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At the time, all this was theoretical. But last week, the company announced they’d linked 16 CS-2s together into a world-class AI supercomputer.

Meet Andromeda

The new machine, called Andromeda, has 13.5 million cores capable of speeds over an exaflop (one quintillion operations per second) at 16-bit half precision. Due to the unique chip at its core, Andromeda isn’t easily compared to supercomputers running on more traditional CPUs and GPUs, but Feldman told HPC Wire Andromeda is roughly equivalent to Argonne National Laboratory’s Polaris supercomputer, which ranks 17th fastest in the world, according to the latest Top500 list.

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In a conversation right before the 2021 Conference on Neural Information Processing Systems (NeurIPS), Amazon vice president and distinguished scientist Bernhard Schölkopf — according to Google Scholar, the most highly cited researcher in the field of causal inference — said that the next frontier in artificial-intelligence research was causal-representation learning.

Where existing approaches to causal inference use machine learning to discover causal relationships between variables — say, the latencies of various interrelated services on a website — causal-representation learning learns the variables themselves. “These kinds of causal representations will also go toward reasoning, which we will ultimately need if we want to move away from this pure pattern recognition view of intelligence,” Schölkopf said.

Francesco Locatello, a senior applied scientist with Amazon Web Services, leads Amazon’s research on causal-representation learning, and he’s a coauthor on four papers at this year’s NeurIPS.

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Tumor cells in the most common pancreatic cancer share nutrients to live and grow. A new discovery by University of California, Irvine biologists and collaborators during a four-year investigation could help lead to better treatments for pancreatic ductal adenocarcinoma, which accounts for over 90 percent of pancreatic cancer cases. The scientists’ paper appears in Nature Cancer. While pancreatic cancer is relatively rare, it is among the leading causes of cancer death in the United States.

One obstacle in treating , known as PDA, is that it generally does not show early symptoms. Another hurdle is the complexity of its dense and fibrous tumors. Consequently, they do not have fully functioning in the tumor. On one front, this makes it difficult to deliver effective chemotherapy. However, it also means the tumors have developed a different kind of metabolism.

“Without blood vessels, PDA cells aren’t getting the normal nutrients they need, so they have come up with other ways to nourish themselves and grow,” said Christopher Halbrook, assistant professor of molecular biology & biochemistry, and lead and co-corresponding author. Understanding this process is essential for devising treatments targeting the cancer’s metabolism.

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Large language models have advanced significantly in recent years (LLMs). Impressive LLMs have been revealed one after the other, beginning with OpenAI’s GPT-3, which generates exceptionally correct texts and ends with its open-source counterpart BLOOM. Language-related problems that were previously unsolvable had become simply a challenge for these systems.

All of this progress is made possible by the vast amount of data available on the Internet and the accessibility of powerful GPUs. As appealing as they may sound, training an LLM is an incredibly expensive procedure in terms of both data and technology needs. We’re talking about AI systems with billions of parameters, so feeding these models with enough data isn’t easy. However, once you do it, they give you a stunning performance.

Have you ever wondered where the development of “computing” gadgets began? Why did individuals devote so much time and energy to designing and constructing the first computers? We can presume it was not for the purpose of amusing people with video games or YouTube videos.

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A team of scientists led by crystallographers from St Petersburg University has succeeded in synthesizing an analog of the Earth’s most structurally complex mineral, ewingite, in a laboratory. The findings of the research are published in Materials.

Ewingite is a that was discovered in the mid-2010s in the abandoned Plavno uranium mine located in the Czech Republic. It is the most complex mineral known to exist on Earth. Moreover, because of the specific thermodynamic conditions required for its formation, the mineral is considered to be very rare.

The researchers managed to synthesize an analog with a composition and crystal structure similar to that of natural ewingite through a combination of low-temperature hydrothermal synthesis and room-temperature evaporation.

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The evolution of a new species by hybridization of two previously described species with no change in chromosomal number is very unusual in the animal world. So far, only a few empirically acknowledged cases of this spontaneous mode of evolution (from one generation to the next) known as homoploid hybridization exist.

A study led by Axel Meyer, Professor of Zoology and Evolutionary Biology at the University of Konstanz, has successfully demonstrated the emergence of a new hybrid species in cichlid fishes. This is likely the first instance of this genetic speciation method in vertebrates. The researchers reveal that a new hybrid species has emerged from the cichlid A. sagittae and A. xiloaensis in the crater lake Xiloá in Nicaragua using whole genome sequencing of more than 120 individuals as well as a number of other techniques.

Their findings were recently published in the journal Nature Communications.

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Dark Matter is both the most abundant and most mysterious substance in the Universe, what properties does it have and what technologies might we create to use it in the future?

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