Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Open Innovation: The New Imperative for Creating and Profiting from Technology

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

1. Interaction. The interactions learners have with each other build interpersonal skills, such as listening, politely interrupting, expressing ideas, raising questions, disagreeing, paraphrasing, negotiating, and asking for help. 2. Interdependence. Learners must depend on one another to accomplish a common objective. Each group member has specific tasks to complete, and successful completion of each member’s tasks results in attaining the overall group objective.

将“Cooperative Learning”融入课堂——浅谈英语素质教育

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just "old wine in new bottles"? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense. We present a new paradigm in digital manufacturing and design innovation, namely cloud-based design and manufacturing (CBDM).We identify the common key characteristics of CBDM.We define a requirement checklist that any idealized CBDM system should satisfy.We compare CBDM with other relevant but more traditional collaborative design and distributed manufacturing systems.We describe an idealized CBDM application example scenario.

/pdf/cloud-based-design-and-manufacturing-26vcfwa639.pdf

Cloud-based design and manufacturing

https://purehost.bath.ac.uk/ws/files/139852237/ACCEPTED_Cloud_manufacturing_Strategic_vision_and_state_of_the_art.pdf

Cloud manufacturing: Strategic vision and state-of-the-art☆

https://purehost.bath.ac.uk/ws/files/146872105/1_s2.0_S2212827116307910_main_SDCM.pdf

Software-defined cloud manufacturing for industry 4.0

https://northumbria-test.eprints-hosting.org/id/document/267524

Smart Packaging: Opportunities and Challenges

The rise of cloud computing is radically changing the way enterprises manage their information technology (IT) assets. Considering the benefits of cloud computing to the information technology sector, we present a review of current research initiatives and applications of the cloud computing paradigm related to product design and manufacturing. In particular, we focus on exploring the potential of utilizing cloud computing for selected aspects of collaborative design, distributed manufacturing, collective innovation, data mining, semantic web technology, and virtualization. In addition, we propose to expand the paradigm of cloud computing to the field of computer-aided design and manufacturing and propose a new concept of cloud-based design and manufacturing (CBDM). Specifically, we (1) propose a comprehensive definition of CBDM; (2) discuss its key characteristics; (3) relate current research in design and manufacture to CBDM; and (4) identify key research issues and future trends.© 2012 ASME

Dirk Schaefer

Papers

Cloud-based design and manufacturing

Cloud manufacturing: Strategic vision and state-of-the-art☆

Software-defined cloud manufacturing for industry 4.0

Smart Packaging: Opportunities and Challenges

Towards a cloud-based design and manufacturing paradigm: looking backward, looking forward