The funeral director said "AI" as if it were a normal element of memorial services, like caskets or flowers. Of all places, I had not expected artificial intelligence to follow me into the small, windowless room of the mortuary. But here it was, ready to assist me in the task of making sense of death. It was already Wednesday, and I'd just learned that I had to write an obituary for my mother by Thursday afternoon if I wanted it to run in Sunday's paper. AI could help me do this.

One of the most surprising biographical tidbits in McQueen, the new documentary about the late fashion designer Alexander McQueen, is that he didn't know a collection could tell a "story" until he was several apprenticeships into the fashion industry. He was obviously a fast learner. Even among top-tier designers, McQueen became well-known for his theatrical runway shows. The half-dozen or so presentations we see in the film evoke Jack the Ripper, a mental asylum, sexual assault, robots, demons, goddesses, and animal chimeras. Not all the shows were well received. Directors Ian Bonhôte and Peter Ettedgui's smartest tactic--the one that makes McQueen such a pleasure to watch, even for fashion outsiders--is giving viewers a front-row seat to the runway, then letting us judge the designer's oeuvre for ourselves.

Google has announced it's acquiring a $1.1bn chunk of HTC's smartphone business, and with it providing the once leading Taiwanese phone brand a much needed lifeline. But what does Google want with part of a smartphone business? Google gains half of HTC's research and development team – about 2,000 people – and a non-exclusive license for HTC's intellectual property, allowing it to take advantage of some of HTC's advances in smartphone technology. HTC gets a cash injection, which will help it survive in some very competitive markets, and Google gets to continue its "big bet on hardware" according to Rick Osterloh, the company's senior vice president for hardware. It's "a business decision to have access to one of the best R&D teams", said Neil Shah, research director at Counterpoint Technology Market Research.

Some designers might say their collections contain a little piece of each artist who has influenced them in their work. But none can mean this as literally as Tina Gorjanc, a fashion student from Central Saint Martins college in London. Ms Gorjanc has designed a collection of handbags and jackets, which she wants to make using leather cultivated from Alexander McQueen's own DNA. Tina Gorjanc, a fashion student from Central Saint Martins college in London, has designed a collection of handbags and jackets, which she wants to make using leather cultivated from Alexander McQueen's own DNA The'Pure Human' project envisions using DNA from McQueen's graduation collection from the same college, called'Jack the Ripper Stalks His Victims'. The cells would be taken from the hair and placed in a culture.

Manifold Learning (ML) is a class of algorithms seeking a low-dimensional nonlinear representation of high-dimensional data. Thus ML algorithms are, at least in theory, most applicable to high-dimensional data and sample sizes to enable accurate estimation of the manifold. Despite this, most existing manifold learning implementations are not particularly scalable. Here we present a Python package that implements a variety of manifold learning algorithms in a modular and scalable fashion, using fast approximate neighbors searches and fast sparse eigendecompositions. The package incorporates theoretical advances in manifold learning, such as the unbiased Laplacian estimator introduced by Coifman and Lafon (2006) and the estimation of the embedding distortion by the Riemannian metric method introduced by Perraul-Joncas and Meila (2013). In benchmarks, even on a single-core desktop computer, our code embeds millions of data points in minutes, and takes just 200 minutes to embed the main sample of galaxy spectra from the Sloan Digital Sky Survey -- consisting of 0.6 million samples in 3750-dimensions -- a task which has not previously been possible.