O brave new world,
That has such people in ’t!
- The Tempest V.i.188-9

The Doctor is On Line

A few months back I wrote an essay on the coming Automation of Knowledge Work. This past week has seen a flurry of activity in my inbox on this subject, so I thought it was time for an update.

In the comments section of my essay I suggested that medical work such as reading radiology films was ripe for automation, and it now appears that IBM agrees with me:

IBM first focused on health care, and that business now accounts for two-thirds of the Watson unit’s employment. Three years ago, IBM experts began working with leading medical centers. And it has spent more than $4 billion buying a handful of companies with vast stores of medical data like billing records, patient histories, and X-ray and M.R.I. images. “A.I. machines are only as smart as the data you give them,” Mr. Kelly noted.

These medical records are the training data that the professional class have been providing to their owners in the course of their current jobs. Within the next ten years I expect that many of them will have lost their jobs to automation based on their own work. Unless you own the data you create, your employer is free to use the results of your labour in almost infinite ways. In the old days, surplus value was a finite quantity. Now, the surplus value of knowledge work is infinitely copiable, and the only value knowledge workers can provide is new knowledge. And that happens far less often.

Upload Wall Street

The other day gjohnst wrote a great historical piece on the unionisation efforts on Wall Street back in the late 1940s. Their descendants are now looking at a very grim future. Consider this conference invitation that appeared in my inbox the other day:

I wanted to give you an update on the Big Data & Analytics for Banking Summit taking place in New York on December 7 & 8.

Blockchain’s VP of Marketing & Communications, Liana Guzman has recently been confirmed to join the likes of Citi, PNC, New York Community Bancorp, Northern Trust & more as they confront your biggest challenges, and teach you how to improve efficiency and modernize your banking practices.

We have also confirmed the presentations from Fifth Third Bank, who will examine the evolvement of big data in the banking industry and reveal how it can help you to optimize risk management, and TD Bank Group who will demonstrate how banks can achieve dramatic performance improvements by harnessing the right data infrastructure coupled with machine learning benefits.

What they are talking about is replacing human risk assessment with machine learning algorithms. Moreover, I have seen other systems that can read the news and make stock picks for clients. So we can add financial professionals to the radiologists on the unemployment line.

Out of the Loop

Until now, there has been an assumption among those working in the Machine Learning space that human expertise was necessary to set up the training conditions for the systems. This may no longer be true. Last year, a team at a small technical institute in Massachusetts demonstrated a system that could out-compete humans in this arena as well:

Last year, MIT researchers presented a system that automated a crucial step in big-data analysis: the selection of a “feature set,” or aspects of the data that are useful for making predictions. The researchers entered the system in several data science contests, where it outperformed most of the human competitors and took only hours instead of months to perform its analyses.

Now they have gone even further and their latest system can even figure out what problems to solve:

This week, in a pair of papers at the IEEE International Conference on Data Science and Advanced Analytics, the team described an approach to automating most of the rest of the process of big-data analysis — the preparation of the data for analysis and even the specification of problems that the analysis might be able to solve.

The claim is that this will make Data Scientists more efficient, but as history has consistently shown, increased efficiency leads to layoffs. Data Scientist is the latest knowledge worker category to be touted as a goal of a college education, and is less than ten years old, but this newness will not stave off the coming downsizing.

Data Ownership

A common thread in these trends is that the professional class has been complicit in their own undermining. The data produced by their labour has been unwittingly given to the ruling class and will now be used to make them mostly obsolete. Ownership of this data - and any other data we generate - is crucial to an economically just society, but the farm has already been given away in many professions.

This is why it is so important for people to own everything that they produce. Any time something is given away that seems unimportant, it produces an opportunity for exploitation. Data is valuable in the modern world. Or as hippie futurist Rudy Rucker put it:

Protect your software at all cost. The rest is just meat.