Code is logical. Code is hackable. Code is rational and reasonable – and these statements are at the core of computing. Yet over the past several years, the biggest tech companies such as Apple, Google, Amazon have all aggressively pursued an approach to computing called machine learning, which may cause problems for the traditional programming.
In traditional programming an engineer writes explicit step-by-step instructions for the computer to follow. With machine learning, programmers don’t encode computers with instructions, they train them. If you want to teach a neural network to recognise an object, you don’t tell it to look for specific characteristics, but rather you show it thousands and thousands of said object and eventually it works things out. This isn’t new, companies have been employing machine learning to decipher and uncover new trends and ontologies for many years.
Nevertheless the implications of an unparsable machine language aren’t just with regard to markets and business, or merely philosophical. For the past two decades, learning to code has been one of the surest routes to reliable employment. A world run by neurally networked deep-learning machines requires a different workforce. Programmers may have just coded themselves out of a job!
Traditional coding will not disappear completely – we will still need coders for a long time yet, but there will likely be less of it, and it will become a meta skill within which machine learning can operate. Traditional programming will remain a powerful tool to explore the world; but when it comes to powering specific functions, machine learning may just do the bulk of the work for us.
Perhaps we just need to paraphrase – its programming, but not as we know it!