​ In the recent past, using machine learning (ML) to make predictions, especially for data in the form of text and images, required extensive ML knowledge for creating and tuning of deep learning models. Today, ML has become more accessible to any user who wants… Read More »Use machine learning without writing a single line of code with Amazon SageMaker Canvas Julia Ang AWS Machine Learning Blog

​ In image recognition, researchers and developers constantly seek innovative approaches to enhance the accuracy and efficiency of computer vision systems. Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and classify visual… Read More »Reimagining Image Recognition: Unveiling Google’s Vision Transformer (ViT) Model’s Paradigm Shift in Visual Data Processing Madhur Garg Artificial Intelligence Category – MarkTechPost

​ Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. Hyperparameters are the knobs and levers that we use to adjust the training process, such as learning rate, batch size, regularization strength, and others, depending on the… Read More »Explore advanced techniques for hyperparameter optimization with Amazon SageMaker Automatic Model Tuning Ümit Yoldas AWS Machine Learning Blog

​ A team of researchers from Lehigh University, Massachusetts General Hospital, and Harvard Medical School recently performed a thorough evaluation of GPT-4V, a state-of-the-art multimodal language model, particularly in Visual Question Answering tasks. The assessment aimed to determine the model’s overall efficiency and performance in… Read More »This AI Paper Introduces a Comprehensive Analysis of GPT-4V’s Performance in Medical Visual Question Answering: Insights and Limitations Sana Hassan Artificial Intelligence Category – MarkTechPost

​ Researchers introduced hand-drawn sketches as an unexplored modality for specifying goals in visual imitation learning. The sketches offer a balance between the ambiguity of natural language and the over-specification of images, enabling users to convey task objectives swiftly. Their research proposes RT-Sketch, a goal-conditioned… Read More »Researchers from Stanford Introduce RT-Sketch: Elevating Visual Imitation Learning Through Hand-Drawn Sketches as Goal Specifications Adnan Hassan Artificial Intelligence Category – MarkTechPost

​ In computer vision and robotics, simultaneous localization and mapping (SLAM) systems enable machines to navigate and understand their surroundings. However, the accurate mapping of dynamic environments, particularly the reconstruction of moving objects, has posed a significant challenge for traditional SLAM approaches. In a recent… Read More »This AI Paper from China Introduces a Novel Time-Varying NeRF Approach for Dynamic SLAM Environments: Elevating Tracking and Mapping Accuracy Madhur Garg Artificial Intelligence Category – MarkTechPost

​ The GPT-4 was tested using a public Turing test on the internet by a group of researchers from UCSD. The best performing GPT-4 prompt was successful in 41% of games, which was better than the baselines given by ELIZA (27%), GPT-3.5 (14%), and random… Read More »UCSD Researchers Evaluate GPT-4’s Performance in a Turing Test: Unveiling the Dynamics of Human-like Deception and Communication Strategies Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​ From ChatGPT to GPT4 to DALL-E 2/3 to Midjourney, the latest wave of generative AI has garnered unprecedented attention worldwide. This fascination is tempered with serious worry about the risks associated with “intelligence” that appears to be even beyond human capacity. Current generative models… Read More »Reconciling the Generative AI Paradox: Divergent Paths of Human and Machine Intelligence in Generation and Understanding Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

​ Clustering serves as a fundamental and widespread challenge in the realms of data mining and unsupervised machine learning. Its objective is to assemble similar items into distinct groups. There are two types of clustering: metric clustering and graph clustering. Metric clustering involves using a… Read More »Google AI Introduces a Novel Clustering Algorithm that Effectively Combines the Scalability Benefits of Embedding Models with the Quality of Cross-Attention Models Rachit Ranjan Artificial Intelligence Category – MarkTechPost