Many have touted GPT-3 as the next-generationartificial intelligence technologythat will usher in a new breed of applications and startups.
Since GPT-3s release, many developers have found interesting and innovative uses for the language model.
And several startups have declared that they will be usingGPT-3to build new or augment existing products.
But creatinga profitable and sustainable business around GPT-3remains a challenge.
A few-shot learning model that must be fine-tuned?
I didnt find technical details on the fine-tuned version of GPT-3 Microsoft used.
But there are generally two reasons you would fine-tune adeep learningmodel.
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A possible tradeoff will be that the model will perform poorly on other tasks (such as question-answering).
But in Microsofts case, the penalty will be irrelevant.
So, whats the point of the few-shot machine learning model that must be fine-tuned for new tasks?
This is where the worlds of scientific research and applied AI collide.
In academic AI research, the goal is to push the boundaries of science.
This is exactly what GPT-3 did.
And they have tested the model on several popular natural language processing benchmarks.
But in commercial product development, youre not running against benchmarks such as GLUE and SQuAD.
And those questions were answered with the optimization of the model for that specific task.
Therefore, the plain vanilla GPT-3 is more of a scientific achievement than a reliable platform for product development.
In July 2019, Microsoft made a $1 billion investment in OpenAIwith some strings attached.
On the other hand, Microsoft already has the pieces required to shortcut OpenAIs path to profitability.
But more importantlyand this is why I think OpenAI chose Microsoft over Amazonis Microsofts reach across different industries.
These applications provide perfect platforms to integrate GPT-3.
Microsofts market advantage is fully evident in its first utility for GPT-3.
It is a very simple use case targeted at a non-technical audience.
Its not supposed to do complicated programming logic.
It just converts natural language queries into data formulas in Power Fx.
Microsoft has another factor working to its advantage.
It has secured exclusive access to the code and architecture of GPT-3.
Meanwhile, Microsoft was sitting back, observing all the different experiments with growing interest.
The GPT-3 API basically served as a product research project for Microsoft.
This gives Microsoft a unique advantage to dominate most markets that take shape around GPT-3.
What kind of companies will the fund invest in?
The first part seems to be in line with OpenAIs mission to use AI for the betterment of humanity.
But the second part seems to be the throw in of profit-generating applications that Microsoft is exploring.
How this will affect the research labs long-term goal ofscientific research on artificial general intelligenceremains an open question.
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