10 Interesting Facts About Machine Learning You Should Know

Machine learning is about creating algorithms and systems that can learn from the data they process and analyze. The more data is processed, the better the algorithm will become. It is actually a science of getting computers to act without explicitly being programmed and is a branch of Artificial Intelligence (AI).

But right now before the possible alarming impact of artificial intelligence, we could in the today, the now, enjoy learning about some interesting facts. In this post, you will know 10 such interesting facts about machine learning!


Here are the 10 interesting facts about machine learning you should know according to our editors:

1: Facebook’s News Feed uses machine learning to personalize each member’s feed.

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Facebook’s News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling in order to read or “like” a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use to patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be included in the dataset and the News Feed will adjust accordingly.

2: Machine learning ignores preexisting knowledge.

Real knowledge is the result of a long process of reasoning and experimentation, which you can’t mimic by running a generic algorithm on a database. Experts in different fields have invested the significant human effort to develop domain knowledge. Domain experts are often better than machines at suggesting features that hold predictive power. As such, domain experts form a key part in defining the input to a machine learning system, from which preexisting knowledge can be extracted, extended, and refined.

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3: The speech recognition by Apple’s Siri or Facebook’s controversial facial recognition technology are great examples of the power of machine learning.

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Genuine learning is the after effect of a long procedure of thinking and experimentation, which you can’t emulate by running a nonexclusive calculation on a database. And, this can be seen quite well when you will get your focus into Apple’s Siri or Facebook’s controversial facial recognition technology which uses an extraordinary algorithm to work and rely on the user’s tasks and working.

4: Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud.

The insights can identify investment opportunities, or help investors know when to trade. Data mining which is a part of machine learning can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.

5: Machine learning is applied in weather forecasting software to improve the quality of the forecast.

Deep learning, a branch of machine learning, is one technique that is showing promise in the field of weather prediction. Deep learning allows researchers to process, analyze and enact on extremely large data sets by leveraging a series of trained algorithms that can learn and make predictions based on past data of weather reports.

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6: Machine learning, reorganized as a separate field, started to flourish in the 1990s.

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. It also benefited from the increasing availability of digitized information, and the ability to distribute it via the Internet.

7: Machine Learning can only discover correlations, not causal relationships.

In fact, one of the most popular types of machine learning consists of trying out different actions and observing their consequences: the essence of causal discovery. For example, an e-commerce site can try many different ways of presenting a product and choose the one that leads to the most purchases. You’ve probably participated in thousands of these experiments without knowing it. And causal relationships can be discovered even in some situations where experiments are out of the question, and all the computer can do is look at past data.

8: The ”dumb” approach of machine learning eventually beats human experts.

Research in many fields (like linguistics/translation) over the last 40 years has shown that these generic learning algorithms that “stir the number stew” out-perform approaches where real people try to come up with explicit rules themselves. The “dumb” approach of machine learning eventually beats human experts.

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9: ML Saves Lives! Computer-Aided Detection (CAD) software can spot 52% of breast cancer cells, a year before patients are diagnosed.

Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine learning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially requires “learning from examples.” One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates.

10: Machine learning combined with linguistic rule creation helps to know what customers are saying about you on twitter.

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Machine learning can also be combined with linguistic rules creation. This application is implemented by Twitter, where you will know what customers say about you.


 

Debobrata Deb

Written by Debobrata Deb

He has always been interested in technology, especially knowing deeply about computers or gadgets since childhood. Gathering and sharing more and more knowledge relating to awesomeness is what he always like to do.