Artificial Intelligence (AI) is all in vogue right now. For better or worse, it is here to stay. So why not have a look at this being part of modern data science? A simple image classifier could do the trick!
It is almost too easy for anyone these days to work with AI or MachineLearning. Tools are aplenty, be it using the graphical based Knime or one of the more common scripting languages such as Python. Combine that with popular tools such a scikit, pytorch, etc using only a few lines of code and you are done. Making a good AI model though, even with all the available tools – another story for another time…
Moving on. What is it you are asking? You can’t/don’t want to get into advanced “stuff”? AI sounds complicated? Too much programming and statistics or whatnot? Forget all that. Not necessary. May I suggest this online course/book by Jeremy Howard and Rachel Thomas from SanFran Uni (no connections or perks exist between us, I simply like their approach). Do start at their blog: https://www.fast.ai/ and choose “Practical Deep Learning for Coders”. It introduces you to all prerequisites in an easy and simple manner, even tips with regards to free cloud services if you don’t have the hardware required. The video sessions go through the book as python notebooks (Jupyter) and introduces you to some basic programming at the same time. All with the attitude that you don’t need a Ph.D. to do AI. (Although, while that is true, a certain level of education or “human intelligence” is necessary to make useful and “safe” models – otherwise you end up with scandals or abuse of models. Check out e.g. Thomas’s course on tech ethics: https://ethics.fast.ai/).
Taking from this course, I present here a very simple AI for image recognition, specifically, one that distinguishes (more or less well) between Bengal cats vs “other cats”, and “cartoon cats”, because, why not. And since I have a Bengal myself… To test this, you won’t even need to install anything, simply use this MyBinder link:
This is a rather neat way to share code with others who don’t (want to / can) code, without having to go through whatever hoops to get it shared. One can even include a simplistic GUI when using something called ‘Voila’. It does have some drawbacks, but for the purpose of this e.g. this blog, it is perfect.
For deeper explanations, you are probably better off viewing the pro description on how to to do this – I followed these two notebooks from FastAi Book: https://github.com/fastai/fastbook/blob/master/05_pet_breeds.ipyn resp. https://github.com/fastai/fastbook/blob/master/06_multicat.ipynb . )
My Bengal Classifier
Anyway, the final code and output looks simply like this, where the actual “AI” is strictly speaking only one line (in paragraph 3 (learn.inf.predict(img)), everything else is preparation and output. Well, that, and the architecture that is being loaded in paragraph 2 (load_learner(….)). This architecture is the model created in the above mentioned separate notebook.
You can have an even simpler view, if you use something called Voila (which is available in the referred notebook):
You can find all this on Github for testing yourself – using MyBinder.org though, you don’t require any local installation/know-how: simply click on the icon, wait for the (rather long) creation of the virtual image of this app (but hey it’s for free!). Or click directly here on the Binder link without the hassle of going through Github:
You will see something like this in your browser (click in that window the “show/hide” text in “Build logs” to expand and see the (slow) startup status):
Finally, you should have the notebook open and you can either run it there directly (click the run button multiple times, or choose menu “Cell > Run all “; ignore the error messages).
Finally, upload an image via the Upload button.
Even simpler, if you don’t want to bother with code, or “Run”, click the “Voila” button (circled in red) and you will only see the text and the upload button (as shown above).
That’s it! Artificial Intelligence (AI) made easy! Although … shouldn’t forget to at least touch upon that mainstream usually forgets to mention that AI isn’t that intelligent at all. It’s actually pretty stupid and depends on (the intelligence of?) the person(s) who sets up a system…. Anyway….
Of course, since I myself am interested in molecules, I want to use AI for different purposes, but that is something for another time.
Thanks for reading, hope you enjoyed the intro to creating your own AI app!
Oh, and Happy New Year!