Researchers from Fudan University, Ohio State University, and Pennsylvania State University, Meta AI, have developed TravelPlanner, an AI benchmark to evaluate agents’ planning skills in realistic scenarios. It challenges AI agents to plan multi-day travel itineraries, highlighting limitations in current AI models. TravelPlanner aims to advance AI planning capabilities and bridge the gap between theoretical…

MeetKai, an influential player in conversational AI, introduced Functionary, an open-source language model for function calling. In contrast to larger models like GPT-4, Functionary offers faster, more cost-effective inference with high accuracy. It seamlessly integrates with OpenAI’s platform and aligns with MeetKai’s vision for the metaverse, inviting developers to shape the future of applied generative…

LMMs have widely expanded using CLIP for vision encoding and LLMs for multi-modality reasoning. Scaling up CLIP is crucial, leading to the EVA-CLIP-18B model with 18B parameters. It achieves remarkable zero-shot top-1 accuracy on 27 benchmarks and demonstrates effectiveness in various image tasks, underlining progress in open-source AI models. [50 words]

Graph Neural Networks (GNNs) leverage graph structures to perform inference on complex data, addressing the limitations of traditional ML algorithms. Google’s TensorFlow GNN 1.0 (TF-GNN) library integrates with TensorFlow, enabling scalable training of GNNs on heterogeneous graphs. It supports supervised and unsupervised training, subgraph sampling, and flexible model building for diverse tasks.

Vision Language Models (VLMs) leverage Large Language Models’ strength to comprehend visual data, demonstrating capability in visual question answering and optical character recognition. A study by Tsinghua University and Zhipu AI introduces Chain of Manipulations (CoM) to enable VLMs for visual reasoning, leading to competitive performance on various benchmarks and highlighting potential for accelerated VLM…

DeepSeekMath, developed by DeepSeek-AI, Tsinghua University, and Peking University, revolutionizes mathematical reasoning using large language models. With a dataset of over 120 billion tokens of math-related content and innovative training using Group Relative Policy Optimization, it achieves a top-1 accuracy of 51.7% on the MATH benchmark, setting a new standard for AI-driven mathematics.

State-space models (SSMs) are being explored as an alternative to Transformer networks in AI research. SSMs aim to address computational inefficiencies in Transformer networks and have led to the proposal of MambaFormer, a hybrid model combining SSMs and Transformer attention blocks. MambaFormer demonstrates superior in-context learning capabilities, offering new potential for AI advancement.

Large Language Models, like GPT-3, have revolutionized Natural Language Processing by scaling to billions of parameters and incorporating extensive datasets. Researchers have also introduced Speech Language Models directly trained on speech, leading to the development of SPIRIT-LM. This multimodal language model seamlessly integrates text and speech, demonstrating potential impacts on various applications.

The introduction of Large Language Models in Artificial Intelligence, propelled by the transformer architecture, has greatly enhanced machines’ ability to comprehend and solve problems akin to human cognition. USC and Google’s researchers have introduced SELF-DISCOVER, improving these models’ reasoning capabilities significantly, bridging the gap between Artificial Intelligence and human cognitive processes.