Pub Date : 2018-06-27DOI: 10.5772/INTECHOPEN.75751
Dov Schafer, David Kaufman
It is clear that our daily reality will increasingly interface with virtual inputs. We already integrate the virtual into real life through constantly evolving sensor technologies embedded into our smartphones, digital assistants, and connected devices. Simultaneously, we seek more virtual input into our reality through intelligent interfaces for the applications that these devices can run in a context rich, socially connected, and personalized way. As we progress toward a future of ubiquitous Augmented Reality (AR) interfaces, it will be important to consider how this technology can best serve the various populations that can benefit most from the addition of these intelligent interfaces. This paper proposes a new terminological framework to discuss the way AR interacts with users. An intelligent interface that combines digital objects in a real-world context can be referred to as a Pose-Interfaced Presentation (PIP): Pose refers to user location and orientation in space; Interfaced means that the program responds to a user’s intention and actions in an intelligent way; and Presentation refers to the virtual object or data being layered onto the perceptive field of the user. Finally, various benefits of AR are described and examples are provided in the areas of education, worker training, and ESL learning.
{"title":"Augmenting Reality with Intelligent Interfaces","authors":"Dov Schafer, David Kaufman","doi":"10.5772/INTECHOPEN.75751","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75751","url":null,"abstract":"It is clear that our daily reality will increasingly interface with virtual inputs. We already integrate the virtual into real life through constantly evolving sensor technologies embedded into our smartphones, digital assistants, and connected devices. Simultaneously, we seek more virtual input into our reality through intelligent interfaces for the applications that these devices can run in a context rich, socially connected, and personalized way. As we progress toward a future of ubiquitous Augmented Reality (AR) interfaces, it will be important to consider how this technology can best serve the various populations that can benefit most from the addition of these intelligent interfaces. This paper proposes a new terminological framework to discuss the way AR interacts with users. An intelligent interface that combines digital objects in a real-world context can be referred to as a Pose-Interfaced Presentation (PIP): Pose refers to user location and orientation in space; Interfaced means that the program responds to a user’s intention and actions in an intelligent way; and Presentation refers to the virtual object or data being layered onto the perceptive field of the user. Finally, various benefits of AR are described and examples are provided in the areas of education, worker training, and ESL learning.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114266300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-27DOI: 10.5772/INTECHOPEN.76027
Zeinab Hajjar, S. Tayyebi, Mohammad Hosein EghbalAhmadi
A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields.
{"title":"Application of AI in Chemical Engineering","authors":"Zeinab Hajjar, S. Tayyebi, Mohammad Hosein EghbalAhmadi","doi":"10.5772/INTECHOPEN.76027","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.76027","url":null,"abstract":"A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128441326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-27DOI: 10.5772/INTECHOPEN.75692
C. Luo, G. E. Jan, Zhenzhong Chu, Xinde Li
Biologically inspired intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society and this trend will continue with biologically inspired neural network techniques. In this chapter, multiple robots cooperate to achieve a common coverage goal efficiently, which can improve the work capacity, share the coverage tasks, and reduce the completion time by a biologically inspired intelligence technique, is addressed. In many real-world applications, the coverage task has to be completed without any prior knowledge of the environment. In this chapter, a neural dynamics approach is proposed for complete area coverage by multiple robots. A bio-inspired neural network is designed to model the dynamic environment and to guide a team of robots for the coverage task. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each mobile robot treats the other robots as moving obstacles. Each robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot position. The proposed model algorithm is computationally sim- ple. The feasibility is validated by four simulation studies.
{"title":"Biologically Inspired Intelligence with Applications on Robot Navigation","authors":"C. Luo, G. E. Jan, Zhenzhong Chu, Xinde Li","doi":"10.5772/INTECHOPEN.75692","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75692","url":null,"abstract":"Biologically inspired intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society and this trend will continue with biologically inspired neural network techniques. In this chapter, multiple robots cooperate to achieve a common coverage goal efficiently, which can improve the work capacity, share the coverage tasks, and reduce the completion time by a biologically inspired intelligence technique, is addressed. In many real-world applications, the coverage task has to be completed without any prior knowledge of the environment. In this chapter, a neural dynamics approach is proposed for complete area coverage by multiple robots. A bio-inspired neural network is designed to model the dynamic environment and to guide a team of robots for the coverage task. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each mobile robot treats the other robots as moving obstacles. Each robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot position. The proposed model algorithm is computationally sim- ple. The feasibility is validated by four simulation studies.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121047610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-27DOI: 10.5772/INTECHOPEN.75959
A. Lezama-León, J. Zárate-Corona, E. León, José Angel Montes-Olguín, Juan Ángel Rosales-Alba, Mariana Carrillo-González
{"title":"High Performance Technology in Algorithmic Cryptography","authors":"A. Lezama-León, J. Zárate-Corona, E. León, José Angel Montes-Olguín, Juan Ángel Rosales-Alba, Mariana Carrillo-González","doi":"10.5772/INTECHOPEN.75959","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75959","url":null,"abstract":"","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114638825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-27DOI: 10.5772/INTECHOPEN.78299
N. Bouhmala
Genetic algorithms (GA) which belongs to the class of evolutionary algorithms are regarded as highly successful algorithms when applied to a broad range of discrete as well continuous optimization problems. This chapter introduces a hybrid approach com- bining genetic algorithm with the multilevel paradigm for solving the maximum constraint satisfaction problem (Max-CSP). The multilevel paradigm refers to the process of dividing large and complex problems into smaller ones, which are hopefully much easier to solve, and then work backward toward the solution of the original problem, using the solution reached from a child level as a starting solution for the parent level. The promis-ing performances achieved by the proposed approach are demonstrated by comparisons made to solve conventional random benchmark problems.
{"title":"A Multilevel Genetic Algorithm for the Maximum Satisfaction Problem","authors":"N. Bouhmala","doi":"10.5772/INTECHOPEN.78299","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78299","url":null,"abstract":"Genetic algorithms (GA) which belongs to the class of evolutionary algorithms are regarded as highly successful algorithms when applied to a broad range of discrete as well continuous optimization problems. This chapter introduces a hybrid approach com- bining genetic algorithm with the multilevel paradigm for solving the maximum constraint satisfaction problem (Max-CSP). The multilevel paradigm refers to the process of dividing large and complex problems into smaller ones, which are hopefully much easier to solve, and then work backward toward the solution of the original problem, using the solution reached from a child level as a starting solution for the parent level. The promis-ing performances achieved by the proposed approach are demonstrated by comparisons made to solve conventional random benchmark problems.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127883141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-27DOI: 10.5772/INTECHOPEN.76146
Masakazu Sato, K. Koga, T. Fujii, Y. Osuga
Background : Remarkable progress has recently been made in the field of artificial intelligence (AI). Objective : We sought to investigate whether reinforcement learning could be used in sur gery in the future . Methods : We created simple 2D tasks (Tasks 1–3) that mimicked surgery. We used a neu ral network library, Keras, for reinforcement learning. In Task 1, a Mac OS X with an 8 GB memory (MacBook Pro, Apple, USA) was used. In Tasks 2 and 3, a Ubuntu 14. 04LTS with a 26 GB memory (Google Compute Engine, Google, USA) was used . Results : In the task with a relatively small task area (Task 1), the simulated knife finally passed through all the target areas, and thus, the expected task was learned by AI. In con trast, in the task with a large task area (Task 2), a drastically increased amount of time was required, suggesting that learning was not achieved. Some improvement was observed when the CPU memory was expanded and inhibitory task areas were added (Task 3) . Conclusions : We propose the combination of reinforcement learning and surgery. Appli cation of reinforcement learning to surgery may become possible by setting rules, such as appropriate rewards and playable (operable) areas, in simulated tasks.
背景:近年来，人工智能(AI)领域取得了显著进展。目的:探讨强化学习在外科手术中的应用前景。方法:我们制作简单的2D任务(任务1-3)来模拟手术。我们使用了一个神经网络库Keras来进行强化学习。在Task 1中，使用的是8gb内存的Mac OS X (MacBook Pro, Apple, USA)。在任务2和任务3中，安装一个Ubuntu 14。使用26gb内存的04LTS(谷歌Compute Engine，谷歌，USA)。结果:在任务区域相对较小的任务(任务1)中，模拟刀最终通过了所有的目标区域，因此，AI学习到了预期的任务。相反，在任务区域较大的任务(任务2)中，需要的时间急剧增加，这表明没有实现学习。当CPU内存扩大和抑制性任务区域增加时，可以观察到一些改善(任务3)。结论:我们建议将强化学习与手术相结合。通过在模拟任务中设置规则，例如适当的奖励和可玩(可操作)区域，将强化学习应用于外科手术可能成为可能。
{"title":"Can Reinforcement Learning Be Applied to Surgery?","authors":"Masakazu Sato, K. Koga, T. Fujii, Y. Osuga","doi":"10.5772/INTECHOPEN.76146","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.76146","url":null,"abstract":"Background : Remarkable progress has recently been made in the field of artificial intelligence (AI). Objective : We sought to investigate whether reinforcement learning could be used in sur gery in the future . Methods : We created simple 2D tasks (Tasks 1–3) that mimicked surgery. We used a neu ral network library, Keras, for reinforcement learning. In Task 1, a Mac OS X with an 8 GB memory (MacBook Pro, Apple, USA) was used. In Tasks 2 and 3, a Ubuntu 14. 04LTS with a 26 GB memory (Google Compute Engine, Google, USA) was used . Results : In the task with a relatively small task area (Task 1), the simulated knife finally passed through all the target areas, and thus, the expected task was learned by AI. In con trast, in the task with a large task area (Task 2), a drastically increased amount of time was required, suggesting that learning was not achieved. Some improvement was observed when the CPU memory was expanded and inhibitory task areas were added (Task 3) . Conclusions : We propose the combination of reinforcement learning and surgery. Appli cation of reinforcement learning to surgery may become possible by setting rules, such as appropriate rewards and playable (operable) areas, in simulated tasks.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129594688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-12DOI: 10.5772/INTECHOPEN.75957
Josep Llorca
Urban sound is one of the main concerns of architects and urban planners in contempo- rary cities: how to control it, what to do about noise pollution, where silent areas should be situated, or which urban decisions must be made. These questions, among others, are based on spatial sound. Virtual reality is a powerful technology that can serve as a design tool to find some answers to these questions. Due to its power to generate realistic images of the environments that are studied, it is easy to see that virtual reality could contribute to the visualization and auralization of spaces before their construction. This task is one of architects’ responsibilities, and such a tool could be very useful to them. This chapter highlights the principles and some applications of virtual reality in urban sound design. Two big differences separate the experience of illuminating and sonic phenomena. The first consists of the fact that most visual objects are not sources of light, but simply objects, in the usual sense of the word, with light shining on them. Physicists are therefore quite accustomed to distinguishing light from the objects that reflect it. If the object itself gives out light, then we say it is a light “source”. With sound there is nothing like this. In the overwhelming majority of sonic phenomena, sound as origi-nating from “sources” is emphasized. However, the classic distinction in optics between sources and objects has not been imposed in acoustics. Attention has been given to the sound (as we say the light) considered as an emanation from a source, its paths and deformations, without the appreciation of the shapes and contours of this sound apart from the reference to its source [2] .
{"title":"Virtual Reality for Urban Sound Design: A Tool for Architects and Urban Planners","authors":"Josep Llorca","doi":"10.5772/INTECHOPEN.75957","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75957","url":null,"abstract":"Urban sound is one of the main concerns of architects and urban planners in contempo- rary cities: how to control it, what to do about noise pollution, where silent areas should be situated, or which urban decisions must be made. These questions, among others, are based on spatial sound. Virtual reality is a powerful technology that can serve as a design tool to find some answers to these questions. Due to its power to generate realistic images of the environments that are studied, it is easy to see that virtual reality could contribute to the visualization and auralization of spaces before their construction. This task is one of architects’ responsibilities, and such a tool could be very useful to them. This chapter highlights the principles and some applications of virtual reality in urban sound design. Two big differences separate the experience of illuminating and sonic phenomena. The first consists of the fact that most visual objects are not sources of light, but simply objects, in the usual sense of the word, with light shining on them. Physicists are therefore quite accustomed to distinguishing light from the objects that reflect it. If the object itself gives out light, then we say it is a light “source”. With sound there is nothing like this. In the overwhelming majority of sonic phenomena, sound as origi-nating from “sources” is emphasized. However, the classic distinction in optics between sources and objects has not been imposed in acoustics. Attention has been given to the sound (as we say the light) considered as an emanation from a source, its paths and deformations, without the appreciation of the shapes and contours of this sound apart from the reference to its source [2] .","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129158961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-04DOI: 10.5772/INTECHOPEN.75924
Lejun Gong
With the enormous volume of biological literature, increasing growth phenomenon due to the high rate of new publications is one of the most common motivations for the biomedical text mining. Aiming at this massive literature to process, it could extract more biological information for mining biomedical knowledge. Using the information will help understand the mechanism of disease generation, promote the development of disease diagnosis technology, and promote the development of new drugs in the field of biomedical research. Based on the background, this chapter introduces the rise of biomedical text mining. Then, it describes the biomedical text-mining technology, namely natural language processing, including the several components. This chapter emphasizes the two aspects in biomedical text mining involving static biomedical information recognization and dynamic biomedical information extraction using instance analysis from our previous works. The aim is to provide a way to quickly understand biomedical text mining for some researchers.
{"title":"Application of Biomedical Text Mining","authors":"Lejun Gong","doi":"10.5772/INTECHOPEN.75924","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75924","url":null,"abstract":"With the enormous volume of biological literature, increasing growth phenomenon due to the high rate of new publications is one of the most common motivations for the biomedical text mining. Aiming at this massive literature to process, it could extract more biological information for mining biomedical knowledge. Using the information will help understand the mechanism of disease generation, promote the development of disease diagnosis technology, and promote the development of new drugs in the field of biomedical research. Based on the background, this chapter introduces the rise of biomedical text mining. Then, it describes the biomedical text-mining technology, namely natural language processing, including the several components. This chapter emphasizes the two aspects in biomedical text mining involving static biomedical information recognization and dynamic biomedical information extraction using instance analysis from our previous works. The aim is to provide a way to quickly understand biomedical text mining for some researchers.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131158087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-03-30DOI: 10.5772/INTECHOPEN.75599
M. Rojc, Z. Kacic, I. Mlakar
Conversation is becoming one of the key interaction modes in HMI. As a result, the con- versational agents (CAs) have become an important tool in various everyday scenarios. From Apple and Microsoft to Amazon, Google, and Facebook, all have adapted their own variations of CAs. The CAs range from chatbots and 2D, carton-like implementations of talking heads to fully articulated embodied conversational agents performing interaction in various concepts. Recent studies in the field of face-to-face conversation show that the most natural way to implement interaction is through synchronized verbal and co-verbal signals (gestures and expressions). Namely, co-verbal behavior represents a major source of discourse cohesion. It regulates communicative relationships and may support or even replace verbal counterparts. It effectively retains semantics of the information and gives a certain degree of clarity in the discourse. In this chapter, we will represent a model of generation and realization of more natural machine-generated output.
{"title":"Advanced Content and Interface Personalization through Conversational Behavior and Affective Embodied Conversational Agents","authors":"M. Rojc, Z. Kacic, I. Mlakar","doi":"10.5772/INTECHOPEN.75599","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75599","url":null,"abstract":"Conversation is becoming one of the key interaction modes in HMI. As a result, the con- versational agents (CAs) have become an important tool in various everyday scenarios. From Apple and Microsoft to Amazon, Google, and Facebook, all have adapted their own variations of CAs. The CAs range from chatbots and 2D, carton-like implementations of talking heads to fully articulated embodied conversational agents performing interaction in various concepts. Recent studies in the field of face-to-face conversation show that the most natural way to implement interaction is through synchronized verbal and co-verbal signals (gestures and expressions). Namely, co-verbal behavior represents a major source of discourse cohesion. It regulates communicative relationships and may support or even replace verbal counterparts. It effectively retains semantics of the information and gives a certain degree of clarity in the discourse. In this chapter, we will represent a model of generation and realization of more natural machine-generated output.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117074496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-03-11DOI: 10.5772/INTECHOPEN.75310
T. McCaffrey
In order to maximize creative behavior, humans and computers need to collaborate in a manner that will leverage the strengths of both. A 2017 mathematical proof shows two limits to how innovative a computer can be. Humans can help counteract these demonstrated limits. Humans possess many mental blind spots to innovating (e.g., functional fixedness, design fixation, analogy blindness, etc.), and particular algorithms can help counteract these shortcomings. Further, since humans produce the corpora used by AI technology, human blind spots to innovation are implicit within the text processed by AI technology. Known algorithms that query humans in particular ways can effectively counter these text-based blind spots. Working together, a human-computer partnership can achieve higher degrees of innovation than either working alone. To become an effective partnership, however, a special interface is needed that is both humanand computer-friendly. This interface called BrainSwarming possesses a linguistic component, which is a formal grammar that is also natural for humans to use and a visual component that is easily represented by standard data structures. Further, the interface breaks down innovative problem solving into its essential components: a goal, sub-goals, resources, features, interactions, and effects. The resulting human-AI synergy has the potential to achieve innovative breakthroughs that either partner working alone may never achieve.
{"title":"Human-AI Synergy in Creativity and Innovation","authors":"T. McCaffrey","doi":"10.5772/INTECHOPEN.75310","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75310","url":null,"abstract":"In order to maximize creative behavior, humans and computers need to collaborate in a manner that will leverage the strengths of both. A 2017 mathematical proof shows two limits to how innovative a computer can be. Humans can help counteract these demonstrated limits. Humans possess many mental blind spots to innovating (e.g., functional fixedness, design fixation, analogy blindness, etc.), and particular algorithms can help counteract these shortcomings. Further, since humans produce the corpora used by AI technology, human blind spots to innovation are implicit within the text processed by AI technology. Known algorithms that query humans in particular ways can effectively counter these text-based blind spots. Working together, a human-computer partnership can achieve higher degrees of innovation than either working alone. To become an effective partnership, however, a special interface is needed that is both humanand computer-friendly. This interface called BrainSwarming possesses a linguistic component, which is a formal grammar that is also natural for humans to use and a visual component that is easily represented by standard data structures. Further, the interface breaks down innovative problem solving into its essential components: a goal, sub-goals, resources, features, interactions, and effects. The resulting human-AI synergy has the potential to achieve innovative breakthroughs that either partner working alone may never achieve.","PeriodicalId":442318,"journal":{"name":"Artificial Intelligence - Emerging Trends and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125118597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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