Today let’s talk about an advance in Bard, Google’s answer to ChatGPT, and how it addresses one of the most pressing problems with today’s chatbots: their tendency to make things up.
Today let’s talk about an advance in Bard, Google’s answer to ChatGPT, and how it addresses one of the most pressing problems with today’s chatbots: their tendency to make things up.
From the day that the chatbots arrived last year, their makers warned us not to trust them. The text generated by tools like ChatGPT does not draw on a database of established facts. Instead, chatbots are predictive — making probabilistic guesses about which words seem right based on the massive corpus of text that their underlying large language models were trained on.
As a result, chatbots are often “confidently wrong,” to use the industry’s term. And this can fool even highly educated people, as we saw this year with the case of the lawyer who submitted citations generated by ChatGPT — not realizing that every single case had been fabricated out of whole cloth.
This state of affairs explains why I find chatbots mostly useless as research assistants. They’ll tell you anything you want, often within seconds, but in most cases without citing their work. As a result, you wind up spending a lot of time researching their answers to see whether they’re true — often defeating the purpose of using them at all.
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AI models are being trained not just to make predictions but also to estimate their confidence or uncertainty in those connections predictions. This helps identify situations where the model might not be certain or where its predictions might be less reliable.
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The article mentions that chatbots, buckshot roulette including ChatGPT, generate text through predictive methods rather than relying on a database of established facts.
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The advance in Bard addressing Flappy Bird the issue of chatbots making things up feels like a promising step towards improving the reliability of these tools, as it tackles a critical problem that has often undermined their usefulness and trustworthiness.
Traditional AI systems are often designed to provide a definitive answer or decision based on input data. However, real-world applications are much more complex, filled with ambiguity, fnf incomplete data, and unpredictable outcomes.
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