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ChatGPT and Generative AI: When machines create human-like responses

ChatGPT and Generative AI: When machines create human-like responses

Generative Artificial Intelligence (AI) has gained significant attention this year, capturing the public’s imagination and sparking a race among tech giants Microsoft and Alphabet to develop products using the technology, which they believe will revolutionize the nature of work. Generative AI is a type of machine learning that learns how to take actions from past data, much like other forms of AI. However, instead of simply categorizing or identifying data, generative AI creates entirely new content, such as text, images, and even computer code, based on the training it receives.

One of the most prominent applications of generative AI is ChatGPT, a chatbot that Microsoft-backed OpenAI released last year. ChatGPT uses a large language model to generate human-like responses from a given text prompt. OpenAI’s latest iteration, GPT-4, is a newer and more advanced model that is “multimodal,” meaning it can not only perceive text but also images. It can take a photo of a hand-drawn website mock-up and generate a real website from it, as demonstrated by OpenAI’s president earlier this week.

Businesses are already utilizing generative AI to create a first draft of marketing copy and to summarize customer reviews to help shoppers decide what used car to buy, as demonstrated by CarMax Inc’s use of OpenAI’s technology. In addition, generative AI can take notes during virtual meetings, draft and personalize emails, and even create slide presentations, as shown by Microsoft Corp and Alphabet Inc’s Google in their product announcements this week.

Despite its potential applications, generative AI is not without its concerns. Schools are worried about students using AI to draft essays, undermining the effort and learning required to complete the task. Cybersecurity researchers also fear that generative AI could lead to the production of far more disinformation than before by bad actors, including governments. Furthermore, the technology is still prone to errors and mistakes, such as factual inaccuracies or erratic responses, which companies have aimed to test before making it widely available.

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