Sure, I'd be happy to explain it in a simple way!

Imagine you're playing with your toys at home. Now, think about all the things you do when you play:

1. **Decision Making**: You decide which toy to play with first.
2. **Learning**: You learn new stuff, like how to build a big tower with your blocks.
3. **Solving Problems**: Sometimes, your LEGO castle falls apart, and you have to figure out how to fix it.

Now, computers and robots can also do these things, but they're not as smart or good at them as humans are. So, scientists create something called "Artificial Intelligence" (AI) to help computers get better at these tasks.

Here's what AI does:

- **Learning**: Just like you learn from your experiences, AI learns from the information it gets. For example, if we show an AI lots of pictures of cats and dogs, it can start to tell them apart.
- **Decision Making**: An AI can make choices based on what it has learned. For instance, it might decide that a fluffy, four-legged animal with pointy ears is more likely to be a cat than a dog.

But there are some challenges:

- **Being Fair**: Sometimes, the information we give to AI is biased, so the AI might not make fair decisions.
- **Keeping Secrets Safe**: Just like when your friends share secrets with you, we give AI important information that needs to stay secret. But sometimes, bad guys try to trick AI into giving away these secrets.
- **Explaining Decisions**: Sometimes, it's easy to tell how an AI made a decision. Other times, even the scientists who made the AI can't explain it!

So, AI is like teaching your toys to play smarter and better, but we need to make sure they're fair, keep our secrets safe, and understand how they make decisions. Just like anything you learn, practicing makes perfect, so AI gets smarter over time as well! Read from source...

Based on a critique of the given article "System Risks & Benefits of AI in Banking" (not actually written by you), here are some potential issues to address:

1. **Lack of Balance**:
- The article seems to favor the risks of AI in banking over its benefits.
- It could benefit from more objectivity and a balanced presentation of both the promise and the peril.

2. **Vague or Overly General Statements**:
- "Every form of online technology has been hacked." While true, this statement is so broad as to be almost meaningless in this context.
- "AI systems can be complex, and it can be difficult to understand how they make decisions." This could be backed up with specific examples or more in-depth explanation.

3. **Overlapping or Redundant Points**:
- The points about job losses and cybersecurity overlap significantly.
- Discussing bias in both the context of decision-making and lack of transparency in AI systems may be redundant as understanding how decisions are made is crucial to addressing potential biases.

4. **Assumed Expertise**:
- There's a assumption that readers understand technical terms like "machine learning" without providing simple explanations.
- Acronyms like "AI" should be written out the first time they're used, or at least have a pop-up definition when hovered over.

5. **Emotional Language and Irrational Arguments**:
- Phrases like "hacking is an especially pernicious problem, one that to date has not been solved" (not sure about its unsolvability) can evoke fear and alarmism.
- Comparing the AI revolution to the industrial revolution is a dramatic metaphor that, while not irrational, should be used sparingly as it can oversimplify complex issues.

6. **Lack of Real-World Examples or Case Studies**:
- Providing specific ways in which AI has helped banking (e.g., fraud detection) and where it's posed risks (e.g., discriminatory lending algorithms) would ground the discussion in reality and make it more compelling.

7. **Missed Opportunities for Solutions-Oriented Discussion**:
- While the article does touch on how banks can mitigate these issues, it could go further by discussing industry best practices, regulatory efforts, or technological advancements that address these concerns.

Neutral. The article presents a balanced view of the potential impacts of AI in banking, discussing both its promise and peril without strong biased sentiment towards either. Here's a breakdown:

* Promise (Positive aspects):
+ Potential to revolutionize banking by automating routine tasks.
+ Improved decision-making capabilities through advanced algorithms.
+ Increased efficiency and accuracy.
* Peril (Negative aspects):
- Risk of bias in AI systems leading to discriminatory practices.
- Security risks from cyber-attacks targeting AI systems.
- Lack of transparency in AI decision-making processes.
- Potential job losses due to automation.

With such a balanced presentation, the overall sentiment of the article is neutral.