Risk of AI : Super Intelligent Systems or Half Baked AI Products

AI products and tools that have limited machine learning components, or those that have not been adequately trained and tested for real-life applications poses higher risk in the sort term:

1. Suboptimal Decision-Making: AI products with insufficient machine learning capabilities can lead to poor decision-making. For example, consider a basic “AI” financial advisor that makes investment recommendations based on simple rules instead of using sophisticated machine learning algorithms. Such a tool might not take into account the complexities of the market, potentially leading to suboptimal investment decisions for users.
2. False Confidence in AI Products: AI tools that are marketed as intelligent but lack proper machine learning components can create a false sense of confidence among users. This overreliance on inadequate AI tools could lead to negative consequences. For instance, imagine a health app that claims to diagnose skin conditions using AI but, in reality, relies on simple pattern matching. Users might trust the app’s diagnosis and forgo seeing a medical professional, which could exacerbate existing health issues.
3. Misrepresentation and Deceptive Marketing: Some companies might exaggerate their AI capabilities to gain a competitive edge, which can mislead consumers. For example, a…