Lifeboat Foundation

Flippy’s first iteration was already pretty impressive. It used machine learning software to locate and identify objects in front of it (rather than needing to have objects lined up in specific spots), and was able to learn from experience to improve its accuracy. Sensors on its grill-facing side took in thermal and 3D data to gauge the cooking process for multiple patties at a time, and cameras allowed the robot to ‘see’ its surroundings.

A system that digitally sent tickets to the kitchen from the restaurant’s front counter kept Flippy on top of how many burgers it should be cooking at any given time. Its key tasks were pulling raw patties from a stack and placing them on the grill, tracking each burger’s cook time and temperature, and transferring cooked burgers to a plate.

The new and improved Flippy can do all this and more. It can cook 19 different foods, including chicken wings, onion rings, french fries, and even the Impossible Burger (which, as you may know, isn’t actually made of meat, and that means it’s a little trickier to grill it to perfection).

Researchers in China have developed a biocompatible magnetic coating that actuates inanimate millirobots for biomedical applications.

It is thrilling to live at a time when robots the size of small insects, known as millirobots, can o.

Apple’s first passenger car could include its own breakthrough battery technology.

Apple is actively working on various automotive projects that could ultimately lead to an “Apple Car” and is targeting 2024 to produce a passenger vehicle.

Honorable Mentions

One more scientific brilliance this year is the use of light in neuroscience and tissue engineering. One study, for example, used lasers to directly print a human ear-like structure under the skin of mice, without a single surgical cut. Another used light to incept smell in mice, artificially programming an entirely new, never-seen-in-nature perception of a scent directly into their brains. Yet another study combined lasers with virtual reality to dissect how our brains process space and navigation, “mentally transporting” a mouse to a virtual location linked to a reward. To cap it off, scientists found a new way to use light to control the brain through the skull without surgery—though as of now, you’ll still need gene therapy. Given the implications of unauthorized “mind control,” that’s probably less of a bug and more of a feature.

We’re nearing the frustratingly slow, but sure, dying gasp of Covid-19. The pandemic defined 2020, but science kept hustling along. I can’t wait to share what might come in the next year with you—may it be revolutionary, potentially terrifying, utterly bizarre or oddly heart-warming.

One of the ongoing questions these past few months has been why so many tech products have been so hard to buy. We’ve made repeated reference to known potential factors like COVID-19, economic disruptions, yield issues, and the impact of scalping bots, but there’s a new argument for what’s causing such general problems across so many markets: Insufficient investment in 200mm wafers.

Today, leading-edge silicon is invariably manufactured on 300mm wafers. Over the past few decades, manufacturers have introduced larger wafer sizes: 100mm, 150mm, 200mm, and 300mm have all been common standards at one time or another. In the PC enthusiast space, 300mm wafers have long been considered superior to 200mm wafers, because the larger wafer size reduces waste and typically improves the foundry’s output in terms of chips manufactured per day.

There aren’t that many commercial foundries still dedicated to 150mm or smaller wafer sizes, but a number of foundries still run 200mm fab lines. TSMC and Samsung both offer the node, as well as a number of second-tier foundries. GlobalFoundries has 200mm facilities, as do SMIC, UMC, TowerJazz, and SkyWater. A great many IoT and 5G chips are built on 200mm, as are some analog processors, MEMS devices, and RF solutions.

Even Apple wants to get into the automobile business it seems.

(Reuters) — Apple Inc is moving forward with self-driving car technology and is targeting 2024 to produce a passenger vehicle that could include its own breakthrough battery technology, people familiar with the matter told Reuters.

The iPhone maker’s automotive efforts, known as Project Titan, have proceeded unevenly since 2014 when it first started to design its own vehicle from scratch. At one point, Apple drew back the effort to focus on software and reassessed its goals. Doug Field, an Apple veteran who had worked at Tesla Inc, returned to oversee the project in 2018 and laid off 190 people from the team in 2019.

Continue reading “Exclusive: Apple targets car production by 2024 and eyes ‘next level’ battery technology” »

While many self-driving vehicles have achieved remarkable performance in simulations or initial trials, when tested on real streets, they are often unable to adapt their trajectories or movements based on those of other vehicles or agents in their surroundings. This is particularly true in situations that require a certain degree of negotiation, for instance, at intersections or on streets with multiple lanes.

Researchers at Stanford University recently created LUCIDGames, a computational technique that can predict and plan adaptive trajectories for autonomous vehicles. This technique, presented in a paper pre-published on arXiv, integrates an algorithm based on game theory and an estimation method.

Continue reading “LUCIDGames: A technique to plan adaptive trajectories for autonomous vehicles” »

A team of scientists at Freie Universität Berlin has developed an artificial intelligence (AI) method for calculating the ground state of the Schrödinger equation in quantum chemistry. The goal of quantum chemistry is to predict chemical and physical properties of molecules based solely on the arrangement of their atoms in space, avoiding the need for resource-intensive and time-consuming laboratory experiments. In principle, this can be achieved by solving the Schrödinger equation, but in practice this is extremely difficult.

Up to now, it has been impossible to find an exact solution for arbitrary molecules that can be efficiently computed. But the team at Freie Universität has developed a deep learning method that can achieve an unprecedented combination of accuracy and computational efficiency. AI has transformed many technological and scientific areas, from computer vision to materials science. “We believe that our approach may significantly impact the future of quantum chemistry,” says Professor Frank Noé, who led the team effort. The results were published in the reputed journal Nature Chemistry.

Central to both quantum chemistry and the Schrödinger equation is the wave function —a mathematical object that completely specifies the behavior of the electrons in a molecule. The wave function is a high-dimensional entity, and it is therefore extremely difficult to capture all the nuances that encode how the individual electrons affect each other. Many methods of quantum chemistry in fact give up on expressing the wave function altogether, instead attempting only to determine the energy of a given molecule. This however requires approximations to be made, limiting the prediction quality of such methods.

The 60-year-old sub is preparing to take its deepest plunge yet. But in the age of autonomous machines, why are humans exploring the ocean floor at all?