Natural language processing lately has come to resemble an arms race, as the big AI companies build models that encompass ever larger numbers of parameters. Microsoft recently held the record — but not for long.

Language models can’t correct your misspellings or suggest the next word in a text without knowing what language you’re using. For instance, if you type “tac-,” are you aiming for “taco,” a hand-held meal in Spanish, or “taca,” a crown in Turkish?

Reinforcement learning can beat humans at video games, but humans are better at coming up with strategies to master more complex tasks. New work enables neural networks to connect the dots.

In any training dataset, some classes may have relatively few examples. A new technique can improve a trained model’s performance on such underrepresented classes. Researchers introduced a method that synthesizes extracted features of underrepresented classes.

Neuroscientists once thought they could train rats to navigate mazes by color. Rats don’t perceive colors at all. Instead, they rely on the distinct odors of different colors of paint. New work finds that neural networks are prone to this sort of misalignment between training goals and learning.

The latest update of the acclaimed real-time object detector You Only Look Once is more accurate than ever. Researchers at Taiwan’s Institute of Information Science Academia Sinica offer YOLOv4 — the first version not to include the architecture’s original creators.

Facebook recently rolled out its entry in the World’s Biggest Chatbot sweepstakes. In keeping with the company’s social-networking dominance, the bot is designed to excel at chitchat on any subject.

Progress in language models is spawning a new breed of chatbots and, unlike their narrow-domain forebears, they have the gift of gab. Recent research tests the limits of conversational AI.

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.

Like nurses who can’t decipher a doctor’s handwriting, machine learning models can’t decipher medical scans — without labels. Conveniently, natural language models can read medical records to extract labels for X-ray images.

Human teachers can teach more effectively by adjusting their methods in response to student feedback. It turns out that teacher networks can do the same.

AI has added an unlikely language to its catalog of translation capabilities: brain waves. Joseph Makin led a group from the University of California San Francisco to render a person’s neural signals as English text while the person while the person read a sentence aloud.

AI’s ability to produce synthetic pictures that fool humans into believing they’re real has spurred a race to build neural networks that can tell the difference. Recent research achieved encouraging results.

Which comes first, training a reinforcement learning model or extracting high-quality features? New work avoids this chicken-or-egg dilemma by doing both simultaneously.

Your body disturbs Wi-Fi signals as you move through them. New research takes advantage of the effect to recognize the presence of people. Yang Liu and colleagues at Syracuse University detected people in a room with a Wi-Fi router by analyzing the signal.