Artifical intelligence AI, Responsible AI, Machine learning ML, Generative AI

We will look at issues of accuracy, bias, and context related to use of LLMs like Chat GPT in software development and digital marketing.  Accuracy and Reliability:   1. Outdated Documentation: A developer relies on outdated documentation found on an online forum to implement a critical feature in their software. When the information from this outdated source is fed into an LLM, it generates code that is no longer compatible with the latest software libraries and APIs, leading to unreliable functionality.   2. Misleading Error Messages: A programmer uses LLM-generated content to create error messages for their application. However, some of the generated messages are unclear and misleading, making it challenging for users to understand and resolve issues, thereby affecting the software's reliability.   3. Inaccurate Data Processing: In data analysis software, a user inputs data preprocessing instructions based on content from a blog post that lacks validat...

These examples illustrate various scenarios where challenges related to accuracy, bias, and context can arise when using LLMs, emphasizing the importance of careful consideration and critical thinking in utilizing LLM-generated content. Accuracy and Reliability:   1. A user extracts content from an unreliable website that contains inaccurate technical information. When this content is fed into the LLM model, it may generate responses that perpetuate these inaccuracies, leading to unreliable insights.   2. An entrepreneur uses LLM-generated content to draft a business proposal. However, the content includes outdated market statistics, causing the proposal to lack accuracy and relevance to the current market conditions.   3. A medical researcher relies on LLM-generated information for a research paper but discovers that some of the data used is not peer-reviewed and lacks scientific rigor, compromising the paper's reliability.   4. A journalist uses an ...

A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other by using deep learning methods to become more accurate in their predictions. Code examples  1.  https://www.geeksforgeeks.org/generative-adversarial-network-gan/ 2. A generative adversarial network (GAN) has two parts: a. The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator. b. The discriminator learns to distinguish the generator's fake data from real data. The discriminator penalizes the generator for producing implausible results. ref https://developers.google.com/machine-learning/gan/gan_structure 3.  GAN- uses and examples https://www.techtarget.com/searchenterpriseai/definition/generative-adversarial-network-GAN Low price course https://www.udemy.com/course/keras-deep-learning-generative-adversarial-networks-gan/