diff --git a/docs/_sources/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.md.txt b/docs/_sources/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.md.txt index c446510..c5cf0a4 100644 --- a/docs/_sources/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.md.txt +++ b/docs/_sources/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.md.txt @@ -41,7 +41,7 @@ We expect that any decision-making system should be able to apply logical rules really not be there, since our network only needs 3 neurons to compute on such knowledge: -It is raining Implies the grass is wet +It is raining Implies the grass is wet Logic also allows us to build high-level decision makers that can reason about outcomes given only partial information about the world. Lets add some more diff --git a/docs/_sources/introduction.md.txt b/docs/_sources/introduction.md.txt index 2965bc4..9ed0f91 100644 --- a/docs/_sources/introduction.md.txt +++ b/docs/_sources/introduction.md.txt @@ -1,6 +1,6 @@ # Logical Neural Networks -LNN structure +LNN structure The LNN is a form of recurrent neural network with a 1-to-1 correspondence to a set of logical formulae in any of various systems of ___weighted, real-valued logic___, in which evaluation performs logical inference. The graph diff --git a/docs/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.html b/docs/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.html index 5068a45..cf743e8 100644 --- a/docs/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.html +++ b/docs/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.html @@ -394,7 +394,7 @@

really not be there, since our network only needs 3 neurons to compute on such knowledge:

- It is raining Implies the grass is wet + It is raining Implies the grass is wet

Logic also allows us to build high-level decision makers that can reason about outcomes given only partial information about the world. Lets add some more diff --git a/docs/introduction.html b/docs/introduction.html index ec153b9..35c91ee 100644 --- a/docs/introduction.html +++ b/docs/introduction.html @@ -264,7 +264,7 @@

ΒΆ

- LNN structure + LNN structure

The LNN is a form of recurrent neural network with a 1-to-1 correspondence to a set of logical formulae in any of various systems of diff --git a/docsrc/source/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.md b/docsrc/source/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.md index c446510..c5cf0a4 100644 --- a/docsrc/source/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.md +++ b/docsrc/source/education/blogs/moving_ai_beyond_its_narrow_view_of_intelligence.md @@ -41,7 +41,7 @@ We expect that any decision-making system should be able to apply logical rules really not be there, since our network only needs 3 neurons to compute on such knowledge: -It is raining Implies the grass is wet +It is raining Implies the grass is wet Logic also allows us to build high-level decision makers that can reason about outcomes given only partial information about the world. Lets add some more diff --git a/docsrc/source/introduction.md b/docsrc/source/introduction.md index 2965bc4..9ed0f91 100644 --- a/docsrc/source/introduction.md +++ b/docsrc/source/introduction.md @@ -1,6 +1,6 @@ # Logical Neural Networks -LNN structure +LNN structure The LNN is a form of recurrent neural network with a 1-to-1 correspondence to a set of logical formulae in any of various systems of ___weighted, real-valued logic___, in which evaluation performs logical inference. The graph