Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Models trained directly with asynchronous updates would exhibit even greater robustness than synchronously trained modelshypothesis0.734
  Hypothesis that motivated the asynchronous robustness comparison experiment
• Under these conditions, what is done next always has a natural and comfortable relation to what existed before: it has a similar structure and never violates the previously existing structure.claim0.715
  Characteristic of a structure-preserving process.
• Modern machines, including quantum computers and machine learning systems, achieve reliable function through uncertain events, making unpredictability a feature rather than a defect.claim0.713
  Argument that predictability is no longer an essential property distinguishing machines from life
• When care is amplified, intelligence is likely to increase as well.hypothesis0.710
  A predictive relation between care and intelligence enhancement.
• Huginn-0125 performance remains constant when extrapolating beyond training recurrences, while Ouro performance deteriorates in the same regimefinding0.708
  Correlates stable fixed-point behavior with out-of-domain generalization performance at test-time
• Reinforcement learning is sufficient for agency.claim0.708
  Argument that RL meets the agency indicator.
• If we do one thing at a time, and if what we do is wholesome and sound, then whatever comes next will work.hypothesis0.708
  A predictive statement encapsulating the confidence of living process.
• Individual floating-point number weights in neural networks become meaningful once you understand the features they connect.claim0.705
  Interpretive claim that circuits render raw weights interpretable as algorithmic structures