Artificial intelligence company Scale Ai just raised $100 million in funding, but what does that even mean? The company claims it’s working on “explainable AI”, a term that’s been getting a lot of buzz lately.
What’s the fuss about explainability?
Explainable AI refers to the practice of making AI models more transparent and understandable. This means being able to explain how a model arrives at its conclusions, rather than just giving a black box answer. Think of it like a doctor diagnosing you with a disease – you want to know the symptoms and test results that led to the diagnosis, not just the diagnosis itself.
Other AI terms you should know
There are many other terms floating around the AI industry, and it’s hard to keep track of them all. Here are a few more that you might encounter:
* Model interpretability: This is the process of understanding how an AI model works and making sense of its predictions. It’s often used interchangeably with explainable AI, but they’re not exactly the same thing.
* Transfer learning: This is a technique where an AI model trained on one task is fine-tuned for another task. It’s like taking a chef who learned how to make a great cake and using those skills to make a great pizza.
* Overfitting: This is when an AI model becomes too specialized in the data it was trained on and doesn’t generalize well to new situations. It’s like a student who only studies for one exam and can’t answer questions on another.
What this means
As AI becomes more ubiquitous, understanding these terms will become increasingly important. It’s not just about being able to talk the talk – it’s about being able to make informed decisions about AI systems and how they’re used. So, next time you hear someone mention explainable AI, you’ll know exactly what they mean.
