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9 Robotic Eye System Schematic (A) Micro servo (B) Mains PSU (C) 30Kv generator (D) Arduino PSU shield with manual control knob (E) Arduino Uno microprocessor (F) GT2005 USB camera (G) Left EAP actuator (H) Photoresistor with 200 Ohm resistor (I) Right EAP actuator

9 Robotic Eye System Schematic (A) Micro servo (B) Mains PSU (C) 30Kv generator (D) Arduino PSU shield with manual control knob (E) Arduino Uno microprocessor (F) GT2005 USB camera (G) Left EAP actuator (H) Photoresistor with 200 Ohm resistor (I) Right EAP actuator

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Fig.2.1 The Uncanny Valley, Mori (1970)
Fig.2.2 Diagram of ML and DL as subsets of AI.
Fig.2.3 Structure of an ANN.
Fig.2.4 Gardner's eight intelligence types combinations and application...
+40 Fig.2.5 Representation of Goertzel's Theoretical Pipeline from ANI to AGI.
A Multimodal Turing Test for Realistic Humanoid Robots with Embodied Artificial Intelligence
Thesis
Full-text available
  • Sep 2020
The human face is the most natural interface for face-to-face communication, and the human form is the most effective design for traversing the human-made areas of the planet. Thus, developing realistic humanoid robots (RHRs) with artificial intelligence (AI) permits humans to interact with technology and integrate it into society in a naturalistic...
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... Although some variants of the TT have been already proposed to compare different robot platforms through standardized measures or using a more comprehensive evaluation method (e.g., [32]- [34]) they all include stages of increasing difficulty or complexity, up to the demonstration of completely autonomous behavior. As modern-day robotics has not advanced yet to a stage where an interrogator would confuse a robot and a human based on their fully developed autonomous behavior these proposals find limited application. ...
Human or AI? The brain knows it! A brain-based Turing Test to discriminate between human and artificial agents.
Conference Paper
  • Aug 2023
Since the introduction of the Turing Test to measure machine intelligence, more and more sophisticated artificial systems have been developed to pass the test. These systems revealed some limitations of the Turing Test and new versions of the test have been developed over time in an attempt to overcome these shortcomings. Yet, all these variants still rely on the subjective judgments of human interrogators which are subject to biases. Here, we propose the brain-based Turing Test, a novel version of the test that uses implicit information encoded in the human brain to discriminate between human and artificial agents. We highlight multiple benefits of the brain-based Turing Test, outline its possible outcomes, present an empirical test using robot and human interactive communication, and explain how research in human-robot interaction can profit from it.
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