The Imitation Game | VR Concept

Virtual reality is recognized as an immersive technology that separates its user from their current, fixed reality. VR is still very young. The shoes that it is expected to fill are waiting patiently in the future, knowing its potential has yet to be reached. The VR concept that I introduce with my project, fills these metaphorical shoes. The user is not aware of a heavy headset weighing down on their face. Instead, they are projected into darkness, and expected to quickly adapt. I present, through photographs that I have taken and edited, a void, much like the black dreamscape in Stranger Things. Through this digital project, I want to introduce a concept that would remove bias of artificial intelligence in an unanticipated manner. The user believes that they will be playing an advanced version of the Turing test in a virtual environment, but come to face a deeper truth inside of themselves. In other words, the game starts simply, and ends with a lesson. As Turing suggests, aren’t we also machines of a similar nature? Flesh and bone rather than wires and circuitry? Instead of holding a pessimistic viewpoint that is spiteful towards the presence of AI in our future, we should attempt to open our eyes to the coexistence of man and machine as well as the bond that we could share. It is okay to acknowledge with some fear, and an even greater hope, that we are different from one another, yet so entirely the same

Interactive intelligence: behaviour-based AI, musical HCI and the Turing Test

The field of behaviour-based artificial intelligence (AI), with its roots in the robotics research of Rodney Brooks, is not predominantly tied to linguistic interaction in the sense of the classic Turing test (or, "imitation game"). Yet, it is worth noting, both are centred on a behavioural model of intelligence. Similarly, there is no intrinsic connection between musical AI and the language-based Turing test, though there have been many attempts to forge connections between them. Nonetheless, there are aspects of musical AI and the Turing test that can be considered in the context of non-language-based interactive environments–-in particular, when dealing with real-time musical AI, especially interactive improvisation software. This paper draws out the threads of intentional agency and human indistinguishability from Turing’s original 1950 characterisation of AI. On the basis of this distinction, it considers different approaches to musical AI. In doing so, it highlights possibilities for non-hierarchical interplay between human and computer agents

Chatbots’ greetings to human-computer communication

In the last years, chatbots have gained new attention, due to the interest showed by widely known personalities and companies. The concept is broad, and, in this paper we target the work developed by the (old) community that is typically associated with chatbot’s competitions. In our opinion, they contribute with very interesting know-how, but specially with large-scale corpora, gathered by interactions with real people, an invaluable resource considering the renewed interest in Deep Nets.info:eu-repo/semantics/publishedVersio

Turingin testi, interrogatiivimalli ja tekoäly

Turingin testi, interrogatiivimalli ja tekoäl

INTELLIGENT SYSTEMS FOR INDUSTRY USING REINFORCEMENT LEARNING TECHNIQUE

The rise of Intelligent Systems has happened gradually, then suddenly. They are gradual because we are aware that this field of computing has come a long way along with the history of computers. Yet, the sudden astonishing changes that affect mankind seem to take everyone in surprise. Their occurrence is reshaping the real world and our interaction with our digital life is changing in profound ways. Can computers think? We don’t have evidence on that, whatever the answer to that question is. But what we know is that computers do learn. Indeed, the whole process of computer evolution revolves around machines that are able to follow instructions and practice and eventually get better at what they are initially produced to accomplish. Consequently, the questions that we try to answer are related to the types of learning that intelligent programs use with special regards to one of the most researched methods of Machine Learning – Reinforcement Learning. On the other hand, it is crucial to apply the intelligent self-learning machines in industry, environment, enterprise, medicine and all the other sectors where we need to see the substantial changes that correspond with the era of machines that can learn. The intersection point in this research is the application of intelligent programs in industry using a very specific learning technique – Reinforcement Learning

A Philosophical Survey of Artificial Intelligence

Thesis written by a student in the UNT Honors College discussing artificial intelligence, philosophy, the anthropomorphization of computers, related science fiction, possible rights of an artificial intelligence, and possible threats

Questioning Turing test

The Turing Test (TT) is an experimental paradigm to test for intelligence, where an entity’s intelligence is inferred from its ability, during a text-based conversation, to be recognized as a human by the human judge. The advantage of this paradigm is that it encourages alternative versions of the test to be designed; and it can include any field of human endeavour. However, it has two major problems: (i) it can be passed by an entity that produces uncooperative but human-like responses (Artificial Stupidity); and (ii) it is not sensitive to how the entity produces the conversation (Blockhead). In light of these two problems, I propose a new version of the TT, the Questioning Turing Test (QTT). In the QTT, the task of the entity is not to hold a conversation, but to accomplish an enquiry with as few human-like questions as possible. The job of the human judge is to provide the answers and, like in the TT, to decide whether the entity is human or machine. The QTT has the advantage of parametrising the entity along two further dimensions in addition to ‘human-likeness’: ‘correctness’, evaluating if the entity accomplishes the enquiry; and ‘strategicness’, evaluating how well the entity carries out the enquiry, in terms of the number of questions asked – the fewer, the better. Moreover, in the experimental design of the QTT, the test is not the enquiry per se, but rather the comparison between the performances of humans and machines. The results gained from the QTT show that its experimental design minimises false positives and negatives; and avoids both Artificial Stupidity and Blockhead