Inflection AI has recently launched Inflection-2.5, a model that competes with all the world’s leading LLMs, including GPT-4 and Gemini. Inflection-2.5 approaches the performance level of GPT-4 but utilizes only 40% of the computing resources for training.
Inflection-2.5 is available to all Pi’s users today, atpi.ai, on iOS, on Android, and via the new desktop app.
Inflection AI’s previous model, Inflection-1, utilized about 4% of the training FLOPs of GPT-4 and exhibited an average performance of around 72% compared to GPT-4 across various IQ-oriented tasks.
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.
Other topics in the video include: the plan and different phases of construction, the robots building the city, structures that are on the surface versus below the surface, pressurizing a habitat on Mars, the soil and how to turn it in Martian concrete, the art of terraforming, and the different materials that can be extracted from the planet. And the future plans of the Mars colony, from building upwards to venturing to the asteroid belt and Jupiter’s 95 moons.
Drawing inspiration from the extraordinary adaptability seen in biological entities such as the octopus, a significant advancement in the field of soft robotics has been made. Under the guidance of Professor Jiyun Kim from the Department of Materials Science and Engineering at UNIST, a research team has successfully developed an encodable multifunctional material that can dynamically tune its shape and mechanical properties in real-time.
This groundbreaking metamaterial surpasses the limitations of existing materials, opening up new possibilities for applications in robotics and other fields requiring adaptability.
Current soft machines lack the level of adaptability demonstrated by their biological counterparts, primarily due to limited real-time tunability and restricted reprogrammable space of properties and functionalities. In order to bridge this gap, the research team introduced a novel approach utilizing graphical stiffness patterns. By independently switching the digital binary stiffness states (soft or rigid) of individual constituent units within a simple auxetic structure featuring elliptical voids, the material achieves in situ and gradational tunability across various mechanical qualities.
