How ChatGPT Can Help Improve Quality of Code
As an AI language model, ChatGPT can offer several suggestions and guidelines that can help you write better-quality code.
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As an AI language model, ChatGPT can offer several suggestions and guidelines that can help you write better-quality code.
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Our study reveals that the potential for bias within AI-driven language models, such as GPT-3, poses a significant threat to the integrity of science. These models are trained on vast amounts of data, primarily from the internet, which can lead to a bias in the data. For example, if the data source is biased or incomplete, this bias will be reflected in the model’s output.
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In this episode of InfoFire, listen to Mark Maybury on Commercializing AI. Maybury is a C-Suite Executive and Board Member with leadership success across public and private sectors with expertise in executive management, innovation commercialization, venture investment, defense and intelligence, cybersecurity, and AI/ML.
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Artificial Intelligence (AI) has the potential to change how we organize work. However, to truly understand the impact of AI, it’s important to understand the key dimensions of the work context in which it is used.
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Introduces ChatGPT and discusses the academic integrity implications and concerns raised by researchers and educational institutions and the importance of rethinking learning assessment approaches and strategies.
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Will automated content moderation work? Researchers and software engineers have made great strides in building automated content moderation tools, but these tools are not good enough yet, and are prone to making mistakes.
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ChatGPT is a simple-to-use conversation agent developed by OpenAI. Of course, this is not the first time we have seen an AI agent that generates information. Google’s LaMDA and Meta’s Galactica are some recent examples for text generation.
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Many AI systems, especially those generating texts and images are starting to give an impression that they understand and even feel things. This illusion create misinformation and misdirection.
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With data as the infrastructure, and machine learning and deep learning as the technology foundations of building AI systems combined, they largely determine the performance, fairness, robustness, reliability, and scalability of AI systems.
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Ethical dilemma is a common problem for humans, but this is as true for humans as it is for machines. Hence, machines need to be enhanced to make the optimal decision in the face of ethical dilemma.
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