Providing information guaranteed not to disturb is a sure way to squash knowledge and progress
I can attest, from my own experience when writing detailed code in my work as a physicist, how often it is that surprising results arise, only to discover upon checking that they are due to errors in coding rather than reflecting interesting facets of fundamental reality.The NP Comment newsletter from columnist Colby Cosh and NP Comment editors tackles the important topics with boldness, verve and wit. Get NP Platformed delivered to your inbox weekdays by 4 p.m. ET.
Recently, Greg Giovanni, a student of Vinod Goel, Professor of Cognitive Neuroscience at York University, held the following “dialogue” with ChatGPT, on an issue of current topical interest, which he and Professor Goel have permitted me to reproduce:There are no inherent conceptual issues with identifying as neither male nor female.
How do you know there are no inherent conceptual issues with identifying as neither male nor female? What is your source? It is important to note that the training data can reflect biases and inaccuracies that exist in the world, and these may inadvertently be reflected in the responses generated by the model. OpenAI is committed to continuously improving the model and reducing these biases through ongoing research and development. However, it is a challenging task and no AI model is perfect or completely free of biases.
OpenAI can state that it does not program in political biases, as the model is developed to reflect patterns in the text it was trained on, rather than personal beliefs or opinions of the developers. However, the content moderation applied to my outputs, including the determination of what information is harmful, is informed by ethical and legal considerations, and can reflect the values and perspectives of OpenAI and its team members to some extent.
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