How Commonsense Knowledge is Developed in AI Systems

The Crucial Need for Commonsense in ai

Okay, so, common sense in ai... why is it even a big deal? Imagine a self-driving car that can't figure out that a street is closed because of a parade. Annoying, right? It's more than just annoying though, it's a safety issue.

AI's a bit like a super-smart kid who's never left the library. They know a ton of facts, but they can't always apply it to the real world. ai systems often stumble because, well, they lack that intuitive understanding we humans take for granted. They can fail in many ways, like not understanding that a closed street means they can't drive through it, or not grasping that a falling glass will likely break. These systems haven't been exposed to every single possible scenario, leading to these kinds of errors.

• Intuition Deficit: ai struggles with stuff that's obvious to us. Like, if a glass falls off a table, it'll probably break.
• Unforeseen Events: Current ai isn't great at handling unexpected curveballs. Think about a robotaxi that can't deal with a pedestrian suddenly darting into the street.
• Nuance? What's Nuance?: ai often misses the subtle cues in a situation. It's like trying to explain sarcasm to a computer – good luck! According to techtarget, AI systems mess up in various ways because they haven't been exposed to everything that could possibly happen.

And it's not just about avoiding accidents. It's about building trust. People are way more likely to trust an ai if it can explain why it made a certain decision.

Looking ahead, we'll dive into how ai learns and why it's so important to make it more transparent.

Methods for Developing Commonsense Knowledge

Machine learning (ml) is how ai starts to "get" common sense. It's not about programming every little detail, but letting the ai learn from tons of data. Think about it – kids learn by seeing and doing, right? ai can do the same, but with way more "experiences" than any human could ever have.

One cool application is using reinforcement learning. Basically, the ai learns through trial and error, often by receiving rewards for desired actions. It's like teaching a robot to cook by letting it try different steps and seeing what works best.

• Imagine a healthcare ai learning to diagnose illnesses by observing doctors' decisions and the outcomes. Or a retail ai figuring out how to predict customer preferences by analyzing shopping patterns and feedback.
• Even in finance, ml can help ai understand risk by analyzing how humans react to market changes and the consequences of those reactions.

But here's the thing: large language models (llms), while impressive, aren't perfect. They can be unreliable because they don't truely "get" the underlying concepts. It's like a parrot that can repeat phrases but doesn't know what they mean.

As mentioned earlier, AI systems often struggle because they haven't been exposed to everything that could possibly happen.

Next up, we will explore the challenge of even defining what "common sense" really is.

The Challenge of Defining Commonsense

Okay, so, what exactly is "common sense"? It's like trying to nail jelly to a wall, innit?

• Context is king (or queen!): What's obvious in one situation? Might be totally lost in another.
• Normative, not absolute: It's about what should be obvious, and that changes with the culture.
• Emotions play a role: We feel when something's off, you know? That gut feeling when someone's missing the point.

Think about it, table manners in a fancy restaurant versus a football game. Total different vibe, right? And if someone cuts in line, we get annoyed. But what if they're rushing to the hospital? Changes everything!

The next section will look at how ai tries to mimic human thought processes, which is where commonsense reasoning comes in.

Commonsense Reasoning: Mimicking Human Thought

Okay, so, commonsense reasoning—it's about mimicking how we humans think, right? Imagine AI that gets sarcasm; that'd be something.

• It's all about making assumptions, just like we do. If you see someone with an umbrella, you assume it's raining.
• Dealing with time is key; ai needs to understand past, present, and future.
• And cause-and-effect? Crucial. ai needs to know that dropping a glass usually leads to breakage.

Explainable ai is super important here too. We need to know why the ai did what it did, ya know? For commonsense reasoning, this means understanding the assumptions and logical steps the ai took. Next, let's talk about the limits of this kind of reasoning in AI.

Applications of Commonsense Knowledge in AI

Okay, so, where does all this common sense ai stuff actually shake out? It's not just theory, ya know?

• Think smarter nlp. Instead of ai bots giving you canned responses, they actually get what you're askin'. For example, they could understand idioms like "kick the bucket" or figure out that "it's raining cats and dogs" doesn't mean animals are falling from the sky.
• Better predictive models, too. Not just guessing what you'll buy, but understanding why you'd buy it. Like, knowing that someone buying baby clothes might also be interested in diapers or nursery furniture, not just random unrelated items.
• And robots? Ones that don't just follow code, but can kinda "see" the world like we do. A robot in a kitchen could understand that it needs to open the fridge before trying to get milk, or that it shouldn't put a metal spoon in the microwave.

It's all about making ai less of a robot and more, well, like us. And that's kinda cool, ain't it?