James Bridle (TED, 2018)

The nightmare videos of childrens’ YouTube — and what’s wrong with the internet today
James Bridle
Jul 13, 2018

Writer and artist James Bridle … From “surprise egg” reveals and the “Finger Family Song” to algorithmically created mashups of familiar cartoon characters in violent situations … our increasingly data-driven world

The code is more or less a black box

code_is_black_boxState-of-the-Art AI: Building Tomorrow’s Intelligent Systems
Peter Norvig, Director of Research, Google
EmTech Digital 2016. May 23, 2016

01:51 the code is more or less a black box, but you can look more carefully at it and figure out what is going on there with some degree

ML is 99% human work

machine_learning_2016AI for the Common Good
Oren Etzioni, Chief Executive Officer, Allen Institute for Artificial Intelligence
EmTech Digital 2016. May 23, 2016

Machine learning is 99% human work
Deep learning inputs:
– target concept
– algorithm
– neural network design
– labeled data

Why Big Tech pays poor Kenyans to teach self-driving cars
Dave Lee
3 November 2018

Office 365 Advanced eDiscovery

Office 365 Advanced eDiscovery (formerly Equivio Analytics)
Nov 17, 2015
by Microsoft Mechanics

This uses advanced text analytics and predictive coding to perform multi-dimensional analyses of data collections

identify and filter data; including training Machine Learning to search for relevant documents

several vectors

document similarity: 65%

recall: 84%

MVA: Data Science and ML

Data Science and Machine Learning Essentials
Microsoft Virtual Academy. Level 300
02 November 2015


Data Science with Microsoft SQL Server 2016 – Free eBook

Deep reinforcement learning

Human-level control through deep reinforcement learning
Nature  518, 529–533 (26 February 2015)
Volodymyr Mnih, et al.
The theory of reinforcement learning provides a normative account1, deeply rooted in psychological2 and neuroscientific3 perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems4, 5, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms3. While reinforcement learning agents have achieved some successes in a variety of domains6, 7, 8, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks9, 10, 11 to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games.

Game-playing software holds lessons for neuroscience
DeepMind computer provides new way to investigate how the brain works.
Nature. 25 February 2015