Should we trust AI?
Pros of Trusting AI
- Efficiency: AI can process vast amounts of data quickly, making it useful for tasks like data analysis and automation.
- Pattern recognition: AI can identify patterns in data that humans might miss, leading to insights and discoveries.
- Consistency: AI systems can perform tasks consistently, reducing the risk of human error.
Cons of Trusting AI
- Bias: AI systems can perpetuate biases present in the data used to train them, leading to unfair outcomes.
- Lack of transparency: Some AI systems can be difficult to interpret, making it hard to understand their decision-making processes.
- Dependence on data quality: AI systems are only as good as the data they’re trained on, and poor data quality can lead to poor performance.
- AI can hallucinate: AI In AI, particularly in large language models, “hallucination” refers to when a model generates information or outputs that aren’t based on any actual input data or facts. This can result in false, nonsensical, or unrelated content. Hallucinations can occur due to various reasons such as:
For instance, if you ask a language model to summarize a news article and it provides information not present in the article, that’s a hallucination.
Other uses of AI
Chunking issues: Chunking in AI
Chunking is a technique used to break down complex information into smaller, more manageable units called “chunks.” These chunks can be phrases, sentences, or even individual words, depending on the context.
Chunking helps AI models in several ways:
Improved processing efficiency: By breaking down large inputs into smaller chunks, models can process information more efficiently.
Better understanding: Chunking can aid in capturing dependencies and relationships within the input data.
Chunking can be a problem in certain situations:
Loss of context: When chunks are too small, the model might lose important contextual information, leading to misinterpretation or inaccurate results.
If chunks are too large, they might not capture local dependencies.
Overlapping or ambiguous chunks: When chunks overlap or have ambiguous boundaries, it can lead to confusion or inconsistencies in processing.
Chunking bias: If chunking is based on biased or flawed assumptions, it can perpetuate those biases in the model’s outputs.
Personal scenarios I have seen major issues
- A medical AI program invented body parts-obviously a huge problem for patients/doctors
- CPA AI-where there are similar numbers AI cannot differentiate between 2 different items 2. Broker input for expert work-AI did not approach the problem correctly-it botched the analysis of paystubs, using the wrong assumptions and doing calculations on the wrong numbers 3.Legal-I have a client who was warned he would be heavily sanctioned if he used AI again, since it made up fake cases in his filings. Many lawyers have been fined tens of thousands of dollars and risk malpractice and worse. AI is wrong up to 90% of the time (see article below)
When to Trust AI
- Well-defined tasks: AI can be trusted for well-defined tasks with clear objectives and high-quality data.
- Human oversight: AI systems should be designed with human oversight and review processes and outputs