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Deep learning for business

To leverage the power of deep learning for business, the first question would be how to choose the problems to solve? In an interview with Andrew Ng, he talked about his opinion, the rule of thumb is:

Anything a typical human can do with up to 1 second of thought, we can probably now or soon automate with AI.

If we look around, we can easily find that companies today, large or small, have already applied deep learning to production with impressive performance and speed. Think about Google, Microsoft, Facebook, Apple, Amazon, IBM, and Baidu. It turns out we are using deep learning based applications and services on a daily basis.

Nowadays, Google can caption your uploaded images with multiple tags and descriptions. Its translation system is almost as good as a human translator. Its image search engine can return related images by either image queries or language-based semantic queries. Project Sunroof (https://www.google.com/get/sunroof) has been helping homeowners explore whether they should go solar - offering solar estimates for over 43 million houses across 42 states.

Apple is working hard to invest in machine learning and computer vision technologies, including the CoreML framework on iOS, Siri, and ARKit (augmented reality platform) on iOS, and their autonomous solutions including self-driving car applications.

Facebook can now automatically tag your friends. Researchers from Microsoft have won the ImageNet competition with better performance than a human annotator and improved their speech recognition system, which has now surpassed humans.

Industry leading companies have also contributed their large-scale deep learning platforms or tools in some way. For example, TensorFlow from Google, MXNet from Amazon, PaddlePaddle from Baidu, and Torch from Facebook. Just recently, Facebook and Microsoft introduced a new open ecosystem for interchangeable AI frameworks. All these toolkits provide useful abstractions for neural networks: routines for n-dimensional arrays (Tensors), simple use of different linear algebra backends (CPU/GPU), and automatic differentiation.

With so many resources and good business models available, it can be foreseen that the process from theoretical development to practical industry realization will be shortened over time.