So far, it has been an encouraging year for science fiction. My favourite new offering to date is probably Hal LaCroix's Here and Beyond, but then, I'm a sucker for a good ark-ship story. In LaCroix's take on the trope, a vessel called Shipworld is heading for HD-40307g, "a habitable Super Earth hug orbiting a simmering red dwarf star". It is a journey of 42 light years – meaning that none of the 600 souls who begin the journey will actually live to see HD-40307g. Only the Seventh Generation will make planetfall. There are rules on board.

We each make decisions based on certain values and principles. But whose do we choose as the template for ethical AI?

Two things can be said about human beings: we like building machines, and we tend to freak out about the machines we build. The Luddites of 19th-century England, an oath-based secret society, looked to the industrial era and saw not liberation but destitution. The most radical among them formed paramilitary groups to raid textile factories and destroy knitting machines and mechanical looms -- devices that would replace workers. Their political descendants include the lamplighters of early-20th-century New York who went on strike to protest the advent of electric streetlights, and the switchboard operators of Bloomington-Normal, Illinois, who in the 1930s took action against the rotary dial system. Did predictions of automation and mass joblessness come true?

Significant advances have been made in artificial systems by using biological systems as a guide. However, there is often little interaction between computational models for emergent communication and biological models of the emergence of language. Many researchers in language origins and emergent communication take compositionality as their primary target for explaining how simple communication systems can become more like natural language. However, there is reason to think that compositionality is the wrong target on the biological side, and so too the wrong target on the machine-learning side. As such, the purpose of this paper is to explore this claim. This has theoretical implications for language origins research more generally, but the focus here will be the implications for research on emergent communication in computer science and machine learning---specifically regarding the types of programmes that might be expected to work and those which will not. I further suggest an alternative approach for future research which focuses on reflexivity, rather than compositionality, as a target for explaining how simple communication systems may become more like natural language. I end by providing some reference to the language origins literature that may be of some use to researchers in machine learning.

It's three in the morning and my room is bathed in the glow of my phone. Like one in three people, I check my smartphone when I wake up in the middle of the night. I can't sleep and so wander from one social-media app to another, my thumbs scrolling through what feels like miles of emptiness. "Siri, what is the meaning of life?" "I have stopped asking myself this kind of question," she answers. I ask again, because I like it better when she says "nothing Niestzche wouldn't teach you".

Since the inception of networked video surveillance, many companies have worked to develop a variety of different analytics to enhance the value of the systems to end-users. Some vendors have been more successful than others in being able to provide reliable video analytics to their customers and, after a period in which the technology was greeted with a healthy amount of skepticism, it has now become commonplace in many surveillance installations across a wide range of vertical markets. While the use cases for analytics have changed, the technology itself has remained relatively the same – algorithms are created to search for certain pre-defined actions within a camera's field-of-view. However, the evolution of artificial intelligence (AI) means that the future of analytics will lie not in the creation of static algorithms but on the ability of machines to learn what operators should and should not be alerted to. For example, for those installations that use virtual trip wires for notification of perimeter breaches, many analytics cannot decipher between a human coming onto the property, which would obviously be the primary concern, vs. an animal, which would be of little interest.