What connects all these clues?

Really, if you had to link all these, what would YOU come up with …   it turns our that all these are linked via George Gilder’s brain and imagination of   in his “Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy “  (more details here)

(rank / xx / subject)

1          60        The System of The World

83        36        Artificial Intelligence

84        36        Machine Learning

88        35        Data Centers

92        34        Information Theory

117      28        Virtual Reality

118      28        Von Neumann

130      26        Computer Science

171      23        Human Beings

193      22        Smart Contracts

230      19        Larry Page

315      16        Open Source

316      16        Peter Thiel

320      16        The Dalles

407      14        Vitalik Buterin

421      13        Billion Dollars

422      13        Deep Learning

437      13        Markov Models

479      12        Brendan Eich

483      12        Central Banks

485      12        Data Centers

486      12        Elon Musk

497      12        Marc Andreessen

508      12        Private Keys

510      12        Public Keys

512      12        Search Engines

517      12        Speed Of Light

526      12        The Machine

553      11        Bell’s Law

558      11        Craig Wright

567      11        Human Intelligence

577      11        Muneeb Ali

578      11        New York Times

592      11        The Real World

614      11        Time and Space

643      10        Cloud Computing

651      10        Economic Growth

670      10        Jaron Lanier

673      10        Market Cap

677      10        Neal Stephenson

678      10        Nick Stab

691      10        Property Rights

834      9          Satoshi Nakamoto

869      9          Venture Capitalists

906      8          Bitcoin Blockchain

918      8          Eric Schmidt

919      8          Face Recognition

932      8          Google Brain

933      8          The Great Unbundling

934      8          The Ground State

945      8          Internet Architecture

960      8          Low Entropy

982      8          Princeton University

94        8          Scarcity Of Time

1024    8          Thiel Fellowship

1162    7          Hidden Markov Models

1163    7          High Entropy

1166    7          Human Brains

1167    7          Human History

1168    7          Human Minds

1184    7          Information Economy

1185    7          Information Technology

1242    7          Private Keys

1246    7          Ray Kurzweil

1253    7          Self Driving Cars

1254    7          Sergey Brin

1286    7          The Great Unbundling

1324    7          Turing Machine

1412    6          Bill Dally

1424    6          Charles Sanders Peirce

1445    6          Economic Activity

1544    6          Lambda Labs

1547    6          Leemon Baird

1639    6          Stephen Balaban

1739    6          Walled Gardens

1887    5          Being Human

1894    5          Bitcoin Maximalists

1898    5          Blockchain Technology

1977    5          Free Will

2001    5          Hal Finney

2019    5          Human Consciousness

2078    5          Kurt Gödel

2099    5          Mathematical Logic

2100    5          Mathematics of Creativity

Beyond the Usual AI and Machine Intelligence

the Beyond topics
  1. George Gilder –Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy worth reading to obtain additional perspectives. Some may be right, some may be wrong. Definitely technologically provocative. Will Google/Alphabet last?Do you know about the Dalles? You should. My first clue was through the book …OK … find out more about Google’s Data Centers. Find out more about other pieces worth knowing.

the Artificial and Machine Intelligence related topics

  1. Gelernter, D. (2016). The tides of mind: Uncovering the spectrum of consciousness. WW Norton & Company.
  2. Marquis, P., Papini, O., & Prade, H. (2014). Some Elements for a Prehistory of Artificial Intelligence in the Last Four Centuries. ECAI.
  3. Scheutz, M. (Ed.). (2002). Computationalism: new directions. MIT Press.
  4. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach.
    This is an updated edition of the 2010 version containing extensive current references. [note the book is getting hard to find sometimes due to demand, and its being the definitive AI textbook. Check the edition you are using/getting]
  5. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press. This is an updated (2nd) edition of the 1998 version
  6. Nilsson, N. J., & Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.
  7. Poole, D. L., Mackworth, A. K., & Goebel, R. (1998). Computational intelligence: a logical approach (Vol. 1). New York: Oxford University Press.
    see also Artificial Intelligence: Foundations of Computational Agents 2nd Edition by the same authors.
  8. Pratt, V. (1987). Thinking Machines—The Evolution of Artificial Intelligence. Oxford: Basil Blackwell. – this is a general history of earlier machines … great reference to get historical insights not easily obtained elsewhere.
  9. Turing, A. M. (1948). Intelligent machinery. NPL. Mathematics Division. See also, Turing, A. (2004). Intelligent machinery (1948). The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma B. Jack Copeland, 395 which provides context and pointers to additional Turing resources.
  10. B. Jack Copeland (2004), Computability: Turing, Gödel, Church, and Beyond, The MIT Press.

Hard(er) Core Science Fiction and Speculative Fiction works

    1. John C. Wright’s Count to the Eschaton series is worth reading … provides interesting glimpse into a possible (far) future. It’s also fun to read … so good ideas and an interesting, universe spanning plot.

Concerning Superintelligence

These are my recommendations of key texts to read  if you really want to get familiar with   Superintelligence. 

SI-1. Good, I. J. (1966). Speculations concerning the first ultraintelligent machine. In Advances in computers (Vol. 6, pp. 31-88). Elsevier.

Irving John (Jack) Good was mathematician who worked with Alan Turing and made significant contribution to braking the Enigma codes. One could regard him as Turing’s statistician. Good later worked with British AI pioneer and computer designer Donald Michie. Good devoted much of his later life to research in Bayesian statistics. Goods paper cited above was the first to clearly spell out ultraintelligent machines and can be rightly viewed as the basis of the superintelligence discipline today. This paper stated:

Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control

This short paragraph not only presages the idea of superintelligent AI, it also laid the groundwork for subsequent Paperclip Apocalypse scenarios and the drive for AI safety considerations. Good was particularly a credible messenger due to his early intimate and highly knowledgeable technical familiarity and experience with highly complex and capable computers.

SI-2. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. New York: Oxford University Press.

Bostrom’s book was much waited by the superintelligence (SI) community, and in some respects provided the academic sanctioning of runaway-AI potential for harm, and AI-safety, as legitimate scholarly topics for discussion. In some ways the runaway SI apocalypse scenarios act to counterbalance Ray Kurzweil’s Exponentiality of all things technological and Singularity visions.

SI-3. Drexler, K.E. (2019): Reframing Superintelligence: Comprehensive AI Services as General Intelligence, Technical Report #2019-1, Future of Humanity Institute, University of Oxford

This is a must read by Eric Drexler, pioneer of nanotechnology . This report projects a possible, if not likely, trajectory of AI development that envisions emergence of asymptotically comprehensive, superintelligent-level AI services. Drexler has been prescient regarding the importance of and trajectory of nanotechnology.

SI-4.Yampolskiy, R. V. (2015). Artificial Superintelligence: a futuristic approach. CRC Press.

While maintaining a focus on AI and superintelligence safety, Roman Yampolskiy brings additional dimensions to discussions of superintelligence. I am not quite sure why the need to use the term Artificial in the title and the discussion. Superintelligence is not now and will never be a normal or natural attribute; I view adding artificial to superintelligence as redundant.

The book includes interesting and useful discussions on topics such as AI-Completeness and AI-Hardness, Mind Design and associated taxonomies of real and speculative mind design space. Most of the intensity and depth of discussion though is focused on the harm that SI can bring (and according to the author and many of the references cited, viewed as very likely to occur.) The detailed references provided are exceptional. Personally, I would prefer to see more discussion of the positive aspects of SI and the hard problems it can and should solve first.

SI-5. Philip Larrey (2017), Would Super-Human Machine Intelligence Really Be Super-Human? in G. Dodig-Crnkovic and R. Giovagnoli (eds.), Representation and Reality in Humans, Other Living Organisms and Intelligent Machines , (Studies in Applied Philosophy, Epistemology and Rational Ethics 28, DOI 10.1007/978-3-319-43784-2_19)

AI Recommended Readings / Possible. Minds

added a section for Artificial / computational / machine intelligence recommended readings on the main site — here.

noted also that  “We’re also paying attention to the applicability of AI/MI concepts to the space of What Sloman calls ‘possible minds’. “The idea is that the space of possible minds encompasses not only the biological minds that have arisen on this earth, but also extraterrestrial intelligence, and whatever forms of biological or evolved intelligence are possible but have never occurred, and artificial intelligence in the whole range of possible ways we might build AI”

The initial list of principal textbooks include:
1. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach.
This is an updated edition of the 2010 version containing extensive current references.
[note the book is getting hard to find sometimes due to demand, and its being the definitive AI textbook, check the edition you are using/getting]

2. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press. This is an updated (2nd) edition of the 1998 version.
In addition to his University of Alberta academic appointment, Richard Sutton is now the head of Alphabet/Google DeepMind Alberta operations.

3. Nilsson, N. J., & Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.

4. Poole, D. L., Mackworth, A. K., & Goebel, R. (1998). Computational intelligence: a logical approach (Vol. 1). New York: Oxford University Press.

5.  Artificial Intelligence: Foundations of Computational Agents 2nd Edition by the same authors.


Stanley & Lehman – Why Greatness Cannot Be Planned

Fascinating insights by Computer Science / Artificial Intelligence profs …


some have summarized their insights by writing: “only by doing activities that fulfill our curiosity without any pre-defined objectives, true creativity can be unleashed. They call this the ‘Myth of the Objective’: Objectives are well and good when they are sufficiently modest … In fact, objectives actually become obstacles towards more exciting achievements, like those involving discovery, creativity, invention, or innovation—or even achieving true happiness… the truest path to “blue sky” discovery or to fulfill boundless ambition, is to have no objective at all.”

some of Stanley’s and Lehmans insights:


  • “The flash of insight is seeing the bridge to the next stepping stone by building from the old ones. ”


  • “[Picbreeder] is just one example of a fascinating class of phenomena that we might call non-objective search processes, or perhaps stepping stone collectors. The prolific creativity of these kinds of processes is difficult to overstate”


  • “ measuring success against the objective is likely to lead you on the wrong path in all sorts of situations”


  • “You can’t evolve intelligence in a Petri dish based on measuring intelligence. You can’t build a computer simply through determination and intellect—you need the stepping stones. ”


  • “ambitious objectives are the interesting ones, and the idea that the best way to achieve them is by ignoring them flies in the face of common intuition and conventional wisdom. More deeply it suggests that something is wrong at the heart of search. ”



I find their books inspiring and insightful. Reframing questions and providing different lines of attack on AI and Search Optimization to Ambitious Goals …





artificial intelligence, natural stupidity.

according to popular legends and urban myths … Amos Tversky is said to have said …

My colleagues, they study artificial intelligence; me, I study natural stupidity.

this, from CoEvolving Innovations which seems like a fascinating resource.

The blog entry there talks about Daniel Kahneman  and Amos Tversky.

The topic is fascinating.  The question of how intelligence and stupidity are related is fascinating.

There’s also a reference to Daniel Kahneman, Paul Slovic, and Amos Tversky book Judgment under uncertainty: Heuristics and biases,  that I now feel compelled to investigate

interesting factoid …Kahneman  was awarded the 2002 Nobel Prize in economic sciences  despite being a psychologist, not an economist.  Which goes to show you … that Forrest Gump’s Mom was right  Life is like a box of chocolates. You never know what you’re gonna get.”

Ford’s Architects of Artificial Intelligence


To place Artificial Intelligence in appropriate context is a complex and intricate challenge.  Marty Ford, a master explainer presents interviews with some of the principal architects of AI.  The Architects in this case are Yoshua Bengio, Stuart Russell, Geoffrey Hinton, Nick Bostrom, Yann LeCun, Fei-Fei Li, Demis Hassabis, Andrew Ng, Rana El Kaliouby, Ray Kurzweil, Daniela Rus, James Manyika, Gary Marcus, Barbara Grosz, Judea Pearl, Jeffrey Dean, Daphne Koller, David, Ferrucci, Rodney Brooks, Cynthia Breazeal, Joshua Tenenbaum, Oren Etzioni, and Bryan Johnson.

What a great list … Dan Ferrucci is of course known from his amazing work with IBM WATSON, and the first ever amazing win of a machine over the best of the best at Jeopardy!  Dennis Hassabis , of Google/Alphabet’s Deep Mind, brought us AlphaGo, AlphaGoZero, and now AlphaGo that exceeds the best of the best in Chess, GO and Shogi (all with the same MCTS algorithm). NYU/FAIR/FaceBook’s Yan LeCun did some serious stuff with Mastering and claiming ‘the prize’ over ImageNet Challenge. Rodney Brooks with iRobot, … each one of the Architects is truly a master architect.  We’ll explore their contributions and significance later … their thoughts are really worth checking out.  I am looking at all kinds of things right now … and there’s just so much.  Maybe I need a nice Intelligent Machine Assistant to help me pull this al together.  🙂

Ford, M. (2018), Architects of Intelligence

Coming up soon – in my book: Regarding Natural, Artificial & Other (?) Intelligences.

One of the main categories of discussion in this book is that of worthwhile tasks for AI. I will devote some time to stating some of the recognized questions, problems, and tasks. I will also mention some notable AI accomplishments and highlight a few of the recognized scholarly achievements. Another topic for discussion is the classification of Intelligences. What is Natural Intelligence? What is Artificial General Intelligence? What is Superintelligence? What about human measures such as IQ? G? What does the AlphaZero algorithm beating the best human players in Chess, Go and Shogi mean? Can the Paperclip Apocalypse really happen?

All these and more … coming soon …