You’re struggling to provide feedback to an AI model. What are the best ways to make it work?

Providing effective feedback to an AI model is like giving instructions to a new teammate – the clearer your expectations, the better the results. Here's why defining goals is crucial: Focus on what matters: Without a clear goal, feedback can be scattered and unhelpful. Knowing the desired outcome allows you to pinpoint areas where the AI is excelling or falling short. Measure success effectively: If you don't define success criteria, how do you know if the AI is actually learning? Metrics like accuracy, relevance, and performance become your compass, guiding your feedback and ensuring it's aligned with the ultimate objective.

In my experience, our inherent inability to communicate with clarity is where feedback to an AI model goes wrong. Let's use a familiar experience most of us have working with a NLP AI model such as ChatGPT. There are additional instructions you must give beyond the question you may be asking in order edit a sentence. Just telling it to improve is wrong. The same has been true for AI models I've worked with over a decade ago in an industrial setting. We make assumption about the AI model understanding us, and that is our failure and not that of the AI model. We as humans communicate poorly. If you cannot communicate with your spouse or partner, you will see the same poor outcome with AI. Prioritize improving the clarity of your feedback.

Treat AI model as child. Thats basic premise on which most models are being developed. Even Best Generative AI models are wholly dependent on right focus on objective, relevant data (often in form of instructions and expected outcomes). There has to be clearly defined evaluation metrics that measure's it performance - quantative and qualitative. While traditional metrics are important, however there is significant rise in task-based metrics.

Providing effective feedback to an AI model involves clear, structured input and regular evaluation. Start by defining specific performance metrics that align with your goals. Use a diverse and representative dataset for training and testing, ensuring the model learns from a wide range of scenarios. Implement feedback loops where the model's predictions are reviewed and corrected, facilitating iterative learning. Regularly update the model with new data to keep it relevant. Communicating the feedback in a machine-readable format is crucial, as is ensuring consistency in the feedback provided. Collaborating with data scientists to interpret the model's output and refine its training process can also lead to significant improvements.

In my role as a project manager, I am dedicated to improving customer satisfaction through the implementation of a chatbot for e-commerce support. Success hinges on meeting specific criteria: responding promptly within 30 seconds, maintaining a high accuracy rate of 90%, achieving a 4-star satisfaction rating, and resolving 80% of inquiries without escalation. To evaluate performance, I will closely monitor metrics such as response time, accuracy, satisfaction ratings, and resolution rates. Feedback will be tailored to address areas of improvement, particularly focusing on enhancing accuracy and reducing response time to better align with our overarching objectives.

Identify areas where the model is underperforming. Is it giving inaccurate answers, misunderstanding your questions, or generating irrelevant content? Don't just say "This is wrong." Provide specific examples of the mistake. The more detail you give, the easier it will be for the AI to learn from your feedback.

Look at the data. If you are struggling to provide feedback consider the following Your sources Type of algorithm you are using e.g. deep learning, machine learning How is the data labeled The context in which you are providing the feedback Does the model need to be pruned Resetting the parameter #AI #ARTIFICIALINTELLIGENCE #GENAI #GENERATIVEAI #DATAINSIGHTS

??. ?????????????????? ???????? ???????? ?????????????????? In AI, data reigns supreme, shaping the destiny of your model. Recognizing the pivotal role of data quality and diversity is your first step toward refining model feedback mechanisms. ? ???????? ??????????: Conduct thorough checks for precision, completeness, and impartiality. Flaws here can skew model training, embedding biases or inaccuracies. ? ???????????? ??????????????: Enrich your model's learning environment by integrating data from a spectrum of sources, enhancing its comprehension and reducing biases.

Mapping the output to the outcome wil lead to a fit gap analysis so that the feedback to the model will make it more acuurate in the next iteration

If you already check all things to consider and you are using data pipeline to create training dataset then one place most people forgot to validate is data lineage of your training data, it can help uncover surprise changes.

From my experience a combination of automated evaluation of user interaction by an AI agent based chain of thought process and a human review of outliers works best. Make sure that the learnings are fed back to the content for continuous improvement.

If you are struggling to provide feedback to an AI model consider the following Everyone associated with the organization would provide feedback in different ways For example - 1. Feedback from engineers might involve relabeling the data 2. Feedback from users could be as simple as submitting a support ticket 3. Feedback from a CEO may involve indirect feedback through delegation to his or her employees #AI #ARTIFICIALINTELLIGENCE #GENAI #GENERATIVEAI #DATAINSIGHTS

Utilize debugging tools like TensorFlow Debugger or PyTorch's TorchDebugger. Leverage model interpretability tools like SHAP or Lime. Experiment with data augmentation techniques. Finally, solicit feedback from domain experts and iterate relentlessly.

Feedback to AI models necessitates a multi-pronged approach. Incorporate techniques like cross-validation for testing accuracy, active learning for data annotation, and user feedback loops for continuous improvement. Employing a blend of these methodologies ensures holistic refinement of the model's performance, enhancing its efficacy across diverse scenarios.

In my experience for developing an AI chatbot for customer support, I've seen the power of using multiple feedback methods. When our initial tests showed inconsistencies, we didn't lose hope. Instead, we turned to rigorous testing, annotation, and feedback loops. Each method played a crucial role: testing ensured accuracy, annotation refined data, and feedback helped gauge user satisfaction. It was through this collaborative effort that our chatbot evolved into a reliable support tool, meeting and exceeding user expectations.

AI systems are not yet able to reason outside of context. So it is important to be specific and use metaphors, analogies and examples of the reasoning behind your feedback. It is also human assisted technology, so it is important to not just put blind faith into AI but actually look for anomalies or inconsistencies when something does not sound right. All this feedback goes into the AI to make it more efficient. #AI #ARTIFICIALINTELLIGENCE #GENAI #GENERATIVEAI #DATAINSIGHTS

??. ???????????????? ?????????????? ???????????????? Precision and uniformity in feedback transform AI models from learners to leaders. Setting the stage with clarity and consistency is key to their evolution. ? ???????????????? ??????????????????: Clearly articulate performance goals, ensuring all feedback is in harmony with these criteria, guiding your model with precision. ? ???????????????????? ??????????????: Utilize uniform metrics for performance assessment to maintain objectivity and track progress, solidifying the feedback's impact.

One thing I've learned is that when giving feedback to an AI model, specificity and consistency are crucial. It's important to avoid any ambiguity or mixed messages, as these can derail the model's learning process. Provide clear, concrete examples to illustrate your points, and maintain a uniform approach in your feedback. This ensures that developers and the model can interpret and apply your insights effectively, leading to more accurate and reliable AI performance.

My experience amidst refining our AI chatbot, feedback was scattered. Engineers suggested data relabeling, users flooded us with support tickets, and the CEO delegated feedback through various channels. Feeling overwhelmed, I emphasized clear, concrete feedback. We established a format and encouraged examples. Chaos faded; engineers tackled data tasks, users provided insights, and the CEO gave consistent feedback. With clear communication, our chatbot flourished, showcasing the power of precision in driving progress. This experience taught us the invaluable lesson that clarity and consistency are the bedrock of effective feedback, propelling our project to success.

When feedback is segmented it is limited. Collective feedback is more valuable than individual feedback. Organizations should arrange feedback sessions or feedback conferences where departments, groups or even customers get together and work together to make the AI better and more efficient. #AI #ARTIFICIALINTELLIGENCE #GENAI #GENERATIVEAI #DATAINSIGHTS

??. ?????????????????? ?????????????????????????? ?????? ?????????????????? The journey of AI development is a collaborative marathon, not a sprint. Emphasizing iterative feedback and cross-disciplinary collaboration molds models that surpass expectations. ? ?????????????????? ??????????????????????: Establish a cyclical feedback loop for continual model evaluation and refinement, fostering gradual perfection. ? ??????????-?????????? ??????????????: Break down silos by involving experts from various domains. Their collective insights ensure the feedback enriches the model’s adaptability and accuracy.

Finally in our AI chatbot journey, collaboration and iteration became our guiding stars. We monitored, evaluated, and refined alongside developers, sharing feedback regularly. Openness to input and flexibility were key. As the model evolved, so did our approach, driving continuous improvement towards excellence.

At the end of the day, we have to mother and father over AI. We have to be better than AI, otherwise it won’t be better for us. This all starts with active engagement and contributions from everyone. #AI #ARTIFICIALINTELLIGENCE #GENAI #GENERATIVEAI #DATAINSIGHTS

Providing feedback to an AI model can be tricky because you are essentially teaching a machine. Clarity is key, some tips like below make your feedback effective: Specific Examples: Focus on pinpointing Specific instances where the model underperformed. Don't just say "it's wrong". Instead explain what the correct answer should be and why the models output was inaccurate. Continuous learning: the most effective AI models Lear and improve over time. Look for ways to create automated feedback loops where the models predictions are constantly compared to real world outcomes. This allows the model to adjust and refine it's performance.

Providing effective feedback to an AI model can be challenging but crucial for improvement. Start by identifying specific areas where the model is underperforming or making errors. Then, provide clear and actionable feedback, focusing on specific examples and suggesting potential adjustments or improvements. Utilize diverse datasets and real-world scenarios to train the model and address its shortcomings comprehensively. Additionally, leverage techniques like transfer learning or fine-tuning to refine the model's performance based on feedback. Finally, iterate and refine the feedback loop continuously to ensure ongoing improvement and optimization of the AI model.

Having a labeling strategy for how end users can give feedback to the model can be key depending on your product. A lot of the time user actions which aren’t direct labeling buttons can give you valueable feedback to your models.

The context matters. While humans bring emotions and personal experiences to the table, AI operates purely based on data and algorithms. Any feedback can be both valuable and limited, depending on the user’s viewpoint and the AI’s capabilities.