Machine learning for kids

The scratch program when they’re machine learning model has less than 50% confidence that it’s correctly recognized to command it replies saying I’m sorry I don’t understand so if they ask it something silly like make me a cheese sandwich instead of trying to turn the lamp or the fan on or off it says I don’t understand so–but children learn that there is one confidence for shot that makes sense for all projects or for all machine learning models they play with it they experiment with it and they get to know the impact that this has on a machine learning project what I mean is they’ll try setting the threshold very very high and they’ll see that their system their project will only take in action when it’s very very confident that it’s correctly recognized the command it very rarely takes the wrong action but it says I don’t understand very often even when it’s actually correctly understood what they’d asked it to do and it’s really interesting hearing the children describe the system like this they describe it as being you know too shy or too timid they describe it as not having enough self-confidence sometimes they’ll describe as being like when they don’t put their hand up in class to answer a question even when they actually know the answer because they weren’t confident enough that they had the right answer and it means that the children can talk about when different types of behaviors are appropriate you know for some ailing a patient’s you want to cross a system that doesn’t risk getting things wrong but for other AI applications it’s better that the system always tries when it has an answer it’s okay if it makes mistakes sometimes and by building systems like this for themselves and seeing how they can control the behavior like this it gives them a bit of insight into how these systems are created. A group of people it makes projects much quicker now that sort of planning logistics .

A lot of successful AI projects  like  machine learning for  kid  and they also learned that successful projects will often look for training data that can be created as a byproduct of something people are already doing or that they’d enjoy doing anyway and that’s a really common strategy amongst successful AI projects this was a fun project this class were training a handwriting recognition system they wanted to train a machine learning model to recognize postcodes by drawing examples of postcodes directly onto an onscreen canvas now in the interest of time they didn’t do all the post codes in the country they just picked a few local cities and they didn’t do the whole post code they just picked the first couple of letters so they could do this pretty quick this helped them you know make a project they could do in a single lesson and once they’d use these examples to train a machine learning model they made projects like this a postman pat themed game where they can write a post code directly onto an onscreen envelope and then click on the stamp to send that letter now when they click on the stamp they’re machine learning model is being used to recognize what they’ve written so that their envelope can be sent to the right postman pattern now the great thing about projects like this isn’t just that they’re learning about the tech although that is really cool but by making simplified examples like this they’re learning about how a AI is used in the real world around them because this for example is essentially how large-scale sorting offices have automated the sorting of mail in this project the class for training a machine learning model to recognize sounds as specifically they came up with secure code words for four different places and the class had a lot of fun inventing sort of made-up gibberish noises to represent hotel stadium gas station and and they trained a machine learning model by recording.

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