The Information said on Monday that Microsoft opened a new tab and is developing an internal AI language model big enough to take on those from Alphabet’s Google and OpenAI.
According to the story, which cited two Microsoft workers with knowledge of the project, Mustafa Suleyman, the recently hired co-founder of Google DeepMind and former CEO of AI firm Inflection, is in charge of the new model, internally known as MAI-1.
Microsoft introduced Phi-3-mini, a more affordable artificial intelligence model, last month to reach a larger clientele.
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The MA-1 model’s precise function is still unknown and will be defined by how well it works. According to the source, Microsoft may unveil the new device at its Build developer conference later this month.
The paper claims that MAI-1 will cost more since it will be “far larger” than the earlier, smaller, open-source models that Microsoft had previously trained. According to the folks, this implies that the AI model will need additional processing power in addition to training data, which could increase its cost.
OpenAI-developed large language models (LLMs) have been used by Microsoft. The business has taken an early lead in the race for generative AI thanks to its billion-dollar investment in OpenAI and integration of ChatGPT Maker’s technology across its suite of productivity applications.
According to the source, Microsoft has been allocating a sizable cluster of servers fitted with Nvidia’s graphics processing units and opening new tab graphic processing units together with enormous amounts of data to improve the model.
To control what models learn during training, MAI-1 is expected to include about 500 billion parameters, or settings. In contrast, smaller open source models made available by companies like Meta Platforms and Mistral contain 70 billion parameters, but OpenAI’s GPT-4 has more than 1 trillion.
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In March, Microsoft appointed Suleyman to lead its recently established consumer AI division and brought on board other Inflection staff members.
The study also stated that while the new model might draw from startup training data, it is not carried over from Inflection.
