What do you do if your AI career is demanding but you still want to keep learning?

Prioritize learning by allocating specific time slots in your schedule dedicated to personal development. Even dedicating just a few hours per week can make a significant difference over time.

In the field of AI, continuous learning is crucial, and the cornerstone of an effective learning process in a demanding business environment lies in setting achievable goals and taking small, steady steps toward them.

Juggling a demanding AI career with learning doesn't have to halt your growth. Make time management your secret weapon. Utilize techniques like the Pomodoro Technique for focused study sessions. Analyze your workload and delegate or streamline tasks where possible. Treat learning like a non-negotiable meeting, scheduling dedicated time for it, even if it's just short bursts. Consistency is key - these focused pockets of learning will snowball into significant knowledge gains over time.

Effectively managing time is essential when juggling the demands of a burgeoning AI career and ongoing learning pursuits. Implementing strategies such as the Pomodoro Technique, with its structured work intervals and brief respites, can enhance productivity and focus. By evaluating commitments and identifying opportunities for delegation or simplification, individuals can optimize their schedules for maximum efficiency. Prioritizing learning as an immutable component of daily routines ensures consistent allocation of time for skill development. Even small, regular study sessions can yield substantial knowledge growth over time, reinforcing the importance of sustained dedication to personal and professional advancement.

It's not easy, but you need to carve out time (I do it after work and before going to bed) to read about recent developments, catch up on new trends, write articles, and watch some of my favorite leaders' recent keynotes.

You can't simply "learn AI". You need to know what you want to focus on - LLMs, Vision Models, Ethical AI, etc. Decide what you want to become, before you start learning

???????????????????? ????????????????: Explore flexible learning options such as online courses, tutorials, podcasts, or audiobooks that you can engage with at your own pace. These resources allow you to learn on the go, fitting learning into your busy schedule. ???????????????????? ?????? ??????????????????????????: Engage with peers, mentors or online communities to exchange ideas, share resources and collaborate on learning initiatives. Networking can provide valuable support and accountability in your learning journey. ?????? ?????????????????? ??????????: Establish achievable learning goals that align with your career aspirations. Break down larger objectives into manageable tasks to maintain motivation and progress steadily.

Establishing clear and attainable goals is pivotal for advancing in your learning journey while avoiding overwhelm. Begin by pinpointing the specific domains of AI you wish to explore, whether it's machine learning, natural language processing, or another specialized area. Break down these domains into manageable sub-objectives and devise a timeline for accomplishing them. This structured approach not only offers guidance but also facilitates progress tracking and sustains motivation throughout your learning endeavors.

While leveraging digital resources, I've also embraced the power of microlearning. Utilizing platforms that offer bite-sized lessons allows me to fit learning into even the shortest gaps in my schedule, making continuous education both feasible and effective.

There are a vast amount of newsletters, online-blogs and tutorials out there. Use a LLM of your choice to let it summarize these for you and extract the most important bits to stay up-to-date!

In today's digital landscape, there's a wealth of resources for learning AI that cater to various schedules and preferences. Online courses, podcasts, and webinars offer flexible learning options, allowing individuals to learn at their own pace and convenience. Moreover, the vibrant AI community provides platforms for networking, sharing insights, and staying updated on the latest trends and advancements. By taking advantage of these resources, one can effectively incorporate AI learning into their daily routine, making it a seamless and enriching experience.

Utilize online platforms, podcasts, webinars, and industry blogs that offer the latest AI insights and can be accessed in your own time. Take advantage of learning resources offered by your employer, such as workshops, courses, or subscriptions to professional journals. Consider setting aside dedicated time each week for self-study or joining a study group to stay motivated.

The symbiotic relationship between learning & contributing is integral to professional growth. 1) In AI, we encounter certain concepts that transcend specific tasks. These universal principles become our toolkit, ready for application across various contexts.2) While theoretical knowledge is essential, the practical application of AI concepts is where true learning occurs. Reflecting on these practical aspects is the bridge between theory & real-world impact. 3)Some ideas fall short, models underperform,assumptions prove flawed. These lessons shape our future endeavors. In summary, the interplay between learning & contribution is dynamic. Embrace both the universal and the practical, & recognize that even failures contribute to learning.

Three 3?? words: Flow of work - This is how I have implemented all the new tools or processes I have been using for years. Do you spend a lot of time writing or reviewing documents? Then Midjourney (generates images ??? from prompts) is not the AI companion for you. Try Deepl Write ?? to get better at word choice, or if you use Microsoft Word, try Microsoft Copilot. Copilot can also help you with all your other Office products, including PowerPoint, Excel ??, and Microsoft Teams. Last but not least, take notes. By hand or however you prefer, writing down when AI was helpful will help you later to see the time savings. ? You can keep yourself accountable by showing your progress. What is documented is true.

Most task you need to perform can be done in a lot of different ways, so use this to your advantage to learn on an actual task by experimenting with 2 or 3 approaches (depending on the deadline). That way you complete you work tasks AND learn while you are doing it. Maybe you will even find a better way to solve things by experimenting, which you can use to improve processes and impress your managers!

Applying new AI concepts directly to projects has been enriching. To amplify this, I maintain a learning log, documenting applications, outcomes, and reflections. This not only enhances my learning but serves as a valuable reference for future projects and career milestones.

Networking with fellow AI professionals can significantly enrich your learning journey by providing access to diverse experiences and insights. Whether attending industry conferences, joining professional associations, or participating in online communities, these interactions offer opportunities to glean valuable knowledge. From mentorship possibilities to collaborative projects and fresh perspectives on AI trends, networking opens doors to a wealth of resources. Even brief engagements during lunch breaks can yield valuable connections and foster professional growth, underscoring the importance of cultivating a robust network within the AI community.

Networking has been invaluable. I've taken it further by organizing bi-weekly "AI Insight Exchanges" within my network, where we share the latest in AI, discuss challenges, and brainstorm solutions. These sessions are incredibly enriching and foster a strong sense of community.

What you think today will be relevant can already be outdated in 3 months. That's why it is important to understand and learn basic concepts, which will help you to transition to new developments.

"Tell me and I forget. Teach me and I remember. Involve me and I learn." My AI career is like yours, demanding and yet we are wanting to learn more knowing that the learning curve will get steeper if we stop our pursuit. So what do I endeavour to do is stay involved ?? How, well start the day with collaboration on SM handles like LinkedIn & share your experiences, during work get involved in tasks where learning is involved like model tuning or even finalising the scope of Ai implementation and after work listen ?? to podcasts or read newsletters during breaks. That's what I personally do and then always volunteer to teach a subject on AI or even better author research papers and deliver thought leadership on AI. You reap as you sow !!