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NASA has turned to AI to help them develop, and build, more robust, lightweight components for its spacecraft of the future.

NASA’s Goddard Space Flight Center in Maryland has been using commercially available AI software to design specialized, bespoke parts, called “evolved structures,” for its missions. They also look a little “out of this world.”

“They look somewhat alien and weird,” Research Engineer Ryan McClelland said, “but once you see them in function, it makes sense.”

Ex-CEO of Google, Eric Schmidt, advocated for implementing AI for the U.S. military use to compete against China and other rivals.

Former Google CEO Eric Schmidt has advocated for the military use of artificial intelligence (AI) to build a more robust and adaptable defense system for the United States against China and other rivals.

“Every once in a while, a new weapon, a new technology comes along that changes things,” he told Wired.


Getty Images.

AI could be just as revolutionary for warfare as nuclear weapons, argued Schmidt, according to an interview published by Wired on Tuesday.

When Ben Reinhardt was an undergrad at Caltech, he often passed a mural painted on the back of a building on campus. It included a quote from Theodore von Kármán, a scientist and engineer who served as the first director of JPL: “Scientists study the world as it is, engineers create the world that never has been.”

But a recent paper published in Nature described a decline in scientific progress over the last few decades.


Some of the other new scientific institutions experimenting with shaking up the traditional structure of research include Arcadia Institute, based in the Bay Area, which is dedicated to a translational program that, “will provide a unique combination of funding, support, and access to accelerate new product development.”

Alexey Guzey is another leader in this space; he recognized a gap in opportunities for young scientists and initiated New Science, which plans to finance entire labs outside of academia, and turn “the process of doing science into an experiment itself.” For a better overview of all the new types of research organizations, check out Sam Arbesman’s The Overedge Catalog.

Reinhardt’s vision for a private DARPA (laid out in the 278 page whitepaper) begins with the simple call to action, “How can we enable more science fiction to become reality?” The document attracted the attention of investors. Today, they announced the launch of Speculative Technologies with initial backing from Schmidt Futures, Patrick Collison, Protocol Labs, the Sloan Foundation. The board for the non-profit includes Kanjun Qiu, founder of Generally Intelligent, an AI research company, and Adam Marblestone, founder and CEO of Convergent Research.

A team of computer programmers at IT University of Copenhagen has developed a new way to encode and generate Super Mario Bros. levels—called MarioGPT, the new approach is based on the language model GPT-2. The group outlines their work and the means by which others can use their system in a paper on the arXiv pre-print server.

Mario Brothers is a first introduced in 1983—it involves two Italian plumbers emerging from a sewer and attempting to rescue Princess Peach, who has been captured and held by Bowser. To rescue her, the brothers must travel (via input from the game player) across a series of obstacles made of pipes and bricks. As they travel, the terrain changes in accordance with the level they have achieved in the game. In this new effort, the team in Denmark has recreated one aspect of the game—the number of levels that can be traversed.

The researchers used Generative Pre-trained Transformer 2 (GPT-2)—an open-source language created by a team at OpenAI, to translate user requests into graphical representations of Super Mario Brothers game levels. To do so, they created a small bit of Python code to help the language model understand what needed to be done and then trained it using samples from the original Super Mario Bros. game and one of its sequels, “Super Mario Bros.: The Lost Levels.”

An international team led by researchers from Nanyang Technological University, Singapore (NTU Singapore) has developed a universal connector to assemble stretchable devices simply and quickly, in a ‘Lego-like’ manner.

Stretchable devices including soft robots and wearable healthcare devices are assembled using several different modules with different material characteristics — some soft, some rigid, and some encapsulated.

However, the commercial pastes (glue), currently used to connect the modules often either fail to transmit mechanical and electrical signals reliably when deformed or break easily.

The concept of synthetic data is almost too good to be true – it can mimic the distinctive properties of a dataset while dodging a number of issues that afflict data. There are zero data privacy concerns around synthetic data since it is artificially generated and isn’t related to real-world persons. It can be manufactured on demand and in the volumes required. In other words, synthetic data is a boon in a world eternally thirsty for data.

And the hectic space of generative AI is offering a helping hand in the easy generation of synthetic data.

The concept of synthetic data has been around for decades until the autonomous vehicle (AV) industry started using it commercially in the mid-2010s. But for how important an issue it resolves, creating synthetic data brings a myriad of complications along with it.

I have to admit, it’s very human like. It makes we wonder if the incident if fabricated, but it may also be true.


SOPA images/Getty.

The concept of a conversational chatbot shot to fame with OpenAI’s ChatGPT, which can provide paraphrased answers for users’ detailed queries and write poetry or code with equal ease. Microsoft, which has provided financial support for OpenAI’s work, has incorporated the chatbot into its Bing search engine, providing users with a new way to search for information. The service’s rollout is still slow, with few users getting access. However, their experience has been interesting.

Professor of Biology at Tufts University Michael Levin shows the remarkable plasticity of somatic (non-neural) cells and the way they communicate through bioelectric signalling to produce different morphologies. He argues that cellular control of growth and form is a type of collective intelligence.

Prof. Levin also shows that by manipulating bioelectric signalling between cells it is possible to change what the cells are going to build. The particular examples include converting one type of tadpole tissue into another, making planaria (a type of flatworm) to regrow two heads, etc. Prof. Levin’s and his team work has profound theoretical contributions towards understanding better biological intelligence, and from the practical side, it may lead to applications in biomedicine (solving birth defects, curing degenerative disease and cancer).

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