I've been refreshing my AI skills lately since they were a little rusty. After getting my master's in AI in 1998, during which the historical victory of Deep Blue over Kasparov took place, the next AI winter set in. For about 15 years AI was about as sexy as Flash is now. But the well known advance in processing power and the staggering price drop of storage AI, and specifically machine learning, became viable. Actually at the time of writing machine learning specialist is probably the most sought after specialism. So it was time for me to get back into the AI game. But mostly to cure my curiosity what the new state of the art was actually about and what was new that could be done. I mean, over the past years we've seen self driving cars, robots, algorithms for social media, all kinds of medical applications, etcetera etcetera come into being solely because of the advances in AI and the needed hardware/software infrastructure. In other words, the AI playground was revived and full of enthusiastically playing children. First thing that surprised me is that although there were a lot of refinements but not many fundamental new technologies. Of course, you run stuff in the cloud, there are more neural net variations, and the time it takes to train a network has decreased dramatically. But the underlying principles are still largely the same. Another thing didn't change were the hoops you have to run through to get your stuff up and running. It was a bit like the current web development situation (those in the know will nod their heads in understanding). Instead of focussing on the algorithms you spend most of your time trying to get your toolset installed and build/deploy street up and running. And that's a bad thing. Fortunately I stumbled across a hidden gem called Google Colaboratory. It's a free service that let's your run Jupypter notebooks on a hosted GPU….for free! If you want you can store the notebooks themselves on Google Drive, or, if you don't want that load them from elsewhere. That is quite amazing and an amazing boon for those that want to get up and running with machine learning as soon as possible. The amount of resources you get for free is, of course, limited, but it's more than enough the experiment and design your data processing pipeline and design, train and test your models. Once your content with your trained model you can take it to more beefy hardware (in case needed). Or to train it on huge training data sets. All in all quite an amazing service that will benefit the machine learning community a great deal. The nice thing about Jupyter notebooks is that you can take them elsewhere and run them there. You are in no way tied to Google, which is a good thing.