How Machine Learning Can Change Our Future

Published: October 18, 2016 3 min. read
Svitla Team
By Svitla Team
AI/ML Development
How Machine Learning Can Change Our Future

For several decades, hardly any science, business, or manufacturing have gone without computers. Every day we use thousands of routine computer processes such as web search, speech and text recognition, and spam filtering, without suspecting that most of these capabilities we owe to such computer science subfield as Machine Learning (ML).

Machine Learning is considered one of the most progressive ways in the domain of the human-like Artificial Intelligence (AI). Engineers can implement ML techniques in many industries: automobiles, medicine, genetics, finances, bioinformatics, marketing, game playing, etc.  Thanks to Machine Learning, we can get accurate results for data analysis in minutes to make better and quicker decisions.

One must have noticed that ad sections on websites and social media show you the goods and information targeted to your tastes. How do they know that a person wants a smartphone like this? The answer is: when you surf through websites, Machine Learning algorithms remember your selected preferences and then recommend the goods that may interest you.

Nowadays, the amounts of accumulated data are so big that ever-growing volumes of data make it possible to construct predictive models to automate processes for even not trivial decision making. The goal of the computer scientists of the new age is to teach machines how to find the “right answer” without being directly programmed where to look. The problem is that unlike programmed applications (that have clear instructions for actions to get some required results) Machine Learning algorithms can only build on statistics, input examples and previous experience, using large (if not infinite) set of possible models. Actually, Machine Learning algorithms should work the same way as a human brain when searching for an answer.

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The Machine Learning is not a brand new idea in IT; however, it now gains increasing popularity among a growing generation of programmers. Developers that deal with Machine Learning can select among a variety of ready solutions for building ML models without the need to dive deep into studying algorithms and technologies. Among the best known MLaaS are AmazonML, Microsoft AzureML, BigML, Google Prediction API, and IBM Watson.

Machine Learning technologies are actively used in real-world projects now. Good examples of ML application are:

  • Spelling correction in web search engines
  • Recommendations for similar products
  • Recommendations of relevant articles according to a user’s previous search
  • Analysis of information from IoT devices
  • Credit card fraud detection
  • Real-time language translation (like in Skype that can translate in real-time a conversation from one language to another)
  • Text and voice recognition, computer vision
  • Email junk detection
  • Speech recognition in smartphones and tablets (Siri, Cortana, Google Now)

The importance of Machine Learning for future science and engineering is evident. Understanding the clients’ needs and interests, being up-to-date with economic global trends, creating marketing campaigns more precisely are only several ideas about how ML can help future entrepreneurs. Self-driven cars that possibly will make our lives safer and eco-friendly also use ML algorithms. Using ML in medicine for more accurate and timely diagnostics, monitoring the effect of taking drugs, inventing new pharmaceuticals in shorter time periods – this is what the near future holds for us with ML.

And what if a computer could predict enemy movements in advance so that the government can prepare and increase security. Even now, when Machine Learning is only at the beginning of its development, there are already projects that use ML algorithms for avoiding different threats.

Written by
Svitla Team
How Machine Learning Can Change Our Future

FAQ

How will Machine Learning impact different industries in the future?

Artificial Intelligence shall change the face of industries with the help of swift and perfect data analytics, supporting in making decisions regarding complex scenarios. It shall assist in the diagnosis and discovery of medicines in healthcare, detecting fraud, and exposing risks in finance. In marketing, supporting highly personalized recommendations and campaigns, smart automation, predictive maintenance, and threat detection from automotive, manufacturing, and security industries; these are just a few benefits that arise from AI. Generally speaking, ML brings novelty by making each sector optimal through intelligent solutions.

What are some real-world examples of Machine Learning changing our daily lives?

Machine Learning is a part of our lives, and often, we might not even be aware of it. For example, error correction of typing errors and search results on the Internet that are related to spelling corrections, and related areas wherein ML algorithms play a role in getting things corrected. The product recommendations that you receive on e-commerce sites and the advertisements that you see on social media that are based on targeted marketing all work on the backbone of Machine Learning. This kind of ML learns your preferences so that suggestions for products are made. Even real-time translated languages that are found in communication applications, your smartphone’s voice-based assistant, and the spam filters in your email make use of Machine Learning to make our online communications smoother and more productive.

Will Machine Learning replace human jobs, or will it create new opportunities?

It will displace jobs in certain roles but at the same time create other opportunities through product, service, and even whole industry innovation that the assistance of human creativity, supervision, and expertise can bring about. The introduction of Machine Learning into business operations creates an immediate demand for professionals capable of developing, managing, and interpreting such systems. Finally, Machine Learning changes the job market by stressing new competencies and inaugurating new positions.

What are the main challenges and ethical concerns associated with the future of Machine Learning?

The major challenge for the machine is learning models to deliver the ‘right answer’ since data and statistics hardly ever respond as clearly as steps in a program with defined instructions. Therefore, any bias that exists in the data will probably be carried on and even enhanced further by the results, making them unfair or discriminatory. Another big concern ethically is privacy because ML systems normally operate and process huge volumes of personal information, not to mention their misuse in surveillance and other forms of autonomous decision-making without human intervention. In addition, ML algorithms must be transparent, accountable, and fair as technology keeps advancing.

How can individuals and businesses prepare for a future shaped by Machine Learning?

They should focus on skills that complement Machine Learning–until now unreplaceable by the human worker, critical thinking, creativity, problem-solving, and emotional intelligence – those aspects wherein humans still vastly outperform current implementations of artificial intelligence. It would also be useful to analyze data and obtain information on how ML systems perform. For business enterprises, the investment preparation means spending funds on Machine Learning solutions that will help to automate the operation, as well as draw insight from their data, plus continuously inculcate a learning and adaptive culture among workers regarding this new technology.