Active (Machine) Learning – Computerphile | JabarPos Media

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Active (Machine) Learning – Computerphile | JabarPos Media

Machine Learning where you put in a fraction of the effort? What’s not to like? – Dr Michel Valstar explains Active & Cooperative Learning.

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham:

Computerphile is a sister project to Brady Haran’s Numberphile. More at

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  1. I don't understand how they use reCAPTCHA to evaluate humans while also training machines. How do they know if the user is correctly labelling things if they weren't labelled in the first place? What do they compare the user's answers against to see if they're right?

  2. That's exactly what I did. Cooperative Learning is a kind of self-supervised learning but there are potential issues with it when confidence is high in falsely labeled data. There is also a problem with over-fitting that arises from selecting the high confidence training data/labels. Great topic!!

  3. Human thought is similar to a zip file full of pointers to the most significant data points which can be referenced, in a relative chronological table.

  4. Hey Sean,
    Please consider doing a video about the intersection of Control Theory and it's practical implementations in real world computing.

    Yesterday I started to research why PID controllers are not used in power supplies. I came across a verbose explanation on this. During the explanation "Type 2" and "Type 3" controllers were passively mentioned. This sent me down the rabbit hole of the control theory wiki. This was a dead end with too much maths for me to gain an abstract overview type understanding.
    I just went through all 5 years of your videos looking for content on the subject (I stacked my watch later list in the process) but didn't find anything. I've seen a lot of info about PID controllers, but I'd really like to understand what other types of controllers are out there in practice in the computing world.
    Thanks for the upload.

  5. I have this voice recognition software that is supposed to learn as you speak and become more efficient as you use it. It's called Dragon Natural Speaking. And at first I could not tell any improvement, but now it's been almost a year it's really fine tuned itself to my voice. When someone else uses it, it goes berserk until it learns a new voice. Very cool.

  6. This is how you train people. Train them on the basics. Then get them to work closely supervised, then with someone they can ask if they get stuck, and then unsupervised.

  7. Let's face it. Researchers aren't labelling their own data, they're using cheap sources of labour such as the Mechanical Turk, click farms and grad students.

  8. Even so, humans have neutral plasticity so that if an small amount of damage to neurons occurrs it can trigger creativity as brain is repaired. Bashing any computer with a hammer though doesn't solve anything, so they don't learn the same way therefore.

  9. This process basically uses the AI to pick out cases that are least like their annotated training data thus far, which is what the AI would learn the most from having next.
    This provides humans with the best bang for the buck, achieving their desired accuracy with the least annotation required.

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