability to perform at a mediocre level, akin to average human performance. The article introduces the concept of ‘realish domains’—areas where mediocre performance is sufficient for survival, contrasting them with stylized domains that require exceptional performance. The discussion connects AI and crypto as intertwined technological advancements that challenge traditional notions of intelligence and economics, suggesting that a deeper understanding of their relationship could lead to what the author calls ‘mediocre computing.’

Signals

name description change 10-year driving-force relevancy
Mediocre Computing Computers achieving parity with mediocre human performance in writing and other realish domains. Shift from AI aiming for excellence to focusing on mediocrity in real-world applications. In 10 years, computing may prioritize practical mediocrity over exceptional performance, transforming productivity. The demand for more relatable and accessible technology in everyday applications drives this change. 5
AI and Crypto Convergence Emerging relationship between AI and cryptocurrency technologies as they automate similar domains. From isolated tech advancements to integrated solutions enhancing each other’s capabilities. 10 years from now, AI and crypto could be foundationally linked, creating new markets and services. The necessity for secure, efficient systems for handling language and finance propels this integration. 4
Realish Domains Concept of ‘realish domains’ where mediocre performance is sufficient for survival. Transition from excellence-focused systems to those accommodating mediocre performance for survival. Realish domains may redefine success metrics in various industries, favoring adaptability over mastery. The complexity and unpredictability of real-world tasks encourage acceptance of mediocrity. 4
Automation of Mediocre Tasks Automation of tasks previously performed by mediocre humans using AI and crypto. Shift from human labor to algorithmic solutions for tasks like writing and financial management. In 10 years, many jobs may be redefined or eliminated as algorithms take over mundane tasks. The pursuit of efficiency and cost-effectiveness is driving the automation of previously human tasks. 5
AI’s Role in Deep Fakes AI technologies generating deep fakes raises concerns about authenticity and trust. From acceptance of AI-generated content to questioning its legitimacy and authenticity. In a decade, society may face greater challenges in distinguishing real from fake content, impacting trust. The proliferation of misinformation and need for verification tools will drive this concern. 4

Concerns

name description relevancy
Mediocre Computing Paradigm The emergence of AI and crypto leading to a focus on mediocrity as a standard for performance, which may hinder true innovation. 4
Human-AI Symbiosis The potential reliance on mediocre AI writing abilities, creating a cultural and cognitive shift towards accepting mediocrity in human and automated outputs. 4
Deep Conceptual Challenges The suggested deep connection between AI and crypto that poses significant unifying challenges for computing and requires careful exploration. 5
Standardization of Mediocrity As mediocrity becomes a design principle for computing, it could set lower standards in various fields, impacting professions and societal values. 3
Automation of Critical Tasks The increasing delegation of critical thinking and economic tasks to AI and algorithms, raising concerns about accountability and decision-making. 5
Vulnerability to Exploitation The convergence of AI and crypto presents new opportunities for scams and fraud due to inherent vulnerabilities in their systems. 4
Ethical Implications The ethical considerations arising from AI-generated content and automated decision-making processes in language and finance. 5
Devaluation of Human Contribution The risk that human contributions to writing and financial decision-making become undervalued as AI systems gain prominence. 4

Behaviors

name description relevancy
Mediocre Computing A new paradigm where computing systems aim for parity with mediocre human performance in open, structured domains. 5
Automation in Realish Domains The trend of AI and blockchain automating tasks traditionally performed by mediocre humans in language and finance. 5
Integration of AI and Crypto The conceptual and technical unification of AI and crypto as complementary technologies for future computing. 4
Shift in Evaluation Standards Changing benchmarks for assessing AI performance from excellence to mediocrity in real-world tasks. 4
Realish Domain Engagement Increased focus on how AIs interact within imperfectly defined but structured environments like modern urban life. 4
Complexity of Realish Domains Recognition that realish domains require a different approach to computing, emphasizing survival over competition. 4
Use of AI for Vulnerability Detection Employing AI to audit and secure blockchain technologies against fraud and hacking. 3

Technologies

description relevancy src
Tools like ChatGPT that can generate human-like text, achieving parity with mediocre human writing. 5 ed83cb3f36a11c36c0bfc25263f13d80
AI models designed to understand and generate human language, transforming how we interact with text. 5 ed83cb3f36a11c36c0bfc25263f13d80
A decentralized digital ledger technology that securely records transactions and data across multiple computers. 5 ed83cb3f36a11c36c0bfc25263f13d80
A subset of AI that uses neural networks to analyze and interpret complex data patterns. 4 ed83cb3f36a11c36c0bfc25263f13d80
Mathematical techniques used to secure information and transactions in digital systems. 4 ed83cb3f36a11c36c0bfc25263f13d80
Using machine learning to identify and mitigate vulnerabilities in blockchain and crypto systems. 4 ed83cb3f36a11c36c0bfc25263f13d80

Issues

name description relevancy
Mediocre Computing The concept of computing systems designed to achieve parity with mediocre human performance, particularly in language and finance. 5
AI and Crypto Interrelationship The deep, conceptual connection between AI and cryptocurrency, suggesting a need for unified research and understanding. 4
Realish Domains The idea that many human activities occur in ‘realish domains’ where mediocre performance is sufficient for survival. 4
Automation of Mediocre Tasks The increasing capability of AI and crypto to automate tasks traditionally performed by average humans, impacting job markets and economies. 5
Ethics of Deep Fakes and Authenticity The implications of AI-generated content, such as deep fakes, and the necessity for robust authentication methods via cryptography. 4
Limits of Current AI Evaluations The inadequacies of existing standards for evaluating AI performance, particularly in creative fields like writing. 3
Mediocrity as an Aspirational Principle The proposal that aiming for mediocre performance in computing could yield more practical and applicable results in complex domains. 4