Direct link to paper: https://arxiv.org/pdf/2009.08449.pdf https://arxiv.org/pdf/2009.08449.pdf Interesting paper, although the headline is of course sensation

Direct link to paper: https://arxiv.org/pdf/2009.08449.pdf https://arxiv.org/pdf/2009.08449.pdf Interesting paper, although the headline is of course sensational. The crux of the paper is that by using "soft labels" (for example a probability distribution rather than one-hot), it's possible to create a decision boundary that encodes more classes than you have examples. In fact, only two examples can be used to encode any finite number of classes. This is interesting because it means that, in theory, ML models should be able to learn decision spaces that are far more complex than the input data has traditionally been thought to encode. Maybe one day we can create complex, generalizable models using a small amount of data. As written, this paper does not provide much actionable information. The problem is a toy problem, and is far from being useful in "modern" AI techniques (especially things like deep learning or boosted trees). The paper also is not practical in the sense that in real life you don't know what your decision boundary should look like (that's what you learn after all), and there's no obvious way to know which data to collect to get a decision boundary you want. In other words, this paper has said "this representation is mathematically possible" and is hoping that future work can actually make it useful in practice.