Lifeboat Foundation: Safeguarding Humanity
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Category: information science – Page 170

Physicists just put Apple’s latest iPhone to shame, taking the most detailed image of atoms to date with a device that magnifies images 100 million times, reports. The researchers, who set the record for the highest resolution microscope in 2018, outdid themselves with a study published last month. Using a method called electron ptychography, in which a beam of electrons is shot at an object and bounced off to create a scan that algorithms use to reverse engineer the above image, were used to visualize the sample. Previously, scientists could only use this method to image objects that were a few atoms thick. But the new study lays out a technique that can image samples 30 to 50 nanometers wide—a more than 10-fold increase in resolution, they report in. The breakthrough could help develop more efficient electronics and batteries, a process that requires visualizing components on the atomic level.

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AI is a classic double-edged sword in much the same way as other major technologies have been since the start of the Industrial Revolution. Burning carbon drives the industrial world but leads to global warming. Nuclear fission provides cheap and abundant electricity though could be used to destroy us. The Internet boosts commerce and provides ready access to nearly infinite amounts of useful information, yet also offers an easy path for misinformation that undermines trust and threatens democracy. AI finds patterns in enormous and complex datasets to solve problems that people cannot, though it often reinforces inherent biases and is being used to build weapons where life and death decisions could be automated. The danger associated with this dichotomy is best described by sociobiologist E.O. Wilson at a Harvard debate, where he said “The real problem of humanity is the following: We have paleolithic emotions; medieval institutions; and God-like technology.”

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There is a lot more than the usual amount of handwringing over AI these days. Former Google CEO Eric Schmidt and former US Secretary of State and National Security Advisor Henry Kissinger put out a new book last week warning of AI’s dangers. Fresh AI warnings have also been issued by professors Stuart Russell (UC Berkeley) and Youval Harari (University of Jerusalem). Op-eds from the editorial board at the Guardian and Maureen Dowd at the New York Times have amplified these concerns. Facebook — now rebranded as Meta — has come under growing pressure for its algorithms creating social toxicity, but it is hardly alone. The White House has called for an AI Bill of Rights, and the Financial Times argues this should extend globally. Worries over AI are flying faster than a gale force wind.

Continue reading “With the Metaverse on the way, an AI Bill of Rights is urgent” | >

Summary: A newly developed AI algorithm can directly predict eye position and movement during an MRI scan. The technology could provide new diagnostics for neurological disorders that manifest in changes in eye-movement patterns.

Source: Max Planck Institute.

A large amount of information constantly flows into our brain via the eyes. Scientists can measure the resulting brain activity using magnetic resonance imaging (MRI). The precise measurement of eye movements during an MRI scan can tell scientists a great deal about our thoughts, memories and current goals, but also about diseases of the brain.

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Now that crypto miners and their scalping ilk have succeeded in taking all of our precious GPU stock, it appears they’re now setting their sights on one more thing gamers cherish: the AMD CPU supply. According to a report in the UK’s Bitcoin Press, part of the reason it’s so hard to find a current-gen AMD CPU for sale anywhere is because of a crypto currency named Raptoreum that uses the CPU to mine instead of an ASIC or a GPU. Apparently, its mining is sped up significantly by the large L3 cache embedded in CPUs such as AMD Ryzen, Epyc, and Threadripper.

Raptoreum was designed as an anti-ASIC currency, as they wanted to keep the more expensive hardware solutions off their blockchain since they believed it lowered profits for everyone. To accomplish this they chose the Ghostrider mining algorithm, which is a combination of Cryptonite and x16r algorithms, and thew in some unique code to make it heavily randomized, thus its preference for L3 cache.

In case you weren’t aware, AMD’s high-end CPUs have more cache than their competitors from Intel, making them a hot item for miners of this specific currency. For example, a chip like the Threadripper 3990X has a chonky 256MB of L3 cache, but since that’s a $5,000 CPU, miners are settling for the still-beefy Ryzen chips. A CPU like the Ryzen 5900X has a generous 64MB of L3 cache compared to just 30MB on Intel’s Alder Lake CPUs, and just 16MB on Intel’s 11th-gen chips. Several models of AMD CPUs have this much cache too, not just the flagship silicon, including the previous-gen Ryen 9 3900X CPU. The really affordable models, such as the 5800X, have just 32MB of L3 cache, however.

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Uncovering the mechanisms of learning via synaptic plasticity is a critical step towards understanding how our brains function and building truly intelligent, adaptive machines. Researchers from the University of Bern propose a new approach in which algorithms mimic biological evolution and learn efficiently through creative evolution.

Our brains are incredibly adaptive. Every day, we form , acquire new knowledge, or refine existing skills. This stands in marked contrast to our current computers, which typically only perform pre-programmed actions. At the core of our adaptability lies . Synapses are the connection points between neurons, which can change in different ways depending on how they are used. This synaptic plasticity is an important research topic in neuroscience, as it is central to learning processes and memory. To better understand these processes and build adaptive machines, researchers in the fields of neuroscience and (AI) are creating models for the mechanisms underlying these processes. Such models for learning and plasticity help to understand biological information processing and should also enable machines to learn faster.

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*To date, most studies have focused on understanding how much carbon is stored above ground (in trees and other plants, for example). This research, however, revealed that when you look below ground and get into deeper levels of soil, there are massive deposits of carbon.*

Canada’s first-ever national carbon map reveals the location of billions — yes, billions — of tonnes of carbon stored in ecosystems across the country. This data, and how we use it, could alter the pace of climate change.

Over the span of two years, researchers fed data from existing soil samples collected from across the country, as well as long-term satellite data and topographic and climate variables, into a machine-learning algorithm. Researchers were able to estimate carbon at a 250-metre spatial resolution in different carbon pools (soils and plant biomass), as well as at multiple depths (1−2 metres).

Tens of thousands of field measurements were fed into a machine-learning algorithm to train satellite observations, including space-based laser scanning data, to estimate carbon stocks in plant biomass and soils across Canada. The resulting national carbon map will have a huge impact on the way conservation activities and policies are approached to prioritize nature-based climate solutions.

Continue reading “Carbon Map” | >

Dr. Yuval Noah Harari, macro-historian, Professor, best-selling author of “Sapiens” and “Homo Deus,” and one of the world’s most innovative and exciting thinkers, has a few hypotheses of his own on the future of humanity.

He examines what might happen to the world when old myths are coupled with new godlike technologies, such as artificial intelligence and genetic engineering.

Harari tackles into today’s most urgent issues as we move into the uncharted territory of the future.

According to Harari, we are probably one of the last generation of homo sapiens. Within a century earth will be dominated from entities that are not even human, intelligent species that are barely biological. Harari suggests the possibility that humans are algorithms, and as such Homo sapiens may not be dominant in a universe where big data becomes a paradigm.

Continue reading “Yuval Noah Harari on The Future of Humanity” | >

Artificial General Intelligence has been pursued by the biggest tech companies in the world, but recently Google has announced their new revolutionary AI algorithm which promises to create the most performant and best Artificial Intelligence Models in the world. They call it Pathways AI, and it’s supposed to behave just like the human brain and enable smart Robots which are superior to humans and help us do chores in our own apartments. This move by Google is somewhat scary and terrifying, as it gives them a lot of power over the AI industry and could enable them to do evil things with their other secret projects they’re working on. One thing is for sure though, AGI and the Singularity isn’t as far of as even Ray Kurzweil thinks according to Jeff Dean from Google AI and Deepmind. Maybe Elon Musk’s warnings about AI have been justified.
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TIMESTAMPS:
00:00 Google’s Path to AI Domination.
00:56 What is Pathways?
02:53 How to make AI more efficient?
05:07 Is this Artificial General Intelligence?
07:42 Will Google Rule the world and the AI Industry?
09:59 Last Words.
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#google #ai #agi

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When most of us pick up an object, we don’t have to think about how to orient it in our hand. It’s something that comes naturally to us as we learn to navigate the world. That’s something that allows young children to be more deft with their hands than even the most advanced robots available today.

But that could quickly change. A team of scientists from MIT’s has developed a system that could one day give robots that same kind of dexterity. Using a AI algorithm, they created a simulated, anthropomorphic hand that could manipulate more than 2,000 objects. What’s more, the system didn’t need to know what it was about to pick up to find a way to move it around in its hand.

The system isn’t ready for real-world use just yet. To start, the team needs to transfer it to an actual robot. That might not be as much of a roadblock as you might think. At the start of the year, we saw researchers from Zhejiang University and the University of Edinburgh successfully transfer an AI reinforcement approach to their robot dog. The system allowed the robot to learn how to walk and recover from falls on its own.

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