Machine Learning (and related Deep Learning, Artificial Intelligence) are all in the news these days. Every enterprise and every person using any form of technology will very soon encounter some form of machine learning, even if they don’t realize it. Examples of this are customer service bots, automation bots, automated phone services, self-driving shuttles or seemingly cute information-gathering robots at malls.
This is because we have become rather good at accumulating vast amounts of data, but are not that good at processing it. Even something simple as figuring out what data is useful and what is not, is in reality very difficult. Well, this is exactly what machine learning techniques are good at, so it makes sense to employ it where humans would not be very efficient.
That inevitably brings about the conversation around job loss. Many people fear that ML/AI will destroy large numbers of jobs, and create employment problems. Others argue against this, saying that in fact this technology will create more jobs. Here are some job loss possibilities:
- Taxi/train/shuttle/truck drivers, pilots
- Store check-out personnel
- Mail/parcel delivery personnel
- Furniture carpenters
Proponents of ML/AI say (with a big grain of truth) that this is nothing new. Throughout the history of mankind, there have been disruptive innovations that brought in increased levels of automation, and jobs were lost. Telephone operators, farmers, horse-cart drivers and many others have seen their job numbers decimated by technology. This is normal – what we do is move on, learn new skills required by the new technologies, and are better off for that.
This has worked well for thousands of years. But it seems to me that now, we are approaching a point rather quickly, where this cycle is showing signs of strain. Gaining new skills is a good thing – it typically results in better paying jobs, an improved lifestyle, and improvements to the economy. But what if we come to a point where the technology changes fast enough that there are vast sections of the human population that cannot gain skills fast enough to stay relevant? What if there are enough people that do not want to put in the effort to gain new skills, and are happy with the work they are doing that is now being made obsolete by new technologies such as machine learning?
What do we do in this case? In fact, I think we are already here – that the pace of technology is already at a point that the majority of people are unable to keep up with it. This is why we see political uncertainty in many countries around the world, including the US.
What is the solution for this problem? Some would say that we should stop creating technology for technology’s sake. We should stop the adoption of machine learning techniques, to be specific to the topic on hand.
Is this realistic? Can we as a population actually stop the march of what we consider progress? I think not. I may be wrong here, but I feel the momentum of technology is too strong, and not enough people feel the reason to stop it, including myself.
The correct approach should be to recognize that there will be many different types of people, including those that either cannot, or do not want, to significantly improve their skills to stay relevant in the new world after a technology disruption. And, part of the solution is to create ways for them to stay relevant in this new world, and still contribute.
So, can we use machine learning to create new jobs, ones that will keep people relevant without having to invest significant effort in obtaining new skills? This is the question I would like to put out to the community at large. I can think of a few to kick it off:
- Furniture / decorative item design: ML can be used to improve on hand-made craftsmanship for materials like woods, metals, etc. This could enable artists to create real-world utilitarian items that are also beautiful and cheap.
- Data labeling / preparation: For supervised learning, the data used to train models must be labeled. This is often a tedious and manual operation that highly-trained data scientists should not be doing.
The big problem is, who would be interested in doing this? Is there enough incentive for business to invest in people? Probably not. Should government play a role? Also probably not. Then what? This is the reason I wrote this blog post, to hopefully kick off a discussion. I came across an interesting point about how things work in Germany. If a company wants to lay off anyone, they have to give a 1-yr notice. Meaning, layoffs are difficult to do. Maybe this forces businesses to forgo this country – however I don’t see that happening in Germany. So maybe what it does is to force businesses to think about re-deploying personnel when they are not needed in what they are doing. Is this the way forward perhaps? Or something like it?