Artificial Intelligence Made Faster, More Efficient: New NVIDIA Processor Chip for AI Helps It to Better Understand Human Language

Nvidia recently announced it would start selling a new artificial intelligence or AI acceleration chip in late 2022, part of the company's initiatives to ensure the security of its leadership in a computer revolution.

A CNET report said that the faster ship should enable the developers of AI to fast-track their research and build more advanced models for machine learning, specifically for multifaceted challenges such as understanding human language and piloting autonomous or self-driving cars.

IN PARTICULAR, theH100 Hopper" processor, which Jensen Huang, the Chief Executive of Nvidia, unveiled in March, is expected to start shipping next quarter.

Additionally, the processor features 80 billion transistors and measures over 800 square millimeters, which is nearly as big as is physically plausible with today's chip-making equipment.

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(Photo: Justin Sullivan/Getty Images)
Nvidia recently announced it would start selling a new artificial intelligence or AI acceleration chip in late 2022, part of the company’s initiatives to ensure the security of its leadership in a computer revolution.

Chips for AI

The H100 is a competitor of massive, power-starving AI processors such as the MI250X of AMD, TPU v4 of Google, and the upcoming Ponte Vecchio of Intel.

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Such chips are considered "giants" most frequently found in the preferred environment for AI training systems, data centers loaded with racks of computing gear and laced with thick copper power cables.

This said new chip incorporates the evolution of Nvidia from a designer of graphical processing units employed for video games to a machine learning powerhouse.

The tech firm did this by adapting GPUs for the specific mathematics of AI, like multiplying arrays of numbers, a related WIRED report said.

Essentially circuitry for speeding up artificial intelligence is turning increasingly essential as the technology arrives in everything "from iPhones to Aurora."

Faster Translation of Human Language

Chips similar to the H100 are crucial for fast-tracking tasks like training an AI model to translate live speech in real-time and live from one language to another or automatically generate captions on video.

Faster performance means developers of AI can tackle more challenging tasks such as autonomous vehicles and accelerate their experimentation, although one of the biggest areas of enhancement is processing language.

TechInsights analyst Linley Gwennap said the H100, along with the software tools of Nvidia, cements its position in the artificial intelligence processor market.

Nvidia's longtime customer, Pindrop, that's using AI-based voice analysis to assist customer service representatives in authenticating legit clients and identifying scammers, says the steady process of the chipmaker has let it expand to determining "audio deepfakes."

These are sophisticated computer simulations that can be employed to perform fraud or disseminate wrong information.

According to Ellie Khoury, the company's director of research, they could not get there if they did not have Nvidia GPUs' latest generation.

Memory and Processing

Training the AI system engages processing a massive quantity of information, including audio data from 100,000 voices, each controlled in many ways to simulate background chatter and poor telephone connections.

This is why H100 advancements such as expanded memory and faster processing are essential to the customers of AI.

Based on Nvidia's estimates, H100 is six times faster in general compared to the A100 predecessor the firm launched in 2020.

One essential area that certainly benefits is natural language processing. Also called NLP, the AI domain helps computers decipher one's speech, summarize documents, translate languages, and perform more important tasks.

Lastly, when it comes to NLP, a field at the forefront of machine learning or AI, Nvidia is considered a strong player.

Related information about Nvidia chips is shown on Cosmic Revolution's YouTube video below: