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Most cutting-edge AI algorithms are available in the public domain, and leading scientists have pledged to continue to publish and open-source their work on these algorithms.In addition, businesses have made AI platforms, such as Google’s Tensor Flow, available as a service. These building blocks, such as machine vision, are more functional than a naked algorithm but are not fully self-operational.AI performance continues to improve, thanks to deep learning and other advanced AI techniques, a staggering level of growth in data, and continuing advances in raw processing power.These developments have led to an explosion in AI-enabled business applications, analogous to the Cambrian era, when the development of eyesight contributed to a remarkable worldwide increase in species diversity.
When Ping An wanted to take advantage of facial recognition software, it was dissatisfied with the performance of the products available on the market—so it built its own.Companies can rent raw computing power in the cloud or deploy it on the premises with specific hardware that can process many tasks in parallel—an essential capability for AI techniques such as deep neural networks.Companies can also access rapidly developing AI data architectures based on open-source code.Finally, it offers some practical guidance on how to introduce and spread AI within large organizations. Companies cannot simply “buy intelligence” and apply it to their problems.Although elements of AI are available in the market, the hard work of managing the interplay of data, processes, and technologies happens in-house.
The data revealed markedly different levels of AI understanding and adoption even within the same industry.