Lifeboat Foundation

In collaboration with the National Museums of Kenya and with financial contributions from the British Council Cultural Protection Fund, ICCROM is engaged in a community-centred project focused on the long-term preservation of fossil footprints that date back to approximately 1.5 million years ago. The fossil was found in Ileret, a village on the northeastern shore of Lake Turkana, Kenya.

https://www.youtube.com/watch?si\u003doKkiYzJ96H2hY9en\u0026v\u003d1_gJp2uAjO0\u0026feature\u003dyoutu.be

Quantum computers have the ability to harness the mysterious effects of quantum physics, making them a game changer for science. Professor Hannah Fry explains how they work on The Future with Hannah Fry.

With the promise of unimaginable computing power, a global race for quantum supremacy is raging. Who will be first to harness this new technological force, and what will they do with it?

Continue reading “The Race to Harness Quantum Computing’s Mind-Bending Power” »

Deep-learning models are being used in many fields, from health care diagnostics to financial forecasting. However, these models are so computationally intensive that they require the use of powerful cloud-based servers.

Twelve hacktivist group targets Russian entities with destructive cyber attacks, using public tools for maximum damage without financial gain.

A massive infostealer malware operation encompassing thirty campaigns targeting a broad spectrum of demographics and system platforms has been uncovered, attributed to a cybercriminal group named “Marko Polo.”

The threat actors use a variety of distribution channels, including malvertising, spearphishing, and brand impersonation in online gaming, cryptocurrency, and software, to spread 50 malware payloads, including AMOS, Stealc, and Rhadamanthys.

According to Recorded Future’s Insikt Group, which has been tracking the Marko Polo operation, the malware campaign has impacted thousands, with potential financial losses in the millions.

Our final estimate of the achievable inter data center bandwidth by 2030 is 4 to 20 Pbps, which would allow for training runs of 3e29 to 2e31 FLOP. In light of this, bandwidth is unlikely to be a major constraint for a distributed training run compared to achieving the necessary power supply in the first place.

Expanding bandwidth capacity for distributed training networks presents a relatively straightforward engineering challenge, achievable through the deployment of additional fiber pairs between data centers. In the context of AI training runs potentially costing hundreds of billions of dollars, the financial investment required for such bandwidth expansion appears comparatively modest.⁴⁴

We conclude that training runs in 2030 supported by a local power supply could likely involve 1 to 5 GW and reach 1e28 to 3e29 FLOP by 2030. Meanwhile, geographically distributed training runs could amass a supply of 2 to 45 GW and achieve 4 to 20 Pbps connections between data center pairs, allowing for training runs of 2e28 to 2e30 FLOP.⁴⁵ All in all, it seems likely that training runs between 2e28 to 2e30 FLOP will be possible by 2030.⁴⁶ The assumptions behind these estimates can be found in Figure 3 below.

In recent years, these technological limitations have become far more pressing. Deep neural networks have radically expanded the limits of artificial intelligence—but they have also created a monstrous demand for computational resources, and these resources present an enormous financial and environmental burden. Training GPT-3, a text predictor so accurate that it easily tricks people into thinking its words were written by a human, costs $4.6 million and emits a sobering volume of carbon dioxide—as much as 1,300 cars, according to Boahen.

With the free time afforded by the pandemic, Boahen, who is faculty affiliate at the Wu Tsai Neurosciences Institute at Stanford and the Stanford Institute for Human-Centered AI (HAI), applied himself single mindedly to this problem. “Every 10 years, I realize some blind spot that I have or some dogma that I’ve accepted,” he says. “I call it ‘raising my consciousness.’”

This time around, raising his consciousness meant looking toward dendrites, the spindly protrusions that neurons use to detect signals, for a completely novel way of thinking about computer chips. And, as he writes in Nature, he thinks he’s figured out how to make chips so efficient that the enormous GPT-3 language prediction neural network could one day be run on a cell phone. Just as Feynman posited the “quantum supremacy” of quantum computers over traditional computers, Boahen wants to work toward a “neural supremacy.”

Mathematician Bernhard Riemann was born #OTD in 1826.

Bernhard Riemann was another mathematical giant hailing from northern Germany. Poor, shy, sickly and devoutly religious, the young Riemann constantly amazed his teachers and exhibited exceptional mathematical skills (such as fantastic mental calculation abilities) from an early age, but suffered from timidity and a fear of speaking in public. He was, however, given free rein of the school library by an astute teacher, where he devoured mathematical texts by Legendre and others, and gradually groomed himself into an excellent mathematician. He also continued to study the Bible intensively, and at one point even tried to prove mathematically the correctness of the Book of Genesis.

Although he started studying philology and theology in order to become a priest and help with his family’s finances, Riemann’s father eventually managed to gather enough money to send him to study mathematics at the renowned University of Göttingen in 1846, where he first met, and attended the lectures of, Carl Friedrich Gauss. Indeed, he was one of the very few who benefited from the support and patronage of Gauss, and he gradually worked his way up the University’s hierarchy to become a professor and, eventually, head of the mathematics department at Göttingen.

New Android malware ‘Ajina. Banker’ targets bank customers in Central Asia, stealing financial data and intercepting 2FA messages via Telegram channels.