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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.