SPACE: Why Send Humans to Space When We Can Send Robots?
Daniel Oberhaus | Motherboard
“The first marketable, personal computers in the late 70s came about after almost 40 years of research and development, which created the technology at public expense. One of the peculiarities, if you’d like, of our system of innovation and development is that it’s radically anti-capitalist in many ways…People who paid taxes in the 50s and 60s may not have known it, but they were creating what was ultimately marketed by Apple. But they don’t get any of the profit. I think that’s a social pathology and the same carries over into space.”
ARTIFICIAL INTELLIGENCE: Growing Pains for Deep Learning
Chris Edwards | Communications of the ACM
“It has taken time for neural networks, initially conceived 50 years ago, to become accepted parts of information technology applications. After a flurry of interest in the 1990s, supported in part by the development of highly specialized integrated circuits designed to overcome their poor performance on conventional computers, neural networks were outperformed by other algorithms, such as support vector machines in image processing and Gaussian models in speech recognition.”
DATA: Who Owns the Digital Map of the World?
Laura Bliss | CityLab
“Last week, Mapbox, a map development company based in Washington, D.C., announced that it has raised some $52.55 million in Series B funding, a sum CEO Eric Gunderson called the biggest ever for a mapping company. Mapbox doesn’t exactly make maps, though. It builds towers of software that organize sets of geo-spatial data for other kinds of businesses—real estate, transportation, agriculture, government, smartphone apps.”
AUGMENTED REALITY: The Real-Life Dangers of Augmented Reality
Eric E. Sabelman & Roger Lam | IEEE Spectrum
“To understand how AR wearables affect the way a typical person perceives the world, we considered various natural impairments to vision….A poorly designed AR interface could interfere with vision to the same degree as these diseases.”
GENETICS: How computers are learning to make human software work more efficiently
John R. Woodward, Justyna Petke And William Langdon | The Conversation
“Genetic improvement involves writing an automated “programmer” who manipulates the source code of a piece of software through trial and error with a view to making it work more efficiently. This might include swapping lines of code around, deleting lines and inserting new ones – very much like a human programmer. Each manipulation is then tested against some quality measure to determine if the new version of the code is an improvement over the old version. It is about taking large software systems and altering them slightly to achieve better results
SCIENCE: Researchers Sharing Data Was Supposed to Change Science Forever. Did It?
Lily Hay Newman | Slate
“If goodwill and curiosity aren’t motivating researchers to work with open-source data on their own, there is still something that probably will: human limitation. ‘We have tiny little brains. We can’t understand the big stuff anymore,’ said Paul Cohen, a DARPA program manager in the Information and Innovation Office. ‘Machines will read the literature, machines will build complicated models, because frankly we can’t.’ When all you have to do is let your algorithms loose on a trove of publicly available data, there won’t be any reason not to pull in everything that’s out there. ”
PHILOSOPHY: Could we do without cause and effect?
Mathias Frisch | AEON
“Feynman argued that the laws of physics do not exhibit a unique, logical structure, such that one set of statements is more fundamental than another. Instead of a hierarchical ‘Euclidean conception’ of theories, Feynman argued that physics follows what he calls the ‘Babylonian tradition’, according to which the principles of physics provide us with an interconnected structure with no unique, context-independent starting point for our derivations. Given such structures, Feynman said: ‘I am never quite sure of where I am supposed to begin or where I am supposed to end.’ I want to suggest that we should think of causal structures in physics in the very same way.”
Image Credit: ESA/Hubble & NASA Acknowledgement: Gilles Chapdelaine
