It's a situation familiar to anyone who's ever communicated with a voice assistant on a smart device. You pose a request: "Hey Voice Assistant, tell me a story about Georgia Tech." More often than not, you get a related response – "Georgia Tech is located in Atlanta, Georgia. Would you like me to provide you with directions?" – but one with slightly unnatural language and only limited information. Despite the enormous strides made in artificial intelligence to develop systems that can answer simple questions and requests, the kinds of natural conversational language humans have with each other when giving more complex directions or telling stories has thus far been out of reach.

PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric. The library consists of various dynamic and temporal geometric deep learning, embedding, and spatio-temporal regression methods from a variety of published research papers. In addition, it consists of an easy-to-use dataset loader and iterator for dynamic and temporal graphs, gpu-support. It also comes with a number of benchmark datasets with temporal and dynamic graphs (you can also create your own datasets). PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy -- see the accompanying tutorial.

You already know that Deep Learning is good at vision, translation, playing games, and other tasks. But Neural Networks don't "learn" the way humans do, instead it's just really good at fast pattern matching. Today's research mainly focuses on bigger models with larger datasets, bigger models, and complicated loss functions. But the next revolution is likely going to be more fundamental. Let's take a look at two approaches: adding logic with Stacked Capsule Auto Encoders and Self-Supervised Learning at scale. This about sums up what most AI scientists already know: Deep Learning is really good at doing narrow, pattern based tasks such as object or speech recognition.

This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods. These events are regarded by many as the beginning of a deep learning revolution that has transformed AI.

A Generative Adversarial Network for AI-Aided Chair Design Researchers present a deep neural network for improving human design of chairs which consists of an image synthesis module and a super-resolution module. They select one of the candidates as a design prototype and create a real-life chair based on it. According to the researcher team, this is the first physical chair created with the help of deep neural networks, which bridges the gap between AI and design. This is the largest NLP model ever trained, with 17 billion parameters. T-NLG has achieved SOTA performance on mainstream NLP tasks.

This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods. These events are regarded by many as the beginning of a deep learning revolution that has transformed AI.

The Thirty-Fourth annual meeting of AAAI just concluded in New York. As expected, it was a huge conference with thousands of AI Researches and practitioners in attendance. One big highlight was presentations from 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio as well as a panel discussion with Daniel Kahneman.