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Reasoning About Uncertainty using Markov Chains | by Nikolaus Correll | Feb, 2024

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Formal strategies to deal with “Trial-and-Error” issues

The power to take care of unseen objects in a zero-shot method makes machine studying fashions very enticing for functions in robotics, permitting robots to enter beforehand unseen environments and manipulating unknown objects therein.

Whereas their accuracy in doing so is unbelievable in contrast with was conceivable just some years in the past, uncertainty will not be solely right here to remain, but additionally requires a special remedy than customary in machine studying when utilized in resolution making.

This text describes latest outcomes on coping with what we name “trial-and-error” duties and clarify how optimum selections may be derived by modeling the system as a continuous-time Markov chain, aka Markov Soar Course of.

Left: Efficiency of the “CLIP” mannequin on precisely offering labels for photos, dramatically outperforming earlier work. Picture from https://arxiv.org/pdf/2103.00020.pdf. Proper: Summarizing a mannequin’s efficiency by a single quantity is just one piece of data. As soon as this info is definitely used to decide, we will even want to grasp the other ways the mannequin can fail. Picture: personal work.

The picture above exhibits the typical efficiency for zero-shot picture labeling from CLIP, a groundbreaking mannequin from OpenAI that kinds the idea for big multi-modal fashions comparable to LLava and GPTv4. Let’s assume, it is ready to label a picture containing a hen with 70% accuracy. Whereas that is unbelievable efficiency, in 30% of the instances, the label will likely be mistaken.

Labeling will not be the use case we’re considering when utilizing this output for resolution making. For instance, if we wish to function an automatic hen repeller, we are going to want a transparent reply as as to whether there’s a hen or not. Sadly, issues usually are not as a “sure” and “no” reply, however we’ve got to contemplate 4 instances:

True Optimistic: There’s a hen and the imaginative and prescient mannequin sees itFalse Optimistic: There’s a hen, however the imaginative and prescient mannequin sees a canine, a cat, or a screwdriver.True Destructive: There isn’t any hen, and the mannequin thinks so too.False Destructive: There’s a hen, however the imaginative and prescient fashions doesn’t see it.

These instances are summarized within the picture above. As you may see, what’s offered as “accuracy” within the mannequin solely covers the “True Optimistic” case. What stays unknown is what the possibilities of the opposite potential outcomes are.



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