The Data Science Behind Artificial Intelligence (AI)
The popular press portrays AI as a single unstoppable phenomenon. In fact AI is based on a collection of data science technologies which are at different stages in their development. To understand AI and how far it has come requires that you understand how each element works separately and together.
This presentation is designed to give you a high level understanding so that you can evaluate for yourself the opportunities that are arising. Bill Vorhies, Editorial Director for DataScienceCentral has written widely about all of these capabilities and will provide both a look at each area separately and a structure for evaluating AI as a whole.
§ Demystifying AI. Understanding what components are required to create a fully functional AI and how those components can be measured.
§ Deep learning defined.
§ Image recognition – Convolutional Neural Nets (CNNs)
§ Natural Language understanding and response – Recurrent Neural Nets (RNNs)
§ Reinforcement learning – the key to self-driving cars.
§ How AI beats humans at chess and GO.
§ AI that uses knowledge to create unique new responses – Watson QAMs (Question Answering Machines)
§ Adversarial training – beating the huge requirements for training data.
§ Beyond Deep Learning - Spiking Neural Nets.
§ Deep learning for everyone – DL by API.
§ AI as a long term business driver.