NVIDIA's Annamalai Chockalingam discusses the rise of LLMs in the latest episode of The AI Podcast. LLMs are deep learning algorithms that can recognize, summarize, translate, predict, and generate language. Enterprises are using LLMs to drive innovation and gain a competitive advantage. The podcast touches on the role of LLMs in the generative AI movement, AI safety, the fast pace of innovation in the LLM world, Nemo and Megatron L in AI model development, and the current state and future direction of LLMs.
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question_answer ANECDOTE
From Firmware To LLMs
Annamalai traced his path from firmware and engineering through strategy consulting to NVIDIA, bringing both tech and business perspective.
He says he started working on LLMs before ChatGPT popularized the field.
insights INSIGHT
What LLMs Actually Are
Large language models are deep neural networks built on Transformer architectures that learn from unlabeled data at scale.
They can recognize, summarize, translate, predict, and generate language across many topics.
volunteer_activism ADVICE
Make Safety Multilayered
Build safety at multiple layers: at model training and at the application layer with guardrails tailored to the use case.
Define safety relative to application context and deploy measures accordingly.
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Generative AI and large language models (LLMs) are stirring change across industries — but according to NVIDIA Senior Product Manager of Developer Marketing Annamalai Chockalingam, “we’re still in the early innings.”
In the latest episode of NVIDIA’s AI Podcast, host Noah Kravitz spoke with Chockalingam about LLMs: what they are, their current state and their future potential.
LLMs are a “subset of the larger generative AI movement” that deals with language. They’re deep learning algorithms that can recognize, summarize, translate, predict and generate language.
AI has been around for a while, but according to Chockalingam, three key factors enabled LLMs.
One is the availability of large-scale data sets to train models with. As more people used the internet, more data became available for use. The second is the development of computer infrastructure, which has become advanced enough to handle “mountains of data” in a “reasonable timeframe.” And the third is advancements in AI algorithms, allowing for non-sequential or parallel processing of large data pools.
LLMs can do five things with language: generate, summarize, translate, instruct or chat. With a combination of “these modalities and actions, you can build applications” to solve any problem, Chockalingam said.
Enterprises are tapping LLMs to “drive innovation,” “develop new customer experiences,” and gain a “competitive advantage.” They’re also exploring what safe deployment of those models looks like, aiming to achieve responsible development, trustworthiness and repeatability.
New techniques like retrieval augmented generation (RAG) could boost LLM development. RAG involves feeding models with up-to-date “data sources or third-party APIs” to achieve “more appropriate responses” — granting them current context so that they can “generate better” answers.
Chockalingam encourages those interested in LLMs to “get your hands dirty and get started” — whether that means using popular applications like ChatGPT or playing with pretrained models in the NVIDIA NGC catalog.
NVIDIA offers a full-stack computing platform for developers and enterprises experimenting with LLMs, with an ecosystem of over 4 million developers and 1,600 generative AI organizations. To learn more, register for LLM Developer Day on Nov. 17 to hear from NVIDIA experts about how best to develop applications.
NVIDIA AI Podcast