Artificial Intelligence (AI) has been a buzzword for decades, yet we find ourselves in a new wave of AI hype, one that holds a genuine promise to reshape how we live and work. What sets this era apart? While it might seem like nothing much has changed, there’s both a technical evolution and a shift in accessibility that are driving the current shift.

Jasper Masemann, investment partner at Cherry Ventures, and Lutz Finger, venture partner at Cherry Ventures discuss exactly why is this new AI hype so special in our latest podcast episode. Technologically, the foundation of AI—sequence prediction in natural language processing—has been around for a while. Consider this: if we talk about Forrest Gump and say, "Life is like a box of...," most people will predict "chocolates." But this prediction hinges on context. Early models struggled with maintaining context over long sentences. The advent of attention models has transformed this by allowing AI to focus on the most relevant parts of lengthy inputs, improving accuracy in tasks like language translation and text generation. So, the core concept hasn’t changed; it’s the implementation that has seen significant refinement.

The most profound shift, however, is in usability. AI, particularly through tools like ChatGPT, has become accessible to everyone. Previously, leveraging AI required specialized knowledge and resources. You'd need a data scientist from a top institution to set up models and process data. Today, anyone can input a query into ChatGPT and receive a coherent response in seconds. AI is no longer just a tool for experts; it’s a utility for the masses, which explains the surge in its adoption and the accompanying hype.

The democratization of AI technology is a game-changer. But it's crucial to temper this enthusiasm with an understanding of AI’s limitations. AI models, while impressive, operate on historical data and probabilistic predictions. They excel at generating human-like text but fall short in understanding context as humans do. For instance, an AI might predict Lutz’s preference for Costa Rican coffee based on his past behavior, but it can’t grasp the nuance of changing tastes or the emotional experiences associated with coffee.

Moreover, AI's way of learning is fundamentally different from learning in humans. Humans develop a gut feeling from experiences and can explain their reasoning—a car changing lanes haphazardly is interpreted as a danger sign based on past observations. AI, on the other hand, makes decisions based on statistical likelihoods without true comprehension.

In conclusion, while the core principles of AI remain unchanged, enhancements in technology and significant improvements in accessibility have sparked the current hype. As we embrace this powerful technology, it’s essential to recognize its limitations and continue refining its capabilities. AI’s journey is far from over, and its potential is vast, but so are its challenges.

Tune in to The Edge, our very own podcast breaking down the latest AI developments, to explore these insights and more!