The Only Metric That Matters: User Engagement and Artificial Intelligence's Quest for Intelligence

The Only Metric That Matters: User Engagement and Artificial Intelligence's Quest for Intelligence

In the ever-evolving world of technology and innovation, two topics have been buzzing in recent years - user engagement and artificial intelligence. On the surface, these may seem like disparate subjects, but they actually share some common threads. Both revolve around the concept of understanding human behavior and harnessing it for success.

When it comes to building a successful product, the only metric that truly matters is user engagement. As Josh Elman of Greylock Perspectives puts it, founders need to ask themselves three key questions: are people using your product, are they using it as intended, and are they using it frequently? These questions help to divide the user base into three distinct categories: cold, casual, and core users.

Core users, as Elman points out, are the lifeblood of any product. These are the individuals who consistently return and engage with the product, forming a loyal user base. They not only provide valuable feedback but also serve as brand advocates, spreading the word to others. Understanding and catering to the needs of core users should be a top priority for any founder.

But how does artificial intelligence fit into this equation? To understand that, we need to delve into the nature of intelligence itself. AI has taken various forms over the years, with the first generation known as Good Old-Fashioned AI, or GOFAI. This approach, rooted in symbolic representations, aimed to mimic human reasoning through syllogistic logic.

However, contemporary AI, often referred to as second-wave AI, takes a different approach. Rather than relying on predetermined conceptual schemes, it leverages vast amounts of data and shallow inference over weakly correlated variables. This approach, known as deep learning, has yielded impressive results in tasks such as game-playing, facial recognition, and medical diagnosis.

The key distinction between first-wave and second-wave AI lies in their treatment of representation. First-wave AI focused on manipulating symbolic representations, while second-wave AI employs distributed representations. These distributed representations capture the nuances and micro details of the world, allowing AI systems to register the world in relevant ways.

This ontological approach, as it turns out, is what sets second-wave AI apart. Humans, too, register the world based on their interests, culture, and communities. But unlike AI systems, humans possess something AI still lacks - judgment. Judgment is the ability to thoughtfully consider and act in a manner that aligns with the context at hand.

To deal with context appropriately, it is not enough to have a predefined conceptual model. Instead, judgment requires an existential commitment to the world, an accountability to its reality, and a defense against falsehoods. This level of judgment is an incredibly high bar, one that AI has yet to reach.

So, what actionable advice can we glean from these two realms? Here are three key takeaways:

1. Focus on user engagement: Regardless of the type of product or service you offer, user engagement should be your top priority. Understand who your core users are and cater to their needs. Continuously gather feedback and iterate based on user behavior.

2. Embrace the power of distributed representations: AI systems have demonstrated the power of distributed representations in capturing the complexities of the world. Consider how you can incorporate this approach into your own projects, whether it's through data-driven decision-making or leveraging neural networks.

3. Strive for judgment, not just intelligence: While AI may excel in certain tasks, it still lacks the ability to exercise judgment. As humans, we possess the capacity for open-minded, deliberative thought. Nurture your own judgment by embracing diverse perspectives, challenging assumptions, and remaining accountable to the realities of the world.

In conclusion, user engagement and artificial intelligence may seem like distinct topics, but they both center around understanding human behavior and leveraging it for success. By prioritizing user engagement, embracing distributed representations, and striving for judgment, we can navigate the ever-changing landscape of technology and innovation with confidence.