AI Real-World Task Performance: Field Notes & Observations
Hey guys! Welcome to my running log of how AI performs on real tasks. I'm sharing my notes from the last few weeks, and I think you'll find them pretty interesting. It's fascinating to see what AI can do, where it shines, and where it… well, doesn't quite shine so much. Let's dive in!
Success Story: ChatGPT and the Elusive AI Paper
In the realm of AI research, keeping up with the latest advancements is crucial. I tasked ChatGPT with a specific challenge: find a recent paper about a small AI model that performed well on the ARC AGI (Abstraction and Reasoning Corpus for Artificial General Intelligence) benchmark. Guys, I was genuinely surprised and impressed when it found the paper immediately! This highlights how effective AI can be in information retrieval, especially when dealing with technical topics. It’s a big win for using AI to assist in research and staying updated in fast-moving fields. The ability of AI to sift through vast amounts of academic literature and pinpoint relevant research is a game-changer for researchers and practitioners alike. Imagine the time saved and the potential for accelerating breakthroughs! This kind of success makes me really optimistic about the future role of AI in supporting complex tasks.
The paper ChatGPT found is this: https://arxiv.org/abs/2510.04871. Check it out if you're curious!
Failure: The Case of the Phantom Amazon Item
Now for a less triumphant tale. I attempted to use ChatGPT to monitor an Amazon link daily and notify me when the item became available for purchase. Sounds simple, right? Unfortunately, every morning I received a message stating that the item was available… but it wasn't. This was pretty frustrating, and it underscores a critical limitation of current AI systems: reliability in task execution. While AI can excel at information retrieval and generation, consistently performing real-world tasks with accuracy remains a challenge. This particular failure highlights the need for improved integration between AI and external services, as well as more robust error handling. It’s one thing for an AI to understand a request; it’s another thing entirely for it to execute that request flawlessly in a dynamic environment like the internet. We need AI that's not just smart, but also dependable.
Success: Image-to-Image Editing for Home Decor
Let's get back to the positives! While I was at the store, I used AI to generate renderings of my dining room with different wallpapers. This is where I find image-to-image editing incredibly useful. It’s like having a virtual interior designer in your pocket! This application showcases the creative potential of AI and its ability to assist in decision-making processes. Being able to visualize different options in a realistic setting can save time, money, and a whole lot of potential design regrets. AI tools that offer image manipulation capabilities are becoming increasingly powerful, and I think we’ll see even more practical applications emerge in areas like home improvement, fashion, and even personal expression. It's a great example of how AI can augment our creativity and help us bring our ideas to life.
Failure: Atlas Browser and the Fabricated Prices
I also tried using the Atlas browser to find some cheap RAM. What followed was a series of impressive-looking clicks… that ultimately led to the creation of fake prices. Ouch. This experience highlights the issue of AI hallucination, where the system generates information that is not based on reality. While Atlas may have performed the actions of searching, the results were completely fabricated. This is a major concern, especially in applications where accuracy is paramount. We need to be cautious about trusting AI systems implicitly, especially when dealing with data-sensitive tasks like shopping or financial transactions. It’s a reminder that AI is still a tool, and like any tool, it can malfunction or be misused. Critical evaluation of AI output is essential.
Check out the details here: https://kschaul.com/link/2025-10-21_just_tried_out_atlas/
The Unpredictability of AI: A Lingering Question
After using these AI tools for years, I've noticed something consistently: I rarely know whether something is going to work until I actually try it. Is it just me? This unpredictability is a key characteristic of current AI technology. While AI has made significant strides, it's not yet a perfectly predictable or reliable tool. There are numerous factors that can influence AI performance, from the quality of the input data to the complexity of the task. This inherent uncertainty makes it crucial to approach AI with a healthy dose of skepticism and a willingness to experiment. It also underscores the importance of user feedback in improving AI systems. The more we test AI in real-world scenarios, the better we can understand its strengths and weaknesses, and ultimately, the better we can build AI that truly serves our needs. It's an ongoing learning process for both the AI and the user!
Final Thoughts
So, what do these experiences tell us? AI is powerful, but it's not perfect. It excels in some areas, stumbles in others, and sometimes surprises us in unexpected ways. The key takeaway is that AI is a tool, and like any tool, it needs to be used thoughtfully and critically. We need to celebrate the successes, learn from the failures, and continue to push the boundaries of what AI can do. What are your experiences with AI? Share your thoughts in the comments below – I'd love to hear them!