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Hardware


AI Emerging Technologies to Watch

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One of the most exciting projects I have been lucky enough to work on at Intel was leading the engineering team tasked with designing, implementing and deploying the software platform that enabled large-scale training based on Habana Gaudi processors for MLPerf. I learned a lot on how to scale AI Training across a large hardware cluster, as well the challenges of building just building a data center. One thing that stood out was the immense amount of hardware, manual labor and power required to drive such a compute-intensive effort. Modern AI/ML solutions have shown that given a large amount of computing resources, we can create amazing solutions to complex problems. Applications leveraging solutions such as DALL·E and GPT-3 to generate images or create human-like research papers are truly mind-blowing.


Building a Speech-Enabled AI Virtual Assistant with NVIDIA Riva on Amazon EC2

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Speech AI can assist human agents in contact centers, power virtual assistants and digital avatars, generate live captioning in video conferencing, and much more. Under the hood, these voice-based technologies orchestrate a network of automatic speech recognition (ASR) and text-to-speech (TTS) pipelines to deliver intelligent, real-time responses. Building these real-time speech AI applications from scratch is no easy task. From setting up GPU-optimized development environments to deploying speech AI inferences using customized large transformer-based language models in under 300ms, speech AI pipelines require dedicated time, expertise, and investment. In this post, we walk through how you can simplify the speech AI development process by using NVIDIA Riva to run GPU-optimized applications.


How to Start a Career in AI

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How do I start a career as a deep learning engineer? What are some of the key tools and frameworks used in AI? How do I learn more about ethics in AI? Everyone has questions, but the most common questions in AI always return to this: how do I get involved? Cutting through the hype to share fundamental principles for building a career in AI, a group of AI professionals gathered at NVIDIA's GTC conference in the spring offered what may be the best place to start. Each panelist, in a conversation with NVIDIA's Louis Stewart, head of strategic initiatives for the developer ecosystem, came to the industry from very different places. But the speakers -- Katie Kallot, NVIDIA's former head of global developer relations and emerging areas; David Ajoku, founder of startup aware.ai;


Smart Glasses for Blind People Using Raspberry pi

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After my internship program and before the start of my senior year, I became inspired to work on this project. I was aware that I wanted something stimulating and entertaining that would allow me to study a lot of topics at once. Artificial intelligence and machine learning have grown in popularity among engineers. I was among those who had a keen interest in this field. Despite the fact that I was unable to implement all of my ideas, I am appreciative of the time and effort put into this project over the course of six months.


NVIDIA Orin Leaps Ahead in Edge AI, Boosting Leadership in MLPerf Tests

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In its debut in the industry MLPerf benchmarks, NVIDIA Orin, a low-power system-on-chip based on the NVIDIA Ampere architecture, set new records in AI inference, raising the bar in per-accelerator performance at the edge. Overall, NVIDIA with its partners continued to show the highest performance and broadest ecosystem for running all machine-learning workloads and scenarios in this fifth round of the industry metric for production AI. In edge AI, a pre-production version of our NVIDIA Orin led in five of six performance tests. It ran up to 5x faster than our previous generation Jetson AGX Xavier, while delivering an average of 2x better energy efficiency. NVIDIA Orin is available today in the NVIDIA Jetson AGX Orin developer kit for robotics and autonomous systems.


How analog AI hardware may one day reduce costs and carbon emissions

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Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Could analog artificial intelligence (AI) hardware – rather than digital – tap fast, low-energy processing to solve machine learning's rising costs and carbon footprint? Researchers say yes: Logan Wright and Tatsuhiro Onodera, research scientists at NTT Research and Cornell University, envision a future where machine learning (ML) will be performed with novel physical hardware, such as those based on photonics or nanomechanics. These unconventional devices, they say, could be applied in both edge and server settings.


NVIDIA AI Platform Delivers Big Gains for Large Language Models

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As the size and complexity of large language models (LLMs) continue to grow, NVIDIA is today announcing updates to the NeMo Megatron framework that provide training speed-ups of up to 30%. These updates–which include two trailblazing techniques and a hyperparameter tool to optimize and scale training of LLMs on any number of GPUs–offer new capabilities to train and deploy models using the NVIDIA AI platform. BLOOM, the world's largest open-science, open-access multilingual language model, with 176 billion parameters, was recently trained on the NVIDIA AI platform, enabling text generation in 46 languages and 13 programming languages. The NVIDIA AI platform has also powered one of the most powerful transformer language models, with 530 billion parameters, Megatron-Turing NLG model (MT-NLG). LLMs are one of today's most important advanced technologies, involving up to trillions of parameters that learn from text.


Democratizing LLMs Needs a Revolution in AI Hardware

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Andrew Feldman is CEO of Cerebras, a startup that specializes in AI hardware. There is growing concern that artificial intelligence (AI)--namely deep learning--is becoming centralized within a few very wealthy companies. This shift does not apply to all areas of AI, but it is certainly the case for large language models (LLMs). Accordingly, there has been growing interest in democratizing LLMs and making them available to a broader audience. However, while there have been impressive initiatives in open-sourcing models, the hardware barriers of LLMs have gone mostly unaddressed.


Seagate Skyhawk AI Drive

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Seagate Skyhawk AI Drive – Seagate Skyhawk AI is a video-optimised drive designed to support NVRs with artificial intelligence in edge applications. SkyHawk AI supports up to 64 HD cameras and 32 additional AI streams while offering capacities up to 20TB. It delivers zero dropped frames with ImagePerfect AI and has enterprise-class workload rates at 550 TB/yr for high reliability. Constant duty workloads can leverage up to 8 TB with drives designed for DVR and NVR systems and SkyHawk video drives are equipped with enhanced ImagePerfect and SkyHawk Health Management to help manage tough challenges. According to Seagate, when integrated with compatible NVR systems, these drives provide overall system reliability increases due to SkyHawk Health Management. In addition, the SkyHawk drives include a 3-year Seagate Rescue Data Recovery Services plan.


Missed Out on Nvidia? 3 Artificial Intelligence Stocks to Buy Now

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World-renowned semiconductor producer Nvidia is widely regarded as a pioneering force behind artificial intelligence (AI), and it remains a leader in the field today. But AI is a rapidly growing industry with plenty of room for other contributors, and in fact, some experts predict the majority of companies will be using AI by 2030, adding $13 trillion in value to the global economy. Therefore, while Nvidia is a $413 billion giant today, three Motley Fool contributors think C3ai (AI -0.70%), Riskified (RSKD 0.69%), and CrowdStrike (CRWD 0.83%) are artificial intelligence powerhouses of the future. Anthony Di Pizio (C3ai): One thing the artificial intelligence industry is missing is accessibility. Typically, only large technology companies with the financial resources and the ability to attract talented developers have been able to use AI in a meaningful capacity.