Artificial intelligence (AI) is a hot topic these days. But not all AI is the same. Some AI is just hype and doesn’t really deliver anything useful. Some AI can help us get more done and be more creative.
AI hype is heaviest when people make big claims or have high expectations about what AI can do, but they don’t really back them up with facts or evidence. All the AI hype in the news today can be confusing and misleading. It can make us think that AI can replace humans immediately or solve any problem without human help. It can also make us feel like we have to use AI for everything, even if we don’t really understand how it works or what it actually does.
In contrast, AI-driven productivity is when we actually use AI to make our work and life better and easier. AI-driven productivity focuses on a specific goal and the real impact of using AI. It also considers how humans and AI can work together to increase productivity and success.
AI-driven productivity can create real value and competitive advantage for us if we use it smartly and responsibly. For example, AI-driven productivity can help us innovate and create new things and opportunities. For example, some of the cool AI stuff that you should know about are:
- Data-centric AI: This is when we focus on making our data better and richer for training our AI algorithms.
- Model-centric AI: This is when we use advanced methods and techniques for creating, training, testing, deploying, and monitoring our AI models.
- Applications-centric AI: This is when we use AI for specific purposes and domains where it can add value and solve problems.
- Human-centric AI: This is when we make our AI systems more friendly and cooperative with humans.
How can we achieve AI-driven productivity?
To achieve AI-driven productivity, we need to have a clear plan and a big picture of how we want to use AI systems. This means:
- Having clear goals and reasons for using AI systems
- Finding the right problems and data sources for using particular AI systems
- Choosing the best AI technologies and techniques for solving our specific problems
- Following best practices for developing, deploying, and maintaining our AI systems
- Measuring the results and impacts of using our AI systems
- Communicating and engaging with our stakeholders and customers about using our AI systems
- Addressing ethical issues, biases and risks associated with using our AI systems
Conclusion
AI hype will not win in the long run, and AI-driven productivity is what will lead individuals and companies to greater success. Using AI systems to make our work and life better and easier will create real value and innovation. But achieving AI-driven productivity at work (or at home) requires a clear plan and a big picture vision of how we want to use AI systems, as well as a focus on how humans and AI can actually work together to create actual value – not just a lot of hype.
References:
https://www.statista.com/chart/23779/ai-productivity-increase/
