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Skild AI Raises $300M Series A To Build A Scalable AI Foundation Model For Robotics

📅 Date:

🔖 Topics: Funding Event, Foundation Model

🏢 Organizations: Skild AI, Lightspeed Venture Partners, Coatue, SoftBank, Carnegie Mellon

Skild AI, an AI robotics company building a scalable foundation model for robotics, announced it has closed a $300M Series A funding round. The round was led by Lightspeed Venture Partners, Coatue, SoftBank Group, and Jeff Bezos (through Bezos Expeditions), with participation from Felicis Ventures, Sequoia, Menlo Ventures, General Catalyst, CRV, Amazon, SV Angel, and Carnegie Mellon University. The funding brings the company to a valuation of $1.5B. The capital will be used to continue scaling the company’s model and training datasets for future commercial deployment of its technology, in addition to hiring for roles across AI, robotics, engineering, operations, and security.

Skild AI is building intelligence that is grounded in the physical world. The company is breaking the data barrier in robotics, training its model on at least 1,000X more data points than competing models. As opposed to vertically designed robots that are built for specific applications, Skild’s model serves as a shared, general-purpose brain for a diverse embodiment of robots, scenarios and tasks, including manipulation, locomotion and navigation. From resilient quadrupeds mastering adverse physical conditions, to vision-based humanoids performing dexterous manipulation of objects for complex household and industrial tasks, the company’s model will enable the use of low-cost robots across a broad range of industries and applications.

Read more at Business Wire

Electric vehicle battery chemistry affects supply chain disruption vulnerabilities

📅 Date:

✍️ Authors: Anthony L. Cheng, Erica R. H. Fuchs, Valerie J. Karplus, Jeremy J. Michalek

🏭 Vertical: Electrical Equipment

🏢 Organizations: Carnegie Mellon

We examine the relationship between electric vehicle battery chemistry and supply chain disruption vulnerability for four critical minerals: lithium, cobalt, nickel, and manganese. We compare the nickel manganese cobalt (NMC) and lithium iron phosphate (LFP) cathode chemistries by (1) mapping the supply chains for these four materials, (2) calculating a vulnerability index for each cathode chemistry for various focal countries and (3) using network flow optimization to bound uncertainties. World supply is currently vulnerable to disruptions in China for both chemistries: 80% [71% to 100%] of NMC cathodes and 92% [90% to 93%] of LFP cathodes include minerals that pass through China. NMC has additional risks due to concentrations of nickel, cobalt, and manganese in other countries. The combined vulnerability of multiple supply chain stages is substantially larger than at individual steps alone. Our results suggest that reducing risk requires addressing vulnerabilities across the entire battery supply chain.

Read more at Nature Communications

Chip startup Efficient Computer raises $16 million led by Eclipse

📅 Date:

🔖 Topics: Funding Event

🏢 Organizations: Efficient Computer, Eclipse Ventures, Carnegie Mellon

Chip startup Efficient Computer said on Thursday it had raised $16 million in a seed funding round led by Silicon Valley venture capital firm Eclipse to help fund work on its low-power chip designs. Pittsburgh-based Efficient developed a new design, or architecture, for its chips that focuses on producing processors that use the least possible amount of energy. Called Fabric, the architecture was developed by Efficient’s founding team over seven years at Carnegie Mellon University.

Efficient has built a test chip called Monza and plans to use the funding help with research and development, and go-to-market to begin to sell chips. The company will market the chips to customers in industries such as health devices, civil infrastructure monitoring, satellites, defense and security. Devices running on chips that use a tiny amount of power will last longer in the field without the need for replacement power.

Read more at Reuters

Closed-loop fully-automated frameworks for accelerating materials discovery

📅 Date:

🔖 Topics: Machine Learning, Materials Science

🏢 Organizations: Citrine Informatics, Carnegie Mellon, MIT

Our work shows that a fully-automated closed-loop framework driven by sequential learning can accelerate the discovery of materials by up to 10-25x (or a reduction in design time by 90-95%) when compared to traditional approaches. We show that such closed-loop frameworks can lead to enormous improvement in researcher productivity in addition to reducing overall project costs. Overall, these findings present a clear value proposition for investing in closed-loop frameworks and sequential learning in materials discovery and design enterprises.

Read more at Citrine Informatics Blog