Neural networks, deep learning, reinforcement learning -- all seem complicated, and the barrier to entry in understanding how these things work can seem too high. In this article, I'm going to explain the mechanics of a neural network in an intuitive way, using a worked example. Lots of explanations try to relate how a neuron in the brain works to an artificial neural network (ANN). However, unless you did biology, medicine, or neuroscience as a degree, you probably don't know how a neuron works in the brain, so it doesn't help you. I did medical degree with a specialism in neuroscience and I found the explanations of neurons'identifying straight lines, and loops' completely baffling, so don't feel disheartened.

When a bank considers whether it would offer a loan to someone or not, it considers a chronological list of questions to decide if it's safe to approve such a loan. The questions under consideration could begin with simple ones such as what's the individual's annual income. Based on the answers, the next set of questions could involve finding out if the person has any existing loans, has defaulted on credit card payments, etc. Assuming the person draws a salary of $30,000, has no existing loans or criminal record, and makes his credit card payments on time, the bank may offer him the loan. You can call this a basic form of a decision tree.

For years, businesses have sought to provide customers with more self-service options and increase automation rates in their contact centers using speech-enabled interactive voice response systems (IVRs). They have also invested heavily in developing web chatbots. However, these systems were complicated to develop and required organizations to purchase, host, and manage a vast array of software, hardware, and equipment. Applications were also created in silos, requiring multiple development projects while making it difficult for applications to share data and context. A number of disruptive innovations have made it easier and more affordable to deploy AI-and-speech-enabled self-service.

Explainable machine learning is a sub-discipline of artificial intelligence (AI) and machine learning that attempts to summarize how machine learning systems make decisions. Summarizing how machine learning systems make decisions can be helpful for a lot of reasons, like finding data-driven insights, uncovering problems in machine learning systems, facilitating regulatory compliance, and enabling users to appeal -- or operators to override -- inevitable wrong decisions. Of course all that sounds great, but explainable machine learning is not yet a perfect science. Figure 1: Explanations created by H2O Driverless AI. These explanations are probably better suited for data scientists than for business users.