Alan Turing's question "Can machines think?" motivated his famous imitation game, which became widely known as the Turing test. Constructing a machine that can pass the test was seen by many as the "holy grail" of artificial intelligence (AI) research because such a machine must be assumed to have intelligence. The test had a tremendous impact on computer science and stirred many philosophical debates over the last decades. Today, however, the test has nearly vanished from research agendas in AI. Here, we argue that the Turing test is still inspirational. Modern computing machinery is now an integral part in myriads of problem-solving processes and has revolutionized how science is done. Machines can make us think, that is, help us refine or develop new theories about the natural world.
The function of the robot living together converges on the problem of communication. We focus on nonverbal communication and relationship between service robots and human. We show nonverbal communication experiments of robot to build relationship between human. We described some ideal fiction robots to live with. We got experience such as; Relationship based on touch communication is managed three elements, appearance, motion and predictable behavior. As the result, these elements are based on embodiment. Human touches robot after feeling safe and natural motion. Motivation to build relationship with robots is decided above three elements in physically but appearance and motion are important. Evaluation of relationship is complicated because relationship grows up depending on spending time and motivation to relate. These experiences were shown by life sized humanoid robot and robot arm in exhibition. Based on these results, evaluation method to understand relationship between robot and human are considered in near future robot development.
Dialogue systems are becoming central tools in human computer interface systems, moreover in educational environments with social robots. The conventional approaches based on traditional artificial intelligence techniques, have been superseded by machine learning approaches and, more recently, deep learning. In this paper we give a view of the current state of dialogue systems, describing the areas of application, as well as the current technical approaches and challenges. We propose two emerging domains of application of dialog systems that may be highly influential in the near future: storytelling and therapeutic systems.
We propose a new CAPTCHA scheme that uses random dot patterns (RDPs) to prevent highly-developed bots attacks. Human beings can recognize a moving figure filled by a RDP from a background that is filled by another RDP; however, it is impossible to find such figures when they are stationary. Since image recognition by bots is usually carried out frame by frame, it is hard for bots to recognize such moving figures. The proposed CAPTCHA scheme exploits this characteristic. Several experiments were carried out to confirm that the proposed CAPTCHA scheme is usable enough and has enough resistance against bot attacks.
CAPTCHA is a kind of challenge response test, which is used to distinguish human users from malicious computer program such as bots. However, the attack technique called relay attack as a method to avoid the CAPTCHA has been devised. This attack relays the CAPTCHA challenges to remote human-solvers, let them to decode CAPTCHA challenges. We used delay time that is caused by communications needed in relay attack. Our new CAPTCHA uses this delay time between communications to prevent relay attacks. We constructed an experimental environment in which relay attack can be simulated, made a series of experiments in order to evaluate the performance of the proposed method.
We propose new CAPTCHA image generation systems by using generative adversarial network (GAN) techniques to strengthen against CAPTCHA solvers. To verify whether a user is human, CAPTCHA images are widely used on the web industry today. We introduce two different systems for generating CAPTCHA images, namely, the distance GAN (D-GAN) and composite GAN (C-GAN). The D-GAN adds distance values to the original CAPTCHA images to generate new ones, and the C-GAN generates a CAPTCHA image by composing multiple source images. To evaluate the performance of the proposed schemes, we used the CAPTCHA breaker software as CAPTCHA solver. Then, we compared the resistance of the original source images and the generated CAPTCHA images against the CAPTCHA solver. The results show that the proposed schemes improve the resistance to the CAPTCHA solver by over 67.1% and 89.8% depending on the system.
A long-standing dream in research on artificial intelligence (AI) is to build a strong AI, which understands and processes the input, unlike a weak AI which just processes it as programmed. Toward realization of this dream, we need a mathematical formulation on what understanding is. In the present study, starting off by revisiting Shannon’s mathematical theory of communication, I argue that it is a model of information transmission but not that of information understanding, because of its common codebook shared by the sender and receiver. I outline the steps to build a model of information understanding, by seeking possibilities of decoding without the shared codebook. Given the model of information understanding, I discuss its relationship to other known problems in AI research, such as the symbol grounding problem and frame problem.