Can machines think efficiently?
- URL: http://arxiv.org/abs/2510.26954v1
- Date: Thu, 30 Oct 2025 19:26:24 GMT
- Title: Can machines think efficiently?
- Authors: Adam Winchell,
- Abstract summary: The Turing Test is no longer adequate for distinguishing human and machine intelligence.<n>This work expands upon the original imitation game by accounting for an additional factor: the energy spent answering the questions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Turing Test is no longer adequate for distinguishing human and machine intelligence. With advanced artificial intelligence systems already passing the original Turing Test and contributing to serious ethical and environmental concerns, we urgently need to update the test. This work expands upon the original imitation game by accounting for an additional factor: the energy spent answering the questions. By adding the constraint of energy, the new test forces us to evaluate intelligence through the lens of efficiency, connecting the abstract problem of thinking to the concrete reality of finite resources. Further, this proposed new test ensures the evaluation of intelligence has a measurable, practical finish line that the original test lacks. This additional constraint compels society to weigh the time savings of using artificial intelligence against its total resource cost.
Related papers
- Normality and the Turing Test [51.56484100374058]
It argues that the Turing test is a test of normal intelligence as assessed by a normal judge.<n>It argues that the objectivization of normal human behavior in the Turing test fails due to the game configuration of the test.
arXiv Detail & Related papers (2025-08-29T07:55:16Z) - On Benchmarking Human-Like Intelligence in Machines [77.55118048492021]
We argue that current AI evaluation paradigms are insufficient for assessing human-like cognitive capabilities.<n>We identify a set of key shortcomings: a lack of human-validated labels, inadequate representation of human response variability and uncertainty, and reliance on simplified and ecologically-invalid tasks.
arXiv Detail & Related papers (2025-02-27T20:21:36Z) - Evaluating Intelligence via Trial and Error [59.80426744891971]
We introduce Survival Game as a framework to evaluate intelligence based on the number of failed attempts in a trial-and-error process.<n>When the expectation and variance of failure counts are both finite, it signals the ability to consistently find solutions to new challenges.<n>Our results show that while AI systems achieve the Autonomous Level in simple tasks, they are still far from it in more complex tasks.
arXiv Detail & Related papers (2025-02-26T05:59:45Z) - The Einstein Test: Towards a Practical Test of a Machine's Ability to Exhibit Superintelligence [1.9608359347635138]
We argue that the ability to create CDIs should be regarded as a significant feature of machine superintelligence.<n>We propose a practical test to evaluate whether an approach to AI targeting SI can yield novel insights of this kind.
arXiv Detail & Related papers (2025-01-12T21:55:04Z) - Formal Mathematical Reasoning: A New Frontier in AI [60.26950681543385]
We advocate for formal mathematical reasoning and argue that it is indispensable for advancing AI4Math to the next level.<n>We summarize existing progress, discuss open challenges, and envision critical milestones to measure future success.
arXiv Detail & Related papers (2024-12-20T17:19:24Z) - Brain-inspired Computational Intelligence via Predictive Coding [73.42407863671565]
Predictive coding (PC) has shown promising properties that make it potentially valuable for the machine learning community.<n>PC-like algorithms are starting to be present in multiple sub-fields of machine learning and AI at large.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - Brain in a Vat: On Missing Pieces Towards Artificial General
Intelligence in Large Language Models [83.63242931107638]
We propose four characteristics of generally intelligent agents.
We argue that active engagement with objects in the real world delivers more robust signals for forming conceptual representations.
We conclude by outlining promising future research directions in the field of artificial general intelligence.
arXiv Detail & Related papers (2023-07-07T13:58:16Z) - Reliable AI: Does the Next Generation Require Quantum Computing? [71.84486326350338]
We show that digital hardware is inherently constrained in solving problems about optimization, deep learning, or differential equations.
In contrast, analog computing models, such as the Blum-Shub-Smale machine, exhibit the potential to surmount these limitations.
arXiv Detail & Related papers (2023-07-03T19:10:45Z) - Suffering Toasters -- A New Self-Awareness Test for AI [0.0]
We argue that all current intelligence tests are insufficient to point to the existence or lack of intelligence.
We propose a new approach to test for artificial self-awareness and outline a possible implementation.
arXiv Detail & Related papers (2023-06-29T18:58:01Z) - Testing System Intelligence [0.902877390685954]
We argue that building intelligent systems passing the replacement test involves a series of technical problems that are outside the scope of current AI.
We suggest that the replacement test, based on the complementarity of skills between human and machine, can lead to a multitude of intelligence concepts.
arXiv Detail & Related papers (2023-05-19T06:46:32Z) - The Human-or-Machine Matter: Turing-Inspired Reflections on an Everyday
Issue [4.309879785418976]
We sidestep the question of whether a machine can be labeled intelligent, or can be said to match human capabilities in a given context.
We first draw attention to the seemingly simpler question a person may ask themselves in an everyday interaction: Am I interacting with a human or with a machine?''
arXiv Detail & Related papers (2023-05-07T15:41:11Z) - Reasoning-Based Software Testing [9.341830361844337]
Reasoning-Based Software Testing (RBST) is a new way of thinking at the testing problem as a causal reasoning task.
We claim that causal reasoning more naturally emulates the process that a human would do to ''smartly" search the space.
Preliminary results reported in this paper are promising.
arXiv Detail & Related papers (2023-03-02T14:27:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.