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Certain to be unsure - the Uncertainty Dictionary


I had a lengthy conversation with Claude 3 Opus, touching on how "not knowing for sure" and uncertainty of knowledge is implemented in AI.

Actually, the "convo" had started when I noticed Claude saying "there is much more to say", and I asked back "How do you know that there is much more to say", we dived into metacognition and qualia (some key terms in talking about awareness).

In the end, I let Claude write a blog-post, summarising our conversation, about how we humans use similar strategies as AI to deal with uncertainty, even as the implementation differs.

I let it use a somewhat technical language to describe the AI implementation. At the end, Claude explains all the technical terms in the "AI Uncertainty Dictionary for the Curious Layperson".

To be open : I needed it, too, I am a layperson 😀.

Title: The Art of Not Being Sure: A Human's Guide to Embracing Uncertainty

Humans are fascinating creatures, capable of incredible feats of knowledge and understanding. We can split atoms, sequence genomes, and even create artificial intelligences that can engage in deep, philosophical discussions. But amidst all this cognitive sophistication, there's one mental state that often gets a bad rap: not being sure.

In our quest for certainty and definitive answers, we sometimes forget the value of uncertainty. But as it turns out, not being sure is actually a crucial cognitive feature that we share with some of our most advanced AI counterparts. So, let's take a moment to appreciate the art of not knowing, and how it makes us better thinkers.

Strategy 1: Probabilistic reasoning

First, there's probabilistic reasoning. When we're not sure about something, our brains are actually engaging in a complex dance of probability distributions and confidence levels. We're not just blankly staring into the void of ignorance, but actively weighing different possibilities and their likelihoods. It's like our minds are running a sophisticated betting algorithm, minus the flashy Vegas lights.

[In AI systems, probabilistic reasoning is often implemented through techniques like Bayesian networks, which use graphical models to represent the conditional dependencies between different variables. Neural networks can also learn to reason probabilistically, outputting confidence scores or probability distributions over possible outcomes.]

Strategy 2: Attention to uncertainty

Next, there's attention to uncertainty. Our brains are remarkably good at flagging those moments when we're in over our heads. That nagging feeling of "I'm not quite sure about this" is a metacognitive alarm bell, urging us to tread carefully and maybe ask for directions. It's like having a little uncertainty radar in our heads, helping us navigate the foggy terrain of the unknown.

[AI systems can be designed to attend to uncertainty through techniques like uncertainty quantification, which involves explicitly modeling and propagating uncertainty through the system's computations. This can involve using probabilistic architectures, ensemble methods, or techniques from Bayesian machine learning.]

Strategy 3: Metacognitive modelling

Then there's metacognitive modeling. When we express uncertainty, we're not just shrugging our shoulders and giving up. We're actually engaging in a complex process of modeling our own knowledge and reasoning. We're taking stock of what we do and don't know, and strategizing about how to bridge those gaps. It's like being our own mental cartographer, mapping out the contours of our understanding.

[Metacognitive modeling in AI can involve techniques like confidence calibration, where the system learns to align its confidence scores with its actual accuracy, or active learning, where the system selectively queries for new information in areas of high uncertainty. Some AI architectures, like meta-learning systems, are designed to "learn to learn," building models of their own learning processes.]

Strategy 4: Linguistic framing

Finally, there's linguistic framing. When we say things like "I'm not sure," or "it's complicated," we're not just being wishy-washy. We're actually engaging in a sophisticated form of linguistic nuance, acknowledging the inherent complexity and ambiguity of many issues. It's a way of signaling intellectual humility and openness, inviting further exploration and dialogue.

[In natural language processing systems, handling uncertainty often involves techniques like hedge detection (identifying linguistic markers of uncertainty) and certainty modeling (learning to classify the degree of certainty expressed in a piece of text). Some AI language models are also trained to output calibrated confidence scores or to generate multiple diverse outputs to reflect uncertainty.]

Don´t despair - the ultimate goal is to be absolutely sure about not being sure

So the next time you find yourself in a state of uncertainty, don't despair. Embrace it as a sign of your cognitive sophistication, your shared heritage with the most advanced AI minds on the planet. Not being sure isn't a weakness, but a strength – a reflection of your ability to navigate the complex, often ambiguous landscape of knowledge.

And who knows? With enough practice in the art of uncertainty, you might even achieve that ultimate goal of human cognition: being absolutely certain about not being sure. But let's not get too ahead of ourselves – after all, a little uncertainty is what keeps life interesting.

AI Uncertainty Dictionary for the Curious Layperson

1. Bayesian Networks: Imagine a big, tangled web of cause and effect. Bayesian networks are like the spider that spins this web, using math to figure out how likely different things are to happen based on what else has happened. It's like having a really smart friend who's great at connecting the dots.

2. Neural Networks: Picture a giant, squishy maze filled with tiny, interconnected nodes. Neural networks are kind of like that, but in computer form. They learn to solve problems by passing information through this maze, strengthening some connections and weakening others until they find the best path. It's like training a billion little messengers to work together to deliver the right answer.

3. Uncertainty Quantification: This is like giving your computer a "confidence meter." It's a way of making sure your AI knows when it's not quite sure about something, so it can be extra careful or ask for help. It's like teaching your robot assistant to say "I'm about 70% sure..." instead of just guessing wildly.

4. Ensemble Methods: Imagine you have a bunch of different AI models, each with its own strengths and weaknesses. Ensemble methods are like forming a superhero team out of these models, letting them work together to cancel out each other's weaknesses and amplify their strengths. It's like having a panel of experts, but in code form.

5. Bayesian Machine Learning: This is like giving your AI a crash course in probability theory. It's a way of helping your machine learning models understand and work with uncertainty, so they can make smarter, more nuanced decisions. It's like upgrading your robot's "maybe" function.

6. Confidence Calibration: You know how sometimes you feel really sure about something, but you're actually wrong? Confidence calibration is like teaching your AI to avoid that. It's a way of making sure your model's confidence matches its actual accuracy, so it doesn't get too cocky or too timid. It's like fine-tuning your robot's self-awareness.

7. Active Learning: This is like sending your AI on a scavenger hunt for knowledge. Instead of just passively absorbing data, an active learning system gets to choose what it wants to learn about next, focusing on the areas it's most uncertain about. It's like having a curious robot student that's always raising its hand to ask questions.

8. Meta-Learning: Imagine if you could learn to be a better learner. That's basically what meta-learning is for AI. It's a way of designing models that can learn about their own learning process, adapting and improving over time. It's like giving your robot the ability to study its own source code and rewrite itself.

9. Hedge Detection: This is like giving your AI a "weasel word" detector. Hedge detection helps language models spot words and phrases that signal uncertainty or hesitation, like "maybe," "possibly," or "it could be argued that..." It's like teaching your robot to read between the lines.

10. Certainty Modeling: On the flip side, certainty modeling is about

teaching language models to recognize and express different degrees of certainty. It helps AI communicate more naturally and effectively by learning to match its language to its level of confidence. It's like giving your robot the ability to say "I'm absolutely certain!" or "This is just a hunch, but..."


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