Example of machine metaphor

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Example of machine metaphor

Today, I was helping out with a Computing summer school for teachers in London. As part of this, I gave a presentation about machine learning to a room full of school teachers — about what it is, why I think we should be introducing it in the classroom, and how I think we could do that.

This morning I want to talk to you about machine learning. In particular, I want to talk with you about machine learning in the context of education and how it could be introduced in the classroom.

Firstly, a quick level set on what I mean by machine learning. Finally, I want to talk about the practicalities of how we could effectively introduce machine learning in an accessible way.

Machine learning is a broad umbrella term that covers a variety of techniques and technologies. Scratch aims to explain the idea behind programming. It teaches that programming is about taking a complex task that you want a computer to do, and describing that as a series of specific steps.

The point of choosing from a set of simple blocks and snapping them together is to give kids an accessible visual metaphor for the concept of programming. Machine Learning is a bit different. With machine learning, to get the computer to perform a complex task, you collect a set of examples of that task being done.

The computer learns how to do that task from the examples that it is given. For example, say that I wanted to teach you how to kick a ball. How fast to move your leg. Maybe examples of different people kicking a ball.

The Trouble With “Balance” Metaphors

Maybe even kicking different types of ball. We use a lot of metaphors in this field. We use words like learn, train, and teach. With that in mind, lets try a more technical example. Imagine you wanted to make your own email spam filter for school.

You decide to make it using programming. So you add a rule to your program that says any email that mentions Nigeria is spam. So you go back to your rules and change it so that only emails that mention Nigeria and money are spam.

Emails from your colleagues are safe! But now that bins emails from your mobile phone network about how much calls cost while abroad. The point is, trying to do this by manually identifying the right set of rules is difficult.

And as the set of rules get bigger and bigger and bigger over time, it becomes really difficult to manage. New rules you add will contradict or break rules that you added a month ago. You start again, and this time you use machine learning.

Example of machine metaphor

You collect a set of examples of the sort of emails that you get. You read through them, and sort them into two piles. Spam emails in one pile. Legitimate not-spam emails in the other.

You use these examples to train the computer to be able to recognise what a spam email looks like. If every spam email in the spam pile was a Nigerian scam email, and there were no emails in the not-spam pile that included a reference to Nigeria, then there is a reasonable chance that the computer could learn that references to Nigeria mean an email is spam.

This is becoming ubiquitous. Machine learning is all around us. You use machine learning systems every day. Spam filters are a good example. So are assistants like Siri, Google Now and Alexa.

Systems that translate one language to another — trained on examples of documents that have been manually translated. Credit card fraud detection — trained on my buying patterns to recognise a purchase that might not actually be me.

And many many many more.

Metaphor - Wikipedia

And this is just in the consumer space.This is a great post, Julian. Balance is the discourse surrounding intellectual property rights, too, with open vs.

closed on the scales. I’ve long thought, however, that a better framing is “effective” IPR, since, after all, they are an incentive that can be structured more effectively.

Today, I was helping out with a Computing summer school for teachers in London.. As part of this, I gave a presentation about machine learning to a room full of school teachers – about what it is, why I think we should be introducing it in the classroom, and how I think we could do that.

Today, I was helping out with a Computing summer school for teachers in London.. As part of this, I gave a presentation about machine learning to a room full of school teachers – about what it is, why I think we should be introducing it in the classroom, and how I think we could do that.

Music, Film, TV and Political News Coverage. (This series of posts is based on an upcoming paper for the AIPLA Spring meeting.) How did the mental steps doctrine come to have such sweeping breadth?

The answer lies at the intersection of the popularity of the “mind as computer” metaphor and aggressive advocacy. This paper will examine. The Verb Recognize a verb when you see one.

Verbs are a necessary component of all iridis-photo-restoration.com have two important functions: Some verbs put stalled subjects into motion while other verbs help to clarify the subjects in meaningful ways.

Metaphor - Wikipedia