Machine Leraning

Machine Learning:

The next generation of Artificial Intelligence 

What is Machine Learning?

   Machine learning is a field of artificial intelligence that uses mathematical techniques to allow computer systems to “learn” from data, rather than being pre-programmed for its full range of expected tasks. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” -- Tom Mitchell, Carnegie Mellon University

AI and Machine learning are very related but not quite the same. AI is a branch of computer science attempting to build machines capable of intelligent behavior, while Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed”. You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.

    You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent. Big technology players such as Google and Nvidia are currently working on developing this machine learning; desperately pushing computers to learn the way a human would in order to progress what many are calling the next revolution in technology – machines that 'think' like humans. Over the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. But how does it work?

    For example. When you make a typo, for instance, while searching in Google, it gives you the message: "Did you mean..."? This is the result of one of Google's machine learning algorithms; a system that detects what searches you make a couple seconds after making a certain search.

Who's using it?

 

    Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.

Financial Services

    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 can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.

Government

    Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.

Health Care

    Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient's health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment. 

Retail

    Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history. Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, price optimization, merchandise supply planning, and for customer insights. 

Oil and Gas

    Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding.

Transportation

    Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.

"Humans can typically create one or two good models a week; machine learning can create thousands of models a week."

Thomas H. Davenport, Analytics thought leader excerpt from The Wall Street Journal

 https://www.sas.com/en_ph/insights/analytics/machine-learning.html