Notes: Creating Human-Level AI: How and When | Ray Kurzweil

  1. Key challenges – Marvin Minsky [MIT] (symbolic school) & Frank Rosenblatt [Cornell] (connectionist school)
  2. Deep neural networks in the 1960’s with Rosenblatt — high on research agenda — (My thought: Why not until the 90’s or 2000’s if high on research agenda?)
  3. Output of each layer needs to be convex otherwise you will not reach a local minima.
  4. Breakthrough in number of layers in 2000’s
  5. 3-D chips on the horizon (My thought: How about N-D dimensional chips — holographic?)
  6. “Life begins with 1,000,000,000 examples”
  7. LSTM – recomputes the whole net for each sequence (My thought: how about caching with dynamic programming to speed up the computation?)
  8. HMM — sequential model (doesn’t do long-term associations)
  9. LSTM — does do long-term associations (My thought: what is the granularity of the sequence until limit reaches Plank?)
  10. Neocortex is a hierarchy of sequential models (RNN+Skip-List)

My journey in kernel code with Apple macOS Sierra 10.12.3

Today, I was interested in writing kernel extensions for the XNU kernel and I ran into the following problem with the example code in the Documentation from Apple.  I was having multiple errors with the “so-called” working example verbatim from Apple technical documentation creators.  I am going to wait on the resolution to this problem.

Fluid intelligence in “Organic” Intelligence

  1. Crystallized intelligence is overrated and static
  2. Fluid intelligence should be the sole focus and is dynamic
  3. Artificial intelligence is on the wrong path due to its heavy dependence on crystallized intelligence
  4. Using long-term memory to do tasks means nothing about the malleability of the working memory operators
  5. Fluid intelligence is about “pattern recognition, abstract reasoning, and problem-solving” [1]
  6. Can machines do pattern recognition, abstract reasoning, and problem-solving that matches the best pattern recognizers, abstract reasoners, and problems solvers in the universe? No.
  7. AI would thus do terrible on fluid intelligence quotient tests and thus, not pass the grade of AI.

[1] https://en.wikipedia.org/wiki/Fluid_and_crystallized_intelligence

 

Steve Jobs – Interview at CHM (2011) – Walter Isaacson (First 20 minutes)

Key points from the interview with Walter Isaacson:

  1. Technology Personification
  2. Traits of Steve Jobs – passion, perfectionism, impatient, storytelling
  3. He changed the way we listen to music
  4. Humanities + Technology
  5. Creativity + Engineering
  6. Brutally Honest
  7. Mind Distortion Reality Field (Game Theory)
  8. Don’t Preach, do the Fareed Zakaria or Christopher Nolan?
  9. Night shift
  10. Simplicity in bridge between computer and machine in terms of games (e.g. iPod, iPhone)
  11. Invention is collaboration (exception: Einstein)
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