PBS Member Stations rely on viewers like you. To support your local station, go to: http://to.pbs.org/DonateSPACE↓ More info below ↓Is all that exists just w…
Do the Past and Future Exist?
Posted in futurism
Last year, I edhost a thrilling conversation between @SabineHossenfelder, Carlo Rovelli, and Eric Weinstein as they debate quantum physics, consciousness and the mystery of reality. \
\
IAI Live is a monthly event featuring debates, talks, interviews, documentaries and music. LIVE. \
Watch the original video \
\
See the world’s leading thinkers debate the big questions for real, LIVE in London. Tickets: https://howthelightgetsin.org/\
\
To discover more talks, debates, interviews and academies with the world’s leading speakers visit https://iai.tv/\
\
We imagine physics is objective. But quantum physics found the act of human observation changes the outcome of experiment. Many scientists assume this central role of the observer is limited to just quantum physics. But is this an error? As Heisenberg puts it, \.
Puzzling ancient galaxies and oddly shaped clusters suggest we have glimpsed cosmic strings travelling at the speed of light – and with them clues to a deeper theory of reality.
By Dan Falk
This Paper Proposes RWKV: A New AI Approach that Combines the Efficient Parallelizable Training of Transformers with the Efficient Inference of Recurrent Neural Networks
Posted in robotics/AI | 1 Comment on This Paper Proposes RWKV: A New AI Approach that Combines the Efficient Parallelizable Training of Transformers with the Efficient Inference of Recurrent Neural Networks
Advancements in deep learning have influenced a wide variety of scientific and industrial applications in artificial intelligence. Natural language processing, conversational AI, time series analysis, and indirect sequential formats (such as pictures and graphs) are common examples of the complicated sequential data processing jobs involved in these. Recurrent Neural Networks (RNNs) and Transformers are the most common methods; each has advantages and disadvantages. RNNs have a lower memory requirement, especially when dealing with lengthy sequences. However, they can’t scale because of issues like the vanishing gradient problem and training-related non-parallelizability in the time dimension.
As an effective substitute, transformers can handle short-and long-term dependencies and enable parallelized training. In natural language processing, models like GPT-3, ChatGPT LLaMA, and Chinchilla demonstrate the power of Transformers. With its quadratic complexity, the self-attention mechanism is computationally and memory-expensive, making it unsuitable for tasks with limited resources and lengthy sequences.
A group of researchers addressed these issues by introducing the Acceptance Weighted Key Value (RWKV) model, which combines the best features of RNNs and Transformers while avoiding their major shortcomings. While preserving the expressive qualities of the Transformer, like parallelized training and robust scalability, RWKV eliminates memory bottleneck and quadratic scaling that are common with Transformers. It does this with efficient linear scaling.
Published 12 August 2020 • © 2020 The Author(s). Published by IOP Publishing Ltd Journal of Physics: Photonics, Volume 2, Number 4 Focus on Photonics for Neural Information Processing Citation Matěj Hejda et al 2020 J. Phys. Photonics 2 044001 DOI 10.1088/2515–7647/aba670
A central challenge for systems neuroscience and artificial intelligence is to understand how cognitive behaviors arise from large, highly interconnected networks of neurons. Digital simulation is linking cognitive behavior to neural activity to bridge this gap in our understanding at great expense in time and electricity. A hybrid analog-digital approach, whereby slow analog circuits, operating in parallel, emulate graded integration of synaptic currents by dendrites while a fast digital bus, operating serially, emulates all-or-none transmission of action potentials by axons, may improve simulation efficacy. Due to the latter’s serial operation, this approach has not scaled beyond millions of synaptic connections (per bus). This limit was broken by following design principles the neocortex uses to minimize its wiring. The resulting hybrid analog-digital platform, Neurogrid, scales to billions of synaptic connections, between up to a million neurons, and simulates cortical models in real-time using a few watts of electricity. Here, we demonstrate that Neurogrid simulates cortical models spanning five levels of experimental investigation: biophysical, dendritic, neuronal, columnar, and area. Bridging these five levels with Neurogrid revealed a novel way active dendrites could mediate top-down attention.
K.B. and N.N.O. are co-founders and equity owners of Femtosense Inc.
As much as id love to see it. Not until someone solves human level hands, i believe will cost about 10+ billion USD. And, a battery can run 8 to 12 hours, and be changed or re charged in under 15 minutes.
HOUSTON/AUSTIN, Texas, Dec 27 (Reuters) — Standing at 6 feet 2 inches (188 centimeters) tall and weighing 300 pounds (136 kilograms), NASA’s humanoid robot Valkyrie is an imposing figure.
Valkyrie, named after a female figure in Norse mythology and being tested at the Johnson Space Center in Houston, Texas, is designed to operate in “degraded or damaged human-engineered environments,” like areas hit by natural disasters, according to NASA.
But robots like her could also one day operate in space.
Non-personalized content and ads are influenced by things like the content you’re currently viewing and your location (ad serving is based on general location). Personalized content and ads can also include things like video recommendations, a customized YouTube homepage, and tailored ads based on past activity, like the videos you watch and the things you search for on YouTube. We also use cookies and data to tailor the experience to be age-appropriate, if relevant.
Select “More options” to see additional information, including details about managing your privacy settings. You can also visit g.co/privacytools at any time.
The CMS experiment has presented its first search for new physics using data from Run 3 of the Large Hadron Collider. The new study looks at the possibility of “dark photon” production in the decay of Higgs bosons in the detector.
Dark photons are exotic long-lived particles: “Long-lived” because they have an average lifetime of more than a tenth of a billionth of a second—a very long lifetime in terms of particles produced in the LHC—and “exotic” because they are not part of the standard model of particle physics.
The standard model is the leading theory of the fundamental building blocks of the universe, but many physics questions remain unanswered, and so searches for phenomena beyond the standard model continue. CMS’s new result defines more constrained limits on the parameters of the decay of Higgs bosons to dark photons, further narrowing down the area in which physicists can search for them.