Lifeboat Foundation

Lithium-ion batteries could get a significant boost in energy density from disordered rock salt (DRX), a versatile battery material that can be made with almost any transition metal instead of nickel and cobalt.

DRX cathodes could provide batteries with higher energy density than conventional lithium-ion battery cathodes made of nickel and cobalt, two metals that are in critically short supply.

Continue reading “New Consortium to Make Batteries for Electric Vehicles More Sustainable” »

Ali Rogin:

With the cost of owning a car out of reach for many today ride sharing gives commuters an alternative. And a handful of U.S. cities, self-driving taxis are getting the green light to pick up passengers. Several companies including Waymo Cruise and Motional are touting driverless taxis as the way of the future.

But the rollout of these robo cabs has hit some speed bumps. Not everyone is comfortable with autonomous cars on the road. And major technical questions remain. Aarian Marshall is a staff writer for WIRED, and she covers transportation. Aarian, thank you so much for joining us.

While many new cars are equipped to assist drivers at the wheel, experts say we’re a long way from seeing cars capable of fully automated driving.

Here is another story from web bike world: “Is water the future of motorbikes”

https://www.webbikeworld.com/water-power-future-motorbikes/

Continue reading “Motorcycle Goes 300 Miles on 1 Liter of Water” »

A new study modeled the behavior of solar vehicles in 100 locations around the world.

According to a new study, solar energy can provide a range of between 6 and 18 miles (11 and 29 kilometers) for electric vehicles each day, cutting down on the requirement for charging by half. The study took into account the capabilities of solar-powered vehicles in urban settings in 100 locations across the world, modeling the behavior of the cars in busy cities.

Used for limited purposes

Continue reading “Solar cars can reduce global charging needs by half” »

The BF350 VTEC motor makes an ideal choice for large pontoon boats to offshore vessels.

Honda’s Variable Valve Timing and Lift Electronic Control (VTEC) engines are known for their performance, refinement, and durability. However, their production engine series lacked a V8 option in its lineup, except for a few versions developed for racing.

Continue reading “Honda releases its first-ever series production V8 engine” »

Candela’s C-8 electric boat sails 420 nautical miles in 24 hours, shattering previous record.

In a groundbreaking achievement, Candela sets an impressive new world record for the longest 24-hour electric boat distance.

Continue reading “Candela C-8: world record in long-distance electric boating” »

Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today’s state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance.

To circumvent these deployment challenges, practitioners often choose to deploy smaller specialized models instead. These smaller models are trained using one of two common paradigms: fine-tuning or distillation. Fine-tuning updates a pre-trained smaller model (e.g., BERT or T5) using downstream manually-annotated data. Distillation trains the same smaller models with labels generated by a larger LLM. Unfortunately, to achieve comparable performance to LLMs, fine-tuning methods require human-generated labels, which are expensive and tedious to obtain, while distillation requires large amounts of unlabeled data, which can also be hard to collect.

In “Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes”, presented at ACL2023, we set out to tackle this trade-off between model size and training data collection cost. We introduce distilling step-by-step, a new simple mechanism that allows us to train smaller task-specific models with much less training data than required by standard fine-tuning or distillation approaches that outperform few-shot prompted LLMs’ performance. We demonstrate that the distilling step-by-step mechanism enables a 770M parameter T5 model to outperform the few-shot prompted 540B PaLM model using only 80% of examples in a benchmark dataset, which demonstrates a more than 700x model size reduction with much less training data required by standard approaches.

Jason Shawhan, Tesla’s director of manufacturing at Giga Texas, recently gave a rare talk about the facility’s existing operations and the company’s plans for the future. The executive shared the information during a keynote address at the State of Manufacturing conference and expo, which was held by the Austin Regional Manufacturers Association.

Tesla is the world’s most valuable automaker by market cap, and its CEO, Elon Musk, is one of the most visible chief executives in the auto industry. Despite this, Tesla has a reputation for being tight-lipped when it comes to the details of its operations. Rare appearances from high-ranking executives such as Shawhan, who serves as director of manufacturing at Gigafactory Texas, are therefore very interesting.

Shawhan did not disappoint, as he did share a number of important insights about the facility. As noted in a report from the Austin Business Journal, the executive confirmed that Giga Texas has become the second-largest private employer in the region because the factory currently employs over 20,000 workers today. This is a notable increase from the 12,277 employees that Tesla confirmed at the end of 2022. Considering Gigafactory Texas’ growth so far, it would appear that the facility would be outpacing Musk’s estimates.