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Machine Learning


Artificial intelligence has been pursued for many years. The algorithms developed and tested failed to make any progress until 2007, when mathematicians used recent learnings about how the brain worked to rewrite their approach. Since this time, machine learning using support vector machines, and then neural networks has opened the door to a more hopeful future of robotics.

Machine Learning Video - Stanford University

In this video, Professor Andrew Ng shares his journey into understanding the maths behind machine learning, and how it may be applied in the future.

Key Takeaways

  1. Machine learning is still in its infancy
  2. Humans are not capable of advancing the mathematics of complex activity, it requires the processing of machines to compute this in a self-learning environment
  3. The potential for humans to be replaced by machines [robots] to carry out menial tasks is getting closer

Machine learning comes about through a combination of offline [manual] and adaptive [automatic] training and learning. We first have to train a machine as to what is correct and incorrect decisions. Once a workable benchmark is developed, the algorithm continues to learn online. With some manual input to further tune the algorithm to detect new responses to particular patterns of data, we can reduce the error rate.

This type of computation requires direct processing of real-time data. Combining such processing with machine learning can provide a reasoning flow and enable runtime updates of the machine-learning model.

Machine learning can reduce the time to insight from weeks to minutes. Its applications are numerous, however we can expect the primary focus to be on scenarios in manufacturing and IT services.

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