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If you are reading this textbook as part of a college course, then you probably already recognize the value of learning. Learning has been a fundamental part of your life for as long as you can remember. Some of the knowledge you have acquired has been "book" knowledge learned in school, while some has been "street smarts" learned while interacting with new people in new places. For example, the first time someone broke a promise to you, it probably affected the way you perceive the trustworthiness of others. As a result, the next time someone promised you something, a small part of you might have questioned whether that person would actually keep his word.This ability of humans to interpret complex situations and social interactions, and remember and learn from past experiences, enables us to survive and thrive in dynamic and rapidly changing environments. What you may not realize is that computers are becoming better at learning in this dynamic way, too.

Learning to Learn Many people perceive computers to be systems with very rigid operations; they can only carry out finite sets of tasks based on seemingly endless lines of computer code that tell them what to do. However, the walls of rigid computer code are breaking down as machine learning becomes more widely used in a number of industries.Machine learning,in a basic sense, is the ability of computers to learn dynamically rather than being explicitly told what to do via restrictive sets of coded instructions. Machine learning is based on the iterative generation of models; the computer has the ability to adapt to these models and interpret them differently over time. This capability is important because it allows the computer to identify patterns and other insights without being directed to the features it should analyze or being told where to find those features in large sets of data.

The ability of computers to learn iteratively and dynamically has important implications for many real-world applications. Machine learning techniques have been used for fraud detection, credit score analysis, network intrusion detection, and image recognition.Future applications of machine learning will likely include the development of artificial intelligence personal assistants and autonomous cars.

As a more specific example, think about the ways in which companies are turning to social media for insight about how their products and services are perceived by customers. Companies use machine learning tools to analyze customer tweets. Then they identify trends in how customers feel about different advertising campaigns. Based on the results,companies can curtail campaigns that are not having the desired effect and roll out more effective campaigns on a larger scale.

Computer scientists and executives are looking to the future of machine learning to see how advancements may change their industries. Many have an eye on the world of quantum computing as the next big advancement for machine learning due to its ability to increase the speed at which computers can process data.

One Quantum Step for Computers, One Giant Step for Learning Quantum computing has been a topic of interest for scientists and innovators for many years. The fundamental difference between a quantum computer and a regular computer is the way in which computers manage informationbits. A standard computer uses 1s and 0s as the basis for all of its operations. For example, when you enter an A on the keyboard, the computer interprets this as "01000001." In contrast, a quantum computer can encode information in what are called qubits, which can be represented as a 1, a 0, or both at the same time. The capacity for this third state allows tasks to be carried out much more quickly and has the potential to yield exponential growth in the processing capability of computers.

Quantum computing also enhances computers' ability to learn. Right now there is still a huge gap between how computers adapt through machine learning and how humans learn. Quantum computing has the potential to narrow this gap as computers become increasingly able to learn how to interpret and adapt more complex datasets with little to no direction.

The implications of quantum computing for machine learning and the creation of a more powerful artificial intelligence are profound. However, even if quantum computers have the capacity to process mammoth data sets, standardization of schemas and metadata will still be needed to ensure that the correct types of data are being considered for analysis.Once infrastructure and data management hurdles are overcome, quantum computing stands to revolutionize almost everything that we do with 1s, 0s, or both!

The article mentions that quantum computers will have tremendous processing power and revolutionize a number of computer applications. Conduct an Internet search to identify specific examples of how quantum computers may be used in the future.Please specify the URL of your source.