The DeAngelis Review

Good decisions begin with good questions. Three of the most important questions businesses should ask require finding causation before reliable answers can be determined. Those question types are: Associational questions (If I see X, what is the probability that I will see Y?); Interventional questions (If I do X, what is the probability that I will see Y?); and Counterfactual questions (What would Y have been, had I done X?) As a result, causal AI, which looks for causation rather than correlation, is essential for growing businesses.

This article explores the risks of Artificial General Intelligence (AGI), a superintelligence that could potentially destroy humanity. Experts warn of AGI causing accidental harm or intentionally wiping out humans. While some believe AGI is unlikely, others advocate for regulation to control its development. The article concludes that caution is needed, but AI has potential benefits.

Data is king in business today. Companies use automation to process data and make decisions. Intelligent automation (IA) is a new kind of automation that uses AI and machine learning to handle complex tasks. IA can improve efficiency, reduce costs, and provide better customer service. While RPA automates repetitive tasks, IA uses AI to make decisions and solve problems. In the future, IA will become even more sophisticated with the use of Autonomous Decision Science. Businesses that adopt IA will be better positioned to compete in today's marketplace.

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.