The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms

When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms

Is morality the last frontier for machines?

This paper examines some ethical and cognitive aspects of machines making moral decisions in difficult situations. We compare the situations when humans have to make tough moral choices with those in which machines make such decisions. We argue that in situations where machines make tough moral choices, it is important to produce justification for those decisions that are psychologically compelling and acceptable by peopl

Machine Ethics, Ethics for Machines: Context-Based Modeling for Machines Making Ethical Decisions

Machine ethics is an emerging, interdisciplinary field that focuses on if – and if so, how – machines can make ethical decisions autonomously. Through a close study of two positions on whether or not machines can be moral agents, this project sheds light on a clash of assumptions that is keeping the field of machine ethics in limbo. After making this clash of assumptions clear, I raise two questions which get at the scope of machine ethics itself: 1) What makes ethical decision-making different from other kinds of decision-making? 2) To what extent can machines engage with ethics and make ethical decisions? I address the first question by arguing that ethics is distinct because it requires the ability to understand and participate in human conventions. I address the second question by arguing that ethics has always been informed by our humanity, but machine ethics is an opportunity to expand our understanding of ethics so that machines can engage with it insofar as they are machines. This project aims to contribute to machine ethics by proposing a major shift in perspective, from a focus on human abilities to a focus on machines and their own radically novel abilities

AI, Algorithms, and Awful Humans

A profound shift is occurring in the way many decisions are made, with machines taking greater roles in the decision-making process. Two arguments are often advanced to justify the increasing use of automation and algorithms in decisions. The “Awful Human Argument” asserts that human decision-making is often awful and that machines can decide better than humans. Another argument, the “Better Together Argument,” posits that machines can augment and improve human decision-making. These arguments exert a powerful influence on law and policy. In this Essay, we contend that in the context of making decisions about humans, these arguments are far too optimistic. We argue that machine and human decision-making are not readily compatible, making the integration of human and machine decision-making extremely complicated. It is wrong to view machines as deciding like humans do, except better because they are supposedly cleansed of bias. Machines decide fundamentally differently, and bias often persists. These differences are especially pronounced when decisions require a moral or value judgment or involve human lives and behavior. Making decisions about humans involves special emotional and moral considerations that algorithms are not yet prepared to make—and might never be able to make. Automated decisions often rely too much on quantifiable data to the exclusion of qualitative data, resulting in a change to the nature of the decision itself. Whereas certain matters might be readily reducible to quantifiable data, such as the weather, human lives are far more complex. Human and machine decision-making often do not mix well. Humans often perform badly when reviewing algorithmic output. We contend that algorithmic decision-making is being relied upon too eagerly and with insufficient skepticism. For decisions about humans, there are important considerations that must be better appreciated before these decisions are delegated in whole or in part to machines

The Dynamics of a Roll Press Nip

The problem presented concerned the dynamics of a roll press nip, a crucial component in a paper-making machine. Modern commercial paper-making machines are huge items of equipment. They may be as long as a football field and cost many millions of dollars each. Integrity of the process is extremely important; the paper in such a machine travels so fast (up to 20 km/sec) that a break is viewed as a major calamity and may take many man-hours and dollars to recover from. The size and speed of the machines means that it is not easy to make measurements as the paper passes through. The hostility of the environment therefore dictates that a thorough theoretical understanding of the important parts of the process is crucial if the processes involved are to be optimized. In this study we do not seek to answer anyone specific question, but rather wish to propose a general framework for modeling the flow and deformation under a roll press nip. Because of the difficulty of making measurements in the nip region and the need to closely control the process, the distributions of pressure, velocity and felt porosity within the nip have traditionally been subjects of great debate. Previous treatments have included lubrication theory models and "Bernoulli" based models. Although some progress may be made using thin layer theory, we shall show the required modeling does not take the form of standard lubrication theory. As far as models based on Bernoulli's equation are concerned, we simply note that the discussion below shows that the drag force exerted by the felt on the liquid is a key physical component of the flow process. Clearly, a full three-phase flow treatment is required. In this study we will thus address the following questions: (i) Is it possible to propose a general theoretical treatment of the roll press nip? (ii) What determines the physics of the water movement within the paper and felt in the roll press nip and how is this connected to the details of the air movement and the deformation of the felt? (iii) When a general model has been proposed, is it possible exploit the geometry within the nip to generate some simple exact solutions? (iv) What are the key non-dimensional parameters in the problem and how large are they likely to be for realistic paper-making machines? A further matter of interest concerns the influence that the size, shape and separation of the rollers have on the whole process. We approach the modeling from a rather general point of view, beginning by including all effects that might be important and then making clearly defined assumptions to simplify the equations. In this way it is possible to make changes to the model if circumstances change

Perceptual Decision Making of Humans and Deep Learning Machines: aBehavioral Study

Human visual perception system is a key issue of the cognitive researches. It is also an inspiring prototype of thecutting-edge artificial intelligence researches - deep learning. It is interesting to investigate the behaviors of humans and deeplearning machines on vision tasks. In this paper, we focus on the perceptual decision making and object recognition on distortedimages. We found that in a wide range of distortion levels, the recognition rates of human subjects are smoothly increased alongwith the decreases of distortions. Although the deep learning machines perform obviously worse than human subjects, theirrecognition rates vary with the similar trends. It indicates that the deep learning machines make a good simulation to the humanbeing on the perceptual decision making tasks

Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression

High-order parametric models that include terms for feature interactions are applied to various data mining tasks, where ground truth depends on interactions of features. However, with sparse data, the high- dimensional parameters for feature interactions often face three issues: expensive computation, difficulty in parameter estimation and lack of structure. Previous work has proposed approaches which can partially re- solve the three issues. In particular, models with factorized parameters (e.g. Factorization Machines) and sparse learning algorithms (e.g. FTRL-Proximal) can tackle the first two issues but fail to address the third. Regarding to unstructured parameters, constraints or complicated regularization terms are applied such that hierarchical structures can be imposed. However, these methods make the optimization problem more challenging. In this work, we propose Strongly Hierarchical Factorization Machines and ANOVA kernel regression where all the three issues can be addressed without making the optimization problem more difficult. Experimental results show the proposed models significantly outperform the state-of-the-art in two data mining tasks: cold-start user response time prediction and stock volatility prediction.Comment: 9 pages, to appear in SDM'1

Life, service and the cost of service of farm machines on 400 Iowa farms

That farmers of Iowa make an extensive use of farm machines is indicated by the 16th Census of the United States which gives the value of farm implements and machinery on Iowa farms, April 1, 1940, as $242,047,158. This is about one-twelfth of the total for the United States and represents an investment of$1,134 for each Iowa farm. Under these circumstances it is recognized that the use of farm machines occupies an important role in the agricultural practices of the state. Farm machines not only give the farm workers control over large units of power, thus making possible large individual productive capacity, but they also make farm labor less arduous. For these reasons the cost of farm-machine service or use as discussed in this bulletin should be of general interest

Two-Way Automata Making Choices Only at the Endmarkers

The question of the state-size cost for simulation of two-way nondeterministic automata (2NFAs) by two-way deterministic automata (2DFAs) was raised in 1978 and, despite many attempts, it is still open. Subsequently, the problem was attacked by restricting the power of 2DFAs (e.g., using a restricted input head movement) to the degree for which it was already possible to derive some exponential gaps between the weaker model and the standard 2NFAs. Here we use an opposite approach, increasing the power of 2DFAs to the degree for which it is still possible to obtain a subexponential conversion from the stronger model to the standard 2DFAs. In particular, it turns out that subexponential conversion is possible for two-way automata that make nondeterministic choices only when the input head scans one of the input tape endmarkers. However, there is no restriction on the input head movement. This implies that an exponential gap between 2NFAs and 2DFAs can be obtained only for unrestricted 2NFAs using capabilities beyond the proposed new model. As an additional bonus, conversion into a machine for the complement of the original language is polynomial in this model. The same holds for making such machines self-verifying, halting, or unambiguous. Finally, any superpolynomial lower bound for the simulation of such machines by standard 2DFAs would imply LNL. In the same way, the alternating version of these machines is related to L =? NL =? P, the classical computational complexity problems.Comment: 23 page