All of the trends we are seeing—and the recent conversations we are having with clients—connect to the fact that we are seeing a real rise in discussion and investment around artificial intelligence. In fact, 71% of organizations perceive AI to be a game-changer, and 61% report that AI and machine learning are their most significant initiatives right now.
In many ways, this surge in interest and investment is because we have come to accept in AI what used to be viewed as miraculous as simply common today. The way machines can beat us at chess; how Siri can respond to the questions we pose; how we solve environmental issues. AI has become so far-reaching across all industries, but we have only just scratched the surface. AI is not just going to be theoretical but rather practical, useful, and transformational.
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As you and your organization come up to speed on AI, here are three things to keep in mind as you start to incorporate AI into your environment:
Understand how AI grows: We have a tendency to overestimate what an AI system can do when it’s first turned on and then we underestimate how much it can grow and change over time. It’s the same thing as if you are trying to look at a 5-year-old and wonder how they will be as a 35-year-old adult. It will have a lot do with the experiences they have and how they develop. AI is the same. We tend to think about it as a miniature adult but it’s not; it’s still growing. The similarities between human pedagogy and AI are so striking.
Teach the AI: AI is fundamentally different from classic development. With AI, you don’t write the code. What you actually write are the instructions and the pedagogy to teach an AI system how to learn. It’s an exciting and different way of thinking. Instead of focusing on developing AI, you focus on training AI. Then, you give the AI time to learn and if the system is working the way it’s supposed to, it is going to learn, change and reprogram itself continually.
Think Bigger: Once an AI starts to demonstrate some reliability and real autonomy, it’s time to explore other, related uses where it can be applied. A classic example of this is IBM Watson, which started as a Jeopardy-playing demonstrator and is now driving myriad IBM solutions. The real scalability and cost effectiveness of AI comes into play when a mature system is turned loose on related but non-identical problems.
Walter was recently featured in Crunch Metric’s “The Book of AI Trends 2021” as one of the top 100+ experts for their AI predictions for the year. Click here to download the report.