You’re an AI professional who wants to improve your work. How can you get constructive feedback?

To receive constructive feedback as an AI professional, engage in peer reviews, seek mentorship, and organize feedback sessions with supervisors and clients. Utilize surveys and online communities to gather diverse perspectives, and prioritize continuous learning and self-assessment for personal growth and improvement.

If you're an organizational leader who's a decision maker, you could establish cross organizational peer groups Say large staffing companies are people centric & would have the breadth of human capital to do this. For AI, establish cross organizational peer groups in various fields of AI such as 1- Robotics peer group 2- Computer vision peer group 3- Data peer group Etc etc It benefits each organization - For the staffing firm it helps attract talent because they know they are entering a collaborative and inclusive organization - For the other company it can help in recruiting initiatives by building relationships with available talent at the staffing company - It can foster an IP creation driven culture #ai #artificialintelligence

Joining Peer Groups: Essential for professional development in AI. Offers a platform for exchanging ideas and receiving constructive feedback. Collaborative Environment: Whether it's through online communities like Reddit, Slack, Discord, or attending local meetups and hackathons, the opportunity to network and learn is unparalleled. Personal Experience: My journey has greatly benefited from these groups. Sharing work and insights leads to tangible improvements in projects and understanding. Highly Recommend: For anyone looking to enhance their AI skills, engaging in these communities is invaluable. It fosters continuous learning and keeps you updated on industry trends.

Find a peer group of fellow AI professionals or colleagues who can provide constructive feedback on your work. Engage in regular discussions, peer reviews, or collaborative projects to solicit diverse perspectives and insights into your AI projects and methodologies.

-- Requesting regular review sessions with your supervisor, mentors, or peers to discuss your work and receive targeted feedback. -- Presenting your projects and findings at meetings, conferences, or workshops to get insights and suggestions from a wider audience. -- Utilizing peer review processes within your organization or in the broader AI community to get detailed critiques of your methodologies and results.

A lot of people misconstrue what a mentor is. You can seek mentors, but AI is an ever-evolving field, and most mentors don’t have time to be there with you every step of the way or teach you what you can easily find yourself. So mentors are not teachers, that should be remember first. If you are going to look for mentors, look for mentors who are well-known in the community and those who publicly active contributors multiple areas in the field of AI. A mentor who hides behind a computer screen will unlikely have much value add. Second to last, is that the best mentor you can find, is yourself. And lastly, a great way to improve in AI is by starting with what you know and doing some mentoring yourself. #ai #artificialintelligence

It is always great to have a mentor from the industry who is much more experienced in the field, who can guide you aright and spot out areas you may need more improvement.

. Seek a mentor with expertise in AI or related fields who can offer guidance, advice, and feedback on your work. A mentor can provide valuable insights, share best practices, and help you identify areas for improvement in your AI projects and career development.

Seeking a Mentor: A cornerstone for advancing in AI. Mentors offer unmatched insights and guidance. Channels for Mentorship: Platforms like MentorCruise, SharpestMinds, or Plato; professional associations; academic connections. My Path: Leveraged mentors for skill enhancement and career growth. Former professors and colleagues were invaluable. Advice and Guidance: Mentors provide targeted support for specific skills, projects, or challenges. Networking: Utilize your existing network for mentorship opportunities. Reach out proactively. Recommendation: Engaging with mentors is crucial for personal and professional development in AI.

A mentor with deep experience in AI can be an invaluable source of feedback for improving your work. They can offer guidance, spot areas for growth, and provide honest critiques in a supportive setting. But how do you find the right mentor? Look for someone whose work you admire, who has experience in a skill you want to develop, or who's simply known to be a great teacher. Don't be afraid to reach out – many professionals are happy to share their knowledge!

Leveraging client feedback is invaluable for staying aligned with market needs. It's a practical approach to ensure your solutions are not just technically sound but also resonate with your clients' expectations. From my experience, incorporating feedback loops at various project stages enriches your work, fostering innovation and client satisfaction. Plus, it's an excellent way to demonstrate your commitment to excellence and continuous improvement. Adopting tools like SurveyMonkey or conducting interviews can provide deep insights, turning feedback into a strategic asset for development. ??

Client Feedback: Crucial for freelance/consultant growth in AI. Offers direct insights on meeting expectations and goals. Feedback Stages: Before, during, after projects. Ensures alignment and improvement. Collection Methods: Surveys (SurveyMonkey, Typeform), interviews, reviews. Facilitates comprehensive understanding. Personal Strategy: Actively sought client feedback to refine projects and strategies. Enhanced efficiency and client satisfaction. Proactive Approach: Use feedback tools or direct communication for actionable insights. Recommendation: Leverage client feedback for continuous improvement and tailored service delivery in AI ventures.

Ask your clients or stakeholders for feedback on your AI projects or solutions. Solicit input on their experience, satisfaction, and any areas for improvement or enhancement. Actively listen to their feedback and incorporate their suggestions into your work to ensure alignment with their needs and expectations.

Clients are on the front lines of seeing how your AI solutions work in the real world. Their feedback can highlight strengths, reveal blind spots, and suggest areas for improvement. What's the best way to collect client feedback? Think surveys, interviews, or even informal chats. Be specific about the kind of feedback you're looking for, and express genuine interest in improving their experience.

As a freelancer, staying connected with your client is crucial. Communication is key at every stage of the project, from setting expectations upfront to checking in periodically as you progress. It's easy to get absorbed in the work and lose sight of the client's vision, risking trust and efficiency. Regular check-ins, adapted to the project's pace, ensure feedback is received, concerns are addressed, and progress stays on course.

Absolutely agree! Putting your work out there and testing it with the actual market is incredibly revealing. It's a direct line to understanding what works, what doesn't, and how to adjust. There's no substitute for this kind of hands-on feedback. It's how I've learned to pivot quickly and keep my projects relevant and user-centric. Real feedback doesn't just guide; it propels us forward.

You could test a model for improvements by using approaches like federated learning or controlled user cases and scenarios. Federated learning allow your users to remain private and secure but it allows you to learn from their model updates vs without revealing the raw data and this is great for regulated industries say like healthcare or finance. And of course controlled user cases and scenarios helps produce simulated environments, excellent for testing reliability in different scenarios. #ai #artificialintelligence #aifeedback #aitesting

I would also consider looking at a holistic prospective of the industry and the company of interest to identify areas where the introduction of AI will innovate and disrupt. This will allow you to anticipate new sources of value creation.

Test your work rigorously to identify any potential issues, errors, or inefficiencies. Utilize testing frameworks, automated testing tools, or manual testing techniques to validate the performance, accuracy, and reliability of your AI models, algorithms, or applications. Analyze test results and iterate on your work to address any identified issues or shortcomings.

Thorough testing is key to ensuring your AI solutions perform as intended! ?? Develop a rigorous testing plan, try to break your models, and collect feedback from users. Remember, even the most sophisticated AI can have unexpected hiccups, so don't skip this crucial step. ??

One piece of advice that resonates deeply with me is the importance of engaging with stakeholders. In the AI field, understanding the needs, wants, and desires of those we're building for is critical. It's a lesson learned the hard way, showing that beyond algorithms and data, AI's true value is realized when it meets real human needs. Embracing this perspective has transformed how I approach projects, ensuring that stakeholder engagement is not just a step in the process but the foundation of all I do. This approach not only improves outcomes but also enriches the journey.

When incorporating different ways to improve your AI model it is important to remember that with improvement there are always tradeoffs. These tradeoffs can come in the form of increased bias, increased costs, increased time to market, or even environmental effects if the improvements require more data computations #ai #artificialintelligence #aifeedback

Additionally, consider factors such as self-assessment, continuous learning, and professional development opportunities to improve your work as an AI professional. Reflect on your own performance, seek opportunities for growth and learning, and stay abreast of emerging trends and best practices in the field of AI. Embrace feedback as a valuable tool for self-improvement and leverage it to enhance your skills, knowledge, and effectiveness as an AI professional.

When seeking feedback to improve your AI work, here are a few additional things to keep in mind: Own your mistakes: We all make 'em! Acknowledge missteps, learn from them, and show how you're striving to do better. ?? Pay it forward: Just as you benefit from feedback, offer your insights to help others grow within the AI community. ?? Stay curious: The AI field evolves rapidly – a commitment to continuous learning is essential for long-term success. ??