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Category: information science – Page 176

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.

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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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“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.

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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.

Check out the new Space Time Merch Store!

Continue reading “Did Time Start at the Big Bang?” | >

According to Klaus Schwab, the founder and executive chair of the World Economic Forum (WEF), the 4-IR follows the first, second, and third Industrial Revolutions—the mechanical, electrical, and digital, respectively. The 4-IR builds on the digital revolution, but Schwab sees the 4-IR as an exponential takeoff and convergence of existing and emerging fields, including Big Data; artificial intelligence; machine learning; quantum computing; and genetics, nanotechnology, and robotics. The consequence is the merging of the physical, digital, and biological worlds. The blurring of these categories ultimately challenges the very ontologies by which we understand ourselves and the world, including “what it means to be human.”

The specific applications that make up the 4-R are too numerous and sundry to treat in full, but they include a ubiquitous internet, the internet of things, the internet of bodies, autonomous vehicles, smart cities, 3D printing, nanotechnology, biotechnology, materials science, energy storage, and more.

While Schwab and the WEF promote a particular vision for the 4-IR, the developments he announces are not his brainchildren, and there is nothing original about his formulations. Transhumanists and Singularitarians (or prophets of the technological singularity), such as Ray Kurzweil and many others, forecasted these and more revolutionary developments,. long before Schwab heralded them. The significance of Schwab and the WEF’s take on the new technological revolution is the attempt to harness it to a particular end, presumably “a fairer, greener future.”

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