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According to the order page, the price in California starts at $1235, 890, and Tesla requires $5000 down. This doesn’t include taxes or annual maintenance. Prices do vary by state. Customers can order up to 1000 Megapacks, and if they do, the costs per unit decline with each additional Megapack order.

On its website, Tesla said that it took everything it knew about battery technology to enable the world’s largest energy projects. For these giga-scale projects, a 1 gigawatt-hour (GWh) project provides enough energy storage capacity to power every home in San Francisco for 6 hours.

For those ordering the Tesla Megapack, the earliest deliveries will occur in 2022, but not for all states. California, Nevada, and Texas have 2022 delivery estimates, while others, including my own state of Louisiana, have 2023 estimated delivery dates. The price of the Megapack for Louisiana is $1252, 810.

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Untether AI, a startup developing custom-built chips for AI inferencing workloads, today announced it has raised $125 million from Tracker Capital Management and Intel Capital. The round, which was oversubscribed and included participation from Canada Pension Plan Investment Board and Radical Ventures, will be used to support customer expansion.

Increased use of AI — along with the technology’s hardware requirements — poses a challenge for traditional datacenter compute architectures. Untether is among the companies proposing at-memory or near-memory computation as a solution. Essentially, this type of hardware builds memory and logic into an integrated circuit package. In a “2.5D” near-memory compute architecture, processor dies are stacked atop an interposer that links the components and the board, incorporating high-speed memory to bolster chip bandwidth.

Founded in 2018 by CTO Martin Snelgrove, Darrick Wiebe, and Raymond Chik, Untether says it continues to make progress toward mass-producing its RunA1200 chip, which boasts efficiency with computational robustness. Snelgrove and Wiebe claim that data in their architecture moves up to 1000 times faster than is typical, which would be a boon for machine learning, where datasets are frequently dozens or hundreds of gigabytes in size.

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The company’s head, Slyusar, also touted the aircraft’s features on Russian state TV, describing the planes as “unique in their class” and adding that they have “a combat radius of 1500 kilometers, the largest thrust-to-weight ratio, shortened takeoff and landing, more than seven tons of combat load, which is an absolute record for aircraft of this class.”

Russian President Vladimir Putin got a sneak peek of a new fifth-generation lightweight single-engine fighter jet at an air show just outside of Moscow on Tuesday.

Russian aircraft makers unveiled a prototype of the stealth fighter dubbed “Checkmate” for the 68-year-old leader at the MAKS-2021 International Aviation and Space Salon in Zhukovsky, ahead of its official unveiling later in the day, according to a statement from Rostec, the state-owned military giant which is responsible for exporting Russian technology.

The head of Rostec, Sergey Chemezov, and the general director of United Aircraft Corporation (UAC), Yury Slyusar, presented the warplane to Putin at the exhibition pavilion of the Sukhoi company.

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Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

This research work is the first of its kind to use psychology to create more fluid and better AI systems. It aims to develop machine commonsense that makes sense because it has innate human qualities – such as intuition, common knowledge, or understanding of social cues.

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Researchers at Honeywell Quantum Solutions have taken a significant step toward demonstrating the viability of large-scale quantum computing on its trapped-ion quantum computing technology.

The Honeywell team can now perform quantum error correction (QEC), which are protocols necessary to detect and correct errors in real time on a quantum computer. They demonstrated the ability to “protect” quantum information (prevent a quantum computation from being quickly corrupted by imperfections and noise) on the System Model H1. This is an important first in the quantum computing industry. Currently, most demonstrations of quantum error correction involve correcting errors or “noise” after the procedure has finished running, a technique known as post-processing.

In a paper published this week on arXiv, researchers detailed how they created a single logical qubit (a series of entangled physical qubits) and applied multiple rounds of quantum error correction. This logical qubit is protected from two main types of errors that occur in a quantum computer: bit flips and phase flips.

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AFRL completed a two-year space experiment that will help better understand the effects of radiation on space hardware in medium Earth orbit.

WASHINGTON — The U.S. Air Force Research Laboratory announced July 19 it has completed a two-year experiment that will help better understand the effects of radiation on space hardware in medium Earth orbit.

Known as the Demonstration and Science Experiments (DSX) mission, the spacecraft was one of 24 payloads launched June 25, 2019, on a SpaceX Falcon Heavy rideshare.

AFRL had spent 16 years developing and testing DSX. The mission was to study the harsh radiation environment of medium orbits — from about 2000 to 35000 kilometers above Earth — where the Defense Department operates critical spacecraft like the Global Positioning System.

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