Almost 8 years ago, there was a post on Wired called The End of Theory: The Data Deluge that Makes the Scientific Method Obsolete. In that article, Chris Anderson, who frequently considered the long view of technology’s impact on humanity, gives the following digital history.
Sixty years ago, digital computers made information readable. Twenty years ago, the Internet made it reachable. Ten years ago, the first search engine crawlers made it a single database. Now Google and like-minded companies are sifting through the most measured age in history, treating this massive corpus as a laboratory of the human condition. They are the children of the Petabyte Age.
What Anderson calls the “Petabyte Age” I would dub the more general “Data Age”. Like the Stone Age, the Iron Age, and the Information Age before it, the Data Age is defined by the principal medium of its technological advancements.
For the nearly two decades that it’s existed, Google has been the biggest proponent of this data-driven mindset. As Anderson puts it, to Google, as long as the results are favorable, “no semantic or causal analysis is required.” He goes on to quote Peter Norvig, Google’s then research director, as saying “increasingly you can succeed without [models].” That is, it’s fine if your algorithm is a black box, so long as the data produces the results.
Fast forward to earlier this month, when Wired editor-at-large Jason Tanz posted an article titled The End of Code in which he says that the imminent shift to machine learning will further marginalize human understanding in the systems we create. Many experts he cites see even the foundational tech skill of computer programming going this way. In agreement here were such tech giants as Andy Rubin, Tim O’Reilly, and Sebastian Thrun, all expressing the similar sentiment that “in the same way that you don’t need to know HTML to build a website these days, you eventually won’t need a PhD to tap into the insane power of deep learning.”
AI is the new electricity. It has that level of transformative potential. Companies are hiring chief data officers or chief AI officers, whereas there used to be VPs of Electricity. Now electricity is just assumed to be everywhere.
In this new paradigm of computing, the “principal medium of its technological advancements” that I mentioned earlier is now the Artificial Intelligence that guides the programming process. This shift would put us in the Intelligence Age. Tanz describes this shift like this:
For much of computing history, we have taken an inside-out view of how machines work. First we write the code, then the machine expresses it. This worldview implied plasticity, but it also suggested a kind of rules-based determinism, a sense that things are the product of their underlying instructions. Machine learning suggests the opposite, an outside-in view in which code doesn’t just determine behavior, behavior also determines code. Machines are products of the world.
Like other shifts in the fundamental way people get work done, it’s nearly impossible to predict all the ways that society will change. However, as technology progresses, we can learn lessons from each shift and prepare for the next shift. In the words of Andrew Ng, we shouldn’t prepare by learning anything specific like the mechanics of a steam engine or how to code or even how to build a machine learning system, but instead we should focus on learning how to learn.