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If a recent project using Google’s DeepMind were a recipe, you would take a pair of AI systems, images of animals, and a whole lot of computing power. Mix it all together, and you’ d get a series of imagined animals dream ed up by one of the AIs. A look through the research paper about the project—or this open Google Folder of images it produced—will likely lead you to agree that the results are a mix of impressive and downright eerie.

But t he eerie factor doesn’t mean the project shouldn’t be considered a success and a step forward for future uses of AI.

From GAN To BigGAN

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In the last video in this series we discussed the ancient origins of artificial intelligence progressing forward to the beginnings of the development of modern computing based artificial intelligence, encompassing the philosophies, theories and inventions of many talented individuals and groups.

The focus of this video will continue right were the last one left off, so sit back, relax and join me on an exploration on the official birth of modern artificial intelligence leading to present day!

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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.

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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.

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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.

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