Lifeboat Foundation

Who should a self-driving car kill?

The infamous “trolley problem” was put to millions of people in a global study, revealing how much ethics diverge across cultures.

The Mars Society is holding a special contest called The Mars Colony Prize for designing the best plan for a Mars colony of 1000 people. There will be a prize of $10,000 for first place, $5,000 for second and $2500 for third. In addition, the best 20 papers will be published in a book — “Mars Colonies: Plans for Settling the Red Planet.”

The Mars colony should be self-supporting to the maximum extent possible – i.e. relying on a minimum mass of imports from Earth. In order to make all the things that people need on Earth takes a lot more than 1000 people, so you will need to augment both the amount and diversity of available labor power through the use of robots and artificial intelligence. You will need to be able to both produce essential bulk materials like food, fabrics, steel, glass, and plastics on Mars, and fabricate them into useful structures, so 3D printing and other advanced fabrication technologies will be essential. The goal is to have the colony be able to produce all the food, clothing, shelter, power, common consumer products, vehicles, and machines for 1000 people, with only the minimum number of key components, such as advanced electronics needing to be imported from Earth.

As noted, imports will always be necessary, so you will need to think of useful exports – of either material or intellectual products that the colony could produce and transport or transit back to Earth to pay for them. In the future, it can be expected that the cost of shipping goods from Earth to Mars will be $500/kg and the cost of shipping goods from Mars to Earth will be $200/kg. Under these assumptions, your job is to design an economy, cost it out, and show that after a certain initial investment in time and money, that it can become successful.

Continue reading “Mars Colony Prize — Design the First Human Settlement on Mars” »

That will require widening of the locations where AI is done. The vast majority of experts are in North America, Europe and Asia. Africa, in particular, is barely represented. Such lack of diversity can entrench unintended algorithmic biases and build discrimination into AI products. And that’s not the only gap: fewer African AI researchers and engineers means fewer opportunities to use AI to improve the lives of Africans. The research community is also missing out on talented individuals simply because they have not received the right education.

If AI is to improve lives and reduce inequalities, we must build expertise beyond the present-day centres of innovation, says Moustapha Cisse.

Researchers at CSIRO & Queensland University of Technology have recently carried out a study aimed at automatically evolving the physical structure of robots to enhance their performance in different environments. This project, funded by CSIRO’s Active Integrated Matter Future Science Platform, was conceived by David Howard, research scientist at Data61’s Robotics and Autonomous Systems Group (RASG).

“RASG focuses on field robotics, which means we need our robots to go out into remote places and conduct missions in adverse, difficult environmental conditions,” David Howard told TechXplore. “The research came about through an identified opportunity, as RASG makes extensive use of 3D printing to build and customise our robots. This research demonstrates a design algorithm that can automatically generate 3D printable components so that our robots are better equipped to function in different environments.”

The main objective of the study was to generate components automatically that can improve a robot’s environment-specific performance, with minimal constraints on what these components look like. The researchers particularly focused on the legs of a hexapod (6-legged) robot, which can be deployed in a variety of environments, including industrial settings, rainforests, and beaches.

Continue reading “Evolving the physical structure of robots to enhance performance in different environments” »

That is changing. This month fast.ai, an education non-profit based in San Francisco, kicked off the third year of its course in deep learning. Since its inception it has attracted more than 100,000 students, scattered around the globe from India to Nigeria. The course and others like it come with a simple proposition: there is no need to spend years obtaining a phd in order to practise deep learning. Creating software that learns can be taught as a craft, not as a high intellectual pursuit to be undertaken only in an ivory tower. Fast.ai’s course can be completed in just seven weeks.

Treating it like a craft is paying dividends.

Continue reading “New schemes teach the masses to build AI” »

Cinemagraphs are all the rage.

Researchers from the Moscow Institute of Physics and Technology (MIPT), Aalto University in Finland, and ETH Zurich have demonstrated a prototype device that uses quantum effects and machine learning to measure magnetic fields more accurately than its classical analogues. Such measurements are needed to seek mineral deposits, discover distant astronomical objects, diagnose brain disorders, and create better radars.

“When you study nature, whether you investigate the human brain or a supernova explosion, you always deal with some sort of electromagnetic signals,” explains Andrey Lebedev, a co-author of the paper describing the new device in npj Quantum Information. “So measuring magnetic fields is necessary across diverse areas of science and technology, and one would want to do this as accurately as possible.”

The results of the poll are in.

Despite the simplicity of their visual system, fruit flies are able to reliably distinguish between individuals based on sight alone. This is a task that even humans who spend their whole lives studying Drosophila melanogaster struggle with. Researchers have now built a neural network that mimics the fruit fly’s visual system and can distinguish and re-identify flies. This may allow the thousands of labs worldwide that use fruit flies as a model organism to do more longitudinal work, looking at how individual flies change over time. It also provides evidence that the humble fruit fly’s vision is clearer than previously thought.

In an interdisciplinary project, researchers at Guelph University and the University of Toronto, Mississauga combined expertise in fruit fly biology with machine learning to build a biologically-based algorithm that churns through low-resolution videos of fruit flies in order to test whether it is physically possible for a system with such constraints to accomplish such a difficult task.

Fruit flies have small compound eyes that take in a limited amount of visual information, an estimated 29 units squared (Fig. 1A). The traditional view has been that once the image is processed by a fruit fly, it is only able to distinguish very broad features (Fig. 1B). But a recent discovery that fruit flies can boost their effective resolution with subtle biological tricks (Fig. 1C) has led researchers to believe that vision could contribute significantly to the social lives of flies. This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: “can we model a fly brain that can identify individuals?”