Why machine learning

Provide a powerful, consistent end-user computer EUC experience—regardless of team size, location, complexity. A subset of artificial intelligence AI , machine learning ML is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction.

Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision making. Initially, the model is fed parameter data for which the answer is known. At this point, increasing amounts of data are input to help the system learn and process higher computational decisions.

Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind.

Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.

Advancements in AI for applications like natural language processing NLP and computer vision CV are helping industries like financial services, healthcare, and automotive accelerate innovation, improve customer experience, and reduce costs. Machine learning takes the approach of letting computers learn to program themselves through experience.

Machine learning starts with data — numbers, photos, or text, like bank transactions, pictures of people or even bakery items , repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program.

From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find patterns or make predictions.

Over time the human programmer can also tweak the model, including changing its parameters, to help push it toward more accurate results. Some data is held out from the training data to be used as evaluation data, which tests how accurate the machine learning model is when it is shown new data. The result is a model that can be used in the future with different sets of data.

Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own.

Supervised machine learning is the most common type used today. In unsupervised machine learning, a program looks for patterns in unlabeled data. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases.

Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.

In the Work of the Future brief, Malone noted that machine learning is best suited for situations with lots of data — thousands or millions of examples, like recordings from previous conversations with customers, sensor logs from machines, or ATM transactions. In some cases, machine learning can gain insight or automate decision-making in cases where humans would not be able to, Madry said.

Google search is an example of something that humans can do, but never at the scale and speed at which the Google models are able to show potential answers every time a person types in a query, Malone said. It's an example of computers doing things that would not have been remotely economically feasible if they had to be done by humans.

Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.

Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers. In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function.

In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.

Deep learning networks are neural networks with many layers. Like neural networks, deep learning is modeled on the way the human brain works and powers many machine learning uses, like autonomous vehicles, chatbots, and medical diagnostics. Deep learning requires a great deal of computing power, which raises concerns about its economic and environmental sustainability.

Others are still trying to determine how to use machine learning in a beneficial way. In a paper , researchers from the MIT Initiative on the Digital Economy outlined a question rubric to determine whether a task is suitable for machine learning. The researchers found that no occupation will be untouched by machine learning, but no occupation is likely to be completely taken over by it. The way to unleash machine learning success, the researchers found, was to reorganize jobs into discrete tasks, some which can be done by machine learning, and others that require a human.

Recommendation algorithms. The recommendation engines behind Netflix and YouTube suggestions, what information appears on your Facebook feed, and product recommendations are fueled by machine learning. The systems learn, identify patterns, and make decisions with minimal intervention from humans. Ideally, machines increase accuracy and efficiency and remove or greatly reduce the possibility of human error. The nearly limitless quantity of available data, affordable data storage, and growth of less expensive and more powerful processing has propelled the growth of ML.

Now many industries are developing more robust models capable of analyzing bigger and more complex data while delivering faster, more accurate results on vast scales. ML tools enable organizations to more quickly identify profitable opportunities and potential risks. New techniques in the field are evolving rapidly and expanded the application of ML to nearly limitless possibilities. Industries that depend on vast quantities of data—and need a system to analyze it efficiently and accurately, have embraced ML as the best way to build models, strategize, and plan.

One new ML algorithm detects cancerous tumors on mammograms; another identifies skin cancer; a third can analyze retinal images to diagnose diabetic retinopathy. Systems that use machine learning enable government officials to use data to predict potential future scenarios and adapt to rapidly changing situations. ML can help to improve cybersecurity and cyber intelligence, support counterterrorism efforts, optimize operational preparedness, logistics management, and predictive maintenance, and reduce failure rates.

This recent article highlights 10 more applications for machine learning within the healthcare industry. Marketing and sales. E-commerce and social media sites use ML to analyze your buying and search history—and make recommendations on other items to purchase, based on your past habits.

Efficiency and accuracy are key to profitability within this sector; so is the ability to predict and mitigate potential problems.