Managing Guest Editor
Mu-Yen Chen, National Cheng Kung University, Taiwan, E-mail: firstname.lastname@example.org
Mario Koeppen, Kyushu institute of technology, Japan, E-mail: email@example.com
Abbas Mardani, University of South Florida, United States, E-mail: firstname.lastname@example.org
Thar Baker, Liverpool John Moores University, UK, E-mail: email@example.com
Scope of the issue
Artificial Intelligence (AI) has seized the attention of the business world. AI is the next step on the journey from big data to full automation. Human needs are the motivation behind improvements in computing paradigms. Examples of this include such things as collecting brainwave data via “wearables” and using that information to monitor health and predict issues, tracking the movements of mobile phones on roads to predict traffic jams (Google Maps), and using natural language processing to learn and “predict” correct spelling and offer human-like speech (Amazon Alexa, Apple Siri). The more data that is collected, the wider the variety of predictions that can be offered. Each of these examples indicates an implicit or explicit need or expectation from humans, and each is an attempt to satisfy that need via a specific approach. However, humans expect more as technology develops. To this end, AI continuously interacts with us by simulating our thinking patterns, behaviors, and bringing other relevant information into play. Given the number of similar studies in this field, we suggest the introduction of a new computing paradigm, “Predictive Intelligence.”
“Predictive Intelligence” utilizes three types of data: (1) training data for building the AI model, (2) input data for prediction functionality, and (3) feedback data for tuning the model parameters and improving the prediction accuracy. Strong predictions also serve as inputs that are factored into subsequent decisions. The economic field has developed a reliable framework that aids in the understanding of how decisions are made. Recent advances in prediction technology have created implications that are not well-understood, and decision theory derived from economics can provide deeper insight. “Predictive Intelligence” outperforms humans when the complex interactions of various dimensions are considered, especially when huge amounts of data are involved. Increasing the number of interacting dimensions exposes the progressive limitation of the human ability to make accurate predictions, especially when compared to the abilities of a machine. On the other hand, humans often outperform machines—especially when small amounts of data are involved—because their ability to understand the process that generates the data gives them a prediction advantage. This phenomenon offers the opportunity to raise challenging issues within the field of computer science.
Machine learning refers to the design and analysis of algorithms with which computers can “learn” automatically, allowing machines to generate rules by analyzing data and employing that data to “predict” unknowns. Machine learning has been applied to solve complex problems in human society for years, and it has been successful because of advances in computing capabilities and sensing technology. As artificial intelligence and soft computing approaches evolve, they will soon have a considerable impact on the field. Recently, deep learning has matured in the field of supervised learning. Machine learning is only incipient in such areas as unsupervised learning and reinforcement learning using methodologies that involve soft computing. Developments in artificial intelligence and high-speed computing performance have brought recent dramatic changes. Thus, deep learning serves as an excellent example of using feature engineering to exceed the limits of machine learning, offering far greater performance and making possible a number of extremely complex applications.
Prediction can be broken into four distinct categories: known knowns, known unknowns, unknown unknowns, and unknown knowns. “Predictive Intelligence” deals extremely well with known knowns. Machine learning works best with rich data. “Predictive Intelligence” is good at filling in the gaps around known unknowns. These are things humans know intuitively, but machines cannot know. The “unknown” is used in the sense of the discovery of something not known previously. Data can be hard to collect because the rarity of some events makes them a challenge to predict. Unlike machines, humans excel at making predictions with small amounts of data. The majority of “deep learning” technologies build on the concept of supervised learning to determine classifiers that allow the system to recognize various data patterns or events. A Generative Adversarial Networks (GAN) can also overcome the “too little data” issue that creates the known unknowns bottleneck. In order to generate a prediction, human experts and knowledge engineers need to inform the machine regarding the kinds of things for which a prediction is valuable. Unprecedented events cannot be predicted by a machine as they have never occurred. Therefore, the machine is disoriented by data of which it is unaware or that is entirely unexpected, i.e., the unknown unknowns. The Google Flu Trends (GFT) was a failed attempt at using machine intelligence to predict unknown unknowns. This means that linking predictions and effective predictions can also incur some risks and bias. “Soft computing and metaheuristic algorithms” are applicable to this situation. The concept of unsupervised learning is then used to determine efficacious solutions within a solution space, the infinite space in which the unknown unknowns issues can be overcome. Finally, the greatest weakness of “Predictive Intelligence” is the unknown knowns, i.e., when the wrong answer is provided with complete confidence that it is actually right. That sends AI down the wrong path. If the decision process by which the data was generated is not fully understood by the machine, prediction failure is likely. Therefore, large scale incremental learning and transfer learning methods can be used to detect possible knowns from the current knowns and ameliorate this weakness.
For this special issue, we solicit original contributions that address challenges and issues relating to the exploitation of soft computing or deep learning methods to build prediction models and resolve situations involving unknown unknowns and unknown knowns. Classification names should not be derived by learning from past knowns but rather from predicting the expected answer. The best predictions are achieved when humans and machines work in combination, as the strengths of each makes up for the weaknesses of the other. The main goal of this special issue is to collect manuscripts reporting the latest advances in standards, models, algorithms, technologies and applications, and to highlight the paradigm shifts in this field.
We solicit original contributions that fall within, and each submission must contribute to soft computing related methodology, the following topics of interest:
– Methodologies, and Techniques
Adaptive machine learning and soft computing algorithms for data streams
New methods combining soft computing and deep learning
New learning methods involving soft computing concepts for extant architectures and structures of predictive intelligence
Evolutionary and soft computing-based tuning and optimization of predictive intelligence
Metaheuristics aspects and soft computing algorithms in deep learning for improved convergence of predictive intelligence
Robust data augmentation methods for predictive intelligence learning
Faster incremental learning and transfer learning methods for predictive intelligence self-learning
– Human Behavior
Human behavior and user interfaces for human-centered predictive intelligence
Human participation and social sensing for human-centered predictive intelligence
The applications of personality and social psychology for predictive intelligence
Artificial intelligence and mental processes in human-centered predictive intelligence
Trust, security, and privacy issues for human-centered predictive intelligence
– Real-World Applications
Economic and financial applications
Intelligent e-learning & tutoring
Internet of Things (IoT) applications
Smart living and smart cities
Virtual Special Issue start: November 1, 2020
First round of reviews: Maximum 3 months after submission date
Submission of revised paper: Maximum 1 month after 1st review notification
Final notification: Maximum 2 months after resubmission
Virtual Special Issue closing date: May 31, 2021
Paper submissions for the special issue should follow the submission format and guidelines for regular papers and submitted at https://ees.elsevier.com/asoc. All the papers will be peer reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance and contributions, as well as their suitability to the special issue. Each submission must contribute to soft computing related methodology. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will be desk-rejected, and will not be sent for review. Authors should select “VSI: Predictive Intelligence” when they reach the “Article Type” step in the submission process. The submitted papers must propose original research that has not been published nor currently under review in other venues.