NextGen machine learning methods have created a positive ripple in the tech world in recent years, vastly improving voice and image recognition, machine translation, vision enhancement, and many other things. Although, the progress may be hindered by a significant problem: it’s often impossible to explain how some of these “deep learning” algorithms reach a decision.
Deep learning has emerged as a powerful way of mimicking human perceptions. It involves training a very large neural network to recognize patterns in data and is lightly inspired by a theory about the way neurons and synapses facilitate learning. Each simulated neuron is a mathematical function, but the complexity of these interlinked functions makes the reasoning of a deep network extremely difficult to untangle. But then deep learning, allows for sophisticated analytics that are useful to the industry, and which can be very difficult to expedite otherwise.
Industries now aim to exploit concerns over the opacity of existing algorithms by using more transparent approaches. But this issue could become more significant over the next few years as deep learning becomes more commonly used. The world is developing several new deep learning approaches, including more complex deep networks capable of learning several things simultaneously. The only hope is that deep learning can be used to go beyond just matching human perceptions. And make sure we’re making decisions for the right reasons.