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.
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.
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 new memories, 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 synaptic plasticity. 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 brain processes and build adaptive machines, researchers in the fields of neuroscience and artificial intelligence (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.
High level partnerships and a focus on using artificial intelligence and big data to find solutions to complex problems are backing VXV’s quiet climb to new all-time highs.
*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.
## ORIGINAL PAPER
Large soil carbon storage in terrestrial ecosystems of Canada
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. Robots and AI will most likely replace us in our jobs once they become intelligent enough.
Although he is hopeful that AI might help us solve many problems, such as healthcare, climate change, poverty, overpopulation etc, he cautions about the possibility of an AI arms race.
Furthermore Dr. Yuval Noah Harari suggests this technology will also allow us to upgrade our brains and nervous systems. For example, humans will be able to connect their minds directly to the internet via brain implants.
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. – 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. – #google #ai #agi
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.
“We are absolutely losing some science,” Jonathan McDowell, an astronomer at the Harvard-Smithsonian Center for Astrophysics, tells The Register. “How much science we lose depends on how many satellites there end up being. You occasionally lose data. At the moment it’s one in every ten images.”
Telescopes can try waiting for a fleet of satellites to pass before they snap their images, though if astronomers are trying to track moving objects, such as near-Earth asteroids or comets, for example, it can be impossible to avoid the blight.
“As we raise the number of satellites, there starts to be multiple streaks in images you take. That’s no longer irritating, you really are losing science. Ten years from now, there may be so many that we can’t deal with it,” he added.
Our universe started with the big bang. But only for the right definition of “our universe”. And of “started” for that matter. In fact, probably the Big Bang is nothing like what you were taught. A hundred years ago we discovered the beginning of the universe. Observations of the retreating galaxies by Edwin Hubble and Vesto Slipher, combined with Einstein’s then-brand-new general theory of relativity, revealed that our universe is expanding. And if we reverse that expansion far enough – mathematically, purely according to Einstein’s equations, it seems inevitable that all space and mass and energy should once have been compacted into an infinitesimally small point – a singularity. It’s often said that the universe started with this singularity, and the Big Bang is thought of as the explosive expansion that followed. And before the Big Bang singularity? Well, they say there was no “before”, because time and space simply didn’t exist. If you think you’ve managed to get your head around that bizarre notion then I have bad news. That picture is wrong. At least, according to pretty much every serious physicist who studies the subject. The good news is that the truth is way cooler, at least as far as we understand it.
