Amazon Unveils Its New Collection of AI Models to Compete with ChatGPT, Google and Microsoft



Amazon on Tuesday unveiled a suite of artificial intelligence models in its boldest move yet to compete with rival tech giants in the fast-growing generative artificial intelligence sector.

The launch of its own line of core models marks Amazon’s latest attempt to strengthen its position over its predecessors Microsoft, GoogleMeta and OpenAI, creator of ChatGPT.

Until now, Amazon’s AI offerings through its AWS cloud service have largely been limited to providing access to models from other companies, including Anthropic, the AI ​​startup it backs.

Even if Google, Microsoft and OpenAI have taken the lead in the field of artificial intelligence, AWS remains the market leader in cloud computing, which is needed to run artificial intelligence tools and products.

“Within Amazon, we have about 1,000 Gen AI apps in motion, and we had a bird’s-eye view of what app makers are still struggling with,” said senior vice president Rohit Prasad, who leads the company’s AI efforts.

“Our new Amazon New the models are intended to help with these challenges,” he added.

The Amazon Nova family includes six AI models that handle tasks from text creation to video generation.

The company says the models are at least 75 percent cheaper than comparable offerings available on AWS servers and faster than similar models.

The initial lineup includes Nova Micro for fast word processing, Nova Lite for basic multimedia tasks, and Nova Premiere, set for release in early 2025, for complex thinking.

Supporting 200 languages, the models can be customized using the user’s proprietary data – a feature Amazon hopes will attract businesses developing specialized AI applications.

Two dedicated models focus on creative content: Nova Canvas for image creation and Nova Reel for video creation.

Amazon emphasized the built-in security measures for the new offerings, which will be available through AWS’s Bedrock service, with usage guidelines detailing specific use cases and limitations.

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