Machine learning and artificial intelligence help in a variety of ways, including predictive analytics, fraud detection, and the detection of data breaches. The latter process is particularly useful when it comes to business, as AI can answer questions in real-time and make better decisions based on a wide variety of inputs. As more businesses adopt AI to solve complex problems, it is imperative that governments think about the broad goals of AI and refrain from cracking open “black boxes” of algorithms. Ultimately, governments should take a pragmatic approach to regulation and encourage innovation, rather than cracking open AI’s “black boxes.”
Machine learning and AI are not without their ethical issues. However, the use of bias in machine learning raises concerns about its use in business. Human processes may generate the training data for AI models that may not be completely objective. While companies typically have good intentions when automating processes, a recent case involved a company called Amazon which used automation to screen male candidates for technical roles. As a result, the company has faced criticism for implementing algorithms that were based on biased data.
Machine learning algorithms use data to identify patterns in an environment. They can mimic human behaviours by analyzing large amounts of data. This is especially useful in manufacturing plants, which generate massive amounts of data. Using these algorithms, they can determine what actions produce a desired output. Often, this means computers can beat humans in computer games and reach superhuman levels of performance. However, some people are still skeptical of the effectiveness of machine learning.
