Artificial Intelligence is Already Being Used Everywhere
Artificial Intelligence Adds Intelligence to Existing Products
Artificial Intelligence (AI) is a set of tools that can add intelligence to existing products and services. The process of analyzing data for AI algorithms can be complex, so it is not always easy to understand how it makes decisions. The process often involves teasing out subtle correlations between thousands of variables.
For example, financial institutions may have difficulty explaining their credit-issuing decisions to their customers, which is why AI algorithms are often referred to as ‘black boxes.’ This nickname is due to the complex nature of AI algorithms, which are difficult for a human being to discern, comprehend and explain in layman’s terms. In short, the “inner workings” of the AI model are not visible to the end user.
The Internet of Things generates vast amounts of data, which is often unanalyzed. The ability of computers to analyze large amounts of data quickly and accurately is key to intelligent processing. In particular, AI can be applied to the Internet of Things, a network of physical devices that exchange data with each other.
This data can provide information about rare events and allow systems to be optimized in different scenarios. Using application programming interfaces (APIs), developers can add AI capabilities to existing products. For example, AI-based security cameras could add image recognition capabilities to home security systems. They could also use Quality Assurance (QA) capabilities to discern common or impactful patterns within data sets. Machine Learning is able to consolidate all of this data and information to structurally assess for executing its next logical steps.
It Predicts Fraud in Online Credit Card Transactions
Fraudulent credit card transactions are common today, primarily due to the advancement of technology and the popularity of online transactions. Not only are these types of transactions costly, but they can also result in substantial financial losses. There are many ways fraudsters can get your credit card information, and this paper aims to use multiple machine learning algorithms to detect fraud more effectively. It will show that Artificial Neural Networks (ANNs) are superior to Support Vector Machines (SVMs) and K-Nearest Neighbour (KNN) algorithms.
As digital processes, payment methods, and currencies become more widespread, fraud poses a significant threat to profits. Businesses can input known fraud outcomes into the system to test any fraud detection solution’s accuracy. By using mathematical models, AI can detect high-risk events that humans could miss. To configure the AI algorithm, businesses should use business models and past data to train it to make accurate predictions. It can also improve the accuracy of financial fraud detection.
Automate Routine, Repetitive Work
In the field of AI, automated systems have the potential to replace humans in repetitive and routine work. These systems can improve decisions and accelerate processes. They can even automate entire tasks or processes. To take advantage of AI, consider the following examples. Repetitive work is more likely to be automated by a well-established solution. Robotic Process Automation (RPA) can complete the same analysis fifteen times faster with almost no human error than a human can.
Adapting to Exponential Change
While AI is already being used to automate routine, repetitive work, the future of these jobs will be different. It will likely require workers to learn new skills or adapt to machines. Some occupations are likely to disappear entirely, while others will expand. For instance, doctors will leverage AI algorithms to read patient scans and suggest treatments. Some repetitive work will shift to managing or troubleshooting the automated systems. For example, some Amazon employees will be robot operators, monitoring the automated arms and resolving problems that arise.
While the proliferation of Machine Learning and Artificial Intelligence is still in its infancy, the rate of change is exponential—not linear. This means that the speed of technological shifts is ever-increasing, multiplying, and businesses that do not adapt to these changes will be left behind by more technology-oriented competition.
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