Is machine learning the same as AI? How are machine learning and artificial intelligence different?
Machine learning is a subfield of AI. Machine learning is an AI application that enables computers to learn from experience and improve the performance of specific tasks. It allows computers to analyze data and use statistical techniques to learn from that data to improve their ability to perform a given task.
Machine learning algorithms are often classified as “supervised” or “unsupervised.”
What is supervised machine learning?
In supervised machine learning, a data scientist guides an AI algorithm through the learning process. The scientist provides the algorithm with training data that includes examples as well as specific target outcomes for each example. The scientist then decides which variables should be analyzed and provides feedback on the accuracy of the computer’s predictions. After sufficient training (or supervision), the computer is able to use the training data to predict the outcome of new data it receives.
What is unsupervised machine learning?
In unsupervised machine learning, algorithms are provided with training
Semi-supervised machine learning algorithms, as the name suggests, combine both labeled and unlabeled training data. The use of a small amount of labeled training data significantly improves prediction accuracy while mitigating the time and cost of labeling huge amounts of data.
Who invented artificial intelligence?
Computer scientist John McCarthy is considered the father of artificial intelligence, coining the term in 1955 and writing one of the first AI programming languages, LISP. But he wasn’t the first to propose the idea of artificial intelligence.
Concepts of artificial intelligence had been floating around in science fiction from the beginning of the 20th century. It wasn’t until the first stored-program computers became operational in 1949 that conditions were established for AI to become a reality. Within a few years, scientists and academics were theorizing that computers might be able to go beyond processing based on logical rules and actually become “thinking machines.”
One of the most prominent was English mathematician Alan Turing who, in his 1950 paper “Computing Machinery and Intelligence,” proposed a method for testing machine intelligence that has become known as the Turing Test. Five years later, Herbert Simon, Allen Newell and John Shaw created Logic Theorist, the first program written to mimic a human’s problem-solving skills.
Until McCarthy dropped the term “artificial intelligence” into a proposal for a summer research conference on the subject, what we now know as AI was an undefined field. McCarthy changed all that when he wrote in the proposal, "The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
How do you get started with AI?
The best way for a business to get started using AI is to use an existing AI platform. While it’s true that building artificial intelligence from scratch is incredibly expensive and complicated, it’s not the only — or even the preferred — way to bring AI to your organization. A better and simpler option for many companies is to implement existing AI platforms within your business.
Already, your business is using sophisticated technology every day without you ever giving a thought to what’s under the hood. The email clients, word processors, spreadsheets, project management software and cloud platforms that are the backbone of your daily operations all rely on
This has been called the “democratization of AI,” and it’s putting some powerful tools in the hands of everyday business users. In fact, Gartner recently predicted that self-service analytics and business intelligence users will produce more analysis than data scientists will by 2019.
It’s not just analytics either. Popular cloud providers, including Google, Amazon
Can small businesses use AI?
Even small businesses can become data-driven companies with the help of AI. With AI-enabled customer resource management (CRM), a business as small as a single-owner operation can parse customer reviews, social media posts, email and other written feedback to tailor its services and product offerings. A small business user can automate repetitive customer service tasks like answering queries and classifying tickets using an AI platform such as Digital Genius. Small businesses can even extract actionable data from existing tools like Google Sheets and ZenDesk by integrating them
Can you use AI if you don’t have a lot of data?
Small companies can use AI even if they don’t have a lot of in-house data. Social media data can be collected directly from its sources and analyzed on the fly. Similarly, an AI system that tracks and analyzes housing prices, a popular AI application in real estate, usually culls this data from publicly available sources.
Bottom line: It’s time to embrace AI
Artificial intelligence and machine learning are more than esoteric computer science research projects at Stanford and MIT. AI algorithms are doing more than unseating world chess champions or powering virtual personal assistants — cognitive computing is transforming healthcare to powering the development of autonomous vehicles. If you’re concerned about experimenting with artificial intelligence, don’t fret. AI technology is more affordable and easier to use than ever before — and both of those factors continue to improve every day.
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