Update app.py

app.py

@@ -4,18 +4,18 @@ from graphviz import Digraph
 
 st.markdown("""
 # Goals of Cognitive AI with Human Feedback (CAHF):
-    1. Use Models to predict **outcomes**
-    2. Use AI to predict **conditions, disease, opportunities** using flavors of AI with **explainability**.
-    3. **Cognitive AI** - Mimics how humans reason through decision making processes.
-    4. **Reasoning cycles** - "Recommended for You" reasoners - what type of person, classification of users, recommend what products
-    5. **High Acuity Reasoners** - Only make decisions on rules of **what it can and cannot do within human feedback** guidelines.
-        -Emphasis on **explainability, transparency, removing administrative burden** and **protocolize** what staff is doing.
-        -Vetted by SME's, adding value of **judgement and training** to pick up **skills from human feedback**.
-        -**Alerts, Recommended Actions, and Clinical Terminology** per entity including LOINC, SNOMED, ICD10, RXNORM. SMILES, HCPCS and CPT codes,
-    6. Non static multi agent cognitive approach - real time series - factors predictive of outcome.
-    7. Cognitive models take form of Ontology - for some type of computable set - relationships stored in Ontology can be ingested by reasoner
-        -Use models of world to build predictions and recommendations with answers that are cumulative with information we know
-    8. Reasoners can standardize to make it easier as possible to do right thing with learned recommendation tools, questions and actions.
+1. Use Models to predict __outcomes__
+2. Use AI to predict **conditions, disease, opportunities** using flavors of AI with **explainability**.
+3. **Cognitive AI** - Mimics how humans reason through decision making processes.
+4. **Reasoning cycles** - "Recommended for You" reasoners - what type of person, classification of users, recommend what products
+5. **High Acuity Reasoners** - Only make decisions on rules of **what it can and cannot do within human feedback** guidelines.
+    -Emphasis on **explainability, transparency, removing administrative burden** and **protocolize** what staff is doing.
+    -Vetted by SME's, adding value of **judgement and training** to pick up **skills from human feedback**.
+    -**Alerts, Recommended Actions, and Clinical Terminology** per entity including LOINC, SNOMED, ICD10, RXNORM. SMILES, HCPCS and CPT codes,
+6. Non static multi agent cognitive approach - real time series - factors predictive of outcome.
+7. Cognitive models take form of Ontology - for some type of computable set - relationships stored in Ontology can be ingested by reasoner
+    -Use models of world to build predictions and recommendations with answers that are cumulative with information we know
+8. Reasoners can standardize to make it easier as possible to do right thing with learned recommendation tools, questions and actions.
 """)
 
 st.markdown("""