AI & ML: Asking the Right Questions

When it comes to the tech industry one of the most talked about topics at the moment is artificial intelligence (AI) and machine learning (ML), but why? In a world where data and efficiency is becoming more important by the day, a hope that one day we can utilize this data to make our technology, and eventually some of our human tasks, more efficient is on everyone’s radar. According to Gartner, 2020, roughly 37% of organizations have implemented AI in some form, that’s an astronomical increase of 270% over the last 4 years. Gartner also ventures that by next year (2021), over 80% of all emerging technology will have AI foundations. Those numbers are clear indicators that this is in fact the technology of the future, so what exactly is it? 

While many think they are one in the same, there are some discrete yet important differences between AI and machine learning. For example, artificial intelligence is not it’s own system, but more so implemented into the system. Applying human intelligence to a machine in hopes that by applying said intelligence it can do things which present humans can do better. Machine learning is where a machine can learn through experience without being explicitly programmed. Machine learning is an application of AI that allows the system to learn and evolve from each task or experience, making it more versatile after each interaction. 

Earlier this year our Co-Founder Aaron Warren sat down with one of our top AI Architects, Kevin, to discuss AI, machine learning, and what it all means. One of the biggest takeaways from the conversation came from Kevin, who claimed the most important aspect of developing AI at the moment comes down to two things: learning to ask the right questions and learning to use the data. With a seemingly infinite amount of data out in the world, it is almost as if there are infinite possibilities for AI- so what can we expect first? At the moment, call centers and similar services are trying to utilize AI with self-service bots that can answer customer questions in real-time, opposed to using human agents. While the input of data is moving the technology along, the main area of focus now is asking the right questions. What obstacles stand in the way of completing a task? How can we minimize said obstacles, and how can we prevent them in the future? 

Kevin and Aaron both use the analogy of asking for a drink, a simple task at first thought but the nuances and phrasing differences prove to make things difficult. You could ask for a drink, or you could ask for a “cold one”, or you could ask for “a round.” When ordering your second drink you could just ask for another, or you could say “hit me again,” or “pour me another.” If these sequences aren’t directly programmed into the AI, these commands won’t make any sense and won’t be completed. This is where machine learning can provide evolution. The technology will progressively learn these phrases as it hears them, eventually becoming easily recognized and a simple task, similar to how we learn language and pick up on new words or phrases as humans. While there might be a current focus on call centers and self-service bots, we can see AI becoming more involved across life every day. Self-driving cars, cyber security, manufacturing, healthcare, and virtual assistants just to name a few. How will you be able to utilize AI and machine learning? It all starts with asking the right questions. 

To learn more about AI and machine learning, listen to Aaron and Kevin's full conversation on Youtube, or follow the link here.