AI Revolution

#AI revolution: 20 key business findings #CIO #CMO

  • To better understand the AI revolution in business, Salesforce Research compiled insights from previous studies, capturing input from 10,800 respondents from sales, services and marketing leaders and 7,000 consumers and business buyers around the globe.
  • AI steps into the business strategy spotlight: while a minority of businesses currently use AI, interest in the technology (machine learning, deep learning, natural language processing, smart data discovery, etc) is pervasive and planned adoption is strong.
  • AI use cases span sales, service, and marketing: AI has the potential to become as ubiquitous as electricity or cloud computing.
  • All lines of business are starting to view AI as technology that can improve customer engagement, improve employee productivity and ultimately accelerate digital transformation and business growth (revenue).
  • The power of AI is the ability to gain more knowledge from data, and to augment sales, service and marketing intelligence to help accelerate and bolster a company’s ability to improve stakeholder (employees, partners and customers) performance and experience.

Artificial Intelligence (AI) could double annual economic growth rates by 2035. – Accenture
According to Accenture, In five years, more than half …
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How to Build a Recurrent Neural Network in TensorFlow

How to Build a Recurrent #NeuralNetwork in TensorFlow

  • The input to the RNN at every time-step is the current value as well as a state vector which represent what the network has “seen” at time-steps before.
  • The weights and biases of the network are declared as TensorFlow variables, which makes them persistent across runs and enables them to be updated incrementally for each batch.
  • Now it’s time to build the part of the graph that resembles the actual RNN computation, first we want to split the batch data into adjacent time-steps.
  • This is the final part of the graph, a fully connected softmax layer from the state to the output that will make the classes one-hot encoded, and then calculating the loss of the batch.
  • It will plot the loss over the time, show training input, training output and the current predictions by the network on different sample series in a training batch.

This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.

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