Nov 13, 2019

In this series exploring why machine learning projects fail, let’s examine the challenge of “small data.” Given 1 million labeled images, many teams can build a good classifier using open source.

The world’s largest stock market is using AI to flag suspicious trading in real time. Nasdaq is testing a deep learning system to monitor trading of ts U.S. equities. Named Chiron, the system watches for behaviors that indicate potential market manipulation.

A word-embedding model typically learns vector representations from a large, general-purpose corpus like Google News. But to make the resulting vectors useful in a specialized domain, they must be fine-tuned on a smaller, domain-specific dataset. Researchers offer a more accurate method.

Neuroevolution, which combines neural networks with ideas drawn from Darwin, is gaining momentum. Its advocates claim that they can achieve faster, better results by generating a succession of new models, each slightly different than its predecessors, rather than relying on a purpose-built model.

A deep learning system is helping biologists who survey offshore fish populations to prevent overfishing. The U.S. agency in charge of protecting ocean resources is using an underwater camera and neural network to count fish in real time.

Looking at images, people see outlines before the details within them. A replacement for the traditional convolutional layer decomposes images based on this distinction between coarse and fine features.

In March 2018, one of Uber’s self-driving cars became the first autonomous vehicle reported to have killed a pedestrian. A new report by U.S. authorities suggests that the accident occurred because the car’s software was programmed to ignore jaywalkers.

In this series exploring why machine learning projects fail, let’s examine the challenge of “small data.” Given 1 million labeled images, many teams can build a good classifier using open source.