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Tech Concepts Explained: Predictive Analytics, Artificial Intelligence, Machine and Deep Learning

Updated on November 7, 2017

Predictive Analytics, Artificial Intelligence, and Machine Learning

Predictive Analytics, Artificial Intelligence, and Machine Learning are not necessarily terms everyone will encounter in the course of their daily business activities.

In the past, these concepts and technology have been the realm of data analysts, software developers, business intelligence experts and tech fanatics. As this technology becomes mainstream – which it is, and at a rapid rate – it is essential that individuals and businesses have, at the very least, a basic understanding of what these applications mean for the world of work and life, as we know it.

To aid you in this journey I have attempted to simplify the definition and value of the various components, namely predictive analytics, artificial intelligence in addition to machine and deep learning.

Predictive Analytics

Predictive analytics is an advanced form of analysis, which uses data mining; statistics, modeling, machine learning and artificial intelligence, make predictions about unknown future events. It achieves this result by establishing patterns from historical and transactional data and identifying risks and opportunities that enable businesses to prepare for or avoid an event. Predictive models can establish the connections and correlations between vast quantities of data, structured and unstructured.

Predictive analytics is already utilised extensively in the fields of insurance, financial services, marketing and general business operations. Below are a few examples of how it is useful to the aforementioned industries.

  • Insurers, brokers and financial services organisations - such as banks, investors and the like – employ predictive analytics to detect fraud. Pattern detection does prevent criminal behavior.
  • Reducing credit risk and insurance claims.
  • Optimise marketing campaigns, by way of, tracking customer responses and purchases and providing real insight on the success of the campaign as well as trends identified.
  • Forecasting allows businesses to plan for the future and proactively manage resources – inventory, workforce, and assets, to name a few.

Artificial Intelligence

By definition (AI) is the theory and development of computer systems, to perform and automate tasks ordinarily requiring human intelligence. In fact, AI is more efficient at accomplishing the same outcomes. It can also be described as the umbrella phrase that encompasses all terminology in this area.

Artificial Intelligence may be confusing to the layman because of technical terminology that makes it challenging to understand. Some such terms are Algorithms (sequence of actions to be performed to solve a problem), heuristics (knowledge from trial and error), machine and deep learning – more information to follow.

AI has already contributed significantly to our economies, moreover, the explorations and dreams of humanity as a whole. Consequently offering us valuable insight. Where the greatest advances are being made is in visual perception and decision-making, in addition to translation between languages using speech recognition, amongst other inputs.

Companies are not the exclusive beneficiaries of the technology. You have probably had more interaction with Artificial Intelligence than you realise. One way that AI has quietly infiltrated our homes is with the development of virtual personal assistants such as, Amazon's Alexa and Apple's Siri.

Both apps are designed to collect information on your requests, apply speech recognition and serve you results that are tailored to your preferences. Alexa and Siri can also be referred to as chatbots (or chatterbots). Chatbots are used for a number of purposes (other than digital assistant services) including customer service as well as talent acquisitions. They simulate conversation, interacting with human users, via a chat interface.

Many advances have been/ and are being made to advance Artificial Intelligence, but the technology is still far off from true AI. Sometime, in the not too distant future, AI will learn on its own, make connections and reach conclusions without relying on algorithms. It will improve on its past iterations; continuously growing smarter and enhancing its capabilities.

Machine Learning

Machine learning is a core component of AI and a method of data analysis that automates the building of analytical models. Machine learning can further be described as learning without any kind of supervision and acting without explicitly being programmed to do so, although this functionality is not the basis of most machine learning applications.

A form of machine learning is a neural network, which echoes the way neurons in the human brain operate. These networks can recognize patterns in data, draw inferences and make predictions, classifying information as the brain does, but much faster and more accurate. Absorbing vast quantities of data enables neural networks to strengthen the connections between neurons.

According to Wikipedia, there are three broad categories of machine learning. These are:

  • Supervised learning – The computer receives data and is given parameters in order to identify connections contained in that information.
  • Unsupervised learning – The algorithm adapts through the input of enormous quantities of data.
  • Reinforcement learning – The computer learns through iterations within a changing environment. Becoming smarter with each move.

Deep Learning

Deep learning is a subset of machine learning. It is another transformative technology that will further pave the way for the advancement and development of a ‘true' artificial intelligence.

Deep learning is similar to machine learning but, with many artificial neural networks and numerous layers. Essentially it is an algorithm inspired by the human brain and can learn to do anything. See, read, write and understand what it hears. It identifies patterns in unstructured data, using this information to evolve and predict future events.

Currently, deep learning is in the research phase to determine the accuracy of its diagnostic abilities and in the medical field – recognizing cancer cells in screening. Additionally, deep learning is being tested in forecasting market prices. Other applications of deep learning today are; image, facial and voice recognition as well as natural language processing.

Risk Management in the Property Industry

In my follow-on articles, I will be giving you more context as to how these components can be employed to make smarter, more scientific decisions, proactively. Moreover, we will analyse the property industry, its inherent risks and other factors that affect all elements of the organization.

© 2017 Elysia Bentley


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