> Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML),[1] is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI.[2] It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision.[3][4] By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively.[5] XAI may be an implementation of the social right to explanation.[6] XAI is relevant even if there is no legal right or regulatory requirement. For example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on.[7] These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions. [8]
- How does [ChatGPT] work?
- Can ChatGPT explain the criteria it uses to filter candidate responses (out of the Powerset of all possible responses)?
- What sorts of rejectable hypotheses and experimental designs is ChatGPT capable of synthesizing?
...
> Prompts that include a train of thought in few-shot learning examples show better indication of reasoning in language models. [7] In zero-shot learning prepending text to the prompt that encourages a chain of thought (e.g. "Let's think step by step") may improve the performance of a language model in multi-step reasoning problems.
``````python
def generate_comment(c_code, temperature=0.19, program_info=None, prompt=None, model=MODEL, max_tokens=MAXTOKENS):
intro = "Below is some C code that Ghidra decompiled from a binary that I'm trying to reverse engineer."
#program_info = get_program_info()
#if program_info:
# intro = intro.replace("a binary", f'a {program_info["language_id"]} binary')
if prompt is None:
prompt = """{intro}
```
{c_code}
```
Please provide a detailed explanation of what this code does, in {style}, that might be useful to a reverse engineer. Explain your reasoning as much as possible. Finally, suggest a suitable name for this function and for each variable bearing a default name, offer a more informative name, if the purpose of that variable is unambiguous. {extra}
""".format(intro=intro, c_code=c_code, style=LANGUAGE, extra=EXTRA)
``````
ChatGPT can help explain code and codebases, but can it explain each response?
Similarly, "Is there any way to get the step-by-step solution in SymPy?" [like e.g. paid WolframAlpha and PhotoMath do]