In the near future when Artificial Intelligence (Intelligence displayed by machines) will start to spread out and autonomously make important decisions that will impact our daily lives, the issue of its accountability under the law will raise.
AI systems are expected to justify their decisions without revealing all their internal secrets, to protect the commercial advantage of the AI providers. Not to mention that map inputs and intermediate representations in AI systems to human-interpretable concepts is a challenging problem because these systems tend to work as black boxes.
As such explanation systems should be considered distinct from AI systems.
This paper written by researchers of the Harvard University highlights some interesting aspects of this debate and shows that this problem is by no mean straightforward.
Gradient boosting ensemble technique for regression
Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. (source: Wikipedia)
This is a great video tutorial from Alexander Ihler, Associate Professor at Information & Computer Science, UC Irvine.
You can found other interesting data science tutorials made by Alexander Ihler in this YouTube channel:
Introduction to Deep Learning with Python
Alec Radford, Head of Research at indico Data Solutions, speaking on deep learning with Python and the Theano library.
An amazing data science YouTube tutorial with emphasis on high performance computing, natural language processing using recurrent neural nets, and large scale learning with GPUs.
This tutorial provides and excellent example of how deep learning can be practically applied to real world problems.
SlideShare presentation is available here: http://slidesha.re/1zs9M11
Baidu hosted SF Analytics Meetup at their Sunnyvale office on August 19th, 2015 – Updates on Speech Recognition, Deep Learning and HPC.
SF Big Analytics Part 1. Deep Learning by Chief Scientist Andrew Ng
SF Big Analytics Part 2. Bryan Catanzaro, Senior Researcher: “Why is HPC So Important to AI?”
SF Big Analytics Part 3. Awni Hannun, Senior Researcher: “Update on Deep Speech”