Logistic Regression

Even if we manage to stop robots from taking over the world, they may still have the last laugh. Researchers at Kyoto University developed a series of neural networks that enable a robot engaged in spoken conversation to chortle along with its human interlocutor.

An object detector trained exclusively on urban images might mistake a moose for a pedestrian and express high confidence in its poor judgment. New work enables object detectors, and potentially other neural networks, to lower their confidence when they encounter unfamiliar inputs.

There was a moment when logistic regression was used to classify just one thing: If you drink a vial of poison, are you likely to be labeled “living” or “deceased”? Times have changed.

The U.S. government’s effort to take advantage of AI has not lived up to its promise, according to a new report. Implementations of machine learning systems by federal agencies are “uneven at best, and problematic and perhaps dangerous at worst".

Bias arises in machine learning when we fit an overly simple function to a more complex problem. A theoretical study shows that gradient descent itself may introduce such bias and render algorithms unable to fit data properly.

Few-shot learning seeks to build models that adapt to novel tasks based on small numbers of training examples. This sort of learning typically involves complicated techniques, but researchers achieved state-of-the-art results using a simpler approach.

A new algorithm can triage programming bugs, highlighting dangerous flaws. Microsoft researchers developed a machine learning model that reads the titles of bug reports and recognizes those describing flaws that compromise security.