Summary of The Ungrounded Alignment Problem, by Marc Pickett et al.
The Ungrounded Alignment Problem
by Marc Pickett, Aakash Kumar Nain, Joseph Modayil, Llion Jones
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract investigates the Ungrounded Alignment Problem, where a machine learning system must be designed to recognize specific patterns in a sequence of images without knowing the exact form or modality of its inputs. A simplified version of this problem is presented, where an unsupervised learner is given a sequence of text corpus images and evaluated on its ability to recognize sequential patterns without labels. The paper shows that leveraging letter bigram frequencies allows an unsupervised learner to associate images with class labels and identify trigger words in the input sequence, providing an approach for encoding desired innate behavior in modality-agnostic models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to recognize certain patterns in pictures without telling it what those patterns are. This is hard because the computer doesn’t know how the pictures will be presented or what they might look like. The researchers looked at this problem and found that if they used a simple technique called letter bigram frequencies, they could train the computer to recognize patterns without giving it any labels. This means the computer can learn to identify certain words or phrases in a sequence of images even though it doesn’t know what those images are. |
Keywords
* Artificial intelligence * Alignment * Machine learning * Unsupervised
