Stats for nerds
1recommend
6mentions
4people
2014–2025years
17% recommend83% neutral
Mentions per year
What Hacker News says
- You may be interested in a little-known older book Computational Semantics: An introduction to Artificial Intelligence and Natural Language Comprehension: https://archive.org/details/computationalsem04char#mainconte...
- Been doing this software thing for 35 years so I guess that makes me an old timer.
Technical books, especially for practitioners, that stay relevant over a long period of time are rather rare. I wish I could say that I've had time to study TAoCP or SICP, but I haven't.
Some of what I did find useful back in the day were: Advanced Programming in the Unix Environment by Stevens, Stan Lippman's C++ book, Code Complete by Steve McConnell, the GoF Design Patterns book, Working Effectively With Legacy Code by Michael Feathers, and Introduction to Artificial Intelligence by Charniak and McDermott.
Whether, and to what extent, any of them are useful now is hard to say. Depends what you want to learn and what you're doing. The Charniak book, for example, is about classic "good old fashioned" AI, not the currently dominant connectionist approach. But it's LISP, and LISP is always relevant.
I'd also add The Systems Bible by John Gall as a book that influenced me. Its not a technology book though.
- A list of the books can be found in the following excel file, which you can also filter for your liking. https://resource-cms.springernature.com/springer-cms/rest/v1...
The following are the 49 ebooks categorized 'Computer Science':
Introduction to Evolutionary Computing Data Structures and Algorithms with Python LaTeX in 24 Hours Automata and Computability The Algorithm Design Manual Principles of Data Mining Fundamentals of Business Process Management UML @ Classroom Foundations for Designing User-Centered Systems Computer Vision Data Mining Computational Geometry Cryptography Made Simple Eye Tracking Methodology The Data Science Design Manual An Introduction to Machine Learning Guide to Discrete Mathematics Modelling Computing Systems Understanding Cryptography Concise Guide to Software Engineering Fundamentals of Multimedia The Python Workbook Recommender Systems Python Programming Fundamentals Object-Oriented Analysis, Design and Implementation Introduction to Data Science Concise Guide to Databases Digital Image Processing Guide to Computer Network Security Foundations of Programming Languages Probability and Statistics for Computer Science Guide to Competitive Programming Introduction to Artificial Intelligence Introduction to Deep Learning A Beginner's Guide to Scala, Object Orientation and Functional Programming Fundamentals of Business Process Management Guide to Scientific Computing in C++ Fundamentals of Java Programming Logical Foundations of Cyber-Physical Systems Introduction to Programming with Fortran Neural Networks and Deep Learning Data Science and Predictive Analytics Systems Programming in Unix/Linux Introduction to Parallel Computing Analysis for Computer Scientists Introductory Computer Forensics Java in Two Semesters A Beginners Guide to Python 3 Programming Advanced Guide to Python 3 Programming - OK, here ya go. This list is, for now, just a raw dump of titles and authors. When I have some more time, I'll turn this into a proper blog post with more commentary, sorting / grouping of the titles, links to complementary resources etc.
One bit of warning... the first half of this list includes everything "AI" related I've read (or am currently reading) for about the past two years. I say "AI" and not "AGI" because I haven't always bothered to make the distinction. So some of this stuff may not be relevant to you.
FYI, this list (especially the first half) is a heterogeneous lot that varies considerably along the "pop sci" to "academic" axis, as well as the "applied" to "theoretical" axis.
I'm also leaving off (for now anyway) most the "related, but not specifically about AI" stuff, like books on Probability, Bayesian Statistics, Graph Theory, etc., and books that are more about contemporary "Machine Learning" and "Deep Learning" topics. I do believe that there is a connection though, and that DL may play a role in building a truly intelligent system, so when I write the expanded blog post, I'll include more of that stuff.
OK, With no further ado, the "AI stuff I've read recently" list.
0. Blackboard Systems - Robert Engelmore & Tony Morgan
1. The Age of Spiritual Machines - Ray Kurzweil
2. The Emotion Machine - Marvin Minsky
3. The Society of Mind - Marvin Minsky
4. Possible Minds: 25 Ways of Looking at AI - John Brockman (ed)
5. AI Super-Powers: China, Silicon Valley and the New World Order - Kai-Fu Lee (more meta than actually about AI per-se)
6. Artificial Intelligence In Practice - Bernard Marr
7. Architects of Intelligence - Martin Ford
8. Gödel, Escher, Bach - Douglas Hofstadter
9. Superintelligence - Nick Bostrom
10. The Master Algorithm - Pedro Domingos
11. On Intelligence - Jeff Hawkins
12. Unified Theories of Cognition - Alan Newell
13. The AGI Revolution - Ben Goertzel
14. How To Create A Mind - Ray Kurzweil
15. Case-Based Reasoning: A Textbook - Michael M. Richter & Rosina O. Weber
16. The Age of Intelligent Machines - Ray Kurzweil
17. The Singularity is Near - Ray Kurzweil
18. An Introduction to MultiAgent Systems - Michael J. Wooldridge
19. Engineering General Intelligence - Volume 1 - Ben Goertzel
20. Artificial Intelligence: A Modern Approach - Peter Norvig & Stuart Russell
----
Below here is the stuff that is in the "to read" queue. This part tends to run more to textbooks and more academic material and less of the "pop sci" stuff. The choices on this list were inspired by other reading and research I've done, which led me to believe this group of titles includes material that will be useful. YMMV.
21. Cause Effect Pairs in Machine Learning - Isabelle Guyon, Alexander Statnikov, & Berna Bakir Batu (eds)
22. Propositional, Probabilistic, and Evidential Reasoning: Integrating Numerical and Symbolic Approaches - Weiru Liu
23. Human Compatible - Stuart Russell
23. Rebooting AI - Gary Marcus & Ernest Davis
24. Artificial Intelligence - Melanie Mitchell
25. Protocol Analysis - K. Anders Ericsson & Herbert A. Simon
26. The Robot's Dilemma: The Frame Problem in Artificial Intelligence - Zenon W. Pylyshyn
27. Image and Mind - Stephen Michael Kosslyn
28. Elements of Episodic Memory - Endel Tulving
29. Things and Places: How The Mind Connects With The World - Zenon W. Pylyshyn
30. Information Algebras: Generic Structures for Inference - Jurg Kohlas
31. Probabilistic Logics and Probabilistic Networks - Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson
32. Knowledge-Based Systems in Artificial Intelligence - Randall Davis & Douglas B. Lenat
33. Building Large Knowledge-Based Systems - Douglas B. Lenat & R.V. Guha
34. Elements of Information Theory - Thomas M. Cover & Joy A. Thomas
35. The Bayesian Choice - Christian P. Robert
35. Machine Learning for Commonsense Reasoning Processes - Xenia Naidenova
36. Reinforcement Learning and Optimal Control - Dimitri P. Bertsekas
37. An Introduction to Kolmogorov Complexity and Its Applications - Ming Li & Paul Vitanyi
38. The Mechanical Mind: A Philosophical Introduction to Minds, Machines, and Mental Representation - Tim Crane
39. Constraining Cognitive Theories - Zenon W. Pylyshyn
40. Neuro-Dynamic Programming - Dimitri P. Bertsekas & John Tsitsiklis
41. The Language Instinct - Steven Pinker
42. How The Mind Works - Steven Pinker
43. Images and Words - Steven Pinker
44. Words and Rules - Steven Pinker
45. The Blank Slate - Steven Pinker
46. Consciousness Explained - Daniel Dennett
47. How to Build a Brain: A Neural Architecture for Biological Cognition - Chris Eliasmith
48. Introduction to Artificial Intelligence - Eugene Charniak & Drew McDermott
49. Metamagical Themas - Douglas Hofstadter
50. I Am A Strange Loop - Douglas Hofstadter
51. Fluid Concepts And Creative Analogies: Computer Models Of The Fundamental Mechanisms Of Thought - Douglas Hofstadter
52. Principles of Synthetic Intelligence - Joshua Bach
53. Anatomy of the Mind - Ron Sun
54. Generic Inference: A Unifying Theory for Automated Reasoning - Marc Pouly & Jurg Kohlas
55. Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach - Michael Gelfond & Yulia Kahl
56. Semantic Networks: An Evidential Formalization and its Connectionist Realization - Lockendra Shastri
57. Automated Planning and Acting - Malik Ghallab, Dana Nau, & Paolo Traverso
58. How Can The Human Mind Occur in the Physical Universe? - John Anderson
59. Spiking Neuron Models - Wulfram Gerstner & Werner Kistler
60. Neural Engineering - Chris Eliasmith & Charles H. Anderson
61. Principles of Neural Design - Peter Sterling & Simon Laughlin
62. Semantic Information Processing - Marvin Minsky
63. The SOAR Cognitive Architecture - John Laird
64. The Architecture of Cognition - John Anderson
65. Human Associative Memory - John Anderson & Gordon E. Bower
66. Knowledge Representation & Reasoning - Ronald J. Brachman & Hector J. Levesque
67. Cognitive Architectures - Maria Isabel Aldinhas Ferreira, Joao Silva Sequeira, & Rodrigo Ventura (eds)
68. Scripts, Goals, and Planning - Roger C. Schank & Robert P. Abelson
69. Reasoning About Plans - James F. Allen, Henry A. Kautz, Richard N. Pelavin, & Josh D. Tenenberg
70. Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning - Craig A. Knoblock
71. Representations of Commonsense Knowledge - Ernest Davis
72. Approaches to Knowledge Representation - G.A. Ringland & D.A. Duce
73. Knowledge Representation - John F. Sowa
74. Conceptual Structures: Information Processing in Mind and Machine - John F. Sowa
75. Inside Computer Understanding - Roger C. Schank & Christopher K. Riesbeck
76. Integrating Rules and Connectionism for Robust Commonsense Reasoning - Ron Sun
77. Probabilistic Logic Networks - Ben Goertzel, et al.
78. The Subtlety of Sameness: A Theory and Computer Model of Analogy Making - Robert M. French
79. Analogy Making as Perception - Melanie Mitchell
80. Real World Reasoning - Ben Goertzel, et al.
81. Artificial General Intelligence - Ben Goertzel & Cassio Pennachin
82. The Hidden Pattern: A Patternist Philosophy of Mind - Ben Goertzel
83. Engineering General Intelligence - Volume 2 - Ben Goertzel, Cassio Pennachin, & Nil Geisweiller
84. Computation and Cognition - Zenon W. Pylyshyn
85. Universal Artificial Intelligence - Marcus Hutter
86. The Cambridge Handbook of Computational Psychology - Ron Sun
87. Handbook of Neuroevolution Through Erlang - Gene I. Sher
88. Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence - Allan Collins & Edward E. Smith -- this one contains a LOT of seminal papers in the field, including one of the earliest papers on Semantic Networks by Ross Quillian. I actually put this on the wrong part of the list, as I'm about halfway through reading this one, but just remembered it and didn't want to renumber everything. Heh.
Eh, that's a pretty good chunk of the stuff I've got lying around here, waiting to be read. There's a handful of others, but my fingers are getting numb.
- I'll be working on adding some new features to our Enterprise Social Network project, Quoddy[1]. One thing I plan to work on is the ability to insert a document, and the associated tools for operating on that document, into the stream of events. We already do a basic version of this for BPM events, where you can approve/reject/delay/transfer events from the stream, but we want to generalize that facility a bit and add something for working on files.
For now, that will probably mean Google Drive integration, but we'll probably also eventually do something for working with files in a repository using something like JCR[2] and/or CMIS[3].
Longer term, I think we'll do something with Apache Wave[4] and the Operational Transformation[5] stuff as part of this whole idea of putting interactive entities in the stream. It's not all fleshed out yet, but some of the ideas I'm toying with harken back to the old idea of "compound documents"[6] and "intelligent documents"[7], or something like Bret Victor's "reactive documents" stuff[8].
Outside of all that, I plan to finish reading Permutation City by Greg Egan, and Philip C. Jackson's Introduction to Artificial Intelligence.
[1]: https://github.com/fogbeam/Quoddy
[2]: https://jcp.org/en/jsr/detail?id=283
[3]: http://en.wikipedia.org/wiki/Content_Management_Interoperabi...
[5]: http://en.wikipedia.org/wiki/Operational_transformation
[6]: http://www.w3.org/2004/CDF/
- Right this minute I'm diving into the Google Drive API, as I'm looking at using GDrive to implement a new feature in one of our projects.
Outside of that, I hope to finish reading most (or all) of Philip C. Jackson's Introduction to Artificial Intelligence, Alvin Toffler's Future Shock and the Predictable Revenue book. I doubt I'll finish all of that over the "holiday season" per-se, but that's what I'll be focusing on in the near term.
Related
Artificial Intelligence
60 recommend · 153
Introduction to Algorithms
274 recommend · 880
Programming Collective Intelligence
86 recommend · 135
Paradigms of Artificial Intelligence
63 recommend · 169
Information Theory, Inference & Learning Algorithms
77 recommend · 111
On intelligence
114 recommend · 228
