AI generated images with different descriptions

More Realistic Pictures From Text: How the Glide Diffusion Model Generates Images from Text

OpenAI’s DALL·E got an upgrade that takes in text descriptions and produces images in styles from hand-drawn to photorealistic. The new version is a rewrite from the ground up. It uses the earlier CLIP zero-shot image classifier to represent text descriptions.
Jurassic-X's software infrastructure

Neural Nets + Rules = Truer Text: Jurassic-X NLP Can Solve Math, Check Facts, and More

A new approach aims to cure text generators of their tendency to produce nonsense. AI21 Labs launched Jurassic-X, a natural language processing system that combines neural networks and rule-based programs.
Deep Symbolic Regression

From Sequences to Symbols: Transformers Extend AI's Mathematical Capabilities

Given a sequence of numbers, neural networks have proven adept at discovering a mathematical expression that generates it. New work uses transformers to extend that success to a further class of expressions.
Grokking: A dramatic example of generalization far after overfitting on an algorithmic dataset

Learning After Overfitting: Transformers Continue Learning After Overfitting Data

When a model trains too much, it can overfit, or memorize, the training data, which reduces its ability to analyze similar-but-different inputs. But what if training continues? New work found that overfitting isn’t the end of the line.
Nvidia Chip

Transformer Accelerator: Nvidia's H100 is Designed to Train Transformers Faster

Is your colossal text generator bogged down in training? Nvidia announced a chip designed to accelerate the transformer architecture, the basis of large language models such as GPT-3.
Diagram with info about AlphaCode

Competitive Coder: AI code writing system can compete alongside humans.

Programming is hard. Programming competitions are harder. Yet transformers proved themselves up to the task.
The performance of different downstream (DS)

The Limits of Pretraining: More pretraining doesn't guarantee a better fine-tuned AI.

The higher the accuracy of a pretrained model, the better its performance after fine-tuning, right? Not necessarily. Researchers conducted a meta-analysis of image-recognition experiments and performed some of their own.
InstructGPT methods

A Kinder, Gentler Language Model: Inside Instruct GPT-3, OpenAI's GPT-3 successor.

OpenAI unveiled a more reliable successor to its GPT-3 natural language model. InstructGPT is a version of GPT-3 fine-tuned to minimize harmful, untruthful, and biased output. It's available via an application programming interface.
Diagram with automated decision systems

Roadblocks to Regulation: Why laws to regulate AI usually fail.

Most U.S. state agencies use AI without limits or oversight. An investigative report probed reasons why efforts to rein them in have made little headway. Since 2018, nearly every proposed bill aimed at studying or controlling how state agencies use automated decision systems.
Transformer Architecture

Transformers See in 3D: Using transformers to visualize depth in 2D images.

Visual robots typically perceive the three-dimensional world through sequences of two-dimensional images, but they don’t always know what they’re looking at. For instance, Tesla’s self-driving system has been known to mistake a full moon for a traffic light.
Overview of Mobile-Former | Cross attention over the entire featuremap for the first token in Mobile→Former

High Accuracy at Low Power: An energy efficient method for computer vision

Equipment that relies on computer vision while unplugged — mobile phones, drones, satellites, autonomous cars — need power-efficient models. A new architecture set a record for accuracy per computation.
Illustration: Board game pieces and puzzle pieces

How to Keep Up in a Changing Field: How to keep up with a fast-changing industry.

Machine learning changes fast. Take natural language processing. Word2vec, introduced in 2013, quickly replaced one-hot encoding with word embeddings. Transformers revolutionized the field in 2017 by parallelizing the previously sequential training process.
A living room made out of cups of coffee: the people, the seats, the chimney, the lamp, all gather around a cozy fire.

One Architecture to Do Them All: Transformer: The AI architecture that can do it all.

The transformer architecture extended its reach to a variety of new domains.What happened: Originally developed for natural language processing, transformers are becoming the Swiss Army Knife of deep learning.
Illustration of a woman riding a sled

Multimodal AI Takes Off: Multimodal Models, such as CLIP and DALL·E, are taking over AI.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive
An illustration shows a cozy cabin where all the furniture is made out of coffee mugs.

Transformers Take Over: Transformers Applied to Vision, Language, Video, and More

In 2021, transformers were harnessed to discover drugs, recognize speech, and paint pictures — and much more.

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