AI is changing the world 


Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.

 

DL is the cornerstone technology behind products for image recognition and video annotation, voice recognition, personal assistants, automated translation and autonomous vehicles. DNN works similarly to the brain by extracting high-level, complex abstractions from data in a hierarchical and discriminative or generative way.

 

A key feature of DL algorithms is their capability to learn from large amounts of data with minimal supervision – contrary to shallow models that normally require less data, but with labels and easily reach an accuracy plateau. DL however, comes with a cost: there is no theory to guide the learning algorithms and architecture optimization and hyper-parameters selection rely on complex and time-consuming heuristics – training a single model can take weeks on well equipped PCs.

 

The implications of DL supported AI in business is tremendous, shaking to the foundations many industries – perhaps the biggest trans formative force after the Internet. In this book I present the most significant algorithms and applications, including Natural Language Processing (NLP), image and video processing, risk assessment, forecasting, control and robotics, gaming and finance. I’ll also refer to some key findings and implications in business of DL, key companies and startups adopting this technology and some investments. I’ll also refer to some frameworks for training the DL models, key methods and tricks to fine-tune the models and finally some products and companies where this technology is being applied.