Nvidia CEO Jensen Huang said AI would drive long-term demand because it is the "single most powerful force of our time." Nvidia reported earnings and revenues that beat analysts' expectations as demand for graphics and artificial intelligence chips picked up in the second fiscal quarter. Huang also said his company's near-term growth will come from gaming and a couple of variants of the company's artificial intelligence chip business: inferencing and AI at the edge. During a conference call with analysts, Huang said artificial intelligence is the "single most powerful force of our time" and that there are more than 4,000 AI startups working with the company -- as compared to 2,000 AI startups in April 2017. In an interview with VentureBeat, Huang said the actual number of AI startups Nvidia is tracking is closer to 4,500.
Nvidia says it's achieved significant advances in conversation natural language processing (NLP) training and inference, enabling more complex, immediate-response interchanges between customers and chatbots. And the company says it has a new language training model in the works that dwarfs existing ones. Nvidia said its DGX-2 AI platform trained the BERT-Large AI language model in less than an hour and performed AI inference in 2 milliseconds making "it possible for developers to use state-of-the-art language understanding for large-scale applications…." Training: Running the largest version of Bidirectional Encoder Representations from Transformers (BERT-Large) language model, an Nvidia DGX SuperPOD with 92 Nvidia DGX-2H systems running 1,472 V100 GPUs cut training from several days to 53 minutes. A single DGX-2 system trained BERT-Large in 2.8 days.
The GPU maker says its AI platform now has the fastest training record, the fastest inference, and largest training model of its kind to date. Nvidia is touting advancements to its artificial intelligence (AI) technology for language understanding that it said sets new performance records for conversational AI. The GPU maker said its AI platform now has the fastest training record, the fastest inference, and largest training model of its kind to date. By adding key optimizations to its AI platform and GPUs, Nvidia is aiming to become the premier provider of conversational AI services, which it says have been limited up to this point due to a broad inability to deploy large AI models in real time. Unlike the much simpler transactional AI, conversational AI uses context and nuance and the responses are instantaneous, explained Nvidia's vice president of applied deep learning research, Bryan Catanzaro, on a press briefing.
Startup Blendid is trekking into the future with smoothie-making robots. The Silicon Valley robotics company is using AI powered by the compact supercomputing of NVIDIA Jetson to quickly serve up customizable juice and vegetable blends. Co-founder Vipin Jain said the original Star Trek was what inspired him to develop Blendid. "Star Trek's replicator was an intelligent machine that knew you, what you liked to eat and how you liked it prepared. That is what I want with Blendid," said Jain, the company's CEO.
Clara Medical Imaging is a collection of developer toolkits built on NVIDIA's compute platform aimed at accelerating compute, artificial intelligence, and advanced visualization. Medical imaging industry is being transformed. A decade ago, the earliest applications to take advantage of GPU computing were image & signal processing applications. Today, GPUs are found in almost all imaging modalities, including CT, MRI, X-ray, and Ultrasound bringing more compute capabilities to the edge devices. Deep Learning research in Medical Imaging is also booming with more efficient and improved approaches being developed to enable AI-assisted workflows.Today, most of this AI research is being done in isolation and with limited datasets which may lead to overly simplified models.
This book starts with the essentials of turning on the basic hardware. It provides the capability to interpret your commands and have your robot initiate actions. In this second edition, you will learn more specifics on how to use the Raspberry Pi's GPIO pins to communicate with and control a wide range of additional hardware. Teaching you to use the Raspberry Pi from scratch, this book will discuss a wide range of capabilities that can be achieved with it. These capabilities include voice recognition, human-like speech simulation, computer vision, motor control, GPS location, and wireless control.
In January 2018, I finished a project I had been working on for quite some time – The Raspbinator; I was very happy with it and it got some good attention. But there were some bugs and limitations to it and I already ideas for the next one. Early in 2019, the Nvidia Jetson Nano was released and it had great capability for running machine learning; I thought this would be perfect for the next version of my project and a great opportunity to get into ML/Neural Nets. So I pre-ordered the Jetson Nano, it was delivered on release and after many months of working and learning; The Nvidianator is finally ready. Let's dive in to the build and code… I wanted to go all out this time and really try and make this look good – so I knew I was going to utilise 3D printing (finally).