Good day AI Business Transformation Community! 🦾 As we dive deeper 🤿 into the AI Transformation journey, we have decided to launch different 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 for you. 💪🏼 Starting today, you can access our ⚙️ 𝗔𝗜 𝗔𝗨𝗧𝗢𝗠𝗔𝗧𝗜𝗢𝗡 (𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝘂𝘀𝗲) 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘! 🔥 We have created our AI automation templates for MAKE.com - where we are also an affiliate partner. IF you want to support the growth of our community, we would very much appreciate your support! Here is the Link: https://www.make.com/en/register?pc=aitransformation. We are applying for a SPECIAL PROMO, where anyone who registers for Make using our link will automatically receive 1 𝗺𝗼𝗻𝘁𝗵 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼 𝗽𝗹𝗮𝗻 𝘄𝗶𝘁𝗵 10,000 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲! (Let us know, so we can provide you with a special link for your PROMO). Access our MAKE templates here. 𝗧𝗵𝗲 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀 𝗮𝗿𝗲 𝗱𝗶𝘃𝗶𝗱𝗲𝗱 𝗶𝗻𝘁𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀: ✍🏻 Content Creation 📲 Social Media Management 🎯 Lead Management & Marketing Campaigns 💸 Sales & Client Management 𝗛𝗮𝗽𝗽𝘆 𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴! 🦾

The prompt used for this image was: A steampunk styled city with a rising sun in a format of 1920*1080. I think for a simple prompt the result is for a first impression rather good. What have you created so far with image generation and what prompts have you used?

In online communities, maintaining a positive and respectful environment is crucial, especially in spaces like Twitch chat, YouTube comments, or social media threads. A keyword filter is a great way to help achieve this, and with the assistance of AI, it’s easier to set up a customized list that captures a wide variety of inappropriate language, from offensive slurs to spam phrases. If you ask ChatGPT for example for such a list, it will not give you a clear answer. Instead it will censor itself, see the attached screenshot. Another important aspect to consider is that some words, like “gay,” can have multiple uses. While such terms may appear in an insulting context, they are also essential for normal, positive communication. For example, if someone wishes to come out as gay, an overly restrictive AI filter might block that message in error. Multilingual contexts present a further challenge. AI systems trained primarily on English data can misinterpret words in other languages as English curse words. A simple example is the Luxembourgish sentence, "Dat ass richteg flott!" which means “This is really nice!” in English. Here, AI could mistakenly censor the word "ass" (which means "is" in Luxembourgish) as the English term "ass." This brings us to an important question: do the benefits of an automated filter outweigh these limitations? And, what countermeasures can we implement to prevent an overly restrictive AI filter?

As AI becomes deeply integrated into business processes, a breakdown in AI systems can have significant consequences, as it impacts everything from operational efficiency, financial losses, customer trust and data security. Now what can we do in order to prevent this? Here are a few base recommendations: 1) Regular Model Monitoring and Maintenance: AI systems are dynamic and require regular updates to stay effective. Implement automated monitoring to detect unusual behavior and retrain models periodically to ensure they continue to provide accurate outputs. 2) Redundancy and Failover Systems: Just as with any IT infrastructure, AI systems should have backup systems in place. Backups are the fundamentals in IT and is still not taken seriously in some branches. 3) Stress Testing and Simulations: Before deploying AI into critical operations, conduct stress testing and run simulations to understand how it will perform under various scenarios. 4) Clear Escalation Protocols: Develop a clear protocol for AI breakdowns, including steps to mitigate the issue and communicate transparently with affected stakeholders. This not only helps in quicker recovery but also assures customers and employees of a proactive response. 5) Data Quality Control: Many AI failures are due to poor data quality. Regularly check and clean data inputs to prevent model drift and maintain consistent output quality. Or to say it in simpler words: A product can only be as good as the factors used in it. 6) Continuous Skill Development for the Team: Ensure your team is equipped with the necessary skills to manage and troubleshoot AI. Regular training can empower teams to identify potential issues and take corrective action swiftly. These are just a few points that could definitely be further expanded. However, they already provide a good impression of what is relevant, and I would appreciate feedback from the community.

Before I get to the issues realted to AI incest, I will explain what I mean with that. "AI incest" is a situation where new AI models are trained on outputs from other AI models instead of fresh, human-created data. This recycling of AI-generated content creates a feedback loop, potentially degrading quality over time. This could result for example in fake news from one AI being confirmed by another AI as they relate to each other. My general recommendations in order to prevent this: 1. Prioritize Human-Created Data: Ensure that training datasets are refreshed with human-generated content. 2. Limit AI Outputs in Training Sets: Minimize reliance on AI-generated data in new AI model training. 3. Audit and Filter Training Data: Regularly check for AI-generated content in datasets to maintain data diversity and accuracy. This point would also create new jobs again. What are your thoughts on this subject?