What are the best ways to answer an AI question you don't know?

Be patient. Neither the person asking the question or answering the question knows everything about AI. Typically these conversations are not a contest about who knows the most, but rather people doing their best to explore AIs potential to solve a problem that they have. Best to bubble up in altitude to the business problem that is on the mind of the questioner so you really understand it. Then perhaps dive back into potential solutions - which may or may not include AI.

The simplest answer is 'admit you don't currently know' the answer. Now depending on the actual question category - is it a critical or nice to know AI answer - you can select you next step. If it is a critical to know for the business then ask yourself if you can quickly get up to speed or if you need to buy-in and transfer the knowledge then do that. But remember to understand the question first so you can provide the answer as needed.

One thing I have found helpful in answering an AI question you don't know is to be honest about your current knowledge limits and express a willingness to research and learn more. Admitting that you don't have all the answers but are eager to find out can be a sign of professionalism and commitment to growth. For example, I've seen a professional in a meeting say, 'I don't have the answer to that right now, but I will investigate and get back to you.' This approach maintains credibility while showing dedication to expanding one's expertise.

Often, AI-related queries can be ambiguous or lack essential details. Probing for more information helps to fully understand the question's intent, scope, and underlying assumptions. This approach prevents misunderstandings and ensures your response is relevant and accurate. For instance, if asked about creating a chatbot, inquire about its intended use, target audience, domain, and desired features. Such clarification not only aids in formulating a more informed response but also demonstrates a thorough and thoughtful approach to problem-solving in the realm of AI.

When faced with an AI question you don't know, be honest and say "I don't know" or explain limitations. Clarify the question by asking for context, identifying keywords, and checking assumptions. Offer alternative information or suggest rephrasing. Log the question and use it to learn and improve your knowledge base. Remember, honesty, helpfulness, and a commitment to learning are key to answering AI questions effectively.

Investigate the topic using reliable sources to gather information, then synthesize a well-informed response. Honestly acknowledge the limits of my current knowledge or training data, and suggest alternative sources or experts

This maybe obvious, but leverage you AI tools like ChatGPT to help with with gaps in your AI knowledge. Leverage the persona of an AI expert and teacher, and have it start to guide your though gaps in your knowledge. ChatGPT never gets tired of your questions, and is always there to help.

It’s also important to lean into the unknown. Once we get comfortable operating in environments with less than 100% of the full picture, we can become better leaders. After-all, it’s really not hard to make decisions when we have all the information. By embracing vulnerability, and approaching others with a mindset that we could learn something, we can then work to clarify the question. However, as stated earlier, it’s ok if it’s not completely clear. Keep going and challenge assumptions at each step.

Break down the question into smaller, more manageable parts. This helps you focus on what you do know and identify potential avenues for exploration.

When faced with an AI question I don't know, honesty is key. I would acknowledge my uncertainty and express a willingness to learn or provide alternative approaches. Transparency fosters trust, and it allows for collaborative problem-solving. Additionally, I might suggest resources or keywords for further research. Emphasizing a proactive and open-minded attitude ensures a constructive engagement with the topic at hand.

Detailing the steps you would take to find a solution showcases your logical thinking and analytical skills, even if you're unsure of the precise answer. This method demonstrates an understanding of AI principles and problem-solving strategies. For instance, if asked about enhancing a machine learning model's accuracy, discuss the various factors impacting accuracy, like data quality, feature selection, model selection, parameter tuning, and evaluation metrics. By outlining a structured approach, you convey your competency in navigating complex AI challenges, highlighting your ability to think critically and methodically in the field.

When faced with an unfamiliar AI question, share your thought process. Outline steps like data analysis, model selection, and evaluation methods. It reflects analytical thinking and opens up collaborative problem-solving avenues.

A practical approach might be to harness the capabilities of General AI. When faced with AI-related questions beyond your expertise, you can engage a Large Language Model (LLM) to simulate a specialist in the specific AI domain you're unfamiliar with. From this point forward, interacting with the LLM can progressively guide you towards the answer. The effectiveness of this process largely hinges on the quality of your questions or prompts.

Share how you plan to approach finding the answer. Mentioning resources you'll consult shows your commitment to finding a solution.

It is always good to admit the limitations of your knowledge. But do state as well the domain that you are more familiar with, and at what level of expertise.

I start by breaking down a difficult AI question that someone poses to me, such as how to improve the accuracy of a machine learning model. It's similar, in my opinion, to determining why a tomato plant isn't developing properly. First, you check the basics: is the soil in good condition? Are the seeds you have the proper kind? Then you consider the attention you're providing it with: adequate watering, moderate sun exposure? AI models work similarly. I would examine the training data, identify the most significant segments of the data, select the most appropriate model, make the necessary adjustments, then run tests to ensure the model is performing as intended. It's all about taking it step by step and tweaking things as you go.

Most of the time, I feel, people think they know how the model is trained without ever having to train one. Thus, it creates a lot of misconceptions about how exactly AI works, how it's different from the other approaches, what are learnable parameters, or why linearity is bad. In this kind of situation, a step-by-step pipeline process can come to the rescue. The data preprocessing, feature selection, training of the model, and evaluation.

AI is not new. However, it's not been great until now. Therefore, most of us in the business world haven't paid great attention to it until the last few years. We must educate ourselves daily and attempt to retain that knowledge. The world champions of memory can repeat hundreds of playing cards because they build stories about the order of the cards. Much like this process, we can build stories in our heads (examples and analogies) to remember complicated ideas. It is also a great way to then pass that knowledge to others. However, I do encourage people to learn more than just examples or analogies. Seek to understand some of the technical aspects, or you risk too many generalizations and might give false information.

Examples and analogies aid in clarifying your own understanding and effectively communicating your thoughts. They bridge the gap between complex AI theories and familiar, everyday situations. For instance, to explain the difference between supervised and unsupervised learning, you might compare it to the relationship between a teacher and a student, or use practical examples like classification tasks (supervised) versus clustering tasks (unsupervised). This approach not only makes the explanation more digestible but also demonstrates your ability to translate technical AI concepts into understandable terms.

If you come across relevant research or resources during your exploration, share them with the asker. This demonstrates your commitment to learning and finding solutions.

It is helpful to use common lingo (i.e. keywords) to make the listener relate better. For example, if someone wants to know the difference between supervised and unsupervised learning... telling the person that that supervised is "trained" and unsupervised plays with "raw" data. This will bridge the gap of understanding because people are familiar with these terms.

This approach can break down intricate concepts into more understandable terms, making it easier to grasp the core ideas. For instance, when explaining the difference between supervised and unsupervised learning, an analogy like comparing it to a teacher guiding a student (supervised) versus a student learning independently (unsupervised) can be effective. Similarly, providing concrete examples, such as classification tasks for supervised learning and clustering tasks for unsupervised learning, can clarify these concepts. This method of using relatable comparisons and specific instances helps in both comprehending and effectively communicating complex AI principles.

When tackling complex AI queries, employ examples and analogies to demystify concepts. They clarify understanding and relate abstract ideas to familiar scenarios, like using teaching analogies for supervised vs. unsupervised learning.

When it comes to describing interesting concepts of AI for instance difference between supervised and unsupervised learning, I like to use analogies. It's similar to learning to cook. It's like if you have a recipe book with detailed instructions (the 'teacher') for each dish with supervised learning. You learn to cook by following these recipes, and you get better at making that particular foods over time. Unsupervised learning, on the other hand, is like being given a bunch of ingredients but no recipe. You must experiment to determine what works best together. Without any supervision, it's all about investigating and discovering patterns. Using examples or analogies like these can help to simplify a complex AI concepts.

By using examples, you can demystify AI concepts and help them relate to everyday activities or experiences. You can also draw historical parallels to show how similar AI challenges have been tackled in the past. As with all technical explanations, by incorporating a storytelling approach you can make the analogies more engaging and relatable.

Examples and analogies are the best way to explain the difference between different types of learning, like metric learning, supervised learning, etc. Even if you have to teach a concept like distilbert, it becomes easier with analogies of teacher and student. It's all because we understand things in terms of similarity.

Every "I don't know" is an opportunity to learn. Add the question to your knowledge gaps, actively seek out answers, and share your learnings with others. This continuous learning cycle makes you a more valuable AI, always evolving and growing. Admitting your limitations is not a sign of weakness, but a testament to your integrity and humility. It shows you're a reliable guide, not a know-it-all, and that together, you and the user can embark on a journey of discovery. So, embrace the unknown, be a research partner, learn from the questions, and watch your knowledge expand with each "I don't know." The path to AI mastery may have its detours, but curiosity and honesty will always guide you towards the light.

🌟 𝐀𝐝𝐦𝐢𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐚𝐭 𝐲𝐨𝐮 𝐝𝐨𝐧'𝐭 𝐤𝐧𝐨𝐰 𝐚 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞 𝐨𝐫 𝐚𝐧 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐢𝐧 𝐀𝐈 𝐢𝐬 𝐚 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡, 𝐧𝐨𝐭 𝐚 𝐰𝐞𝐚𝐤𝐧𝐞𝐬𝐬 🌟 It builds trust and encourages a culture of learning and growth. You should do FOUR things in such a situation- 1. Continuous Learning 📚 - Use this as an opportunity to learn. Research the topic to enhance your knowledge base 2. Seek Help 👥 - Don't hesitate to ask for assistance from more knowledgeable colleagues or AI tools 3. Provide Resources 📄 - If possible, direct to resources or experts who can answer the question accurately 4. Follow Up 🔄 - Once you've found the right answer, circle back to provide a well-informed response.

Saying 'I don't know' might be the smartest answer sometimes. It opens doors to learning and finding the right solutions. Follow up with a commitment to research, learn and provide the solution.

In navigating the ever-evolving landscape of AI and technology, I’ve learned that a critical skill is being able to make informed decisions with incomplete information. Embracing ambiguity isn’t just about being comfortable with the unknown; it requires vulnerability and a constant challenge to our own assumptions. Recognizing and admitting our limitations in this journey is vital. When faced with questions beyond our current expertise, acknowledging these gaps demonstrates honesty, humility, and an eagerness to grow. Whether it’s about an unfamiliar algorithm or a new framework, it’s an opportunity to express curiosity and a commitment to continuous learning, fostering a culture of open dialogue and development in the dynamic world of AI.

In responding to AI questions beyond your scope, honesty is key. Admitting limitations displays humility and a readiness to learn. Express interest in exploring new algorithms or frameworks with your colleagues and friends.

Admitting that you don't know is courageous... It sends a signal that you know your limitations... You should also show your willingness to learn what you don't know

It's important to be honest when you don't know the answer or require additional information. This candor demonstrates humility, integrity, and a willingness to learn and grow. For example, if asked about implementing a specific algorithm or framework you're unfamiliar with, openly state your lack of experience with it, but express eagerness to explore and understand it further. This approach not only maintains credibility but also opens doors for collaborative learning and potential guidance from others more knowledgeable in that area. Admitting limitations is a strength, reflecting a professional commitment to continuous learning and improvement in the rapidly evolving field of AI.

If it is not an absolute basic of AI, there is no need to be ashamed if you cannot answer a question in such a complex and rapidly developing environment. Basically, you should always be honest with your statements and answers in the interests of your own integrity. This pays off more than being able to answer the very last question. However, if the question is one of the basics, you should think again about whether you should answer questions in this context at all ...

¡No hay ningún problema en no saber sobre algo y decirlo! Nos da miedo, pero generalmente refuerza nuestra autoridad y no la socava (salvo que sea un tema que debiésemos conocer, claro)

It’s about outlining a plan for further research, experimentation, or exploration to find answers or refine solutions. Suggest practical actions, like identifying relevant data sources, technologies, and methodologies you would employ, and anticipate potential challenges and opportunities. For instance, in designing an AI system for a specific domain, detail the types of data you'd seek, the technologies you'd consider, and the methods you'd apply. This forward-looking approach shows your ability to think critically and creatively about AI problems, emphasizing your commitment to continual learning and problem-solving in the field.

Encourage supplementary reading from credible sources. Google, Microsoft and Amazon have plenty of basic 101 publications cater for people of various understanding and needs. Definitely take it with a pinch of salt of what news sources says, as it's often sensationalized/exaggerated truths.

When given a challenging AI issue concerning a field I'm not experienced in, I'll offer a strategy for finding the solution. Let's take the example of discussing the development of an AI healthcare system. I would say, "Let's start by examining the types of patient data we have access to and how best to manage them securely." Next, we should to figure out which artificial intelligence developments are now gaining traction in the medical field and consider how we may utilise them. Naturally, we'll also need to consider accuracy and privacy rules. It's about demonstrating that you're prepared to get right in, finding out what's required, and outlining the steps to take on the challenge.

If you truly don't know the answer to a question related to AI, I'd suggest delaying a response. There is a wealth of knowledge that is available on the topic to build confidence in answering that question and is worth investing the time evaluating available evidence, rather than making up a "smart sounding response."

When facing an unfamiliar AI question, be honest about your uncertainty. Express a willingness to learn and offer a thoughtful approach to finding the answer. Highlight relevant knowledge and indicate your plan for further research. Demonstrating humility, curiosity, and a proactive attitude enhances your credibility and shows resilience in the face of unfamiliar challenges.

Share what knowledge you do have, even if it doesn't fully address the question. Offer insights or related information that might be helpful. Focus on providing value and demonstrating a genuine effort to assist. Ask follow-up questions to gain a deeper understanding of the user's intent and information needs. Use this information to tailor your response and ensure it's relevant and helpful. Invite the user to contribute their own knowledge and expertise. Facilitate discussions and collaboration to reach a better understanding of the topic together. Recognize the collective intelligence of the community and leverage it for problem-solving. Share your research and learning journey with the user.

Ask AI... ha. :) In all seriousness, start with looking for other credible sources, people, and assets that can help you. In the reverse, all of us need to remember that the rising tide floats all boats. We are all better off when we are all better off.

hahaha.. I find this question funny because if you do not know the answer to a question you are asked, I do not think there is any better way to answer it than to say "I do not know, but I will find out and get back to you". Indeed we can never know the answer to everything, so it is important to admit when we do not know something, so we do not give out any erroneous information because we are trying to claim knowledge of what we do not know. :)

There are few AI questions that many people can answer and many AI questions that few people can answer. The chances are that most of the pressing questions about AI will require some research. The best source of answers for technical questions is arXiv, which is where most of the AI research papers are published by academics and company research labs. Pro tip: don't just read one paper and think you have the definitive answer. There are generally a couple of papers that take different approaches and may arrive at different conclusions. For use case information, industry trade publications tend to have the most information. They will have coverage even if not a lot of depth but it will give you a place to start.

When dealing with unfamiliar AI topics, emphasize the importance of questioning assumptions. AI often operates on implicit assumptions. So, by challenging these assumptions, you can lead yourself to innovative problem-solving approaches. Encourage questioning the data sources, algorithms, and even the ethical implications of AI solutions. So while answering AI questions, by employing a critical perspective, you can foster a more responsible and innovative approach while deepening the understanding as well.

Approach the unknown question as a chance to learn and expand your knowledge. This positive attitude fosters collaboration and can lead to unexpected discoveries.