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AMD to offer five Ryzen Threadripper Pro 5000WX CPUs for workstations.

Dual-processor workstations are the stomping grounds of companies like Dell, HP, and Lenovo. They tend to cost as much as a car and are aimed at the most performance-demanding professionals with very deep pockets. It is hard to expect motherboard makers to offer dual-socket sWRX8 platforms at this time since 128-core/256-thread machines are complete overkill even for the workstation segment (which is why this capability might be canned if AMD feels that it is easier to offer Epyc platforms for the same market segment instead). Meanwhile, the report also says that Asus and Gigabyte intend to release all-new single-socket motherboards for the upcoming Ryzen Threadripper Pro 5000WX CPUs.

AMD’s Ryzen Threadripper Pro retains eight memory channels to provide loads of bandwidth and support for plenty of memory for professional applications. The CPUs will continue to use the sWRX8 socket, though we do not know whether the new products will be drop-in compatible with the existing sWRX8 platform (probably they will, albeit with a BIOS update).

Since the Ryzen Threadripper Pro processors are designed for professional workstations, not gamers (so you shouldn’t expect to see them in our list of the best CPUs for gaming), it shouldn’t come as a surprise that all the CPUs have a similar rather conservative 4.55 GHz boost clock at a maximum TDP of 280W. The chips will also come with the B2 stepping.

Continue reading “Threadripper Pro 5000WX’s Secret Weapon: Up to 128 Cores per Workstation” | >

Agility Robotics’ Cassie just became the first bipedal robot to complete an outdoor 5K run, completing the jaunt on a single charge.

Agility Robotics’ Cassie just became the first bipedal robot to complete an outdoor 5K run — and it did so untethered and on a single charge.

The challenge: To create robots that can seamlessly integrate into our world, it makes sense to design those robots to walk like we do. That should make it easier for them to navigate our homes and workplaces.

Continue reading “Bipedal robot completes 5K after learning to run” | >

Korea Zinc buys wind and solar developer Epuron, delivering a wind and solar portfolio of up to 9GW for its green metals and hydrogen ambitions.

Korean Zinc, the world’s biggest zinc, lead and silver producer, has bought Australian-based renewable energy developer Epuron as part of its move towards 100 per cent renewables, green metals and green hydrogen.

The purchase is a significant move, and underlines the determination of some of the world’s biggest metals companies to switch to green products, in moves that will surely turbo-charge the development of wind and solar projects in Australia and across the globe.

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The Air Force Research Laboratory’s (AFRL)and Northrop Grumman’s Space Solar Power Incremental Demonstrations and Research (SSPIDR) Project announced that they are one step closer to collecting solar energy in space and transmitting it to Earth using radio frequency (RF). The team has successfully conducted the first end-to-end demonstration of key hardware for the Arachne flight experiment.

A ground demonstration of novel components for the “sandwich tile” was used to successfully convert solar energy to radiofrequency (RF) – a fundamental step required to pave the way for a large-scale solar power collection system in space. For this to work, it is necessary to use receiving antennas on Earth to convert RF energy into usable power.

Space solar power is a key focus of AFRL, which awarded Northrop Grumman a $100 million contract in 2018 for the development of a payload to demonstrate the key components of a prototype space solar power system. The sandwich tile is currently under development as an essential payload component for Arachne and as a building block for a large-scale operational system.

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In a bit of good news, the spot price for solar grade polysilicon is dropping quite rapidly. If the trend holds, the cost of solar panels in Australia should follow suit soon-ish.

Polysilicon is used in the manufacture of conventional photovoltaic cells used in solar panels. The sought-after stuff was as cheap as chips in July last year, when it was below USD $7/kg. But a series of events including impacts from the pandemic and a couple of factory fires saw it skyrocket.

Polysilicon spot prices were as high as US$36.64/kg at the beginning of this month. But here’s what’s happened in the last few weeks as reported by Bernreuter Research.

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KEAR (Knowledgeable External Attention for commonsense Reasoning) —along with recent milestones in computer vision and neural text-to-speech —is part of a larger Azure AI mission to provide relevant, meaningful AI solutions and services that work better for people because they better capture how people learn and work—with improved vision, knowledge understanding, and speech capabilities. At the center of these efforts is XYZ-code, a joint representation of three cognitive attributes: monolingual text (X), audio or visual sensory signals (Y), and multilingual (Z). For more information about these efforts, read the XYZ-code blog post.

Last month, our Azure Cognitive Services team, comprising researchers and engineers with expertise in AI, achieved a groundbreaking milestone by advancing commonsense language understanding. When given a question that requires drawing on prior knowledge and five answer choices, our latest model— KEAR, Knowledgeable External Attention for commonsense Reasoning —performs better than people answering the same question, calculated as the majority vote among five individuals. KEAR reaches an accuracy of 89.4 percent on the CommonsenseQA leaderboard compared with 88.9 percent human accuracy. While the CommonsenseQA benchmark is in English, we follow a similar technique for multilingual commonsense reasoning and topped the X-CSR leaderboard.

Although recent large deep learning models trained with big data have made significant breakthroughs in natural language understanding, they still struggle with commonsense knowledge about the world, information that we, as people, have gathered in our day-to-day lives over time. Commonsense knowledge is often absent from task input but is crucial for language understanding. For example, take the question “What is a treat that your dog will enjoy?” To select an answer from the choices salad, petted, affection, bone, and lots of attention, we need to know that dogs generally enjoy food such as bones for a treat. Thus, the best answer would be “bone.” Without this external knowledge, even large-scale models may generate incorrect answers. For example, the DeBERTa language model selects “lots of attention,” which is not as good an answer as “bone.”

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The contemporaneous development in recent years of deep neural networks, hardware accelerators with large memory capacity and massive training datasets has advanced the state-of-the-art on tasks in fields such as computer vision and natural language processing. Today’s deep learning (DL) systems however remain prone to issues such as poor robustness, inability to adapt to novel task settings, and requiring rigid and inflexible configuration assumptions. This has led researchers to explore the incorporation of ideas from collective intelligence observed in complex systems into DL methods to produce models that are more robust and adaptable and have less rigid environmental assumptions.

In the new paper Collective Intelligence for Deep Learning: A Survey of Recent Developments, a Google Brain research team surveys historical and recent neural network research on complex systems and the incorporation of collective intelligence principles to advance the capabilities of deep neural networks.

Collective intelligence can manifest in complex systems as self-organization, emergent behaviours, swarm optimization, and cellular systems; and such self-organizing behaviours can also naturally arise in artificial neural networks. The paper identifies and explores four DL areas that show close connections with collective intelligence: image processing, deep reinforcement learning, multi-agent learning, and meta-learning.

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A research team, led by Assistant Professor Desmond Loke from the Singapore University of Technology and Design (SUTD), has developed a new type of artificial synapse based on two-dimensional (2D) materials for highly scalable brain-inspired computing.

Brain-inspired computing, which mimics how the human brain functions, has drawn significant scientific attention because of its uses in artificial intelligence functions and low energy consumption. For brain-inspired computing to work, synapses remembering the connections between two neurons are necessary, like .

In developing brains, synapses can be grouped into functional synapses and silent synapses. For functional synapses, the synapses are active, while for silent synapses, the synapses are inactive under normal conditions. And, when silent synapses are activated, they can help to optimize the connections between neurons. However, as artificial synapses built on typically occupy large spaces, there are usually limitations in terms of hardware efficiency and costs. As the human brain contains about a hundred trillion synapses, it is necessary to improve the hardware cost in order to apply it to smart portable devices and internet-of things (IoTs).

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Autonomous weapon systems—commonly known as killer robots—may have killed human beings for the first time ever last year, according to a recent United Nations Security Council report on the Libyan civil war. History could well identify this as the starting point of the next major arms race, one that has the potential to be humanity’s final one.

The United Nations Convention on Certain Conventional Weapons debated the question of banning at its once-every-five-years review meeting in Geneva Dec. 13–17, 2021, but didn’t reach consensus on a ban. Established in 1983, the convention has been updated regularly to restrict some of the world’s cruelest conventional weapons, including land mines, booby traps and incendiary weapons.

Autonomous weapon systems are robots with lethal weapons that can operate independently, selecting and attacking targets without a human weighing in on those decisions. Militaries around the world are investing heavily in autonomous weapons research and development. The U.S. alone budgeted US$18 billion for autonomous weapons between 2016 and 2020.

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