#245 Can We Make Generative AI Cheaper? With Natalia Vassilieva, Senior VP & Field CTO & Andy Hock, VP, Product & Strategy at Cerebras Systems

9 snips

Sep 19, 2024

Guest

Andy Hock

Guest

Natalia Vassilieva

Natalia Vassilieva, VP & Field CTO of ML at Cerebras Systems, and Andy Hock, Senior VP of Product & Strategy, dive into the world of cost-effective generative AI. They discuss how Cerebras Systems’ specialized processors are revolutionizing AI efficiency, contrasting them with traditional GPUs. Topics include leveraging sparsity in neural networks for resource savings, strategies for tailored inference models, and the balance between centralized and decentralized AI computing. Together, they envision a future where local AI inference transforms personal computing across various industries.

Ask episode

AI Snips

Chapters

Transcript

Episode notes

INSIGHT

Early Stages of AI

Generative AI is still in its early stages, similar to computer graphics 20 years ago.
Wider adoption of use cases will drive advancements in AI technology.

INSIGHT

Cost Pressure and Innovation

Cost pressure is a key driver of innovation in AI, especially regarding infrastructure.
Traditional general-purpose processors are suitable but not optimal for AI workloads.

ADVICE

AI-Specific Processors

Build processors specifically for AI workloads to achieve better efficiency and speed.
Cerebras' wafer-scale engine, designed for AI, is faster and more efficient than GPUs.

Get the Snipd Podcast app to discover more snips from this episode

Get the app

With AI tools constantly evolving, the potential for innovation seems limitless. But with great potential comes significant costs, and the question of efficiency and scalability becomes crucial. How can you ensure that your AI models are not only pushing boundaries but also delivering results in a cost-effective way? What strategies can help reduce the financial burden of training and deploying models, while still driving meaningful business outcomes?

Natalia Vassilieva is the VP & Field CTO of ML at Cerebras Systems. Natalia has a wealth of experience in research and development in natural language processing, computer vision, machine learning, and information retrieval. As Field CTO, she helps drive product adoption and customer engagement for Cerebras Systems' wafer-scale AI chips. Previously, Natalia was a Senior Research Manager at Hewlett Packard Labs, leading the Software and AI group. She also served as the head of HP Labs Russia leading research teams focused on developing algorithms and applications for text, image, and time-series analysis and modeling. Natalia has an academic background, having been a part-time Associate Professor at St. Petersburg State University and a lecturer at the Computer Science Center in St. Petersburg, Russia. She holds a PhD in Computer Science from St. Petersburg State University.

Andy Hock is the Senior VP, Product & Strategy at Cerebras Systems. Andy runs the product strategy and roadmap for Cerebras Systems, focusing on integrating AI research, hardware, and software to accelerate the development and deployment of AI models. He has 15 years of experience in product management, technical program management, and enterprise business development; over 20 years of experience in research, algorithm development, and data analysis for image processing; and 9 years of experience in applied machine learning and AI. Previously he was Product Management lead for Data and Analytics for Terra Bella at Google, where he led the development of machine learning-powered data products from satellite imagery. Earlier, he was Senior Director for Advanced Technology Programs at Skybox Imaging (which became Terra Bella following its acquisition by Google in 2014), and before that was a Senior Program Manager and Senior Scientist at Arete Associates. He has a Ph.D. in Geophysics and Space Physics from the University of California, Los Angeles.

In the episode, Richie, Natalia and Andy explore the dramatic recent progress in generative AI, cost and infrastructure challenges in AI, Cerebras’ custom AI chips and other hardware innovations, quantization in AI models, mixture of experts, RLHF, relevant AI use-cases, centralized vs decentralized AI compute, the future of AI and much more.

Links Mentioned in the Show:

New to DataCamp?

Learn on the go using the DataCamp mobile app
Empower your business with world-class data and AI skills with DataCamp for business