You probably already know that machine learning is an incredibly powerful technology that has the ability to solve difficult problems in a surprisingly effective manner. What you may not have realized, however, is that since machine learning algorithmically builds its “gray matter” by learning from previous patterns, trends, and data models, we are at present witnessing only the very early stages of what machine learning can do for us.
Recently the science behind machine learning hit a significant milestone in fields that hadn’t really moved the needle for some time like speech recognition and image understanding. With the recent proliferation of sufficiently capable computing hardware, we witnessing a BIG BANG in machine learning technology that represents a major step forward in how computers can learn and perform.
For several years now, the use of machine learning has been used as a form of automation for low-value tasks that are easy to do but time-consuming when carried out by human hands. As we move into the near future, expect to see an explosion of applied machine learning as the necessary computing power and software implementation proliferate around you but it won’t all be easy; machine learning algorithms tend to have errors, and it is very interesting to know how we humans-in-the-loop in “coach” of those errors out of the result sets through training and deep-instruction in neural networks.
With that said, machine learning will have a great impact on all areas of business. One of the important things for enterprises to bear in mind is that they need to look beyond the AI hype for practical ways to incorporate machine learning into their operations. Expect too much too fast and we will find ourselves in another “AI Winter”: a season we have witnessed before during which confidence in machine learning plummets and investment stops. Machine learning algorithms should be regarded as a child in need of time and instruction to become truly effective. Goldcorp – a mining company that uses immense vehicles to haul tailings and other debris away from mining sites – is taking this step-by-step approach with great results by iterating a machine learning algorithm over time that now predicts with over 90 % accuracy, when will their machines need maintenance. Since a vehicle breakdown can cost Goldcorp over $2 million per day, it’s hard to argue the economy of this kind of applied machine learning; however, had Goldcorp expected for machine learning to first be able to make all of their monster vehicles self-driving, it is very possible that the program would have failed and the more simple, but extremely useful, algorithms would have never been implemented.
Short Term Predictions
More enterprises will begin their machine learning journey over the next 18 months than any other time in history. The smarter ones will create competitive separation for their enterprise by getting started with machine learning now while still learning from others’ mistakes. Resisting the urge to expect too much too fast will pay off handsomely – as was the case with Goldcorp – while machine learning quietly takes hold beneath a cacophony of AI marketing speak.
Some of the gloomier predictions will end on a higher note: machine learning will automate some human jobs out of the equation, but those jobs will be replaced with higher-value, more stimulating work. Retail and sales jobs are primed for machine learning implementation and automation. We will see robots in hospitals delivering medicines, materials, and meals. Self-driving cars will rule the highways in the next few years. In fact, we will see autonomous trucks, tractors, taxis, forklifts, cargo handlers, etc.
Automation of such large parts of our workforce is going to require that our governments come together in a very bipartisan way to avoid economic strive, but on the positive side, the world will see a tidal wave of creativity and innovation like never before due to the freeing of creative thought afforded by machine learning-based automation.
Machine Learning will become so powerful in the future that it will shape culture by driving us to make better decisions and providing us a more profound vision for the pursuit of happiness and showing you the outcomes, explanations, or evidence that you might be missing in topics both big and small. And it will not only show you those missing elements but will also support you in weighing and making sense of them.
Machine learning will also bring about revolutionary personalization in the services and products based on your tastes, historical choices, location, even your DNA. This of course changes the way products are made, consumed, and marketed.
In conclusion, machine learning is changing everything quietly at the moment with the volume increasing dramatically over the next two years. Ignoring the technology is not an option, but it is important to measure your expectations and have a long game for machine learning in order to reap the highest rewards.
It’s impossible to predict exactly where this phenomenon will lead us but in the words of Peter Thiel,
“Not being able to get the future exactly right doesn’t mean you don’t have to think about it”.
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