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OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization
Designing autonomous agents that can navigate complex web environments raises many challenges, in particular when such agents incorporate both textual and visual information. More classically, agents have limited capability since they are confined to synthetic, text-based environments with well-engineered reward signals, which restricts their applications to real-world web navigation tasks. A central challenge is that…
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The Best Ever Game Controller May Be Set for a Comeback
The GameCube’s controller remains a titan, which is why so many have “borrowed” from its design. But now things are afoot that signal Nintendo could be about to resurrect this gaming gem.
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Meta AI Releases Sparsh: The First General-Purpose Encoder for Vision-Based Tactile Sensing
Tactile sensing plays a crucial role in robotics, helping machines understand and interact with their environment effectively. However, the current state of vision-based tactile sensors poses significant challenges. The diversity of sensors—ranging in shape, lighting, and surface markings—makes it difficult to build a universal solution. Traditional models are often developed and designed specifically for certain…
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This AI Paper from Google Research Introduces Speculative Knowledge Distillation: A Novel AI Approach to Bridging the Gap Between Teacher and Student Models
Knowledge distillation (KD) is a machine learning technique focused on transferring knowledge from a large, complex model (teacher) to a smaller, more efficient one (student). This approach is used extensively to reduce large language models’ computational load and resource requirements while retaining as much of their performance as possible. Using this method, researchers can develop…
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Decoding Arithmetic Reasoning in LLMs: The Role of Heuristic Circuits over Generalized Algorithms
A key question about LLMs is whether they solve reasoning tasks by learning transferable algorithms or simply memorizing training data. This distinction matters: while memorization might handle familiar tasks, true algorithmic understanding allows for broader generalization. Arithmetic reasoning tasks could reveal if LLMs apply learned algorithms, like vertical addition in human learning, or if they…
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Cornell Researchers Introduce QTIP: A Weight-Only Post-Training Quantization Algorithm that Achieves State-of-the-Art Results through the Use of Trellis-Coded Quantization (TCQ)
Quantization is an essential technique in machine learning for compressing model data, which enables the efficient operation of large language models (LLMs). As the size and complexity of these models expand, they increasingly demand vast storage and memory resources, making their deployment a challenge on limited hardware. Quantization directly addresses these challenges by reducing the…
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Leopard: A Multimodal Large Language Model (MLLM) Designed Specifically for Handling Vision-Language Tasks Involving Multiple Text-Rich Images
In recent years, multimodal large language models (MLLMs) have revolutionized vision-language tasks, enhancing capabilities such as image captioning and object detection. However, when dealing with multiple text-rich images, even state-of-the-art models face significant challenges. The real-world need to understand and reason over text-rich images is crucial for applications like processing presentation slides, scanned documents, and…
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Why multi-agent AI tackles complexities LLMs can’t
While AGI and fully autonomous systems are still on the horizon, multi-agents will bridge the current gap between LLMs and AGI.Read More
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How a PhD Student Discovered a Lost Mayan City From Hundreds of Miles Away
WIRED spoke with the researchers responsible for the discovery of Valeriana, a lost Maya city in the middle of the jungle of Campeche.
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Best MacBooks (2024): Which Model Should You Buy?
New MacBooks are here. Having a hard time choosing which Apple laptop to buy? Let us help.