Artificial intelligence (AI) is continually evolving, with new technologies always pushing the limits of what's possible. Despite the astonishing progress that's been made over the past few decades, all AI can be grouped into the following four categories explored below: Reactive Machines are the original form of AI and replicate the ability of humans to respond to different stimuli. Capabilities are limited as the machines aren't equipped with memory-based intelligence and can't use past experiences to "learn" and make smarter decisions. AlphaGo, an advanced computer program developed by Google subsidiary DeepMind Technologies, is an example of Reactive Machine AI. In a 2016 showdown, the program beat international Go champion Lee Se-dol four times and was defeated just once.

Artificial Intelligence (AI) environment has risen from data scientists to reach the boardroom as a pre-curser to digital transformation. Clayton Christensen, author of The Innovator's Dilemma, a disruptive technology adds to the premise writing AI "enables new markets to emerge" to disrupts an existing market status-quo. The adoption path of AI needs a well thought out strategy to evolve in response to the dynamically changing technology parlance. These changes are well equated to the waves in an ocean, where either CIOs need to learn how to ride the wave or be overpowered by its force. Artificial Intelligence defined in business parlance are algorithms that imitate human thinking applying to compute systems using logic, decision trees and if-then rules.

A Google search for AI use cases turns up millions of results, an indication of the many ways in which AI is applied in the enterprise -- or at least can be applied (see section "Adoption in the enterprise"). AI use cases span industries from financial services -- an early adopter -- to healthcare, education, marketing and retail. AI has made its way into every business department, from marketing, finance and HR to IT and business operations. Additionally, the use cases incorporate a range of AI applications. Among them: natural language generation tools used in customer service, deep learning platforms used in automated driving, and biometric identifiers used by law enforcement. Here is a sampling of current AI use cases in multiple industries and business departments with links to the TechTarget articles that explain each one in depth.

While neural networks have achieved impressive successes in domains like image classification or machine translation, standard models still struggle with tasks that require very longterm memory without interference and would thus benefit from a separation of memory and computation [Graves et al., 2016, Collier and Beel, 2018]. Neural Turing Machines (NTM) attempt to address these tasks by augmenting recurrent neural networks with an explicit memory to which the network has read and write access [Graves et al., 2016, Collier and Beel, 2018]. Unfortunately, such models are notoriously hard to train, even compared to other deep learning models [Collier and Beel, 2018]. In our contribution, we propose to address this training problem by replacing the learned recurrent neural network controller of a NTM with an echo state network (ESN) [Jaeger and Haas, 2004]. In other words, we only learn the controller for the read and write head of our memory access as well as the output mapping, all of which is possible via standard linear regression.

In this digital era, industries are witnessing the ability of multifaceted artificially intelligent systems performing tasks that mimic intelligent human behavior or even beyond. Artificial Intelligence today, manage large chunks of data and perform redundant tasks, allowing the human workforce to focus on core tasks. This saves cost and time and improves productivity significantly. According to Gartner, the number of industries adopting AI has grown over 270% in the last 4 years. Technology giant, Google pledges $25 million USD in a new AI challenge named'AI For Social Good'.