In the heart of Silicon Valley, a fresh face has emerged in the AI game. Inception, a startup from Palo Alto, has introduced a cutting-edge AI model. This model is based on something called "diffusion" technology. It's a new way to tackle text generation.  The model is called a diffusion-based large language model, or DLM for short.

Diffusion models have been making waves in the AI world. They're used to create images, videos, and audio. Think of tools like Midjourney and OpenAI's Sora. These models work differently than traditional language models. They start with a rough idea and then refine it all at once. This is unlike traditional language models, which build text word by word.

Inception's DLM can do everything traditional language models can, like writing code and answering questions. But it does so much faster and cheaper. This is a big deal because traditional models can be slow. They have to generate each word one after the other. This sequential process can be a real drag on performance.

The brains behind this breakthrough is a Stanford professor who has been tinkering with diffusion models for text for years. He and his student finally made a major leap forward. They published their findings last year. This discovery led to the birth of Inception. The company was founded last summer and is co-led by two other professors from UCLA and Cornell.

Inception has already landed some big fish. Fortune 100 companies are on board, drawn by the promise of faster AI and lower costs. The company offers various ways to use their models, including an API and options for on-premises and edge devices. They also provide tools for fine-tuning models and a suite of ready-to-use DLMs.

Inception claims their DLMs are up to 10 times faster and cheaper than traditional models. They say their "small" coding model can outperform competitors like OpenAI's GPT-4 and Meta's Llama. But can they back up these claims? Only time will tell.

The AI world is always evolving, and Inception's DLM is a exciting development. It could change how we build and use language models. But it's important to approach these claims with a critical eye. The tech world is full of promises, but not all of them deliver. So, let's keep an eye on Inception and see if they live up to the hype.

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