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Talking Machines: Common sense problems & learning about machine learning with Kevin Murphy
On episode three of Talking Machines we sit down with Kevin Murphy, who is currently a research scientist at Google. We talk with him about the work he’s doing there on the Knowledge Vault, his textbook, Machine Learning: A Probabilistic Perspective (and its arch nemesis, which we won’t link to), and how to learn about machine learning (Metacademy is a great place to start).
We tackle a listener question about the dream of a one-step solution to strong Artificial Intelligence and if Deep Neural Networks might be it.
Plus, Ryan introduces us to a new way of thinking about questions in machine learning from Yoshua Bengio’s Lab at the University of Montreal, as outlined in their new paper, Identifying and attacking the saddle point problem in high-dimensional non-convex optimization, and Katherine brings up Facebook’s release of open source machine learning tools and we talk about what it might mean.
If you want to explore some open source tools for machine learning we also recommend giving these a try:
- Super big list of ML Open Source Projects!
- Torch
- Gaussian Process Machine Learning Toolbox
- PyMC
- Mallet
- Stan
- Weka
- Theano
- Caffe
- Spearmint
tags: Algorithm AI-Cognition, c-Research-Innovation, Google, machine learning, podcast, Yoshua Bengio
