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Building artificial intelligence aptitudes – Examining myths and realities

Artificial intelligence is all the rage, and everyone’s fantasizing about the possibilities – or dreading the dangers. From self-driving cars taking the stress out of morning commutes to Terminator-like fears of a robot takeover, everyone thinks they have a prediction about what the AI-enabled future will look like and how quickly it’ll arrive.

There’s so much noise, in fact, that it can be difficult to separate fact from fiction.

Here are some popular myths surrounding AI and whether they align with reality.

Myth: Before we know it, AI will blow up everything we know about how we interact with technology.

Reality: AI will have a big impact, but its path will be one of evolution, not revolution.

It takes more than just a great idea for AI to become a reality. It takes great people working hard to bring it to life. It also takes a lot of data and time to refine systems in AI and automation to deliver on their full potential.

Let’s take natural language processing (NLP) for virtual assistants as an example. As the market for AI solutions continues to grow, there have been significant advancements in this area – serving as a good example for us to view the scope of AI’s progress. These technologies are no longer new, and you’re likely familiar with them if you’ve ever used Siri to help you look up a song on your iPhone or navigated the latest version of Windows with the help of Microsoft’s Cortana.

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A NAO humanoid robot, developed by Softbank Corp. Photographer: Krisztian Bocsi/Bloomberg

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.

But Duplex is limited. Google has said it can only carry out natural conversations after being deeply trained in such domains, 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. But it may be years before this technology reaches its full potential.

Myth: Any talented developer can create a game-changing AI solution.

Reality: AI is a collaborative effort.

Much of AI’s expansion will come from teaching machines to think like humans, which is no simple feat. The advancement of NLP capabilities will be essential to train and improve voice-enabled commands for AI systems – but getting to that point will require more than developing better voiceovers.

Making strides with AI solutions takes a lot of human intelligence and a lot of data. Data scientists spend countless hours in the data structuring processes required to create and refine AI, which serves as 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.

Many businesses complete this function in-house and find it to be difficult to manage and scale, which is why a growing number are looking at outside resources. Services that provide the infrastructure for vetting, training and managing global workers to handle tasks like data annotation and transcription allow businesses to focus on innovation and growth, rather than scaling a workforce for routine, time-consuming tasks. To engage a contingent workforce strategically, it’s important to evaluate how each option might be beneficial in context with market needs and business objectives.

Myth: AI is going to kill thousands of jobs.

Reality: AI will create as many jobs as it kills – if not more.

Critics of AI argue that AI technologies will replace humans and take jobs, but the truth is AI might create more opportunities, particularly as solutions continue to be created and refined. In fact, Gartner recently suggested 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 will augment human intelligence to help us make more efficient decisions – much like the Siri and Cortana applications of the world. And while these systems already are shaping the future of work, people will always have a place in the AI and technology stack – doing the hard work it takes behind the scenes to turn data into actionable information.

As the future of AI comes into clearer focus, new business opportunities and job paths will emerge. Along the way, it’s important for business leaders and job seekers alike to consider the role of people in AI development, so we can maximize returns on investments both in the technology itself and the people who contribute to building it.

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