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Aamer Baig is a senior partner in McKinsey’s Chicago office, Lareina Yee is a senior partner in the Bay Area office, and Alex Singla is a senior partner in the Chicago office and the global leader of QuantumBlack, AI by McKinsey. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. That’s why ChatGPT—the GPT stands for generative pretrained transformer—is receiving so much attention right now.

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r\n\r\nMachine learning is the act of optimizing a model, which is a mathematical, summarized representation of data itself, such that it can predict or otherwise determine an appropriate response even when it receives input that it hasn’t seen before.There have been numerous cases of self-driving cars making incorrect decisions and putting their owners in dangerous situations. AI-powered image generators have made photos that tricked art judges into thinking they were human-made, and voice generating software has preserved voices of people suffering from degenerative diseases such as ALS. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Generally, the learning process requires huge amounts of data that provides an expected response given particular inputs. In machine learning, the algorithms use a series of finite steps to solve the problem by learning from data.

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However, bias still gets into the computer and taints the results it provides in a number of ways:\r\n

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• Data: The data itself can contain mistruths or simply misrepresentations. You need to distinguish between regression problems, whose target is a numeric value, and classification problems, whose target is a qualitative variable, such as a class or tag. A machine learning solution always generalizes from specific examples to general examples of the same sort.Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. Feed a computer massive amounts of data, and it eventually can recognize patterns and predict outcomes. Today, when you go to the doctor with a problem that can’t be easily diagnosed, there is a chance the doctor might send you to a blood-testing lab or a radiologist. As with the training and validation steps, any data you use during this step must reflect the problem domain you want to interact with using the machine learning model.