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Researchers from UCLA, University of Washington, and Microsoft Introduce MathVista: Evaluating Math Reasoning in Visual Contexts with GPT-4v, BARD, and Other Large Multimodal Models
MathVista is introduced as a comprehensive benchmark for mathematical reasoning in visual contexts. It amalgamates challenges from various multimodal datasets, aiming to refine mathematical reasoning in AI systems. Researchers from UCLA, University of Washington, and Microsoft extensively evaluate foundation models and highlight the potential of GPT-4V in achieving a state-of-the-art accuracy of 49.9%.
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This Machine Learning Paper from DeepMind Presents a Thorough Examination of Asynchronous Local-SGD in Language Modeling
This text discusses the advancements in language modeling through the use of large language models (LLMs) and the challenges faced in optimizing these models for distributed training. It introduces an innovative asynchronous method that combines delayed Nesterov momentum updates and dynamic local updates, showcasing significant improvements in training efficiency for language models.
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Few companies apply New York’s new automated AI hiring law
New York City enacted Law 144, regulating automated employment decision tools (AEDTs) to combat biases in hiring. The law requires auditing for bias, transparency notices, and sets fines for non-compliance. However, researchers from Cornell University found low compliance due to vague definitions and employer discretion. This raises questions about its effectiveness in addressing bias in…
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What’s next for robotaxis in 2024
The promise of robotaxis seemed imminent in 2023, but it came crashing down after tragic accidents involving Cruise, suspending its operations in California. While other companies like Waymo and Baidu continue their robotaxi services, challenges such as high costs, scalability issues, and safety concerns persist. The industry is poised for significant changes in 2024, but…
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Why does AI being good at math matter?
Google DeepMind recently created AlphaGeometry, an AI system combining a language model and a symbolic engine to solve complex geometry problems, demonstrating progress in AI reasoning skills. However, human understanding of technology is crucial to harness AI’s potential, as argued by Conrad Wolfram. AI is also being deployed to address racial segregation in South Africa…
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FDA approves DermaSensor’s AI skin cancer detector
The FDA approved DermaSensor’s AI-powered handheld skin cancer detector for US sale. Skin cancer, a common and fatal disease, often goes undetected. DermaSensor’s non-invasive device uses ESS to detect skin cancer with 96% accuracy and will be available through a subscription model. It aims to aid PCPs in making referrals to dermatologists and reduce unnecessary…
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CMU Research Introduces CoVO-MPC (Covariance-Optimal MPC): A Novel Sampling-based MPC Algorithm that Optimizes the Convergence Rate
Model Predictive Control (MPC) is widely used in fields such as power systems and robotics. A recent study from Carnegie Mellon University focused on the convergence characteristics of a sampling-based MPC technique called Model Predictive Path Integral Control (MPPI). The research led to the development of a new method called CoVariance-Optimal MPC (CoVO-MPC), which outperformed…
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This AI Paper from Meta and NYU Introduces Self-Rewarding Language Models that are Capable of Self-Alignment via Judging and Training on their Own Generations
Researchers from Meta and NYU introduce Self-Rewarding Language Models, addressing limitations in traditional reward models by training a self-improving reward model. Utilizing LLM-as-a-Judge prompting and Iterative DPO, the model iteratively improves instruction-following and reward-modeling abilities, outperforming existing models. This novel approach signifies promising progress in language model training beyond human-preference-based reward models.
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Researchers from CMU, Bosch, and Google Unite to Transform AI Security: Simplifying Adversarial Robustness in a Groundbreaking Achievement
Researchers from Google, Carnegie Mellon University, and Bosch Center for AI have developed a pioneering method to enhance adversarial robustness of deep learning models. The innovative approach achieves top-tier adversarial robustness using pretrained models, without the need for complex fine-tuning. The groundbreaking research has significant implications for various domains, including autonomous vehicles, cybersecurity, healthcare, and…
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Best Image Annotation Tools in 2024
After human annotation, a machine-learning model automatically replicates the same annotations from tagged pictures, aiming to meet defined standards. Image annotation categorizes and labels images for object identification, crucial for computer vision, robotics, and autonomous driving. Notable image annotation tools for 2024 include Markup Hero, Keylabs, Labelbox, Scale, Supervisely, and others, each offering unique features…