Ep. 5 What do Multimodal AI and Smaller LMs Mean for Enterprises? | AI Insights and Innovation
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In episode 5 of AI
In episode 5 of AI Insights and Innovation, theCUBE Research principal analyst David Linthicum discusses how recent advancements in AI are focusing on the development of multimodal systems and smaller, more efficient language models.
Check out the latest from theCUBE, including upcoming tech events https://www.thecube.net/
Multimodal AI integrates and processes multiple types of data, such as text, images and audio, to better understand and generate content, enabling tasks such as generating text from images and creating visuals from descriptions.
Follow theCUBE's wall-to-wall coverage as the roving news desk for SiliconANGLE reports live from tech's top events https://siliconangle.com/category/cub...
Additionally, there is a growing trend towards creating smaller language models that deliver high performance while being less resource-intensive. This shift is driven by both technological innovations in model architecture and practical necessities such as GPU shortages and rising cloud costs. These developments enhance AI capabilities and make sophisticated technology more accessible and sustainable.
Visit theCUBE Research for the latest in tech news https://thecuberesearch.com/
Linthicum’s conversation covers:
Multimodal AI and smaller language models represent significant trends in the field of AI. Here's an overview of these concepts and the factors driving their prominence:
Multimodal AI:
Multimodal AI refers to AI systems that can process and integrate multiple types of data (e.g., text, images, audio and video) to understand better and generate content. This capability allows AI to perform complex tasks that involve more than one data type simultaneously, such as:
-Generating textual descriptions from images
-Making sense of video content to produce summaries or captions
-Creating images or videos from textual descriptions
Advances in multimodal AI:
-Enhanced understanding: By integrating multiple data types, multimodal AI systems can achieve a more holistic understanding of context and content.
-Improved generation capabilities: These systems can create more sophisticated and contextually appropriate output across different media.
Smaller and more efficient language models:
-Traditional large language models such as GPT-3 require substantial computational resources, which makes them expensive to deploy and scale. There's a growing trend towards developing smaller, more efficient models that maintain high performance but are less resource-intensive.
Driving factors for smaller models:
1. Technological advancements:
-Optimized architectures: Innovations in model architectures that deliver similar performance with fewer parameters.
-Efficient algorithms: Improved training techniques that enhance model efficiency.
2. Practical Necessities:
-GPU shortages: The global shortage of GPUs has made it challenging and costly to train and run large-scale AI models.
-Rising cloud costs: Higher cloud infrastructure costs have driven the need for more efficient models that are cheaper to deploy and operate.
Catch up on all episode of AI Insights and Innovation AI Insights & Innovation
#theCUBE # AIInsightsAndInnovation #theCUBEResearch #AI #MultimodalAI #LangaugeModels #AITrends
Check out the latest from theCUBE, including upcoming tech events https://www.thecube.net/
Multimodal AI integrates and processes multiple types of data, such as text, images and audio, to better understand and generate content, enabling tasks such as generating text from images and creating visuals from descriptions.
Follow theCUBE's wall-to-wall coverage as the roving news desk for SiliconANGLE reports live from tech's top events https://siliconangle.com/category/cub...
Additionally, there is a growing trend towards creating smaller language models that deliver high performance while being less resource-intensive. This shift is driven by both technological innovations in model architecture and practical necessities such as GPU shortages and rising cloud costs. These developments enhance AI capabilities and make sophisticated technology more accessible and sustainable.
Visit theCUBE Research for the latest in tech news https://thecuberesearch.com/
Linthicum’s conversation covers:
Multimodal AI and smaller language models represent significant trends in the field of AI. Here's an overview of these concepts and the factors driving their prominence:
Multimodal AI:
Multimodal AI refers to AI systems that can process and integrate multiple types of data (e.g., text, images, audio and video) to understand better and generate content. This capability allows AI to perform complex tasks that involve more than one data type simultaneously, such as:
-Generating textual descriptions from images
-Making sense of video content to produce summaries or captions
-Creating images or videos from textual descriptions
Advances in multimodal AI:
-Enhanced understanding: By integrating multiple data types, multimodal AI systems can achieve a more holistic understanding of context and content.
-Improved generation capabilities: These systems can create more sophisticated and contextually appropriate output across different media.
Smaller and more efficient language models:
-Traditional large language models such as GPT-3 require substantial computational resources, which makes them expensive to deploy and scale. There's a growing trend towards developing smaller, more efficient models that maintain high performance but are less resource-intensive.
Driving factors for smaller models:
1. Technological advancements:
-Optimized architectures: Innovations in model architectures that deliver similar performance with fewer parameters.
-Efficient algorithms: Improved training techniques that enhance model efficiency.
2. Practical Necessities:
-GPU shortages: The global shortage of GPUs has made it challenging and costly to train and run large-scale AI models.
-Rising cloud costs: Higher cloud infrastructure costs have driven the need for more efficient models that are cheaper to deploy and operate.
Catch up on all episode of AI Insights and Innovation AI Insights & Innovation
#theCUBE # AIInsightsAndInnovation #theCUBEResearch #AI #MultimodalAI #LangaugeModels #AITrends
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