Turing Test 2.0: The General Intelligence Threshold (2505.19550v1)

Abstract: With the rise of artificial intelligence (A.I.) and LLMs like Chat-GPT, a new race for achieving artificial general intelligence (A.G.I) has started. While many speculate how and when A.I. will achieve A.G.I., there is no clear agreement on how A.G.I. can be detected in A.I. models, even when popular tools like the Turing test (and its modern variations) are used to measure their intelligence. In this work, we discuss why traditional methods like the Turing test do not suffice for measuring or detecting A.G.I. and provide a new, practical method that can be used to decide if a (computer or any other) system has reached or surpassed A.G.I. To achieve this, we make two new contributions. First, we present a clear definition for general intelligence (G.I.) and set a G.I. threshold (G.I.T.) that can be used to distinguish between systems that achieve A.G.I. and systems that do not. Second, we present a new framework on how to construct tests that can detect if a system has achieved G.I. in a simple, comprehensive, and clear-cut fail/pass way. We call this novel framework the Turing Tests 2.0. We then demonstrate real-life examples of applying tests that follow our Turing Tests 2.0 framework on modern A.I. models.

Analyzing Turing Test 2.0: The General Intelligence Threshold

The paper "Turing Test 2.0: The General Intelligence Threshold" by Georgios Mappouras proposes a novel approach to detect artificial general intelligence (AGI) in AI systems. Current methodologies, such as the traditional Turing test, are inadequate for precisely measuring AGI. Thus, the author delineates a new framework designated as Turing Test 2.0, aimed at establishing a clear threshold for general intelligence (GI) in computational systems, which could be adapted for potential tests to identify AGI.

Definition and Threshold

At the heart of Mappouras's methodology is a newly devised definition of GI and the establishment of the General Intelligence Threshold (G.I.T.). Unlike broad interpretations which align GI with human cognitive abilities, Mappouras posits that true GI embodies the ability to generate new useful information (U.I.) through generative processes from previously non-useful information (N.U.I.). This generative capability, where new insights are developed independently from pre-existing system parameters, defines a system's achievement of crossing the G.I.T.

Turing Test 2.0 Framework

To operationalize the detection of GI, Mappouras reimagines the traditional Turing test into a structured framework that focuses on task-based assessments, requiring systems to autonomously generate missing U.I. A system's intelligence, under this framework, is not merely gauged by task performance but by its ability to autonomously develop solutions using resources in novel ways. The framework delineates rules ensuring that tests are unbiased against human thresholds and specifically target areas where AI could exhibit transformative insights.

Practical Application and Results

The paper exemplifies the application of Turing Test 2.0 through tests like the image alteration tasks, where modern LLMs are inadequately equipped to alter familiar shapes to unfamiliar ones if those shapes aren't explicitly encoded within their training data (e.g., generating a hexagonal stop sign). These tests illustrate that current AI models do not possess the capability to derive solutions beyond their programmed understanding without external information. The tests reveal consistent failures across prominent LLMs to infer required modifications autonomously, framing an essential critique in today's AI development landscape.

Theoretical Implications and Speculation on AI Development

Mappouras's framework prompts a critical inquiry into the future of AI advancements. The paper questions the assumption that scaling computational power and data sets will naturally lead to AGI, emphasizing the need for algorithmic innovation that transcends traditional machine learning paradigms. The work contemplates whether AI will achieve AGI, suggesting that this aspiration hinges on whether the process of generating new U.I. is fundamentally algorithmic or requires non-computable faculties.

Additionally, his insights have practical repercussions for AI's role in society: while today's systems summarize existing knowledge adeptly, they face limitations in advancing new ideas independently. This recognition should guide efforts in the continued pursuit of specialized AI applications, balancing the aspirations toward AGI against realistic expectations.

Conclusion

Mappouras’s contributions furnish an essential analytical lens for evaluating AI's trajectory toward GI and AGI. The Turing Test 2.0 framework offers a robust structural approach, redirecting focus from merely emulating human tasks to innovating independently. As such, it advocates for a reassessment of the current strategies and positions within AI research and development to prioritize breakthroughs that genuinely reflect autonomous intelligence generation. In delineating these concepts, the paper serves as a foundational dialogue piece in considering the iterative future of artificial intelligence.