In the early hours of April 12, 1812, a crowd of men approached Rawfolds Mill, a four-story stone building on the banks of the River Spen, in West Yorkshire. This was Brontë country--a landscape of bleak moors, steep valleys, and small towns nestled in the hollows. The men, who'd assembled on the moors hours earlier, were armed with muskets, sticks, hatchets, and heavy blacksmith's hammers. When they reached the mill, those at the front broke windows to gain entry, and some fired shots into the darkened factory. But the mill's owner, William Cartwright, had been preparing for trouble.

Whilst many will be familiar with our Women in AI lists which include those currently pushing boundaries in the present day, we thought we would put together a list of women who have been instrumental in the advancement of Computer Science and Data Science, providing the foundations for AI in the 21st Century. How many of the below are you familiar with? Dame Mary Cartwright was a University of Oxford Graduate in Mathematics at a time in which Women had only just been allowed to take degree classifications at the prestigious school. Mary then obtained a Yarrow fellowship at Cambridge University, later pursuing research in the theory of Functions through until her retirement in 1968, becoming one of the first to study what would later become known as Chaos theory. Cartwright had a distinguished career in analytic function theory and university administration, publishing over 100 papers on classical analysis, differential equations and related topological problems.

WHAT is the collective noun for a group of economists? Options include a gloom, a regression or even an assumption. In January, when PhD students jostle for jobs at the annual meeting of the American Economic Association, a "market" might seem the mot juste. Or perhaps, judging by the tendency of those writing economic papers to follow the latest fashion, a "herd" would be best. This year the hot technique is machine learning, using big data; Imran Rasul, an economics professor at University College, London, is expecting to read a pile of papers using this voguish technique.

WHAT is the collective noun for a group of economists? Options include a gloom, a regression or even an assumption. In January, when PhD students jostle for jobs at the annual meeting of the American Economic Association, a "market" might seem the mot juste. Or perhaps, judging by the tendency of those writing economic papers to follow the latest fashion, a "herd" would be best. This year the hot technique is machine learning, using big data; Imran Rasul, an economics professor at University College, London, is expecting to read a pile of papers using this voguish technique.

Systems that find music recordings based on hummed or sung, melodic input are called Query-By-Humming (QBH) systems. Such systems employ search keys that are more similar to a cappella singing than the original recordings. Successful deployed systems use human computation to create these search keys: hand-entered MIDI melodies or recordings of a cappella singing. Tunebot is one such system. In this paper, we compare search results using keys built from two automated melody extraction system to those gathered using two populations of humans: local paid singers and Amazon Turk workers.