Application of a Feedforward Neural Network to Predicting the Thermal Characteristics of Machine Tools

he paper presents a procedure for studying the stability of modeling an artificial neural network as applied to the thermal characteristics of machine tools. The topicality of this procedure is dictated by the ambiguity of the results generated by the neural network when constructing the predicted thermal characteristics of machine tools. Therefore, to select one of the possible solutions generated by the neural network, it was proposed to use two criteria. The effectiveness of their use is confirmed by the presented machine experiments. The methodology proposed in this work has made it possible to form a generalized concept for studying the effectiveness of the use of neural network technologies in thermal modeling of machine tools. This concept defines a typical set of variable modeling parameters, a basic mathematical model based on a modal approach, and an architecture of a typical software tool that can be developed to study the effectiveness of artificial neural network modeling. For each variant of the input data of the network, the following parameters were varied: the number of neurons in the hidden layer; the size of the input and output vectors; input vectors error; the size of the training, validation and test sample; functional features of thermal characteristics supplied to the network input or their multimodality; the presence and absence of normalization of the input vector. The paper presents experimental thermal characteristics for two spindle speeds of a vertical CNC machine. The results of the machine experiment are presented for six variants of the variable parameters of the mathematical model. The software tool used to carry out the machine experiment was developed in Matlab.

How Important are Ethical Dilemmas to Potential Adopters of Autonomous Vehicles?

The Moral Machine Experiment (MME) reported large-scale public opinion about how AVs should make ethical decisions (e.g., how should AVs choose between protecting passengers vs. pedestrians if harm was unavoidable). But several academics and industry practitioners have decreed that such trolley-type dilemmas are essentially a distraction and should not be used as design inputs or for making policy about AVs. While both sides of this debate have speculated upon the usefulness of AV dilemmas, the opinion of the potential adopters has been ignored. In two studies it is found that people consider the AV ethical dilemma as significantly more important to address as compared to the other technical, legal, and ethical challenges facing AVs. This suggests that the skepticism about the MME and related approaches to gauge public opinion about AV ethics may be unwarranted.

Programming Away Human Rights and Responsibilities? “The Moral Machine Experiment” and the Need for a More “Humane” AV Future

Dilemma situations involving the choice of which human life to save in the case of unavoidable accidents are expected to arise only rarely in the context of autonomous vehicles (AVs). Nonetheless, the scientific community has devoted significant attention to finding appropriate and (socially) acceptable automated decisions in the event that AVs or drivers of AVs were indeed to face such situations. Awad and colleagues, in their now famous paper “The Moral Machine Experiment”, used a “multilingual online ‘serious game’ for collecting large-scale data on how citizens would want AVs to solve moral dilemmas in the context of unavoidable accidents.” Awad and colleagues undoubtedly collected an impressive and philosophically useful data set of armchair intuitions. However, we argue that applying their findings to the development of “global, socially acceptable principles for machine learning” would violate basic tenets of human rights law and fundamental principles of human dignity. To make its arguments, our paper cites principles of tort law, relevant case law, provisions from the Universal Declaration of Human Rights, and rules from the German Ethics Code for Autonomous and Connected Driving.

Modeling of moral decisions with deep learning

One example of an artificial intelligence ethical dilemma is the autonomous vehicle situation presented by Massachusetts Institute of Technology researchers in the Moral Machine Experiment. To solve such dilemmas, the MIT researchers used a classic statistical method known as the hierarchical Bayesian (HB) model. This paper builds upon previous work for modeling moral decision making, applies a deep learning method to learn human ethics in this context, and compares it to the HB approach. These methods were tested to predict moral decisions of simulated populations of Moral Machine participants. Overall, test results indicate that deep neural networks can be effective in learning the group morality of a population through observation, and outperform the Bayesian model in the cases of model mismatches.

Theorizing Moral Cognition: Culture in Action, Situations, and Relationships

Dual-process theories of morality are approaches to moral cognition that stress the varying significance of emotion and deliberation in shaping judgments of action. Sociological research that builds on these ideas considers how cross-cultural variation alters judgments, with important consequences for what is and is not considered moral behavior. Yet lacking from these approaches is the notion that, depending on the situation and relationship, the same behavior by the same person can be considered more or less moral. The author reviews recent trends in sociological theorizing about morality and calls attention to the neglect of situational variations and social perceptions as mediating influences on judgment. She then analyzes the moral machine experiment to demonstrate how situations and relationships inform moral cognition. Finally, the author suggests that we can extend contemporary trends in the sociology of morality by connecting culture in thinking about action to culture in thinking about people.

Mother machine image analysis with MM3

The mother machine is a microfluidic device for high-throughput time-lapse imaging of microbes. Here, we present MM3, a complete and modular image analysis pipeline. MM3 turns raw mother machine images, both phase contrast and fluorescence, into a data structure containing cells with their measured features. MM3 employs machine learning and non-learning algorithms, and is implemented in Python. MM3 is easy to run as a command line tool with the occasional graphical user interface on a PC or Mac. A typical mother machine experiment can be analyzed within one day. It has been extensively tested, is well documented and publicly available via Github.

The Immoral Machine

In a recent paper in Nature1 entitled The Moral Machine Experiment, Edmond Awad, et al. make a number of breathtakingly reckless assumptions, both about the decisionmaking capacities of current so-called “autonomous vehicles” and about the nature of morality and the law. Accepting their bizarre premise that the holy grail is to find out how to obtain cognizance of public morality and then program driverless vehicles accordingly, the following are the four steps to the Moral Machinists argument:1)Find out what “public morality” will prefer to see happen.2)On the basis of this discovery, claim both popular acceptance of the preferences and persuade would-be owners and manufacturers that the vehicles are programmed with the best solutions to any survival dilemmas they might face.3)Citizen agreement thus characterized is then presumed to deliver moral license for the chosen preferences.4)This yields “permission” to program vehicles to spare or condemn those outside the vehicles when their deaths will preserve vehicle and occupants.This paper argues that the Moral Machine Experiment fails dramatically on all four counts.