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In order to improve an AI assistant, initiate by imitating the unpredictable actions of humans.

Algorithms, Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, UncategorizedJuly 25, 202428Views0Likes0Comments
Researchers from MIT and the University of Washington have developed a method to model the behaviour of an agent, including its computational limitations, predicting future behaviours by examining prior actions. The method applies to both humans and AI, and has a wide range of potential applications, including predicting navigation goals from past routes and forecasting…

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This miniature microchip can protect user information while facilitating effective computing on a mobile phone.

Artificial Intelligence, Computer science and technology, Cybersecurity, Data, Electrical Engineering & Computer Science (eecs), Electronics, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Privacy, Research, School of Engineering, UncategorizedJuly 25, 202431Views0Likes0Comments
Health-monitoring apps that assist people in managing chronic diseases or tracking fitness goals work with the help of large machine-learning models, which are often shuttled between a user's smartphone and a central memory server. This process can slow down the app's performance and drain the energy of the device. While machine-learning accelerators can help to…

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Researchers at Apple suggest LazyLLM: a unique AI strategy for productive LLM inference, specifically in situations with extended context.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJuly 24, 202434Views0Likes0Comments
Large Language Models (LLMs) have improved significantly, but challenges persist, particularly in the prefilling stage. This is because the cost of computing attention increases with the number of tokens in the prompts, leading to a slow time-to-first-token (TTFT). As such, optimizing TTFT is crucial for efficient LLM inference. Various methods have been proposed to improve…

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PILOT: An Innovative Machine Learning Procedure for Linear Model Trees that Offers Speed, Regularization, Stability, and Comprehensibility

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Technology, UncategorizedJuly 24, 202430Views0Likes0Comments
Before the development of PILOT (PIecewise Linear Organic Tree), linear model trees were slow to fit and susceptible to overfitting, notably with large datasets. The traditional regression trees faced challenges capturing linear relationships efficiently. Linear model trees also encountered problems with interpretability when integrating linear models in leaf nodes. The research points out the need…

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LaMMOn: A Comprehensive Multi-Camera Tracking System utilizing Transformers and Graph Neural Networks for Improved Instant Traffic Control.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 24, 202430Views0Likes0Comments
Multi-target multi-camera tracking (MTMCT) has become indispensable in intelligent transportation systems, yet real-world applications are complex due to a shortage of publicly available data and laborious manual annotation. MTMCT involves tracking vehicles across multiple camera lenses, detecting objects, carrying out multi-object tracking, and finally clustering trajectories to generate a comprehensive image of vehicle movement. MTMCT…

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Benchmark for Visual Haystacks: The Inaugural Image-Focused Needle-In-A-Haystack (NIAH) Benchmark for Evaluating LMMs’ Proficiency in Long-Context Visual Search and Analysis

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Computer vision, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 24, 202440Views0Likes0Comments
In the domain of visual question answering (VQA), the Multi-Image Visual Question Answering (MIQA) remains a major hurdle. It entails generating pertinent and grounded responses to natural language prompts founded on a vast assortment of images. While large multimodal models (LMMs) have proven competent in single-image VQA, they falter when dealing with queries involving an…

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