I need to
[BRIEF DESCRIPTION OF BUG/FEATURE]
in the workshops system.
Please:
| import os | |
| import subprocess | |
| import sys | |
| from pathlib import Path | |
| import json | |
| from txtai import Embeddings, LLM, RAG | |
| # --- Add this line to handle the OpenMP error, especially on macOS --- | |
| os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" |
COMPLETE RULES
| #!/usr/bin/env python3 | |
| """ | |
| Fixed CSV Country Counter - Properly extracts clean country names | |
| Handles complex entries like: "United States (Note: This is a corporation, not a university)" | |
| """ | |
| import re | |
| import csv | |
| def extract_clean_country(country_field): |
AI verification is critically important because it ensures that AI systems behave as intended—safely, reliably, and ethically. Here's a breakdown of why it's so vital and where it matters most:
AI systems can cause real-world harm if they fail—especially in high-stakes domains like:
| import csv | |
| import time | |
| import re | |
| import requests | |
| import json | |
| from typing import List, Tuple, Dict | |
| import concurrent.futures | |
| import threading | |
| class UniversityClassifier: |
I think AI governance should involve much broader participation than what we've seen so far. The current approach - primarily driven by researchers, tech companies, and policymakers - leaves out too many voices that will be affected by these systems.
Democratic Participation We need genuine public input, not just expert committees. This could include citizen assemblies, deliberative polling, or other methods that help ordinary people engage with these complex issues. Taiwan's vTaiwan platform and Ireland's citizens' assemblies on contentious issues offer models.
Affected Communities First People who will bear the brunt of AI impacts - workers whose jobs might be automated, communities subjected to algorithmic policing, students evaluated by AI systems - should have the strongest voice in setting rules, not just token consultation.
Multiple Governance Layers Different rules for different contexts: international coordination on existential risks, national laws for broad frameworks, local regulati
| #!/usr/bin/env python3 | |
| """ | |
| Production Transcript Processor | |
| A robust script with JSON output, restart capabilities, and metadata tracking | |
| Enhanced version with better mathematical content capture | |
| """ | |
| import requests | |
| import json | |
| import os |
| Here are some examples of IoCs: | |
| Network IoCs: | |
| Unusual network traffic patterns: Abnormal outbound network traffic, a sudden increase in traffic from a specific IP address, or communication with unknown or malicious domains. | |
| Unusual DNS requests: Requests for known malicious domains or unusual DNS queries. | |
| Mismatched port-application traffic: An application or process communicating over a network port it shouldn't be using. | |
| Host-Based IoCs: | |
| Unauthorized access to system resources: Access to servers, databases, or sensitive data without proper authorization. | |
| Changes to system files or configurations: Unexplained or unauthorized modifications to system configurations or settings. | |
| Unexpected software installations or updates: Unusual or unexpected software being installed or updated on a system. | |
| Suspicious registry changes: Changes to the Windows registry that suggest malicious activity. |