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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.
Ben Dickson, software engineer and the founder of TechTalks says, A new AI research paper shows machine learning algorithms can use "less-than-one-shot" learning to classify data with fewer training examples than labels.
If I told you to imagine something between a horse and a bird—say, a
flying horse—would you need to see a concrete example? 
Machine learning with less than one example
Such a creature does not exist, but nothing prevents us from using our imagination to create one: the Pegasus.
The human mind has all kinds of mechanisms to create new concepts by combining abstract and concrete knowledge it has of the real world. We can imagine existing things that we might have never seen (a horse with a long neck—a giraffe), as well as things that do not exist in real life (a winged serpent that breathes fire—a dragon). This cognitive flexibility allows us to learn new things with few and sometimes no new examples.
In contrast, machine learning and deep learning, the current leading fields of artificial intelligence, are known to require many examples to learn new tasks, even when they are related to things they already know.
Overcoming this challenge has led to a host of research work and innovation in machine learning...
New venues for machine learning research
“For instance-based algorithms like k-NN, the efficiency improvement of LO-shot learning is quite large, especially for datasets with a large number of classes,” Susholutsky said. “More broadly, LO-shot learning is useful in any kind of setting where a classification algorithm is applied to a dataset with a large number of classes, especially if there are few, or no, examples available for some classes. Basically, most settings where zero-shot learning or few-shot learning are useful, LO-shot learning can also be useful.”
For instance, a computer vision system that must identify thousands of objects from images and video frames can benefit from this machine learning technique, especially if there are no examples available for some of the objects. Another application would be to tasks that naturally have soft-label information, like natural language processing systems that perform sentiment analysis (e.g., a sentence can be both sad and angry simultaneously).
Source: TechTalks
