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ARTIFICIAL INTELLIGENCE with Machine Learning & Deep Learning

Artificial Intelligence, Machine Learning, and Deep Learning 

  • Artificial Intelligence is a branch of computer science dealing with the simulation of intelligent behavior in computers and the capability of a machine to imitate human intelligence. This broad term can be used to characterize something as “simple” as recognizing an object in an image to applying reason and ethical values in problem solving, something far beyond current capabilities.
  • Machine Learning is a specific field of Artificial Intelligence that evolved from the study of pattern recognition and computational learning theory while
  • Deep Learning is a branch of Machine Learning. Today, there are two approaches to Machine Learning.

The first and perhaps most widely understood approach uses statistical analysis modeling, now often called “classical” Machine Learning. Typical use cases include resource allocation, predictive analytics, predictive maintenance, text classification, bioinformatics, trend discovery, forecasting, face detection, and pricing. Statistical approaches are generally well suited for uncovering trends and categories in numerical data and do not require massive training datasets or hardware accelerators.

The second approach is Deep Learning which has stoked much publicity and hype in recent years. Deep Learning is now widely used for computer vision, speech recognition, natural language processing, social network processing, autonomous driving, image processing and classification, and financial market modeling. 

In general, Deep Learning is used to solve multi-variant problems and queries that are beyond the scope of the traditional Machine Learning techniques. While Deep Learning is a powerful tool, it’s a “black box” methodology, as one cannot easily determine exactly why a neural network produced a certain result. Therefore, it may not be appropriate for domains where the decision process must be transparent and auditable.

Artificial intelligence (AI) is a transformative technology that will change the way organizations interact and will add intelligence to many products and services through new insights currently hidden in vast pools of data. In 2017 alone, venture capitalists invested more than $11.7 billion in the top 100 Artificial Intelligence startups, according to CB Insights, and the breadth of Artificial Intelligence applications continues to grow. While human-like intelligence will remain the stuff of fantasy novels and movies for the near future, most organizations can and should explore practical Artificial Intelligence projects. This technology has the real potential to:

  • improve productivity of internal applications,
  • increase revenue through enhanced customer interaction and improved customer acquisition,
  • reduce costs by optimizing operations,
  • and enhance products and services with “smart” functionality such as vision and voice interaction and control.

Business Drivers for AI
The many possible benefits of Artificial Intelligence − increases in productivity, revenues, product improvements, etc. – are creating surging demand for Artificial Intelligence technologies. In fact, IDC forecasts total spending in Artificial Intelligence will ramp to tens of billions of dollars by the early 2020s. Many drivers fuel this interest which we believe comes down to two primary motivations:

  • to improve operational efficiencies and
  • to enhance products and services through data-driven insights and use of unstructured data types such as voice and images to enhance human-machine interaction.

For simplicity, we call these “Smart Operations” and “Smart Products and Services”, respectively.

Smart Operations includes e-commerce product recommendation engines, cyber security, customer sales and support chatbots, financial trading, fraud detection, enhanced public safety services, and supply chain optimization.

Smart Products and Services includes medical diagnosis and treatment, drug discovery, hospital clinical care management, autonomous vehicles, drones, consumer electronics, and threat intelligence and prevention. We believe Artificial Intelligence will become pervasive and impact virtually every product and business process over the next decade.

Selecting Right Projects with a Crawl, Walk and Run Strategy
Getting started on the right foot with Artificial Intelligence and Machine Learning requires that you select a project that solves a pressing business problem for which you have access to the requisite data, and for which you have the right skills to tackle. Start by compiling a list of these projects and prioritize them based on ROI, risk, and scope. It can be tempting to jump into the deep end of the pool, replete with racks of servers and high-end Graphic Processor Units, to analyze such data as photo images for plant security, or translate speech into text for sales force or customer support automation. Instead, take time to learn about

  • what problems are most appropriate to solve with ML for your organization,
  • what shape your organization’s data is in, and
  • what projects the team can handle. The old mantra of “Crawl, Walk, Run”! is good to keep in mind.

We recommend starting small, perhaps extending Big Data analytics to include one or two Machine Learning capabilities where the ROI is relatively easy to achieve and measure. Subsequent projects can then harness Deep Learning for voice, image, or text processing for smart offerings or smart operations. Here are a few ideas that may help point the way:

  1. Analyze customer service call length and topic recurrence to identify products that may have quality or documentation shortcomings.
  2. Improve direct mail marketing with subject lines based on specific customer historical buying behavior.
  3. Explore how your CRM or ERP vendor is enhancing their products with Machine Learning features.
  4. If you sell products online, consider deploying a Machine Learning-based product recommendation engine to increase sales to existing customers.
  5. Analyze your product pricing and forecasting methodologies and explore whether you have sufficient data to improve these with Machine Learning.
  6. Look into your customer communications processes for areas where Machine Learning can automate more mundane tasks, such as analyzing emails for common issues and resolution routing by using Natural Language Processing.
  7. Work with your product definition teams to uncover areas where the addition of language, image, or vision-based control and interfaces could yield a competitive edge or improve customer satisfaction.

Moving Forward
Many industry leaders proclaim we are entering the era of Artificial Intelligence, where machines can be trained to do tasks which traditional procedural programming models are unable to tackle. While the technology can seem daunting, a stepwise, practical approach to embracing Artificial Intelligence in the enterprise does not have to be intimidating. Solid ROIs can be achieved with relatively straight-forward extensions to existing Big Data infrastructure already in place in most enterprises.
We believe enterprises and government agencies would be well served to examine how Artificial Intelligence can transform their products and services with Smart Products and Services and their internal business processes with Smart Operations. 

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