Update app.py

app.py

@@ -427,7 +427,7 @@ def display_papers(papers):
     for idx, paper in enumerate(papers):
         papercount = papercount + 1
         if (papercount<=20):
-            with st.expander(f"📄 {paper['title']}", expanded=True):
+            with st.expander(f"{papercount}. 📄 {paper['title']}", expanded=True):
                 st.markdown(f"**{paper['date']} | {paper['title']} | ⬇️**")
                 st.markdown(f"*{paper['authors']}*")
                 st.markdown(paper['summary'])
@@ -820,6 +820,87 @@ def main():
         if st.button("❌ Close"):
             st.session_state.viewing_prefix = None
 
+    markdownPapers = """
+    
+    # Levels of AGI
+
+## 1. Performance (rows) x Generality (columns)
+- **Narrow**  
+  - *clearly scoped or set of tasks*  
+- **General**  
+  - *wide range of non-physical tasks, including metacognitive abilities like learning new skills*  
+
+## 2. Levels of AGI
+
+### 2.1 Level 0: No AI
+- **Narrow Non-AI**  
+  - Calculator software; compiler  
+- **General Non-AI**  
+  - Human-in-the-loop computing, e.g., Amazon Mechanical Turk  
+
+### 2.2 Level 1: Emerging  
+*equal to or somewhat better than an unskilled human*  
+- **Emerging Narrow AI**  
+  - GOFAI; simple rule-based systems  
+  - Example: SHRDLU  
+    - *Reference:* Winograd, T. (1971). **Procedures as a Representation for Data in a Computer Program for Understanding Natural Language**. MIT AI Technical Report. [Link](https://dspace.mit.edu/handle/1721.1/7095)  
+- **Emerging AGI**  
+  - ChatGPT (OpenAI, 2023)  
+  - Bard (Anil et al., 2023)  
+    - *Reference:* Anil, R., et al. (2023). **Bard: Google’s AI Chatbot**. [arXiv](https://arxiv.org/abs/2303.12712)  
+  - LLaMA 2 (Touvron et al., 2023)  
+    - *Reference:* Touvron, H., et al. (2023). **LLaMA 2: Open and Efficient Foundation Language Models**. [arXiv](https://arxiv.org/abs/2307.09288)  
+
+### 2.3 Level 2: Competent  
+*at least 50th percentile of skilled adults*  
+- **Competent Narrow AI**  
+  - Toxicity detectors such as Jigsaw  
+    - *Reference:* Das, S., et al. (2022). **Toxicity Detection at Scale with Jigsaw**. [arXiv](https://arxiv.org/abs/2204.06905)  
+  - Smart Speakers (Apple, Amazon, Google)  
+  - VQA systems (PaLI)  
+    - *Reference:* Chen, T., et al. (2023). **PaLI: Pathways Language and Image model**. [arXiv](https://arxiv.org/abs/2301.01298)  
+  - Watson (IBM)  
+  - SOTA LLMs for subsets of tasks  
+- **Competent AGI**  
+  - Not yet achieved  
+
+### 2.4 Level 3: Expert  
+*at least 90th percentile of skilled adults*  
+- **Expert Narrow AI**  
+  - Spelling & grammar checkers (Grammarly, 2023)  
+  - Generative image models  
+    - Example: Imagen  
+      - *Reference:* Saharia, C., et al. (2022). **Imagen: Photorealistic Text-to-Image Diffusion Models**. [arXiv](https://arxiv.org/abs/2205.11487)  
+    - Example: DALL·E 2  
+      - *Reference:* Ramesh, A., et al. (2022). **Hierarchical Text-Conditional Image Generation with CLIP Latents**. [arXiv](https://arxiv.org/abs/2204.06125)  
+- **Expert AGI**  
+  - Not yet achieved  
+
+### 2.5 Level 4: Virtuoso  
+*at least 99th percentile of skilled adults*  
+- **Virtuoso Narrow AI**  
+  - Deep Blue  
+    - *Reference:* Campbell, M., et al. (2002). **Deep Blue**. IBM Journal of Research and Development. [Link](https://research.ibm.com/publications/deep-blue)  
+  - AlphaGo  
+    - *Reference:* Silver, D., et al. (2016, 2017). **Mastering the Game of Go with Deep Neural Networks and Tree Search**. [Nature](https://www.nature.com/articles/nature16961)  
+- **Virtuoso AGI**  
+  - Not yet achieved  
+
+### 2.6 Level 5: Superhuman  
+*outperforms 100% of humans*  
+- **Superhuman Narrow AI**  
+  - AlphaFold  
+    - *Reference:* Jumper, J., et al. (2021). **Highly Accurate Protein Structure Prediction with AlphaFold**. [Nature](https://www.nature.com/articles/s41586-021-03819-2)  
+  - AlphaZero  
+    - *Reference:* Silver, D., et al. (2018). **A General Reinforcement Learning Algorithm that Masters Chess, Shogi, and Go through Self-Play**. [Science](https://www.science.org/doi/10.1126/science.aar6404)  
+  - StockFish  
+    - *Reference:* Stockfish (2023). **Stockfish Chess Engine**. [Website](https://stockfishchess.org)  
+- **Artificial Superintelligence (ASI)**  
+  - Not yet achieved  
+
+    """
+    st.sidebar.markdown(markdownPapers)
+    
     if st.session_state.should_rerun:
         st.session_state.should_rerun = False
         st.rerun()