The Future of Work: The Newest Revolution

The Future of Work: The Newest Revolution

The Future of Work 

So far, 2018 is seeing even stronger developments in emerging IoT & frontier tech trends, especially AI and automation. US politics has been mindful of the jobs we’ve lost. Although most jobs lost are the result of automation, much of the campaign rhetoric we heard was about the threat of immigration and globalization: The American people were promised resurrected industries, rejuvenated careers, and overall, jobs, jobs, and more jobs. President Trump promised 25 million new jobs in the next decade 

The workers in the Rust Belt who voted for Trump bought into these promises, and now they want those jobs. Many workers there blame NAFTA for the decline of manufacturing jobs, but the reality is that automation is replacing more jobs than is globalizationFrom 2000 to 2010, 87% of manufacturing jobs lost was due to advances in productivity, not trade. (Of course, automation and AI creates other jobs, but the future balance is unknown.)  

The United States’ manufacturing output is at an all-time high. But the jobs aren’t what they used to be.   

“The next wave of economic dislocations won’t come from overseas. It will come from the relentless pace of automation that makes a lot of good middle class jobs obsolete,” President Obama said in his farewell address. 

The Relentless Pace 

A report from the Obama Administration (“Artificial Intelligence, Automation, and the Economy”) in December 2016 says that the current AI wave began in 2010, when machine learning, big data, and enhanced computation power began changing our technology landscape.   

Strategies for adapting to this rapidly changing environment must be found, and we must find them quickly. The most sensible solution, though not without its downfalls, is to invest in and develop AI, while educating and training workers for jobs of the future.  

Just shy of 18 months later, we’re already on the precipice of this shift. Let’s explore some interesting elements of the AI landscape.  

Moore’s Law 

We’re nearing the end of Moore’s law: The market of shrinking transistors is drying up. But two new modes are replacing that, one hardware and one software. We’ve been living out Moore’s Law for decades, but our paradigm is shifting in the very near future.  

Hardware  

Quantum computation has moved from hypothetical solution to reality. It’s no longer binary; bits will be 1 and 0 with positional attributes. (Right now, even small quantum computers are available for hobbyists.)

Even if you’re not ready to invest in your own quantum computer, the cloud will enable more heavy computation, so you can buy computation credits on a quantum computer.   

Ubiquitous Machine Learning  

Machine learning, and more accurately, deep learning has lots of applications. It needs new technology to enable is applications: FPGAs, GPUs, TPUs are all needed to process neural networks. Even our powerful PCs really excel at linear computation; to further machine learning, we’ll need massive parallel computational capabilities.  

The Shift to the City 

The Industrial Revolution shifted life as humans knew it globally: Production sites and output relocated and increased, powered by steam and coal instead of hands. Humans had to adapt quickly to create a society that worked. As workplaces shifted from the village to the city center, families relocated, and new careers were born. 

Our Future 

As we continue to lose jobs, how will we look at the world? Like we did with climate change, will we continue to blame others until we eventually arrived at a crisis situation? Will we chalk up the loss of jobs to the hands of outsourced work, aggressive importing, cheaper overseas manufacturing?  

Will we ignore the shift and lament days gone by?  

Or will we accept the change, welcome the challenge, and aspire to grow with this revolution? Will we pick up the pace of innovation to support a larger and more diverse AI workforce, accelerating productivity? 

Will we lead the charge instead of lagging behind? 

Want to learn more? 

Check out our previous #FutureOfWork articles – Part 1, Part 2, and Part 3.

Let us know what you think: @EarlyGrowthFS

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