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In this episode of Eye on AI, join us as we explore the cutting-edge world of GPU optimization with Ronen Dar, CTO and co-founder of Run:ai.

Delve into the intricacies of managing and maximizing GPU utilization in an era marked by a severe GPU shortage. Ronen shares his insights on how Run:ai's innovative software is revolutionizing AI infrastructure, making GPU resources more efficient and accessible.

The conversation spans the technical challenges of scaling AI models, the evolution of GPU demands from basic algorithms to complex systems like GPT-4, and the strategic innovations helping enterprises overcome these hurdles. Ronen also reflects on the future of AI development, predicting an exponential increase in demand for computational power and the innovative solutions poised to meet these needs.

Tune in to uncover the technological advancements that are propelling AI capabilities forward and shaping the future of AI deployment across industries.

Don't forget to like, subscribe, and hit the notification bell for more deep dives into the technologies that are transforming our digital landscape.

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Craig Smith Twitter: https://twitter.com/craigss

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(00:00) Preview

(02:46) Introducing Ronen Dar

(04:00) Ronen's background and RunAI's origins

(09:13) The need for efficient GPU utilization in AI

(13:14) RunAI's core value proposition

(15:33 RunAI's deployment model

(18:55) The growing demand for compute power and GPUs

(22:08) Challenges in scaling models beyond 70 billion parameters

(27:52) RunAI's open platform approach

(31:00) Addressing latency and throughput challenges in inference

(34:36) RunAI's integration with AI tools and frameworks

(39:37) Reducing the cost of inference with GPU virtualization

(43:54) Challenges in auto-scaling for large language models

(47:06) The future of the GPU market and demand

(50:49) NVIDIA's dominance and the role of competitors like Cerebras

(54:20) RunAI's global customer base and demand patterns

(57:52) NVIDIA's vision and the evolution of GPU architectures

(01:01:25) Compute requirements for the metaverse and future AI applications

(01:03:56) Concerns about power consumption and carbon footprint

This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more.

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Unlock the secrets of clinical trial predictions with Saurabh Jain and Damon Rasheed from Opyl & TrialKey.ai on this episode of Eye on AI.

In today’s discussion, delve into how TrialKey's pioneering AI model can predict the success of clinical trials with astonishing accuracy—about 90%. Learn about the innovative use of over 400,000 past trials to enhance the precision and efficiency of new pharmaceutical trials. Saurabh and Damon share how their technology not only predicts outcomes but also optimizes trial design, saving substantial time and resources in the drug development process.

The episode also covers the sophisticated data processing techniques employed by TrialKey, including natural language processing to extract vital variables from complex datasets, and the strategic use of these insights to improve trial designs. Discover the implications of poorly designed trials and how AI can revolutionize their structure for better success rates.

Tune in to gain a deeper understanding of how AI and machine learning are transforming the pharmaceutical landscape, making clinical trials more predictive and efficient. Saurabh and Damon’s expertise offers a glimpse into the future of medical research and investment, emphasizing the role of AI in enhancing decision-making and resource allocation in healthcare.

Remember to like, subscribe, and hit the notification bell to keep up with the latest innovations and discussions reshaping the future of artificial intelligence and healthcare.

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Dive into the cutting-edge realm of neuromorphic computing with André van Schaik, a professor of electrical engineering at the Western Sydney University, and director of the International Centre for Neuromorphic Systems, in Penrith, New South Wales, Australia

In this episode of Eye on AI, André unveils the capabilities of DeepSouth, an innovative brain-scaled neuromorphic computing system designed to simulate up to 100 billion neurons in real time. Discover how DeepSouth leverages spiking neurons and synapses to process information more efficiently than traditional AI models, and how this technology could transform our understanding of brain computation and unlock new AI architectures.

The conversation explores the unique hardware setup of DeepSouth, utilizing FPGAs (Field-Programmable Gate Arrays) for a flexible, reconfigurable approach that mimics the asynchronous and spiking communication of biological neurons.

André discusses the initial testing phase focusing on balanced excitation-inhibition networks, reflecting common neural activities in the human cortex, and outlines the system's potential to facilitate large-scale simulations previously unachievable due to computational constraints.

André's insights are invaluable for anyone interested in the intersection of neuroscience, artificial intelligence, and computational technology.

Don't forget to like, subscribe, and hit the notification bell to stay updated on the latest breakthroughs and discussions in the world of artificial intelligence.

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Craig Smith Twitter: https://twitter.com/craigss

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(00:00) Preview and Introduction

(02:19) André van Schaik's background

(03:39) What is neuromorphic computing?

(05:45) Differences between Von Neumann and neuromorphic architectures

(09:27) How DeepSouth simulates neurons

(12:20) What are FPGAs?

(16:42) Current status of DeepSouth

(19:04) Running neural network architectures on DeepSouth

(22:33) DeepSouth as an open source, commercial hardware

(24:40) Potential for cheaper model training

(28:35) Number of neurons and connections in DeepSouth

(30:01) Power consumption comparison

(34:21) Mimicking brain structures in DeepSouth

(35:42) André van Schaik's background in neuroscience

(39:12) Goals of understanding brain activity vs solving problems

(41:44) Interest from AI research community

(43:03) Summary of DeepSouth's goals