In the near future, we will see an increase in the development and use of all sorts of AI applications. Some of the more promising areas will be Finance, Healthcare, IoT, Manufacturing, Journalism, and Cybersecurity. Many of these applications generate a great amount of complex information. Natural Language Understanding is one of the most clear examples. Traditional ways of visualizing complex information, namely linear text, web pages and hyperlink-based applications, have serious productivity problems. Users need a lot of time to visualize the information and have problems seeing the whole picture of the results. Mind mapping is probably the only way of reducing the problems inherent in these traditional ways of visualizing complex information. Most people have no clear idea about the advantages of mind mapping or the problems created by the traditional ways of visualizing complex information. The goal of Mind Mapping and Artificial Intelligence is to provide readers with an introduction to mind mapping and artificial intelligence, to the problems of using traditional ways of visualizing complex information and as an introduction to mind mapping automation and its integration into Artificial Intelligence applications such as NLU and others. As more applications of Artificial Intelligence are developed in the near future, the need for the improvement of the visualization of the information generated will increase exponentially. Information overload will soon also happen in AI applications. This will diminish the advantages of using AI. Author José Maria Guerrero is a long-time expert in mind mapping and visualization techniques. In this book he also introduces readers to MindManager mind mapping software, which can considerably reduce the problems associated with the interpretation of complex information generated by Artificial Intelligence software.
Author(s): Jose Maria Guerrero
Publisher: Academic Press
Year: 2022
Language: English
Pages: 257
City: London
Front Cover
MIND MAPPING AND ARTIFICIAL INTELLIGENCE
MIND MAPPING AND ARTIFICIAL INTELLIGENCE
Copyright
Contents
Foreword
Prologue
1 - What is mind mapping
1.1 Introduction to the mind mapping technique
1.2 Some definitions
1.2.1 Main components of a mind map
1.3 How to read a mind map
1.4 Why do we need mind mapping?
1.5 Surveys. What do users of mind mapping think about this technique?
1.6 Mind mapping case studies
1.7 Mind mapping literature reviews
1.8 Mind mapping theses
1.9 Mind mapping studies and experiments
1.9.1 Positive effect of mind mapping
1.9.2 Inconclusive or negative effect
References
2 - How to work with mind mapping software
2.1 Mind mapping software
2.1.1 The first mind mapping software
2.1.2 MindManager history
2.1.3 What to avoid when mind mapping
2.1.4 Evolution of mind mapping software
2.2 The MindManager ecosystem
2.3 MindManager desktop programs
2.3.1 Desktop MindManager for Windows Single
2.3.2 MindManager enterprise
2.3.3 Desktop MindManager for Mac single
2.4 MindManager apps
2.4.1 MindManager Snap [25]
2.4.2 MindManager Go
2.4.3 MindManager for MS Teams
2.4.4 MindManager Web
2.4.5 MindManager Server for MS SharePoint [35]
2.4.6 MindManager Reader for MS SharePoint
2.5 Services
2.5.1 Co-editing
2.6 Ways of working with MindManager
2.6.1 Locations to store maps
2.6.2 What can be done while using MindManager
2.7 Creating mind maps
2.7.1 Basic elements for creating mind maps
2.7.1.1 Background
2.7.1.2 Map templates
2.7.1.3 Map parts
2.7.1.4 Map themes
2.7.1.5 Central topic
2.7.1.6 Main topic
2.7.1.7 Subtopic
2.7.1.8 Floating topic
2.7.1.9 Callout topic
2.7.1.10 Relationship
2.7.1.11 Boundary
2.7.1.12 Markers
2.7.1.13 Images
2.7.1.14 Note
2.7.1.15 Hyperlink
2.7.1.16 Attachment
2.7.1.17 Tasks
2.7.2 How to create a mind map with MindManager
2.7.3 Complements
2.7.3.1 Design
2.7.3.2 Spreadsheet/chart
2.7.3.3 Background objects
2.7.4 Content Control
2.7.5 Map Roll-ups
2.7.6 Properties, formulas, and AutoCalc
2.7.6.1 Formulas
2.7.6.2 Autocalc
2.7.7 HTML5 maps
2.7.8 Export, publishing, and share
2.7.9 Tools for task and project management
2.7.10 Other types of diagrams
2.8 MindManager automation
2.8.1 MindManager automation (using no-code)
2.8.2 MindManager automation using macros
2.8.3 The MindManager API
2.8.4 MindManager automation using an XML framework
2.8.5 MindManager automation using an XML framework and no-code
2.8.6 MindManager automation using a database framework
2.9 Learning mind mapping and MindManager
References
3- Fundamentals of neuroscience for mind mapping
3.1 Introduction
3.2 The eye
3.3 The primary visual pathway
3.4 The eye movements
3.5 Memory
3.5.1 Background
3.5.2 The anatomy of memory
3.5.3 Memory effects
3.6 The central executive and the prefrontal cortex
3.7 Sensation, attention, and perception
3.8 Human neural networks, the human connectome
3.9 Basal ganglia
References
4 - Why mind mapping is a vital software tool for the modern worker?
4.1 Introduction
4.2 Advantages attributable to both analog and digital mind mapping
4.2.1 Hierarchy
4.2.2 Chunking and memory
4.2.3 Combination of diagrams, images, and text
4.2.4 Complex MindManager maps
4.2.5 Integration with external software
4.2.6 Color
4.3 Advantages attributable only to digital mapping
4.3.1 Automatic reorganization of the components of the mind map when adding or modifying more components
4.3.2 Searching and filtering
4.3.3 Knowledge management
4.3.4 Communication with other stakeholders
4.3.5 Collaboration
4.3.6 Actionability
4.3.7 Automation
References
5 - The history of modern artificial intelligence
5.1 Introduction to modern artificial intelligence
5.2 Technical component of AI
5.3 Social component of AI
5.4 Evolution of modern AI
5.5 Milestones of modern AI
5.5.1 1940
5.5.2 1943
5.5.3 1945
5.5.4 1946
5.5.5 1948
5.5.6 1949
5.5.7 1950
5.5.8 1951
5.5.9 1953
5.5.10 1955
5.5.11 1956
5.5.12 1958
5.5.13 1959
5.5.14 1960
5.5.15 1961
5.5.16 1962
5.5.17 1963
5.5.18 1964
5.5.19 1965
5.5.20 1966
5.5.21 1967
5.5.22 1969
5.5.23 1970
5.5.24 1971
5.5.25 1972
5.5.26 1973
5.5.27 1974
5.5.28 1978
5.5.29 1979
5.5.30 1980
5.5.31 1981
5.5.32 1982
5.5.33 1983
5.5.34 1984
5.5.35 1985
5.5.36 1986
5.5.37 1987
5.5.38 1988
5.5.39 1989
5.5.40 1990
5.5.41 1991
5.5.42 1992
5.5.43 1993
5.5.44 1995
5.5.45 1997
5.5.46 1998
5.5.47 1999
5.5.48 2000
5.5.49 2002
5.5.50 2004
5.5.51 2005
5.5.52 2006
5.5.53 2007
5.5.54 2008
5.5.55 2009
5.5.56 2010
5.5.57 2011
5.5.58 2012
5.5.59 2013
5.5.60 2014
5.5.61 2015
5.5.62 2016
5.5.63 2017
5.5.64 2018
5.5.65 2019
5.5.66 2020
5.5.67 2021
5.5.68 2022?
References
6 - Artificial narrow intelligence
6.1 Introduction to narrow artificial intelligence
6.2 Modern NAI
6.2.1 Machine learning
6.2.2 Deep learning
6.2.3 Deep reinforcement learning
6.2.4 Feedforward neural networks
6.2.5 Recurrent neural networks
6.2.6 Convolutional neural networks
6.2.7 Generative adversarial networks
6.2.8 Transformers
6.2.9 Transfer learning
6.3 Some examples of real and productive NAI
6.3.1 Entertainment (video games)
6.3.1.1 Dota 2 [48,49]
6.3.2 Smart voice assistants
6.3.3 Smart town lightning
6.3.4 Content creation
6.3.5 Understanding and summarizing
6.3.6 Translation
6.3.7 Predictive maintenance
6.3.8 Fact checking
6.3.9 Hiring platforms
6.3.10 Malware detection
6.3.11 Robotic process automation
6.3.12 Predictive maintenance
6.3.13 Logistics
6.3.14 Manufacturing
6.3.15 Unmanned systems
6.3.16 Healthcare
6.4 NAI APIs
6.5 Applications of mind mapping in NAI
6.5.1 Visual project management in the development of NAI applications
6.5.2 Visualization of dashboards generated by NAI applications
6.5.3 Visualization of detailed information generated by NAI applications
6.5.4 Visualization of conversations from dialogue systems
6.5.5 Generation of mind maps using NAI applications
6.5.6 Understanding scientific articles
6.5.7 Translation of mind maps
6.5.8 Generation of knowledge mind maps as NFTs
6.5.9 Mind mapping in metaverses
6.5.10 Speech to text transcription
References
7 - Artificial general intelligence
7.1 Introduction
7.2 The path to AGI
7.3 Artificial superintelligence
7.4 The artificial intelligence singularity
7.5 The risks of AGI and ASI
7.5.1 Generic
7.5.2 Economic
7.5.3 Social
7.5.4 Military
7.5.5 Safety
7.5.6 Ethical
7.5.7 Existential risk
7.6 Applications of mind mapping in AGI and ASI
7.6.1 Analysis of scientific articles, reports, and books
7.6.2 Brainstorming of ideas about AGI and ASI
7.6.3 Design of the software
7.6.4 Documentation of the software
7.6.5 Visual project development
References
8 - Examples
8.1 Introduction
8.2 Analysis of speech and sound recordings
8.3 AI project management
8.4 Decision trees
8.5 Translation of mind maps
8.6 Creation of mind maps of scientific articles
8.7 Mind maps from NLU IBM Watson
8.8 Mind maps for recommender systems
8.9 Knowledge mind maps
8.10 Hospital discharge instructions
8.11 Voice generated mind maps
8.12 Mind-generated mind map
8.13 Brainstorming with mind mapping
8.14 Color harmonization
8.15 Mind mapping+AI+extended reality (XR)
References
Appendices
A.1 Surveys
A.2 The scientific method
A.3 Experiments
A.3.1 Introduction
A.3.2 Validity of experiments
A.3.3 Types of experiments
A.3.4 Design of experiments
A.3.5 Differences between surveys and experiments
A.4 Case studies
A.5 Literature reviews
A.6 How to do better mind mapping experiments
A.7 A short history of human brain imaging
References
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
Back Cover
