Dec. 8, 2016

Machine learning is fast becoming a part of our lives. From the order in which your search results and news feeds are ordered to the image classifiers and speech recognition features on your smartphone. Machine learning may even have had a hand in choosing your spouse or driving you to work. As with cars, only the mechanics need to understand what happens under the hood, but all drivers need to know how to operate the steering wheel. Listen to this podcast to learn how to interact with machines that can learn, and about the implications for humanity.

My guest is Dr. Pedro Domingos, Professor of Computer Science at Washington University. He is the author or co-author of over 200 technical publications in machine learning and data mining, and the author of my new favourite book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.

Here’s the outline of this interview with Dr. Pedro Domingos, PhD:

[00:01:55] Deep Learning.

[00:02:21] Machine learning is affecting everyone's lives.

[00:03:45] Recommender systems.

[00:03:57] Ordering newsfeeds.

[00:04:25] Text prediction and speech recognition in smart phones.

[00:04:54] Accelerometers.

[00:04:54] Selecting job applicants.

[00:05:05] Finding a spouse.


[00:06:49] Robot scientists.

[00:07:08] Artificially-intelligent Robot Scientist ‘Eve’ could boost search for new drugs.

[00:08:38] Cancer research.

[00:10:27] Central dogma of molecular biology.

[00:10:34] DNA microarrays.

[00:11:34] Robb Wolf at IHMC: Darwinian Medicine: Maybe there IS something to this evolution thing.

[00:12:29] It costs more to find the data than to do the experiment again (ref?)

[00:13:11] Making connections people could never make.

[00:14:00] Jeremy Howard’s TED talk: The wonderful and terrifying implications of computers that can learn.

[00:14:14] Pedro's TED talk: The Quest for the Master Algorithm.

[00:15:49] Craig Venter: your immune system on the Internet.

[00:16:44] Continuous blood glucose monitoring and Heart Rate Variability.

[00:17:41] Our data: DUTCH, OAT, stool, blood.

[00:19:21] Supervised and unsupervised learning.

[00:20:11] Clustering dimensionality reduction, e.g. PCA and T-SNE.

[00:21:44] Sodium to potassium ratio versus cortisol.

[00:22:24] Eosinophils.

[00:23:17] Clinical trials.

[00:24:35] Tetiana Ivanova - How to become a Data Scientist in 6 months a hacker’s approach to career planning.

[00:25:02] Deep Learning Book.

[00:25:46] Maths as a barrier to entry.

[00:27:09] Andrew Ng Coursera Machine Learning course.

[00:27:28] Pedro's Data Mining course.

[00:27:50] Theano and Keras.

[00:28:02] State Farm Distracted Driver Detection Kaggle competition.

[00:29:37] Nearest Neighbour algorithm.

[00:30:29] Driverless cars.

[00:30:41] Is a robot going to take my job?

[00:31:29] Jobs will not be lost, they will be transformed

[00:33:14] Automate your job yourself!

[00:33:27] Centaur chess player.

[00:35:32] ML is like driving, you can only learn by doing it.

[00:35:52] A Few Useful Things to Know about Machine Learning.

[00:37:00] Blood chemistry software.

[00:37:30] We are the owners of our data.

[00:38:49] Data banks and unions.

[00:40:01] The distinction with privacy.

[00:40:29] An ethical obligation to share.

[00:41:46] Data vulcanisation.

[00:42:40] Teaching the machine.

[00:43:07] Chrome incognito mode.

[00:44:13] Why can't we interact with the algorithm?

[00:45:33] New P2 Instance Type for Amazon EC2 – Up to 16 GPUs.

[00:46:01] Why now?

[00:46:47] Research breakthroughs.

[00:47:04] The amount of data.

[00:47:13] Hardware.

[00:47:31] GPUs, Moore’s law.

[00:47:57] Economics.

[00:48:32] Google TensorFlow.

[00:49:05] Facebook Torch.

[00:49:38] Recruiting.

[00:50:58] The five tribes of machine learning: evolutionaries, connectionists, Bayesians, analogizers, symbolists.

[00:51:55] Grand unified theory of ML.

[00:53:40] Decision tree ensembles (Random Forests).

[00:53:45] XGBoost.

[00:53:54] Weka.

[00:54:21] Alchemy: Open Source AI.

[00:56:16] Still do a computer science degree.

[00:56:54] Minor in probability and statistics.

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