A previously unreported project is intended to improve how facial recognition algorithms track children over time.
Could food delivery robots with zero carbon emissions influence a customer’s decision to buy food using them instead of robot vehicles that emit carbon into the atmosphere? This is what a recent study published in the International Journal of Hospitality Management hopes to address as a tea of researchers from Washington State University (WSU) investigated how a customer’s knowledge of an automatic delivery robot’s (ADR) environment impact influences their choice regarding which type of robot they want delivering their food. This study holds the potential to help scientists, environmental conservationists, and the public better understand the benefits of eco-friendly delivery robots for both the short and long term.
“Much of the marketing focus has been on the functionality and the convenience of these automatic delivery robots, which is really important, but it would enhance these efforts to promote their green aspects as well,” said Jennifer Han, who is a doctoral student in WSU’s Carson College of Business and lead author of the study.
For the study, the researchers used the Amazon crowdsourcing platform, MTurk, to conduct an online survey comprised of 418 adults who were instructed to watch videos about ADRs followed by a questionnaire regarding the environmental impact and the risk of using ADRs for their food delivery service. In the end, the team discovered a connection between participants who found ADRs were less risky and wanted an eco-friendly ADR compared to participants who thought ADRs were riskier but weren’t concerned about the environmental consequences.
Inspired by biology we 1) get adversarial robustness + interpretability for free, 2) turn classifiers into generators & 3) design attacks on vLLMs.
Stanislav Fort, Balaji Lakshminarayanan August 2024 https://www.arxiv.org/abs/2408.
Adversarial examples pose a significant challenge to the robustness, reliability and alignment of deep neural networks. We propose a novel, easy-to-use approach to achieving high-quality representations that lead to adversarial robustness through the use of multi-resolution input representations and dynamic self-ensembling of intermediate layer predictions. We demonstrate that intermediate layer predictions exhibit inherent robustness to adversarial attacks crafted to fool the full classifier, and propose a robust aggregation mechanism based on Vickrey auction that we call \textit{CrossMax} to dynamically ensemble them.
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Studies find wildly divergent views on risk and suggest that regulations could be tightened.
Studies suggest that regulations could be tightened to head off AI misbehavior.
48 likes, — designdelulu on May 23, 2024: ‘Jensen Huang says designing computer chips and writing software can no longer be done without AI and he wants to turn NVIDIA into one giant AI’
Identifying one faulty turbine in a wind farm, which can involve looking at hundreds of signals and millions of data points, is akin to finding a needle in a haystack.
Engineers often streamline this complex problem using deep-learning models that can detect anomalies in measurements taken repeatedly over time by each turbine, known as time-series data.
But with hundreds of wind turbines recording dozens of signals each hour, training a deep-learning model to analyze time-series data is costly and cumbersome. This is compounded by the fact that the model may need to be retrained after deployment, and wind farm operators may lack the necessary machine-learning expertise.
“This is going to be the fastest AI computer ever launched to space,” Yanni Barghouty, CSC’s cofounder and CEO, told Space.com. “The goal of this mission is simply to demonstrate the successful operation of an AI-capable Nvidia GPU on orbit with minimal to no errors while operating.”
The GPU will fly aboard a cubesat built by San Francisco-based company Aethero, a maker of high-performance, space-rated computers. The GPU’s only task during its four-month orbital mission will be to make mathematical calculations, the results of which will be beamed to Earth and carefully checked.
Even the best AI large language models (LLMs) fail dramatically when it comes to simple logical questions. This is the conclusion of researchers from the Jülich Supercomputing Center (JSC), the School of Electrical and Electronic Engineering at the University of Bristol and the LAION AI laboratory.
In their paper posted to the arXiv preprint server, titled “Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models,” the scientists attest to a “dramatic breakdown of function and reasoning capabilities” in the tested state-of-the-art LLMs and suggest that although language models have the latent ability to perform basic reasoning, they cannot access it robustly and consistently.
The authors of the study—Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti and Jenia Jitsev—call on “the scientific and technological community to stimulate urgent re-assessment of the claimed capabilities of the current generation of LLMs.” They also call for the development of standardized benchmarks to uncover weaknesses in language models related to basic reasoning capabilities, as current tests have apparently failed to reveal this serious failure.
