Their research was published Sept. 26 in the journal Optics Express.
The five dimensions in question aren’t new or hidden spatial dimensions. Instead, a team headed by Tingting Wu, a Ph.D. student in the McKelvey School of Engineering’s imaging sciences program, was able to design a system that could tell the orientation of a molecule in 3D space as well as its position in 2D: five parameters from a single, noisy, pixelated image.
Start listening with a 30-day Audible trial and your first audiobook is free. Visit. http://www.audible.com/isaac or text “isaac” to 500–500. Our Universe is billions of years older than our planet, and we often contemplate alien civilizations that might have arisen early than us in our galaxy, but just how early could life have arisen?
Credits: Civilizations at the Beginning of Time. Science & Futurism with Isaac Arthur. Episode 272; January 7, 2021 Produced, Written, and Narrated by Isaac Arthur.
A black hole x-ray binary (XRB) system forms when gas is stripped from a normal star and accretes onto a black hole, which heats the gas sufficiently to emit x-rays. We report a polarimetric observation of the XRB Cygnus X-1 using the Imaging X-ray Polarimetry Explorer. The electric field position angle aligns with the outflowing jet, indicating that the jet is launched from the inner x-ray emitting region. The polarization degree is 4.01 ± 0.20% at 2 to 8 kiloelectronvolts, implying that the accretion disk is viewed closer to edge-on than the binary orbit. The observations reveal that hot x-ray emitting plasma is spatially extended in a plane perpendicular to the jet axis, not parallel to the jet.
Optimus, also known as Tesla Bot, is a general-purpose robotic humanoid under development by Tesla. The first prototype was announced at the company’s Artificial Intelligence (AI) Day event in September 2022!
Large Language Models have the ability to store vast amounts of facts about the world. But little is known, how these models actually do this. This paper aims at discovering the mechanism and location of storage and recall of factual associations in GPT models, and then proposes a mechanism for the targeted editing of such facts, in form of a simple rank-one update to a single MLP layer. This has wide implications both for how we understand such models’ inner workings, and for our ability to gain greater control over such models in the future.
OUTLINE: 0:00 — Introduction. 1:40 — What are the main questions in this subfield? 6:55 — How causal tracing reveals where facts are stored. 18:40 — Clever experiments show the importance of MLPs. 24:30 — How do MLPs store information? 29:10 — How to edit language model knowledge with precision? 36:45 — What does it mean to know something? 39:00 — Experimental Evaluation & the CounterFact benchmark. 45:40 — How to obtain the required latent representations? 51:15 — Where is the best location in the model to perform edits? 58:00 — What do these models understand about language? 1:02:00 — Questions for the community.
Abstract: We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuron activations that are decisive in a model’s factual predictions. This reveals a distinct set of steps in middle-layer feed-forward modules that mediate factual predictions while processing subject tokens. To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME). We find that ROME is effective on a standard zero-shot relation extraction (zsRE) model-editing task, comparable to existing methods. To perform a more sensitive evaluation, we also evaluate ROME on a new dataset of counterfactual assertions, on which it simultaneously maintains both specificity and generalization, whereas other methods sacrifice one or another. Our results confirm an important role for mid-layer feed-forward modules in storing factual associations and suggest that direct manipulation of computational mechanisms may be a feasible approach for model editing. The code, dataset, visualizations, and an interactive demo notebook are available at this https URL
Authors: Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov.
“China was leading the global recovery after the Covid-19 pandemic and accounted for half of worldwide robot installations in 2021,” said Marina Bill, President of the International Federation of Robotics. “Growth is strong across all industries with electrical and electronics being the dominant sector – up 30% to 81,600 installations. The automotive industry also showed a strong recovery. This was mainly driven by electric vehicle manufacturing in China. It rose by 89% in 2021 with 50,700 installations.”
Chinese government supports robotic automation
In China aging population’s demographics causes shortage of labor and drives the growth of robotic automation. The continued robotization of industries has been announced earlier this year by the government. The Five-Year plan for the robotics industry, released by the Ministry of Industry and Information Technology (MIIT) in Beijing, focuses on promoting innovation — making China a global leader of robot technology and industrial advancement.
Stability AI, the venture-backed startup behind the text-to-image AI system Stable Diffusion, is funding a wide-ranging effort to apply AI to the frontiers of biotech. Called OpenBioML, the endeavor’s first projects will focus on machine learning-based approaches to DNA sequencing, protein folding and computational biochemistry.
The company’s founders describe OpenBioML as an “open research laboratory” — and aims to explore the intersection of AI and biology in a setting where students, professionals and researchers can participate and collaborate, according to Stability AI CEO Emad Mostaque.
“OpenBioML is one of the independent research communities that Stability supports,” Mostaque told TechCrunch in an email interview. “Stability looks to develop and democratize AI, and through OpenBioML, we see an opportunity to advance the state of the art in sciences, health and medicine.”