This article discusses a new problem with large language models (LLMs) like Bard: AI hallucination, where the LLM generates incorrect but seemingly believable information. Experts say this is dangerous because it can spread misinformation. People can limit AI hallucination by carefully phrasing their prompts, giving the AI relevant data, and instructing it on its role. However, some argue users shouldn't have to fact-check AI outputs, and developers need to find better solutions. Large tech companies say AI needs regulation, but regulation struggles to keep pace with technology.

AI is improving supply chain forecasting by going beyond correlations to identify causal relationships. This allows for better predictions and decisions, reducing stockouts and disruptions. Causal AI uses data science to understand why things happen, not just that they happen together. This is a more powerful approach for complex systems like supply chains.

The pandemic exposed weaknesses in global supply chains. AI, while promising, wasn't a magic bullet. Data fragmentation between different parts of the supply chain limited AI's effectiveness. Sharing data is crucial but faces competition hurdles. AI can still help companies navigate disruptions and make better decisions.

Social media spreads lies faster than truth. AI creates realistic fakes like images, videos, and celebrity voices. Text-to-image and text-to-speech AI raise ethical concerns. AI can be used to create dangerous content that is difficult to detect. We need to consider ethics in AI development and use.