LimX Dynamics has revealed that the W1 quadruped robot can walk on two feet across a smooth floor. In the future, it might also climb stairs bipedally as with other robots in the company.
Category: robotics/AI – Page 399
A novel algorithm enables robots to flexibly squish, bend, or stretch for tasks such as obstacle avoidance or item retrieval.
A video depicting a Chinese humanoid robot factory reveals scores of robots at different stages of development, evoking surprise and concern.
Engineers at Stanford University have successfully combined AI and holographic imagery to develop the augmented reality headset of the future.
According to a statement by the firm, the study showed that AI models trained on hand images achieve comparable accuracy to those using facial images, with an average error of 4.1 and 4.7 years in predicting chronological age.
The AI model in the study was primarily trained by employing the Indian population dataset to ensure representation of diverse skin tones and address AI’s bias challenges, especially pertaining to ethnicity-specific considerations in age prediction.
By focusing on the Indian population, the study aimed to develop an AI model tailored to this demographic, mitigating biases and promoting fairer AI solutions. Additionally, the research’s market relevance in India’s growing skincare and AI sectors underscores the strategic importance of using an Indian dataset for this study.
Chapters 00:00 — Intro + Background 05:06 — From KART to KAN 07:56 — MLP vs KAN 16:05 — Accuracy: Scaling of KANs 26:35 — Interpretability: KAN for Science 38:04 — Q+A Break 57:15 — Strengths and Weaknesses 59:28 — Philosophy 1:08:45 — Anecdotes Behind the Scenes…
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Abstract: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes (\.
(TNS) — The global race for computational power is well underway, fueled by a worldwide boom in artificial intelligence. OpenAI’s Sam Altman is seeking to raise as much as $7 trillion for a chipmaking venture. Tech giants like Microsoft and Amazon are building AI chips of their own. The need for more computing horsepower to train and use AI models—fueling a quest for everything from cutting-edge chips to giant data sets—isn’t just a current source of geopolitical leverage (as with US curbs on chip exports to China). It is also shaping the way nations will grow and compete in the future, with governments from India to the UK developing national strategies and stockpiling Nvidia graphics processing units.
I believe it’s high time for America to have its own national compute strategy: an Apollo program for the age of AI.
In January, under President Biden’s executive order on AI, the National Science Foundation launched a pilot program for the National AI Research Resource (NAIRR), envisioned as a “shared research infrastructure” to provide AI computing power, access to open government and nongovernment data sets, and training resources to students and AI researchers.
Editor’s note: This story is being highlighted in ASU Now’s year in review. Read more top stories from 2018 here.
In a major advancement in nanomedicine, Arizona State University scientists, in collaboration with researchers from the National Center for Nanoscience and Technology (NCNST) of the Chinese Academy of Sciences, have successfully programmed nanorobots to shrink tumors by cutting off their blood supply.
“We have developed the first fully autonomous, DNA robotic system for a very precise drug design and targeted cancer therapy,” said Hao Yan, director of the ASU Biodesign Institute’s Center for Molecular Design and Biomimetics and the Milton Glick Professor in the School of Molecular Sciences.
ChatGPT is about to become a lot more useful.
OpenAI on Monday announced its latest artificial intelligence large language model that it says will make ChatGPT smarter and easier to use.
The new model, called GPT-4o, is an update from the company’s previous GPT-4 model, which launched just over a year ago. The model will be available to unpaid customers, meaning anyone will have access to OpenAI’s most advanced technology through ChatGPT.
Scientists have published the most detailed data set to date on the neural connections of the brain, which was obtained from a cubic millimeter of tissue sample.
A cubic millimeter of brain tissue may not sound like much. But considering that that tiny square contains 57,000 cells, 230 millimeters of blood vessels, and 150 million synapses, all amounting to 1,400 terabytes of data, Harvard and Google researchers have just accomplished something stupendous.
Led by Jeff Lichtman, the Jeremy R. Knowles Professor of Molecular and Cellular Biology and newly appointed dean of science, the Harvard team helped create the largest 3D brain reconstruction to date, showing in vivid detail each cell and its web of connections in a piece of temporal cortex about half the size of a rice grain.
Published in Science, the study is the latest development in a nearly 10-year collaboration with scientists at Google Research, combining Lichtman’s electron microscopy imaging with AI algorithms to color-code and reconstruct the extremely complex wiring of mammal brains. The paper’s three first co-authors are former Harvard postdoc Alexander Shapson-Coe, Michał Januszewski of Google Research, and Harvard postdoc Daniel Berger.