Update app.py

app.py

@@ -208,3 +208,111 @@ st.markdown("""
 """)
 
 
+st.title("AI Development Levels and Capabilities")
+
+# Create a DataFrame with the information from the image
+data = {
+    "Level": ["Level 0: No AI", "Level 1: Emerging", "Level 2: Competent", "Level 3: Expert", "Level 4: Virtuoso", "Level 5: Superhuman"],
+    "Description": [
+        "No AI capabilities",
+        "Equal to or somewhat better than an unskilled human",
+        "At least 50th percentile of skilled adults",
+        "At least 90th percentile of skilled adults",
+        "At least 99th percentile of skilled adults",
+        "Outperforms 100% of humans"
+    ],
+    "Narrow AI": [
+        "Calculator software, compiler",
+        "GOFAI (Boden, 2014); simple rule-based systems, e.g., SHRDLU (Winograd, 1971)",
+        "Toxicity detectors such as Jigsaw (Das et al., 2022); Smart Speakers such as Siri (Apple), Alexa (Amazon), or Google Assistant (Google); VQA systems such as PaLI (Chen et al., 2023); Watson (IBM); SOTA LLMs for a subset of tasks (e.g., short essay writing, simple coding)",
+        "Spelling & grammar checkers such as Grammarly (Grammarly, 2023); generative image models such as Imagen (Saharia et al., 2022) or Dall-E 2 (Ramesh et al., 2022)",
+        "Deep Blue (Campbell et al., 2002); AlphaGo (Silver et al., 2016, 2017)",
+        "AlphaFold (Jumper et al., 2021; Varadi et al., 2022), AlphaZero (Silver et al., 2018), StockFish (Stockfish, 2023)"
+    ],
+    "General AI": [
+        "Human-in-the-loop computing, e.g., Amazon Mechanical Turk",
+        "ChatGPT (OpenAI, 2023), Bard (Anil et al., 2023), Llama 2 (Touvron et al., 2023), Gemini (Pichai & Hassabis, 2023)",
+        "Not yet achieved",
+        "Not yet achieved",
+        "Not yet achieved",
+        "Artificial Superintelligence (ASI) - not yet achieved"
+    ],
+    "Narrow AI Achievement": [0, 20, 50, 90, 99, 100],
+    "General AI Achievement": [0, 20, 0, 0, 0, 0]
+}
+
+df = pd.DataFrame(data)
+
+# Display the DataFrame as an interactive table
+st.dataframe(df, use_container_width=True)
+
+# Create a line chart to visualize AI achievement levels
+fig = px.line(df, x="Level", y=["Narrow AI Achievement", "General AI Achievement"], 
+              title="AI Achievement Levels",
+              labels={"value": "Achievement Percentage", "variable": "AI Type"},
+              markers=True)
+st.plotly_chart(fig, use_container_width=True)
+
+# Add some explanatory text
+st.markdown("""
+This dashboard summarizes and visualizes the levels of AI development, from no AI capabilities (Level 0) to superhuman AI (Level 5). 
+It categorizes AI into Narrow AI (designed for specific tasks) and General AI (capable of a wide range of tasks).
+
+Key points:
+- As of 2023, most advanced AI systems are at Level 1 or 2 for General AI tasks.
+- Narrow AI has achieved higher levels in specific domains (e.g., AlphaFold for protein folding).
+- General AI at Level 2 (Competent) and above has not yet been achieved.
+- The development of Artificial Superintelligence (ASI) remains a theoretical concept.
+
+Note: The achievement levels in the chart are approximate and for illustration purposes.
+""")
+
+# Add a section for user interaction
+st.subheader("Explore AI Levels")
+selected_level = st.selectbox("Select an AI Level to learn more:", df['Level'])
+
+# Display details for the selected level
+if selected_level:
+    level_data = df[df['Level'] == selected_level].iloc[0]
+    st.write(f"**Description:** {level_data['Description']}")
+    st.write(f"**Narrow AI Examples:** {level_data['Narrow AI']}")
+    st.write(f"**General AI Status:** {level_data['General AI']}")
+
+# Add a comparison tool
+st.subheader("Compare AI Levels")
+col1, col2 = st.columns(2)
+with col1:
+    level1 = st.selectbox("Select first level:", df['Level'], key="level1")
+with col2:
+    level2 = st.selectbox("Select second level:", df['Level'], key="level2")
+
+if level1 and level2:
+    data1 = df[df['Level'] == level1].iloc[0]
+    data2 = df[df['Level'] == level2].iloc[0]
+    
+    comparison_df = pd.DataFrame({
+        "Aspect": ["Description", "Narrow AI Examples", "General AI Status", "Narrow AI Achievement", "General AI Achievement"],
+        level1: [data1['Description'], data1['Narrow AI'], data1['General AI'], f"{data1['Narrow AI Achievement']}%", f"{data1['General AI Achievement']}%"],
+        level2: [data2['Description'], data2['Narrow AI'], data2['General AI'], f"{data2['Narrow AI Achievement']}%", f"{data2['General AI Achievement']}%"]
+    })
+    
+    st.table(comparison_df)
+
+# Add a section for future predictions
+st.subheader("Future of AI")
+st.write("""
+Based on the current trajectory of AI development:
+
+1. **Narrow AI:** We can expect continued rapid progress in specific domains, with more tasks reaching expert and virtuoso levels.
+2. **General AI:** Progress towards Competent AGI (Level 2) is ongoing, but the timeline remains uncertain.
+3. **Ethical Considerations:** As AI capabilities expand, ethical guidelines and responsible development practices become increasingly crucial.
+4. **Interdisciplinary Approach:** Future advancements will likely require collaboration across multiple fields, including computer science, neuroscience, and philosophy.
+
+What are your thoughts on the future of AI? How might these advancements impact various industries and society as a whole?
+""")
+
+# Add a user input section for predictions
+user_prediction = st.text_area("Share your predictions or thoughts on the future of AI:")
+if st.button("Submit Your Prediction"):
+    st.write("Thank you for sharing your thoughts! While we can't store your prediction, it's valuable to consider diverse perspectives on AI's future.")
+