Memoria Técnica : máquina de efectos encadenados

Se trata de una unidad didáctica para el área de tecnología, se intenta acercar al alumno al diseño de pequeñas máquinas, para que con su propio esfuerzo y con los principios elementales adquiridos a lo largo del curso pueda construir mecanismos sencillos a fin de integrar a los alumnos en dinámicas de trabajo de grupo.

Prevenção dos riscos profissionais nas paragens e manutenções industriais : o caso da máquina de pasta de uma fábrica de pasta de papel

Todas as profissões envolvem riscos associados à sua especificidade e ao ambiente laboral, podendo originar acidentes de trabalho ou doenças profissionais. A manutenção realiza-se em todos os setores e locais de trabalho, envolvendo grande variedade de tarefas, englobando perigos e riscos elevados. Esta dissertação tem como objetivo principal a prevenção de riscos profissionais em paragens industriais de manutenção na indústria papeleira. A metodologia usada teve como princípio a participação direta em paragens de manutenção, sendo uma pesquisa exploratória e participativa. Verificou-se a necessidade de identificar perigos e consequentes riscos profissionais a que trabalhadores externos, responsáveis pela manutenção, estão sujeitos, bem como medidas de prevenção. A metodologia usada teve como princípio a participação direta em paragens de manutenção, sendo uma pesquisa exploratória e participativa. O método qualitativo de avaliação de riscos usado, CRAM, 4x4, define três níveis de risco: Baixo, Médio e Alto. A aplicação deste método à operação de substituição de rolos da prensa de uma máquina de pasta de papel permitiu avaliar e hierarquizar os riscos presentes nesta operação. Na sequência dos resultados obtidos a redução do risco é feita através do estabelecimento de medidas que diminuam a sua probabilidade de ocorrência e minimizem a severidade dos respetivos danos, estruturando o planeamento detalhado da execução das tarefas, dando informação/formação aos trabalhadores e à fiscalização dos trabalhos, através de um acompanhamento mais ativo.

Turing Test: Mindless Game?

The Turing Test, originally configured for a human to distinguish between an unseen man and unseen woman through a text-based conversational measure of gender, is the ultimate test for thinking. So conceived Alan Turing when he replaced the woman with a machine. His assertion, that once a machine deceived a human judge into believing that they were the human, then that machine should be attributed with intelligence. But is the Turing Test nothing more than a mindless game? We present results from recent Loebner Prizes, a platform for the Turing Test, and find that machines in the contest appear conversationally worse rather than better, from 2004 to 2006, showing a downward trend in highest scores awarded to them by human judges. Thus the machines are not thinking in the same way as a human intelligent entity would.

Hidden interlocutor misidentification in practical Turing Tests

Based on insufficient evidence, and inadequate research, Floridi and his students report inaccuracies and draw false conclusions in their Minds and Machines evaluation, which this paper aims to clarify. Acting as invited judges, Floridi et al. participated in nine, of the ninety-six, Turing tests staged in the finals of the 18th Loebner Prize for Artificial Intelligence in October 2008. From the transcripts it appears that they used power over solidarity as an interrogation technique. As a result, they were fooled on several occasions into believing that a machine was a human and that a human was a machine. Worse still, they did not realise their mistake. This resulted in a combined correct identification rate of less than 56%. In their paper they assumed that they had made correct identifications when they in fact had been incorrect.

Testing Turing's five minutes, parallel-paired imitation game

Purpose – The purpose of this paper is to consider Turing's two tests for machine intelligence: the parallel-paired, three-participants game presented in his 1950 paper, and the “jury-service” one-to-one measure described two years later in a radio broadcast. Both versions were instantiated in practical Turing tests during the 18th Loebner Prize for artificial intelligence hosted at the University of Reading, UK, in October 2008. This involved jury-service tests in the preliminary phase and parallel-paired in the final phase. Design/methodology/approach – Almost 100 test results from the final have been evaluated and this paper reports some intriguing nuances which arose as a result of the unique contest. Findings – In the 2008 competition, Turing's 30 per cent pass rate is not achieved by any machine in the parallel-paired tests but Turing's modified prediction: “at least in a hundred years time” is remembered. Originality/value – The paper presents actual responses from “modern Elizas” to human interrogators during contest dialogues that show considerable improvement in artificial conversational entities (ACE). Unlike their ancestor – Weizenbaum's natural language understanding system – ACE are now able to recall, share information and disclose personal interests.

Deception-detection and machine intelligence in practical Turing tests

Deception-detection is the crux of Turing’s experiment to examine machine thinking conveyed through a capacity to respond with sustained and satisfactory answers to unrestricted questions put by a human interrogator. However, in 60 years to the month since the publication of Computing Machinery and Intelligence little agreement exists for a canonical format for Turing’s textual game of imitation, deception and machine intelligence. This research raises from the trapped mine of philosophical claims, counter-claims and rebuttals Turing’s own distinct five minutes question-answer imitation game, which he envisioned practicalised in two different ways: a) A two-participant, interrogator-witness viva voce, b) A three-participant, comparison of a machine with a human both questioned simultaneously by a human interrogator. Using Loebner’s 18th Prize for Artificial Intelligence contest, and Colby et al.’s 1972 transcript analysis paradigm, this research practicalised Turing’s imitation game with over 400 human participants and 13 machines across three original experiments. Results show that, at the current state of technology, a deception rate of 8.33% was achieved by machines in 60 human-machine simultaneous comparison tests. Results also show more than 1 in 3 Reviewers succumbed to hidden interlocutor misidentification after reading transcripts from experiment 2. Deception-detection is essential to uncover the increasing number of malfeasant programmes, such as CyberLover, developed to steal identity and financially defraud users in chatrooms across the Internet. Practicalising Turing’s two tests can assist in understanding natural dialogue and mitigate the risk from cybercrime.

Not another look at the Turing Test!

Practical application of the Turing Test throws up all sorts of questions regarding the nature of intelligence in both machines and humans. For example - Can machines tell original jokes? What would this mean to a machine if it did so? It has been found that acting as an interrogator even top philosophers can be fooled into thinking a machine is human and/or a human is a machine - why is this? Is it that the machine is performing well or is it that the philosopher is performing badly? All these questions, and more, will be considered. Just what does the Turing test tell us about machines and humans? Actual transcripts will be considered with startling results.

Some implications of a sample of practical Turing tests

A series of imitation games involving 3-participant (simultaneous comparison of two hidden entities) and 2-participant (direct interrogation of a hidden entity) were conducted at Bletchley Park on the 100th anniversary of Alan Turing’s birth: 23 June 2012. From the ongoing analysis of over 150 games involving (expert and non-expert, males and females, adults and child) judges, machines and hidden humans (foils for the machines), we present six particular conversations that took place between human judges and a hidden entity that produced unexpected results. From this sample we focus on features of Turing’s machine intelligence test that the mathematician/code breaker did not consider in his examination for machine thinking: the subjective nature of attributing intelligence to another mind.