How do you learn from AI and optimization experts?

For me it is about learning how we can apply AI.I am continually learning the basics but leave the technical part to the experts and together we can offer the best solutions to companies.

At my core, I am passionate about leveraging AI to enhance business efficiency and drive growth in marketing and operations. In today's digital economy, I focus on developing AI solutions that empower creators and business leaders to build sustainable enterprises.

To learn from AI and optimization experts, it's essential to start with the basics and build a strong foundation. Begin by grasping the key concepts, principles, methods, and tools that form the core of these fields. Developing your mathematical, statistical, and programming skills is crucial for effective implementation and testing of ideas. Online platforms like Khan Academy, Coursera, or edX offer valuable resources for learning and skill development. Building this foundational knowledge will prepare you for more advanced techniques and applications in AI and optimization, allowing you to benefit from the expertise of experts in these fields.

That’s certainly one way. If your company works these types of projects and problems, see if you can shadow a leader or expert in this space for a day or for a series of meetings. Explore if you might be able to support them on a temporary assignment or as part of a career development initiative (10-20% of your time). The closer you can get to the actual person, the more tangible your learnings will likely be.

You must learn from the experts as there are no real other sources available for you specific problem (and if you are lucky to find one rare blog about your problem it probably will not detail and adapt the exposed solution to your specific context). AFAIK there isn't an academic curriculum that tells you how to combine several AI fields. Worst, it is really not easy to combine them. Some experts sometimes get it wrong themselves! This is what happens when you try to do something that is not as ripe as other approaches. That said, the benefits - when done right - are huge and worth the investment!

Build stuff. We don't have to be as extreme as Yoda — "Do or do not. There is no try." — but we should convert learning into action. Learn from experts so you're not building upon their knowledge rather than trying to reinvent everything yourself, but don't stop with reading articles, watching videos, or taking courses. Put the learning into practice by building prototypes, minimum viable products, or other experimental applications. Such efforts will ensure that you actually understood the material, but will also provide significant opportunity to learn even more in the process and still more when you share your efforts with others.

In order to get feedback on your learn from AI & optimization techniques, you need to start with ur peers first - whom you work closely , then look for cross functional team feedback, and look for manager & leadership feedback. Everyone see your role from their perspective, so great opportunity to get diverse view and follow through with action items .

It takes an insurmountable courage to keep pushing learning in the AI domain. The field is enormously vast and the pace is exponential. But one thing can keep us in track: if stuff is not intuitive to explain, better keep it at bay for a while and delve into those topics that are really easy to explain. That will give the confidence to keep moving in place bigger as the Hilbert Space. Also establish relationships among techniques, for example, you may use PCA for reducing dimension but further, you can see it also as a sparse linear autoencoder. There are plenty of relationships and analogies to explode within AI.

Learning from AI and optimization experts involves engaging with their research papers, attending conferences, webinars, and workshops they participate in, and actively participating in online communities and forums dedicated to these topics. Engaging in collaborative projects or internships under their guidance can provide hands-on learning experiences. Additionally, reading relevant textbooks and staying updated with the latest advancements through academic courses or online tutorials can further enhance your understanding of AI and optimization.

Hybridization of several fields is really the way forward. In particular, several types of combinations of ML (Machine Learning) and OR (Operations Research) are extremely powerful and I would even argue that the future of AI lies in one of those combination (because of current results but also anticipating what will be possible to do in the future).