| Computers are automation tools that increase human efficiency by doing the grunt work for you - but they are limited to automating the tasks that can be captured as a set of rules in code. When we figure a new way to model more complex tasks in code, a whole new set of things can be automated. Here's a concrete example: Before spreadsheets existed, there used to be legions of accountants who created complex ledgers on paper and added up all numbers to track how a business was doing. You'd literally mail off your sales numbers to an accounting team somewhere and wait three days to get the latest report generated and sent back. Sure they had calculators to add numbers, but the computers of the day didn't understand how those numbers related to each other. The human still had to do most of the work to create the reports. The big idea of spreadsheets was to make the computer manage the more complex task of knowing how different numbers in a report related to each other. It made most ledger tasks totally automatic once the initial report was defined. Now a single accountant could do the work of the entire accounting team - and more accurately and in less time! There were stories of the first spreadsheet testers having to delay mailing back their financial reports by a few days because their clients would be suspicious if they mailed them back too fast. Nearly overnight accounting got a lot more efficient and companies made more money. T"What If" modeling that used to be too slow and cost prohibitive to do was now it was quick and easy. Companies could plan more intelligently. The spreadsheet was a true game changer. This same pattern happens every time the bar is raised on the complexity of what can be automated and Machine Learning raises the bar one giant notch. Previously we were limited to automating tasks that a smart coder could describe as discrete steps in code. But with ML, the computer can figure out it's own rules just by looking at data. That means in many cases you can solve very hard problems just by collecting a lot of data. Lots and lots of things that used to be done by large groups of people will now be able to be done with a single computer. In that sense, ML is a total game changer. Don't focus on the specific applications thus far. Focus on the idea that all kinds of tasks that used to require humans can now be automated with a little bit of applied ML. The opportunities are literally everywhere. In a few years, ML won't be some esoteric technique used by a few people. It will be a core skill that everyone uses or touches in some way. It's going to creep into everything everywhere because it's just so darn useful. |