How to Accelerate AI Development Globally

artificial intelligence ai

The promise of artificial intelligence has been looming over technology for decades, but growth has been much slower than anticipated. Although developments are being made, we don’t have the widespread AI use many experts predicted we would have by now. In fact, many of the main components of AI run on the same systems they did 20 years ago. To accelerate the development of this tech, the industry is in need of skilled individuals and people who want to make AI come to fruition, and some serious changes need to be implemented. Here are three ways to speed up the progress in the field of machine learning and artificial intelligence:

Make AI Accessible

Part of the reason that artificial intelligence has stayed in technology limbo for decades has been that people don’t really see how it can work in their life. They don’t necessarily want a robot in the house like what is shown in movies, and developers are often focusing on solving big-picture problems like cracking encryption and managing nuclear weapons. To get people excited about this tech and gain public support, tech companies need to show how their technology will help people in their everyday lives. Not everyone wants a fully autonomous car, but drivers likely want semi-autonomous features that keep them safe, like lane assist and automatic parking, which are based in machine learning. Businesses can also benefit from the use of this tech, particularly in their customer service sectors. However, much of that knowledge is lost when AI developers start talking in jargon and sharing only their big projects. To make this clear, companies should focus on sharing breakthroughs that people can get behind.

Grow AI Talent

Recent efforts have helped science, technology, and engineering gain popularity among teens and college students, but that effort also needs to extend to AI. One of the main reasons artificial intelligencedevelopment has been so slow is that there simply isn’t enough brainpower to make it happen. To truly be successful, there needs to be a strong pipeline of people entering the AI world.

Working with artificial intelligence isn’t easy, especially because it uses extremely difficult, high-level math, and most positions require a Ph.D. However, if younger students can be introduced to the many possibilities of working with artificial intelligence from an earlier age, they can plan accordingly and gain the skills and education they need to join the field some day. Some tech companies are even willing to pay for people’s schooling who show an aptitude for AI in hopes that they will join their company post-graduation. The AI talent pool is weak, so the top priority to building a stronger network is to get the right people in position and preparing to be in position. A lot of this comes back to the previous idea of making artificial intelligence accessible—when young people see what the tech can do, they will be more inclined to want to work in the industry.

Define Basic AI Regulations

Artificial intelligence has long been somewhat controversial, largely because of what people have seen AI and robots do in movies. It however has a world of possibilities, and the technology can be used for good or bad and can blur privacy and security lines. Before development continues in earnest, the developers in the public and private sectors need to come together to set the groundwork for what AI can and cannot do. Developing artificial intelligence is moving into uncharted territory, which means many of the issues like computers thinking and acting like people have never before been addressed. Creating a governance framework will let companies know the boundaries of their development and can ultimately help the public be more accepting of AI by knowing what it won’t be allowed to do. A regulatory group could be a good start to establishing the basic rules for AI and for setting the precedent of what to do when future issues arise.

Artificial intelligence has the power to transform huge amounts of existing data and turn them into amazing new technology that can transform how we live and work. However, to make that happen, things within the development space need to change.

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