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NVIDIA’s Annamalai Chockalingam on the Rise of LLMs - Ep. 206
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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.
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230 episodes
MP3•Episode home
Manage episode 381626077 series 2447382
Content provided by The AI Podcast. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The AI Podcast or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
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.
…
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230 episodes
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