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This is a sci-fi documentary, looking at what it takes to build an underground city on Mars. The choice to go underground is for protection, from the growing storm radiation that rains down on the surface every day. And to further advance the Mars colonization efforts.

Where will the materials to build the city come from? How will the crater be covered to protect the inhabitants? And what will it feel like to live in this city, that is in a hole in the ground?

It is a dream of building an advanced Mars colony, and showing the science and future space technology needed to make it happen.

Personal inspiration in creating this video comes from: The Expanse TV show and books, and The Martian.

Why do I find this so alarming?


ChatGPT-maker OpenAI is looking to fuse its artificial intelligence systems into the bodies of humanoid robots as part of a new deal with robotics startup Figure.

Sunnyvale, California-based Figure announced the partnership Thursday along with $675 million in venture capital funding from a group that includes Amazon founder Jeff Bezos as well as Microsoft, chipmaker Nvidia and the startup-funding divisions of Intel and OpenAI.

Figure is less than two years old and doesn’t have a commercial product but is persuading influential tech industry backers to support its vision of shipping billions of human-like robots to the world’s workplaces and homes.

1.6 billion people globally are homeless.


Homelessness has been a growing issue in the United States for decades — it affects millions of people each year. Despite efforts to address the problem, the number of individuals experiencing homelessness continues to rise, with the COVID-19 pandemic and an ongoing lack of affordable housing exacerbating an already difficult situation.

Audio samples for the paper “BASE TTS: Lessons from building a billion-parameter text-to-speech model on 100K hours of data

Abstract: We introduce a text-to-speech (TTS) model called BASE TTS, which stands for Big Adaptive Streamable TTS with Emergent abilities. BASE TTS is the largest TTS model to-date, trained on 100K hours of public domain speech data, achieving a new state-of-the-art in speech naturalness. It deploys a 1-billion-parameter autoregressive Transformer that converts raw texts into discrete codes (“speechcodes”) followed by a convolution-based decoder which converts these speechcodes into waveforms in an incremental, streamable manner. Further, our speechcodes are built using a novel speech tokenization technique that features speaker ID disentanglement and compression with byte-pair encoding. Echoing the widely-reported “emergent abilities” of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences.

Jesse Watters discusses the very vague alleged threat to Americans that lawmakers are keeping under wraps on ‘Jesse Watters Primetime.’

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