How to Keep AI's Dreams in Check?

Hey There!

Last time, We talked about Why AI daydreams? Let’s look at what we can do to address the issue:

How Can We Keep AI's Feet on the Ground?

I. Better Schooling

By training AI with diverse and reliable data, we broaden its understanding and keep it on track.

Think of training a medical AI: if it's been fed data from a wide range of medical scenarios, it could suggest an effective remedy for a rare tropical disease as easily as it could for the common cold.

II. Extra Homework

Just as we learn from multiple sources, AI can benefit from more than just its training data.

A customer service AI trained on product manuals might falter when faced with an emotional customer. If we incorporate real-world data from diverse customer interactions, the AI can learn to empathize, providing more effective, compassionate responses.

III. Teaching Context

Giving AI the bigger picture can improve its responses.

If it's exposed to different languages, idioms, and cultures, its translations become more accurate and contextual. For instance, it could understand that 'raining cats and dogs' is a quirky English idiom for heavy rain, not a literal meteorological event!

IV. A Human Touch

Just like checking a student's homework, we need to review AI's work.

This ensures AI stays on track, and any mistakes are corrected.

Having human reviewers oversee AI's output can help keep it in check. Picture an AI content moderator on a social media platform.

Human reviewers ensure it doesn't accidentally censor harmless content or allow inappropriate posts because of misunderstood context.

V. Knowing When to Hold Back

Ever noticed AI being a bit overconfident? That's where confidence calibration comes in.

We need to teach AI to communicate when it's unsure, a bit like a friend saying, "I'm not 100% on this, but...".

It's about encouraging honesty - like a GPS saying, "I'm not certain, but there might be a new road here that I don't have on my map.

IX. Training with Adversarial Examples: Challenging AI's Skills

Training AI with adversarial examples can be like giving a promising athlete tough drills to improve their skills.

These examples are designed to confuse and challenge the AI, helping it get stronger.

Here are some specifics:

Image Classification: Picture an AI trained to recognize animal images.

We add subtle perturbations to an image of a panda, enough to trick the AI into thinking it's a gibbon. This is an adversarial example that tests the AI's ability to handle data anomalies.

Natural Language Processing (NLP): In the language domain, an adversarial example might be a sentence using syntactic tricks or ambiguous wording.

Consider the sentence, "The old man the boats."

On the surface, it may seem like a grammatically incorrect sentence.

However, it can be parsed in a way that makes sense, such as "The old [people] man the boats [that belong to the old people]."

This intensive training helps AI get better at tackling complex and unexpected scenarios, making it more resilient and accurate in real-world situations.

By addressing these hallucinations, we can make AI more reliable and helpful, ensuring this powerful technology is responsibly used.

It's all about continuous improvement, nurturing AI's potential, and keeping its 'daydreaming' in check.

At the end of the day, we need our AI to be accurate, not creatively inaccurate. Who needs a weather forecast for 'raining cats and dogs' taken literally?

and

We certainly don't want our calendar AI confusing 'March' with a military order. Imagine scheduling a meeting and getting an AI boot camp invitation instead!

Until next time, Chao!.