ArtificiaI intelligence

ArtificiaI intelligence, APIs and the transformation of computer science

ArtificiaI intelligence, APIs and the transformation of computer science
These are exciting times in the world of artificial intelligence as advanced capabilities escape the labs and become central to mainstream products — think Amazon Echo — and increasingly accessible to everyday software developers.

Recently, we’ve seen a run of large companies open-sourcing their A.I. platform APIs. Google has open-sourced TensorFlow, its machine learning library. Not to be outdone, Amazon has released its TensorFlow competitor DSSTNE (or Deep Scalable Sparse Tensor Network) and Facebook has done the same with fastText, its machine learning engine for text classification and language recognition. Those big-company A.I. platforms join — and in some sense compete with — notable open source deep learning libraries like Caffe, Theano and Torch.

In many ways, open sourcing access to A.I. represents the culmination of the “API economy,” where software developers set aside their not-invented-here pride of creation and instead access the work done by others — and in the process accelerate their own development work — via API hooks. If software development-by-API made sense in the past, it may be the only practical path forward when it comes to fully leveraging new capabilities in the artificial intelligence field.

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Indeed, the impact of these moves can’t be overstated. On a business level, they significantly lower the barrier for entrepreneurs looking to start up the next wave of artificial intelligence-driven companies by democratizing access to machine learning resources. That’s hugely significant.

But I’d argue for an even more fundamental change: Open source A.I. APIs represent the beginning of a transformation of computer science (CS) into a field focused on impact.

At its most basic, CS can be described as people working on new algorithms or improvements to existing ones, with new “inventions” being built on the back of what came before it. In CS, there are two standard ways of doing this: creating libraries of functionality that developers can build into their own applications; or providing access to functionality in one fell swoop via API.

Software development via API is hardly new. Microsoft became a behemoth on the strength of its platform APIs and the swarms of developers that consumed them. More recently, SaaS companies like Salesforce and Google have offered developers the ability to extend their platforms via API. And in the latest trend, new programming concepts like microservices and bots are centered on assembling applications via modular, distributed processes accessed via open APIs.

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The latest surge in A.I. APIs opens up a whole new world of capabilities to software developers.

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