The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing. Through self-paced labs and, partnering with Scan to deliver instructor-led workshops, DLI teaches the latest techniques for designing, training, and deploying neural networks across a variety of application domains. DLI also teaches you how to optimise your code for performance using NVIDIA, CUDA and OpenACC. DLI provides unique hands-on training using the latest AI frameworks, SDKs, and GPU-accelerated technologies. Students get exposure to industry experts, along with the training and insights to help solve some of the world's most challenging problems with deep learning and accelerated computing.

Groq's tensor streaming processor (TSP) silicon is now available to accelerate customers' AI workloads in the cloud. Cloud service provider Nimbix now offers machine learning acceleration on Groq hardware as an on-demand service for "selected customers" only. While there are several startups building AI silicon for the data center, Groq now joins Graphcore as the only two with accelerators commercially available for customers to use as part of a cloud service. Graphcore previously announced its accelerators are available as part of Microsoft Azure. "Groq's simplified processing architecture is unique, providing unprecedented, deterministic performance for compute intensive workloads, and is an exciting addition to our cloud-based AI and Deep Learning platform," said Steve Hebert, Nimbix' CEO.

OpenAI yesterday unveiled its Open AI Microscope, which provides visualizations of every significant layer and neuron in eight of today's most popular computer vision (CV) models. Interactions between neurons indicate the abilities of neural networks, and with machine learning trending toward increasingly complicated neural networks it is important for researchers to be able to quickly and easily conduct a closer inspection of these thousands of interactions. This is where AI Microscope comes in. Just as biologists gain insights into organisms by putting model specimens under their microscopes, AI Microscope was designed to help researchers analyze the features that form inside leading CV models. OpenAI explains that its Microscope models are composed of a graph of nodes -- neural network layers connected via edges.

Artificial Intelligence is moving at the speed of light, with multiple companies creating software, products and services in not just a vertical way – more of a horizontal disruption. Form Healthcare to Security, from Real Estate to Telecom, here is a look into 28 companies powering the disruption with AI – 1st Edition. Sherpa.ai was founded in 2012 after deep research into Artificial Intelligence, with the conviction of creating a personal assistant that would be not just useful, but indispensable for users. In order to do this, Sherpa brought together a team of experts in Artificial Intelligence who, coupled with a fantastic design, have been able to create the next generation of Digital Assistants which will help users make their life not just more exciting, but also more enjoyable. WellSaid Labs has developed state of the art text-to-speech technology that creates life-like synthetic voice, from the voices of real people.

High quality labeled datasets remain one of the biggest obstacles for the mainstream adoption of machine learning technologies. While we are seeing unprecedented advancements in machine learning research and technology, many of those methods can't be widely adopted due to limitations in the creation of training datasets. That hurtle has propelled research in alternative methods such as semi-supervised and self-supervised learning which are able to operate by pretraining with unlabeled datasets. In the language analysis front, we have seen remarkable achievements of these type of techniques with models such as Google BERT or Microsoft Turin-NLG breaking records in performance and efficiency. Other deep learning domains remain behind in the adoption of semi-supervised and self-supervised models.

There's a common refrain among the chronically disappointed, it goes a little something like this: "if this is the future, where is my jetpack?" Juxtaposing this longing for a retro-future against the wonder-world of ubiquitous computing, programmable cells, and renascent space exploration can make the gripe sound out-of-sorts on a cursory examination. For some people this misplaced nostalgic futurism can be remarkably persistent. This causes a tendency to cling to predictions which look quaint in retrospect, ignoring the astounding reality that nobody could have predicted. However, with deep learning for drug discovery we are now able to predict so much more!

Two British firms have partnered to accelerate the adoption of binarized neural networks (BNNs), a technology that will drastically reduce memory footprint for AI models in endpoint applications such as voice control and person detection. The adoption of BNNs, which reduce parameters to 1-bit numbers, requires both new neural network models and special hardware that can support the 1-bit operations. Xcore.ai is one of the first non-ASIC parts with native support for the 1-bit vector arithmetic required for BNN inference. "We're making deep learning tiny and computationally radically more efficient," Roeland Nusselder, CEO of Plumerai told EETimes. "For this, we have been developing software for the most efficient form of deep learning, which is binarized neural networks."

Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.

Multi-Agent Reinforcement Learning (MARL) encompasses a powerful class of methodologies that have been applied in a wide range of fields. An effective way to further empower these methodologies is to develop libraries and tools that could expand their interpretability and explainability. In this work, we introduce MARLeME: a MARL model extraction library, designed to improve explainability of MARL systems by approximating them with symbolic models. Symbolic models offer a high degree of interpretability, well-defined properties, and verifiable behaviour. Consequently, they can be used to inspect and better understand the underlying MARL system and corresponding MARL agents, as well as to replace all/some of the agents that are particularly safety and security critical.

Successfully training a deep neural network demands a huge corpus of labeled data. However, each label only provides limited information to learn from and collecting the requisite number of labels involves massive human effort. In this work, we introduce LEAN-LIFE, a web-based, Label-Efficient AnnotatioN framework for sequence labeling and classification tasks, with an easy-to-use UI that not only allows an annotator to provide the needed labels for a task, but also enables LearnIng From Explanations for each labeling decision. Such explanations enable us to generate useful additional labeled data from unlabeled instances, bolstering the pool of available training data. On three popular NLP tasks (named entity recognition, relation extraction, sentiment analysis), we find that using this enhanced supervision allows our models to surpass competitive baseline F1 scores by more than 5-10 percentage points, while using 2X times fewer labeled instances. Our framework is the first to utilize this enhanced supervision technique and does so for three important tasks -- thus providing improved annotation recommendations to users and an ability to build datasets of (data, label, explanation) triples instead of the regular (data, label) pair.