In the next several years, artificial intelligence will transform how humans interact with businesses. This technical transition is one of the most powerful shifts in computing that the world has ever seen—and it has the potential to be a force of good. According to research from the McKinsey Global Institute, AI could add $13 trillion to global output by 2030 and raise GDP by 1.2% annually.
For this reason, companies will benefit from a long-horizon view of their investments in AI technology. The allure of heightened automation is strong from both cost-cutting and revenue-generating perspectives. But what are the consequences to workers who fear losing their livelihoods? Research from the World Economic Forum (WEF) warns, for instance, that AI has the potential to eradicate jobs, such as customer service, administration, and telemarketing that women typically hold. At the same time, the World Economic Forum says that AI could create as many jobs as they displace.
To fuel job creation, it’s important to understand AI’s two foundational technologies. Read this guide for an overview, along with ideas for AI-related jobs that can make the global economy even stronger.
Natural Language Understanding and Processing (NLU/NLP)
Machines are now smart enough to process voice and text-based communication. The underlying technology is NLU/NLP, a sub-field of AI and information engineering. This tech uses text classification methods to teach machines how to analyze and respond to human communication.
NLU and NLP go hand-in-hand, using algorithms to answer questions and respond to requests. Some notable examples include Google Assistant and Amazon Alexa, which process requests at almost near-perfect accuracy.
Deep learning is a field of artificial intelligence that relies on neural networks. In a nutshell, neural networks are models of the human brain, designed to create connections between images, speech, and other data.
Neural networks are applicable to use cases ranging from human language translation to pedestrian detection, and object identification. The goal of deep learning is to help technology think independently.
Relying on high processing power, neural networks create connections faster than is possible with the human mind.
Job Creation Impact
NLU/NLP and deep learning are only as effective as their accuracy. Consider the case of Facebook in 2017, for instance, which rolled back development of its AI chatbot. The reason was a 70% failure rate that left users frustrated.
What robots cannot replace is empathy and nuanced situational awareness. Human attention is vital in helping robots do their job better.
While AI has the potential to replace human jobs, one opportunity that will arise is the need for data validation—for human beings to troubleshoot problems and to be available when customers escalate their demands. After all, research from Accenture shows that half of people quit doing business with a company after a bad sales and marketing experience.
Another opportunity that will arise is the need for storytellers who can write copy and interpret analyses. AI, at scale, requires large-scale data input, which results in large-scale data output.
There will be an increased need for humans to explain the “why” behind a machine’s analysis, along with prescriptive steps forward.
Humans are wired for empathetic connections. Behind the scenes of every robot is a team of humans solving puzzles, architecting user experiences, and writing code. And long-term, the core technologies behind artificial intelligence will give rise to human creativity—creating a movement that the world hasn’t yet seen.