“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” Larry Page
“The key to artificial intelligence has always been the representation.” Jeff Hawkins
Earlier in my career, I served as the Director of Data Solutions for the Large Business and International Division of the IRS. In that role, I had the privilege of working with some brilliant technologists who utilized the Naive Bayes algorithm—a probabilistic classifier developed in the mid-20th century—for risk assessment across populations. While not cutting-edge at the time, it revolutionized several internal classification processes and demonstrated the potential of applying even basic AI techniques to solve complex challenges.
I observed with great interest as AI evolved from these simpler methods to more advanced models like decision trees, support vector machines (SVMs), and early neural networks. Today, large language models (LLMs) like GPT-4 represent the pinnacle of AI evolution. These systems are capable of complex reasoning, creative problem-solving, and multi-modal understanding, benefiting from fine-tuning and reinforcement learning. This remarkable trajectory illustrates how AI has progressed from basic statistical tools to sophisticated systems reshaping industries across the globe.
So, how is that working for you?
While technologists are achieving amazing breakthroughs, the benefits haven’t fully trickled down to everyday life. In many cases, the promise of technology has even introduced new frustrations. Here are a few examples:
Navigation Apps and Distance Miscalculations
Navigation apps often fail to account for real-world road conditions. Try it yourself: search for “Mexican food near me” or the “nearest Walmart.” You’ll notice the initial results often show straight-line distances, but once you hit “Take me there,” the actual driving distance often doubles. Why can’t these apps provide the true driving distance instead of the “as the crow flies” approximation?
Automated Customer Support
AI-powered chatbots rarely provide the answers users are looking for. Many people find themselves stuck in endless loops with bots that misunderstand their needs. Even worse, escalation to a human agent isn’t always an option. The promise of seamless and efficient customer support remains largely unfulfilled.
Microsoft Copilot and Productivity Issues
Microsoft recently introduced its AI Copilot, which is supposed to enhance productivity. Yet, I’ve already encountered issues that have decreased mine. For example, I can no longer print a date range from my calendar—a function I relied on frequently. Maybe it’s a coincidence, but how is this an improvement?
Recommendation Algorithms and Streaming Platforms
Recommendation engines for TV shows and movies remain frustratingly inaccurate. Searching for a title often fails to scan across all your streaming platforms, leading to incorrect or incomplete results. Worse, the algorithm might direct you to a platform that charges for a movie while ignoring others where it’s available for free.
Predictive Text and Auto-Correct
Tools like predictive text and auto-correct can save time, but their frequent misunderstandings of user intent often cause more frustration than they solve. This is especially problematic in professional or sensitive communications, where accuracy matters most.
Smart Home Devices and Interoperability
I own several “smart” devices—thermostats, TVs, appliances, a Ring doorbell, and even a Wi-Fi-enabled garage door opener—but none of them work together. Each exists within its own ecosystem, and I simply don’t have the time to figure out how to integrate them. As a result, they each perform their one function, but the promised “smart home” remains out of reach.
A Complete Lack of Privacy
Have you ever discussed a topic in person, only to see your social media feeds suddenly filled with ads related to that conversation? We all have. While AI can’t always figure out what I’m trying to type, it somehow manages to infer products I might want to buy. That’s a chilling tradeoff.
While AI continues to advance at an impressive pace, these examples highlight the gap between its potential and the reality of our shared user experiences. Bridging this gap will require a renewed focus on user-centric design to ensure AI genuinely improves our lives.
Coda
I’ve just completed my first year as a self-employed entrepreneur. After spending 32 years with the Federal Government—24 of those as an executive—stepping out on my own was both daunting and exciting. I’m thrilled to share that 2024 was a resounding success! Over the past year, I led 66 classes, helping 753 students advance their careers.
I also had the privilege of conducting in-person sessions across the U.S., visiting cities such as Boston, Gainesville, Washington, D.C., Cleveland, Jacksonville, Miami, Orlando, McAllen, TX, Baltimore, Sierra Vista, AZ, Chicago, St. Louis, and Des Moines.
A heartfelt thank you to all my channel partners who contributed to this incredible milestone. Here’s to another year of growth and collaboration!
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