Our robotic future: The question of jobs

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Cat Mules

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business automation robotics

This century held the breakthrough moment for the robotics revolution: the ability for robots to work together – using connected systems.

Since the dawn of the Industrial Revolution, the ability of machines to do work once done by people has been a concern.

Many of us might still have niggly fears about the rise of the robot as a threat to our jobs; while others argue the reality is that robots are designed by us, so can be guided by our real-world needs.

How the Industrial Revolution Gave Rise to Violent 'Luddites ...

The Luddites in the 19th Century famously smashed machinery in an effort to save jobs. Credit: Mary Evans Picture Library/Tom Morgan/Everett.

An augmented reality

There are some 50 billion connected devices in 2020. These ‘robot sidekicks’ – including digital assistants, wearable fitness trackers, kitchen utensils in homes, manufacturing robots in factories – are both becoming more invisible and more impactful in our lives.

As robots are increasingly building new, better versions of themselves, they’ll be able to better help us tackle future challenges. Support a robot with the right sensors, algorithms and processing powers, and a system can be built that is self-improving.

They audit and learn from data – and then adapt and personalise their responses to human needs accordingly.

Mundane be gone

There is every sign the robotic transformation will continue to see the role of human creativity as central – augmenting our needs, as we programme them. As robots – as well as the learning and technology required to make them – is available to all, then the path takes will be up to us.

These new job areas are extending upon – and starting to replace – jobs of the past in traditional market areas such as manufacturing, retail and administration. Looking back five years, differences can already be noted. Less important, for example, is service orientation and quality control, while more important is cognitive flexibility and emotional intelligence.

Today’s technologies are creating jobs, including in emerging areas such as crowdsourcing, the sharing economy and autonomous vehicles. The McKinsey Global Institute says, in the US, one-third of jobs created over the past 25 years – such as systems management and IT development – barely existed or did not exist a quarter of a century ago.

Robots are just better at humans at some things. As global economist John Tamny says, they’re increasingly useful in the workplace: “robots won’t call in sick, won’t require days off, and won’t quit”.

Machines can take on almost any predictable task. They’re also becoming more able to unite forces with computers – to analyse data to pass tests once considered to be signs of self-awareness.

The productivity of robots could free up humans to focus on other things – and keep the economy running and improve output.

These arguments show why in our new AI-augmented world, we’d do best to understand and align our skills with growing technological capabilities.

McKinsey forecasts that by 2030, “roughly 14 percent of the global workforce—may need to switch occupational categories as digitisation, automation, and advances in artificial intelligence disrupt the world of work.

“The kinds of skills companies require will shift, with profound implications for the career paths individuals will need to pursue.”

Ultimately, robots are extensions of ourselves – so shouldn’t the right question be to ask, what are the best ways to guide them? With that, what if AI could make us more – not less – human, in the age of robotic automation?

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Cat Mules

Umbrellar's Digital Journalist, coming from a background in tech reporting and research. Cat's inspired by the epic potential of tech and helping kiwi innovators share their success stories.

Data, AI, BI & ML

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Artificial Intelligence and Machine Learning are the terms of computer science. Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system. There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.” Therefore it is an intelligence where we want to add all the capabilities to machine that human contain. Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”

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