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Bringing AI into the Workplace At first thought, many people think of artificial intelligence as something of the future or from a sci-fi movie—machines that think and behave like humans and might take over the world. Though you don’t have to worry of a machine takeover anytime soon, businesses today are already discovering the benefits artificial intelligence can bring to the workplace. Leading organizations are employing artificial intelligence to work alongside its employees for more effective and efficient results. These technological capabilities are being used in the form of cognitive computing, machine learning, and deep learning. Cognitive Computing Businesses are looking towards cognitive computing to handle complex, ambiguous situations and enable more “human-like” interactions with software. These self-learning systems simulate human thought processes through data mining, intelligence automation, and natural language processing. Cognitive computing is being used across industries: Hospitals use cognitive machines to communicate the best course of treatment for patients. Call centers pair virtual agents with their human co-workers for improved customer data analysis and problem solving. Humans synergizing with machines is important in order to produce better outcomes and improve efficiencies. Machine Learning A branch of artificial intelligence, machine learning automates the building of systems that learn from data. It identifies patterns and predicts future results with minimal human intervention. In the past decade, machine learning has given way to smart technology like selfdriving cars and speech recognition, as well as technology you encounter everyday with email spam filtering, real-time Web ad placements, and online recommendations. Machine learning derives business insights through three pillars: 1. Data: Machines gather data through model input, customer history, purchased/third parties. 2. Discovery: Algorithms are automated step-by-step sets of operations performed to solve a business problem. Algorithm learning methods could either be supervised or unsupervised. Supervised algorithms discover patterns in data that relate attributes to labels, which are then used to predict values in future data; they are used in situations like customer predictive analytics, recommender systems, and pattern recognitions. Unsupervised algorithms deal with data that have no label attributes, so the goal is to explore the data to find some intrinsic structures; they’re used in customer segmentation, recommendations, and outlier detection. 3. Deployment: Machine learning are deployed into production without any manual coding. They’re automated and integrate analytical models with business rules, track model performance, and retrain models when necessary. These elements give marketers the confidence to make the right decisions. Deep Learning Deep learning branches from machine learning’s algorithms; it’s where machine learning meets Big Data and analytics. Deep learning has been represented from large-scale unlabeled data inspired by deep neural networks. Neural networks are used in a way to structure a computer like the human brain—complete with neuron-like nodes connected together. People have turned to deep learning for tasks like speech and image recognition and it offers more benefits like improving accuracy of Artificial Intelligence approaches, enabling very deep networks, Big Data handling, and more “human-like” interfaces. Companies see huge opportunities with Artificial Intelligence, since it has the great ability to continually learn from the data it collects. The more data collected and analyzed, the more powerful the machine becomes, and the better humans are able to work. While the sci-fi movies make Artificial Intelligence seem detrimental, businesses can reap the benefits from them today.