Open synthetic data generation pipeline for training LLMs.
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive license similar to Apache 2.0. These models perform competitively to open access models on a wide range of evaluation benchmarks, and were sized to fit on a single DGX H100 with 8 GPUs when deployed in FP8 precision.
The researchers compared two variants of their MatMul-free LM against the advanced Transformer++ architecture, used in Llama-2, on multiple model sizes.
Interestingly, their scaling projections show that the MatMul-free LM is more efficient in leveraging additional compute resources to improve performance in comparison to the Transformer++ architecture.
The researchers also evaluated the quality of the models on several language tasks. The 2.7B MatMul-free LM outperformed its Transformer++ counterpart on two advanced benchmarks, ARC-Challenge and OpenbookQA, while maintaining comparable performance on the other tasks.
Computer scientist Lance Fortnow writes that by embracing the computations that surround us, we can begin to understand and tame our seemingly random world.
What does “equals” mean? For mathematicians, this simple question has more than one answer, which is causing issues when it comes to using computers to check proofs. The solution might be to tear up the foundations of maths.
By Alex Wilkins
The complex computer model takes into account weather and climate systems as well as our impact on the planet.
Faithful transfer of quantum states between different parts of a single complex quantum circuit will become more and more important as quantum computing devices grow in size. Here, the authors transfer single-qubit excitations, two-qubit entangled states, and two excitations across a 6 × 6 superconducting qubit device.
While carbon nanotubes are the materials that have received most of the attention so far, they have proved very difficult to manufacture and control, so scientists are eager to find other compounds that could be used to create nanowires and nanotubes with equally interesting properties, but easier to handle.
So, Chiara Cignarella, Davide Campi and Nicola Marzari thought to use computer simulations to parse known three-dimensional crystals, looking for those that—based on their structural and electronic properties —look like they could be easily “exfoliated,” essentially peeling away from them a stable 1-D structure. The same method has been successfully used in the past to study 2D materials, but this is the first application to their 1-D counterparts.
The researchers started from a collection of over 780,000 crystals, taken from various databases found in the literature and held together by van der Waals forces, the sort of weak interactions that happen when atoms are close enough for their electrons to overlap. Then they applied an algorithm that considered the spatial organization of their atoms looking for the ones that incorporated wire-like structures, and calculated how much energy would be necessary to separate that 1-D structure from the rest of the crystal.
Embark on a captivating journey through the intricate pathways of the brain. This video delves into the fascinating realm where neuroscience and the philosophy of knowledge converge. Explore how brain structures facilitate learning, the dynamic interplay between cognition and perception, and the profound mysteries of consciousness and self-awareness. Discover the roles of language, emotion, and sensory integration in shaping our reality. Delve into the ethical considerations of brain manipulation and the revolutionary potential of educational neuroscience and brain-computer interfaces. Join us as we push the boundaries of knowledge, uncovering the secrets of the mind and envisioning the future of human cognition.
#Neuroepistemology #BrainScience #Cognition #Neuroplasticity #BrainComputerInterface.
Continue reading “The Brain’s Pathways to Knowledge: Neuroepistemology” »
Stanford researchers have developed a speech brain-computer interface (BCI) they say can translate thoughts into text at a record-breaking speed — putting us closer to a future in which people who can’t talk can still easily communicate.
The challenge: “Anarthria” is a devastating condition in which a person can’t speak, despite being able to understand speech and knowing what they want to say. It’s usually caused by a brain injury, such as a stroke, or a neurological disorder, such as Parkinson’s disease or ALS.
Some people with anarthria write or use eye-tracking tech to communicate, but this “speech” is far slower than the average talking speed. People with anarthria due to total paralysis or locked-in syndrome can’t even move their eyes, though, leaving them with no way to communicate.
Led by UMass Amherst, goal is to explore grid decarbonization and reduce carbon in computing.
