These are the latest articles and videos I found most interesting.

1. Who Are We To Say We Are Intelligent?
2. Key moments in evolution
3. Inside Alzheimer’s disease
4. Introduction to Machine Learning
5. Restoring America’s First Muscle Car

Who Are We To Say We Are Intelligent?”

Neil deGrasse Tyson

A discussion on the search for intelligent life

Key moments in evolution

Max Planck Society

Nematodes show scientists how evolution works. Tried and tested processes are used in different ways than usual and recombined with others. This way, an organism can quickly evolve new features.

Inside Alzheimer’s disease

Nature

Our understanding of Alzheimer’s disease has come along way in the last century. In this animation, Nature Neuroscience takes us inside the brain to explore the cells, molecules and mechanisms involved in the onset and progression of this devastating condition – from the latest advances to the remaining gaps in our scientific knowledge.

Introduction to Machine Learning

Zoubin Ghahramani, Cambridge University

Zoubin Ghahramani FRS is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group, and the Cambridge University Liaison Director of the Alan Turing Institute, the UK’s national institute for Data Science. He is also the Deputy Academic Director of the Leverhulme Centre for the Future of Intelligence, and a Fellow of St John’s College Cambridge. He has worked and studied at the University of Pennsylvania, MIT, the University of Toronto, the Gatsby Unit at UCL, and CMU. He is co-founder of Geometric Intelligence and advises a number of AI and machine learning companies. He has served as programme and general chair of the leading international conferences in machine learning: AISTATS, ICML, and NIPS. In 2015 he was elected a Fellow of the Royal Society.

Zoubin’s current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, deep learning, probabilistic programming, Bayesian optimisation, and automating data science. His Automatic Statistician project aims to automate the exploratory analysis and modelling of data, discovering good models for data and generating a human-interpretable natural language summary of the analysis. He and his group have also worked on automating inference (though probabilistic programming) and on automating the allocation of computational resources. More information can be found at http://mlg.eng.cam.ac.uk/.

The goal of these lectures: To introduce very basic concepts, models, and algorithms.
Much more on this topic:

• Videolectures.net
• Probabilistic Machine Learning
• Coursera
• edX
• Udacity
• Excllent textbooks by Kevin P. Murphy; Chris Bishop; David MacKay; Hastie, Tibshirani, and Friedman

Three Types of Learning

Imagine an organism or machine which experiences a series of sensory inputs:

1. Supervised Learning: The machine is also given desired outputs , and its goal is to learn to produce the correct output given a new input.
2. Unsupervised Learning: The goals of the machine is to build a model of that can be used for reasoning, decision making, predicting things, communicating, etc.
3. Reinforcement Learning: The machine can also produce actions , which affect the state of the world, and receives rewards (or punishments) . Its goal is to learn to act in a way that maximizes rewards in the long term.

Restoring America’s First Muscle Car

Wall Street Journal

Curt Brohard and his brother Allan took 20 years to restore the only surviving 1906 Stanley Steamer Model H steam car, which had the world’s land-speed record of over 127 miles an hour when it launched.