The philosophy of X, where X is a science, involves philosophers analyzing the concepts of X and sometimes commenting on what concepts are or are not likely to be coherent. Artificial intelligence (AI) has closer scientific connections with philosophy than do other sciences, because AI shares many concepts with philosophy, e.g. This article treats the philosophy of AI but also analyzes some concepts common to philosophy and AI from the standpoint of AI. The philosophy of X often involves advice to practitioners of X about what they can and cannot do. We partly reverse the usual course and offer advice to philosophers, especially philosophers of mind.

Today there's no legislation regarding how much intelligence a machine can have, how interconnected it can be. If that continues, look at the exponential trend. We will reach the singularity in the timeframe most experts predict. From that point on you're going to see that the top species will no longer be humans, but machines.

We present a new application and covering number bound for the framework of "Machine Learning with Operational Costs (MLOC)," which is an exploratory form of decision theory. The MLOC framework incorporates knowledge about how a predictive model will be used for a subsequent task, thus combining machine learning with the decision that is made afterwards. In this work, we use the MLOC framework to study a problem that has implications for power grid reliability and maintenance, called the Machine Learning and Traveling Repairman Problem ML&TRP. The goal of the ML&TRP is to determine a route for a "repair crew," which repairs nodes on a graph. The repair crew aims to minimize the cost of failures at the nodes, but as in many real situations, the failure probabilities are not known and must be estimated. The MLOC framework allows us to understand how this uncertainty influences the repair route. We also present new covering number generalization bounds for the MLOC framework.

STA4273H : index

Challenges for Computing include Learning for Life (Taylor et al, 2008). Grand Research Challenges in Information Systems identifies the need to "provide a teacher for These are amongst the key challenges that AIED responds to. What will next generation AIED learning environments be like? GROE report (Woolf, 2010), in order to highlight the expected role of AIED research.

The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed.

Due to the large variety of CAD systems in the market, data exchange between different CAD systems is indispensable. Currently, data exchange standards such as STEP and IGES, etc. provide a unique approach for interfacing among different CAD platforms. Once the feature-based CAD model created in one CAD system is input into another via data exchange standards, many of the original features and the feature-related information may not exist any longer. The identification of the design features and their further decomposition into machining features for the downstream activities from a data exchanged part model is a bottleneck in integrated product and process design and development. In this paper, the feature panorama is succinctly articulated from the viewpoint of product design and manufacturing.

Design for manufacturability (DFM) requires product designers to simultaneously consider the manufacturing issues of a product along with the geometrical and design aspects. This paper reports a computer-aided DFM tool for product designers to evaluate the manufacturability of their designs. A fuzzy set-based manufacturability evaluation algorithm is formulated to generate relative manufacturability indices (MIs) to provide product designers with a better understanding of the relative ease or difficulty of machining the features in their designs. This computer-aided DFM system is developed for rotational parts. The MI of machining a part is decomposed into three components, namely, the support index, the clamping index, and the feature index.

Brake forming is widely applied in the high variety and small batch part manufacturing of sheet metal components, for the bending of straight bending lines. Currently, the planning of the bending sequences is a task that has to be performed manually, involving many heuristic criteria. However, set-up and bend sequencing procedures and knowledge have to be formally formalized and modeled, for the development of computer-aided process planning systems for sheet metal forming. This paper describes the application of fuzzy set theory for the normalization and modeling of the set-up and bend sequencing process for sheet metal bending. A fuzzy-set based methodology is used to determine the optimal bending sequences for the brake forming of sheet metal components, taking into account the relative importance of handling and accuracy.

Artificial Intelligence and other smart technologies are used to empower businesses from every industry, regardless of location. Moreover, smart technologies help companies gain an advantage in front of their competitors, so they are changing the business landscape at an increasing speed. Therefore, today we'll have a look at some of the most innovative and useful AI-based products that help businesses stay competitive and get the lead in their niche. While the logistics industry benefits from a wide range of sensors and GPS-enabled devices, the product that brought the most improvements is the AI-based dash cam for trucks. This product is mounted on a truck's dashboard and has a wide range of features that allow fleet managers to monitor drivers and optimize routes. Due to these devices, trucks get in fewer traffic accidents, transport speed has improved, and costs have dropped.