Assimilate… or be Eliminated?
The Future of a Bionic Workforce.

We (both in the sense of “we here at Dataspace” as well as in the sense of humanity as a whole) have done a lot of pondering about the future of AI in the workplace, and there are a plethora of theories out there on what this future will look like.

Some imagine a utopia where mundane labor has been automated, freeing up humans for more leisure and creativity.

Others fear a future where robots and AI make human workers redundant – taking their jobs and creating a spike in unemployment, particularly for those in already low-paying work.

Then there are those with a healthy dose of skepticism – doubtful that Artificial Intelligence will ever truly be “intelligent” enough to truly replace a human-based workforce.

This final point of view does have some strong evidence to support it, as there are any number of areas where AI is clearly not ready to “take over”:

Working the stock market is just making predictions, right? Should be a perfect job for AI, right? Maybe not, as this domain contains a multitude of subtleties that can spell disaster for an algorithm trying to operate by a set of logical rules.

(Hotels, bars, Environmental Services)
At first consideration, such tasks as checking customers in/out, and cleaning should be easily within the scope of automation. However, the experiment of a robot run hotel has been run… and in an interesting turn of events, most of the hotel’s robot staff was recently “laid off” as they were found to be more expensive, less reliable, and less hospitable than human workers.

Facial Recognition:
(Law enforcement, ID checks, Job interviews, Anything that involves interpretation of human facial expressions)
The examples of AI shortcomings are almost endless in this domain, and have not only opened up some interesting discussions on the ethical issues of using this technology in the context of law enforcement, but also highlight some of the problematic racial bias that is unconsciously built into the AI itself.

It is also telling with how simple it is to “fool” an AI facial recognition algorithm, and there is even a simple game you can play to help flesh out some of the processes that an AI uses while trying to identify a face, and similarly demonstrates how easily they can be led to draw the wrong conclusions about the human it is evaluating.

In addition to the above career fields, it is commonly agreed upon that the kind of jobs that require a college degree – due to the critical thinking, domain knowledge, literacy, and collaborative skills that are acquired through these studies – are not likely to be replaced by automation. In general, it will be the repetitive, unskilled labor jobs that will be able to be turned over to robots. However this article takes it a bit further, and provides examples of jobs across all education levels where humans will likely continue to be the preferred workers – thanks to our abilities for problem solving, creativity, and interpersonal relationships.

In a recent Dataspace blog post, we gave another example of an incident supporting the view that robots are not “ready” to take human jobs – an AI controlled camera that had confused a referee’s bald head for the ball during a soccer match, much to the dismay of all of the fans watching from home. This “AI fail” gave rise to a discussion about a shift away from a “humans or robots” dichotomy to a “humans and robots” mindset – where the focus is not one proving to be “better” than the other, but both working in harmony in new ways.

Another framing (key term) of this viewpoint comes from researcher Jason Bell discussing his app to generate ideas and content for new startups, and how he came to the realization that the utility of the app was all in how it was framed for the human elements of the team. Taken at face value, the suggestions that the algorithm would come up with would just seem so outlandish as to be ridiculous, as if they had totally missed the mark. However, Bell points out that if the humans on his team approached the suggestions from the AI with the right framing in mind – not taking the suggestions literally, but more as a jumping off point – there was actual value to be gleaned. It just needed to be unpacked and re-framed by the humans. This provides another demonstration of AI not doing the work for the humans, but functioning as just one more part of the collaborative process.

Ready or not, AI is coming. But more likely than not, the future that we should prepare for is not one where technology TAKES human jobs, but rather CHANGES them. The keys to success both on an individual and enterprise level will be that mindset of adaptability, adjustment, and innovation that allows for optimal use of a bionic workforce.

Until next time,

Cheers, everyone!


Some very cool stuff is going on with Golden Record, the data matching technology we’ve been developing. Since our last newsletter we’ve…

  • Moved it to the cloud
  • Upgraded our algorithms with the ability to load data without matching, as well as to undo existing matches and redo them with modified matching rules
  • Released a beautiful, new website
  • Officially started our free trial program

We’re working on some big enhancements for this month’s release so keep your eyes open for what’s coming next. And, of course, if you have a need to bring together data from multiple data sets, let’s talk; I’d love to give you a demo and a free account.

You can reach me at
That’s all for now. Thanks for reading and stay safe!
– Ben


We’ve posted several new articles to our Dataspace Blog since our last newsletter. If you missed them the first time around, here’s your chance to check ’em out!



What do you think? How can we make this blog more useful to you? What topics would you like to see more of? Want to contribute an article? Just want to catch up and chat?

I’d love to hear from you! Email me at
Thanks for reading!


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