Breaking down some major myths and realities surrounding AI and examining how people are helping drive Artificial Intelligence forward.

As Artificial Intelligence is set to hit the mainstream, big dreams abound. Recent headlines demonstrate fantasies that range from self-driving cars perfectly propelling people through their morning commutes to nightmare scenarios of a sudden robot takeover. With so much speculation it can be difficult to separate fact from fiction.  

The truth is that AI is poised to change the world, but developments aren’t necessarily happening in the same way – or on the same trajectory – as many have envisioned. One thing remains clear as we do begin to understand more about AI’s path forward: people will be essential in driving AI’s progress.  

Future of Work Forecast

One of the biggest questions hovering over the industry has been: what does AI mean for the future jobs? The reality is that AI might create more opportunities than it eliminates.  Gartner recently suggested that AI will generate 2.3 million jobs by 2020 – exceeding the 1.8 million that it will replace. The report predicts AI-related job creation will reach two million net-new jobs by 2025.

Contrary to fears of sweeping job displacements, many theorize that AI systems won’t replace people. Instead, they will augment human intelligence to help us make more efficient decisions – much more like the Siri and Alexa-style applications of the world. And, while these systems are already shaping the future of work, the truth is that people will always have a place in the AI and technology stack – doing the hard work it takes behind the scenes to develop and progress new solutions.

The Power of People  

One of the biggest reasons AI technologies call for so much human intelligence is the data they require. Data scientists spend countless hours in the data structuring processes required to create and refine AI, which is a major challenge for companies when it comes to getting solutions to market fast. For many businesses to remain competitive and survive in the AI boom – particularly those seeking to innovate and disrupt the status quo in their industries – they will have to examine workforce strategies to accommodate growing data demands.

Businesses still need core, strategic functions in-house, but a growing number are looking at outside resources for less strategic data processing work. Services that provide the infrastructure for vetting, training and managing global workers to handle routine, time-consuming tasks like data annotation and transcription allow businesses to focus on innovation and growth. To do this tactically, businesses must evaluate how these techniques might be beneficial in context with market needs and their own unique objectives.

AI Advancement: Fast, but Not That Fast

Businesses that evaluate people and their workforce strategically stand to benefit as AI continues to advance. While the buzz around AI can make it seem like innovation is happening rapidly (and in some cases it is), a lot still needs to occur before some of AI’s most promising improvements reach their full potential. This conversion will require a lot of data and a lot of support from the people who are refining systems to deliver on their promises.

Natural language processing (NLP) for virtual assistants serves as a good example for us to view the scope of AI’s progress and the role people have in the ongoing progression. These technologies are nothing new, and many people have used one or two themselves. Perhaps they’ve asked Siri to help look up a song on their iPhone, or navigated the latest version of Windows with the help of Microsoft’s Cortana.

But it wasn’t until Google released Duplex, its technology to create natural conversations for tasks over the phone, that we really started to see the power of AI to interact with people more naturally. Google used Duplex to call a hair salon to schedule an appointment and played back a recording onstage at Google I/O earlier this year, amazing the audience with the technology’s ability to “speak” with a real person.

Even so, Duplex’s limitations are clear. Google has said it can only carry out natural conversations after being deeply trained in such domains – trained by people and complex algorithms, that is – so it cannot yet carry out general conversations.

This trend is consistent with most virtual assistant programs that use NLP today, which tend to be great for a handful of tasks but are limited when it comes to other tasks. In the future, these technologies will advance so that one virtual assistant will be able to handle a wider variety of tasks and do so in ways that’s even more surprisingly human.

As the future of AI comes more into focus, innovative developments – and thus new opportunities and job paths – will emerge. Along the way, it’s important for business leaders and those in the industry to consider the role of people in AI development, so we can maximize returns on investments in the technology and the people who contribute to building it.

 

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