Machine learning is the science of getting computers to act without being explicitly programmed. It’s a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes. Machine learning is a science that’s not new – but one that’s gaining fresh momentum.
It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks, researchers interested in artificial intelligence wanted to see if computers could learn from data.
It is no doubt that the field of machine learning and artificial intelligence has increasingly gained more popularity in the past couple of years. It already has given us self-driving cars, speech recognition, effective web search, and a vastly improved understanding of the human genome in the last decade.
Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results.
As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on big data. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.
Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.
Machine learning algorithms are often categorized as supervised, unsupervised and reinforcement learning.
Supervised algorithms require humans to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during training. Once training is complete, the algorithm will apply what was learned to new data. During training for supervised learning, systems are exposed to large amounts of labelled data, for example images of handwritten figures annotated to indicate which number they correspond to. Given sufficient examples, a supervised-learning system would learn to recognize the clusters of pixels and shapes associated with each number and eventually be able to recognize handwritten numbers, able to reliably distinguish between the numbers 9 and 4 or 6 and 8. Training these systems typically requires huge amounts of labelled data, with some systems needing to be exposed to millions of examples to master a task.
Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms are used for more complex processing tasks than supervised learning systems. The algorithm isn’t designed to single out specific types of data, it simply looks for data that can be grouped by its similarities, or for anomalies that stand out.
Where Reinforcement learning falls between these 2 extremes – there is some form of feedback available for each predictive step or action, but no precise label or error message.
Once training of the model is complete, the model is evaluated using the remaining data that wasn’t used during training, helping to gauge its real-world performance. To further improve performance, training parameters can be tuned.
In 2010, the Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.
In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.
In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences between artists.
Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.
Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of investment.
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