Once upon a time the term “bewilderment” was meant to describe the feeling of being in a wild place with no human presence let alone any discernable paths.  While probably the majority of people have stopped being bewildered in the original sense of the word, it’s still an apt term for other wild landscapes – like that of AI. 

Navigating the ever-accelerating wilds of artificial intelligence often feels like trying to cross a mountain range without a pre-established path, a map or a compass.  Every journey from the comfort of our pre-AI basecamp (here meaning the time since the most recent AI winter) brings new learnings, perhaps a new breakthrough, a novel application, or a paradigm-shifting research paper.  Some trails are starting to be formed, patterns on the landscape as implementation approaches such as RAG go from novel to 200 page books.  Offshoots from these main trails may someday prove to be the defacto paths of the future, as new rabbit holes of research and learning and notebooks upon notebooks of python code lead us from the primary arteries.   

If you feel overwhelmed or even defeated by navigating without a clear path or by the sheer volume of information, you’re not alone.  Yet, amidst this whirlwind, we've found some ways to stay up to date or at least reasonably convince ourselves that we are.  Here's how we manage to convince ourselves that we’re keeping pace with the relentless march of progress. 

Embracing the Impossibility of Knowing It All 

The first step in my journey was accepting that fully keeping up with AI is, frankly, impossible.  This might sound counterintuitive, but acknowledging this reality lifted a tremendous weight off my shoulders.  Just like I don’t stress about being able to read every book that I would like to read, I no longer feel FOMO about grasping every new algorithm or read every research paper the moment it's published.  Keeping up is futile and, while not worthless, would definitely consume all time and have diminishing returns.  Instead, I focus on absorbing what I can, knowing that it's okay to have gaps in my knowledge.  This mindset has made learning about AI more chillaxed. 

Curating Quality Over Quantity 

In an age where information is abundant, curation becomes essential. I subscribe to a select few reputable newsletters and blogs like AlphaSignal (and follow Eric Vyacheslav on LinkedIn) and use tools like arXiv Sanity Preserver to sift through the flood of research papers.  

These curated sources, like cairns marketing a trail, highlight the most significant developments without drowning me in oceans of content.  By focusing on quality over quantity, I can stay informed about impactful advancements without feeling overwhelmed. 

Engaging with the AI Community 

Being part of a community has been invaluable.  Conferences, expert co-workers as well as platforms like Reddit's r/MachineLearning and Stack Overflow allow me to engage with fellow enthusiasts and professionals.   

Participating in discussions, asking questions, and sometimes even helping others not only deepens my understanding but also keeps me connected to the latest trends and practical applications that might not yet be widely publicized.  Local meetups (including some that we host with the simple goal of creating community) and webinars offer additional opportunities to network, share, and learn from others' experiences. 

Setting Realistic Learning Goals 

To help prevent the feeling of helplessness, I've long set realistic and quasi-structured learning goals for myself.  Whether it’s a technical topic, some hobby or sport, I’ve found that having a bit of structure driving the learning can be helpful. 

The first part of this structure is a context for what the learning is about.  Sometimes that’s just experimental, or sometimes it’s with a particular project or business objective in mind. 

Dedicating just fifteen minutes a day to reading or watching something helps make the learning process manageable.   

By breaking down my learning goals into achievable snack-size bites, I maintain steady progress without overwhelming myself or forgetting to sleep.  It also helps to stay focused and not explore every side path – I try to stick to the branch I’m on unless it becomes clear that it’s not going to get me where I want to go.  

A consistent, measured approach ensures that I'm at least attempting to continually advancing my knowledge without sacrificing other must-do things. 

Hands-On Experimentation 

I find there's no substitute for practical experience.  When possible, I get hands-on time with new tech as a way to get first-hand familiarity with it.  That could be using open-source frameworks like TensorFlow and PyTorch or spinning something up in a commercial platform.  Calling new API endpoints is also a particularly fun thing to do because usually it takes very little time and can have tremendous benefits (it’s kind of the whole point of APIs, really).  

As someone who learns best through a combination of observation and doing, building something tangible not only reinforces what I've learned but also exposes me to challenges that deepen my understanding.  I want to hike the trail, not just read about it. 

Experimenting with real data and models bridges the gap between theoretical knowledge and practical application, making the learning process both engaging and effective, plus it’s a lot more interesting that way. 

Prioritizing What's Relevant 

With so much happening in AI, it's crucial to prioritize.  I focus on developments that align with what I think will help our customers and partners.  By zeroing in on what I think is going to be relevant to them, I help ensure that my learning and experimenting has practical, potentially high-ROI benefits. 

Viewing Learning as a Continuous Journey 

Perhaps most importantly, I keep a mindset that staying updated is an ongoing journey rather than a destination.  This perspective alleviates the anxiety of feeling like I'm constantly playing catch-up.  It reminds me that learning is a lifelong process, especially in a field as dynamic as AI.  Embracing this journey keeps me open to new experiences and discoveries, making the process rewarding in itself. 

Staying Informed on Ethical and Societal Implications 

It’s also important to remember that AI doesn't exist in a vacuum and it has and will continue to have profound impacts on society.  I make it a point to stay informed about the ethical, legal, and societal implications of AI by following organizations like the AI Ethics Lab and the Partnership on AI.  Understanding and staying grounded in these aspects enriches my perspective and ensures that my engagement with AI is responsible and considerate of its broader effects. 

Conclusion 

Convincing myself that I'm staying up to date with AI is a balancing act of strategic learning, community engagement, and a healthy mindset. While I certainly will never know everything or even a small fraction of everything the steps I take help me stay informed.  In embracing the journey of continuous learning, I find not just solace but excitement in the ever-evolving landscape of artificial intelligence. 

After all, staying up to date with AI isn't about reaching a finish line; it's about enjoying the trail, even as the horizon is continuously expanding.