Initially designed for continuous control tasks, Proximal Policy Optimization (PPO) has become widely used in reinforcement learning (RL) applications, including fine-tuning generative models. However, PPO’s effectiveness relies on multiple heuristics for stable convergence, such as value networks and clipping, making its implementation sensitive and complex. Despite this, RL demonstrates remarkable versatility, transitioning from tasks like…
Artificial intelligence (AI) is transforming healthcare, bringing sophisticated computational techniques to bear on challenges ranging from diagnostics to treatment planning. In this dynamic field, large language models (LLMs) are emerging as powerful tools capable of parsing and understanding complex medical data, thus promising to revolutionize patient care and research. A key issue confronting the healthcare…
Boston Dynamics has been at the forefront of robotics innovation for decades, and its latest offering—the fully electric Atlas robot—marks a significant milestone in the field. As it announces the retirement of its hydraulic Atlas, a new era begins with an electric version poised to transform real-world applications across various industries. Image Source A Decade…
Graph Transformers (GTs) have successfully achieved state-of-the-art performance on various platforms. GTs can capture long-range information from nodes that are at large distances, unlike the local message-passing in graph neural networks (GNNs). In addition, the self-attention mechanism in GTs permits each node to look at other nodes in a graph directly, helping collect information from…
Artificial intelligence is constantly advancing, and there’s always something new to be excited about. A few moments ago, a cutting-edge AI model called “gpt2-chatbot” was making waves in X’s AI community (Twitter). This new large language model (LLM) has generated a lot of discussion and curiosity among AI experts and enthusiasts, who are eager to…
With the significant development in the rapidly developing field of Artificial Intelligence driven healthcare, a team of researchers has introduced OpenBioLLM-Llama3-70B & 8B models. These state-of-the-art Large Language Models (LLMs) have the potential to completely transform medical natural language processing (NLP) by establishing new standards for functionality and performance in the biomedical field. The release…
Instant Voice Cloning (IVC) in Text-to-Speech (TTS) synthesis, also known as Zero-shot TTS, allows TTS models to replicate the voice of any given speaker with just a short audio sample without requiring additional training on that speaker. While existing methods like VALLE and XTTS can replicate tone color, they need more flexibility in controlling style…
Physics-Informed Neural Networks (PINNs) have become a cornerstone in integrating deep learning with physical laws to solve complex differential equations, marking a significant advance in scientific computing and applied mathematics. These networks offer a novel methodology for encoding differential equations directly into the architecture of neural networks, ensuring that solutions adhere to the fundamental laws…
The success of many reinforcement learning (RL) techniques relies on dense reward functions, but designing them can be difficult due to expertise requirements and trial and error. Sparse rewards, like binary task completion signals, are easier to obtain but pose challenges for RL algorithms, such as exploration. Consequently, the question emerges: Can dense reward functions…
Evaluating Multimodal Large Language Models (MLLMs) in text-rich scenarios is crucial, given their increasing versatility. However, current benchmarks mainly assess general visual comprehension, overlooking the nuanced challenges of text-rich content. MLLMs like GPT-4V, Gemini-Pro-Vision, and Claude-3-Opus showcase impressive capabilities but lack comprehensive evaluation in text-rich contexts. Understanding text within images requires interpreting textual and visual…