Section 1.1: The Testing Dilemma

One core issue with the AI systems we're putting our trust in is the lack of thorough testing. In simpler terms, these AI tools haven't been rigorously vetted. There are assessments available, but they come from an external viewpoint, largely conducted by the companies that developed the AI. This raises questions about bias, as the creators may not be fully transparent about their software's limitations.

Subsection 1.1.1: Understanding External vs. Internal Testing

When we discuss how AI software is evaluated, it often involves observational testing, where data is input into the system and the output is monitored. This leads us to the phenomenon known as "hallucinations," where the AI generates outputs that are completely irrelevant or nonsensical. This scenario typically arises from a "garbage in, garbage out" situation, where the input may be valid, but the results are far from it.

The creators of these AI systems often cannot explain why such hallucinations occur. When I approached an AI expert about identifying these errors, he brushed it off, claiming it couldn't be done. This refusal to acknowledge the issue is concerning. The AI community seems to have accepted releasing products into the market without adequate testing. Furthermore, we must scrutinize the code extracted from the internet, as it could harbor vulnerabilities, which we are currently ill-equipped to detect.

Section 2.1: The Need for Comprehensive Documentation

Software development, particularly in critical domains like nuclear energy, requires meticulous documentation of errors and bugs. For example, when Microsoft developed Windows and Word, they utilized user feedback to address flaws. This level of transparency is sorely lacking in current AI systems, where we see little public documentation regarding the types of errors that occur.

The absence of such records raises alarms, especially when we consider the potential ramifications of undocumented bugs in life-critical applications. Unlike the nuclear industry, where new technologies undergo extensive testing for years, AI software is often seen as experimental and rushed into deployment. This rush to market can lead to dire consequences.

Section 2.2: The Non-Deterministic Nature of AI

AI programs operate on a non-deterministic basis, meaning their actions can be unpredictable and challenging to debug. This characteristic complicates our ability to trace errors and understand the underlying code. While traditional software is deterministic, AI's probabilistic nature makes it difficult to predict outcomes, which poses a significant risk.

The rapid advancement of AI technology is often prioritized over thorough testing, a process that can consume a considerable portion of development time. However, the pressure from investors to deliver results quickly often overshadows the need for safety and reliability.

The second video, "Machine Learning for Everybody – Full Course," provides a foundational understanding of AI and machine learning principles, highlighting the necessity for responsible implementation.

Section 2.3: The Future of AI and Human Safety

As we consider the trajectory of AI development, it is crucial to remain skeptical of claims suggesting that machines will evolve intelligence spontaneously. This notion undermines the structured process behind human intelligence development, which is driven by survival principles. Relying on the belief that AI will autonomously reach human-like cognition is not only unrealistic but potentially dangerous.

In conclusion, trusting AI to generate reliable and safe code is fraught with risks. The current state of AI technology, marked by hallucinations and insufficient testing, raises serious concerns about its application in software development. Therefore, I urge you to reconsider the wisdom of allowing AI to handle your coding tasks. It’s essential to advocate for comprehensive testing of AI systems before they are deployed in any capacity.