Acclaimed physicist, author and educator Stephen Hawking has claimed that artificial intelligence may cause the end of civilization as we know it. 

While I don't think we’re headed for a dire, Terminator-like state, there’s little doubt that artificial intelligence has the capacity to change the world.

That change first affected industrial and mechanical industries by assisting in mass production, but today businesses of all types are discovering the benefits of artificial intelligence in the workplace. Leading organizations are employing artificial intelligence to work alongside employees for more effective and efficient results. 

Artificial intelligence capabilities can be grouped into three main categories: cognitive computing, machine learning and deep learning. 

Cognitive Computing: Enabling 'Human-Like' Interactions 

Cognitive computing is the most widely used category of artificial intelligence in the workplace today. Businesses look to 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 used across industries to deliver a better customer experience. Some examples include:

  • Incoming audio transposed across retail, telecom and financial services industries, and then routed automatically for better customer service results
  • Cognitive machines used in health care to communicate the best course of treatment for patients
  • Marketers across all industries interacting with software via voice to execute and iterate on marketing initiatives

Humans working in concert with machines to classify and address customer concerns will be more and more widespread in the workplace and will produce faster, more effective outcomes and improve efficiencies. 

Machine Learning: Identifying Patterns

Machine learning is considered to be one the biggest up-and-coming capabilities headed for the everyday workplace. 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 self-driving cars and speech recognition, as well as technology you encounter every day such as 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 and subsequent output scoring; customer history and session data across traditional and digital channels; and purchased or third-party data that is fed into the machine. Streaming analytics, or collecting data from the Internet of Things (think mobile devices, manufacturing machines, vehicles, oil platforms, etc.), and using that data in the workplace for sales, service, support and marketing, is already happening and will continue to get smarter and more prevalent.

2. Discovery

Algorithms allow this data to be translated into usable insights. Algorithm learning methods are either 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. 

Learning Opportunities

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. 

More structured and unstructured algorithms will be built into software in the workplace, which will drive businesses forward. As a marketer, I am particularly excited about the opportunity of embedding more intelligence into marketing software through better customer analytics, product recommendations and pattern detection capabilities. All of this will allow a brand’s marketing programs to be more contextually relevant to consumer groups.

3. Deployment

Machine learning methods are deployed automatically in many cases. They integrate analytical models with business rules to often predict necessary future actions. Marketing software being developed today is already embedding capabilities like automatically derived segment creation, applied optimization and analytical asset insights directly into the software for the marketer to use without even asking for it.

These elements will give marketers, and others, the confidence to make the right decisions — backed by predictive analytic techniques and surfaced via machine learning methods. 

Deep Learning: Where Machine Learning Meets Big Data and Analytics

Deep learning is quite possibly the most exciting area of artificial intelligence — in part because of the new frontiers ahead. 

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 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.

Some examples of deep learning in the workplace include:

  • Teaching machines to create and refine a task, like answering customer inquiries in a human-like fashion
  • Machines iterating on accompanying humans with workplace tasks, refining themselves and improving their efficiencies
  • Machines automatically creating a product or service when needed, based on information sourced and fed to the machine

Artificial intelligence has the great ability to continually learn from and iterate on the data it collects — and inspires companies with the opportunities it promises. The more data collected and analyzed, the more powerful the machine becomes, and the better humans are able to work alongside these machines. 

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