Artificial Intelligence
Large language models (LLMs) have impressive few-shot learning capabilities, but they still struggle with complex reasoning in chaotic contexts. This article proposes a technique that combines Thread-of-Thought (ToT) prompting with a Retrieval Augmented Generation (RAG) framework to enhance LLMs’ understanding and problem-solving abilities. The RAG system accesses multiple knowledge graphs in parallel, improving efficiency and…
This article provides a beginner’s guide to writing AI agents for games. It can help you get started and create game-winning agents.
This text discusses a customized copilot used to streamline research and development for a type of artificial neural network known as PINN. The copilot assists in improving efficiency and productivity in the development process.
Researchers from Duke and Johns Hopkins Universities have developed an approach called SneakyPrompt that bypasses safety filters in generative AI models like Stable Diffusion and DALL-E to generate explicit or violent images. By replacing banned words with semantically similar ones, the researchers were able to trick the models into generating the desired images. To prevent…
The text discusses whether AI-powered Business Intelligence is a hype or a reality. More information can be found on Towards Data Science.
Leverage ChatGPT and generative AI to achieve the same results in 2023 as described in the article on Towards Data Science.
OpenAI has removed Sam Altman as its CEO due to communication transparency issues. Mira Murati, the former CTO, will serve as interim CEO. Greg Brockman, the president and co-founder, has also resigned. OpenAI’s success with ChatGPT and its partnership with Microsoft remain important as it navigates this transition and negotiates a new funding round.
OpenAI co-founder Greg Brockman has resigned as company president following the departure of CEO Sam Altman. In a statement, Brockman expressed pride in OpenAI’s achievements since its start eight years ago. The company has named Mira Murati as the interim replacement for Altman, and this move raises questions about OpenAI’s future direction in the AI…
This text discusses how to improve the learning and training process of neural networks by tuning hyperparameters. It covers computational improvements, such as parallel processing, and examines hyperparameters like the number of hidden layers, number of neurons, learning rate, batch size, and activation functions. The text also provides a Python example using PyTorch and references…
TRL (Training with Reward Learning) is a full-stack library that enables researchers to train transformer language models and stable diffusion models using reinforcement learning. It includes tools such as Supervised Fine-tuning (SFT), Reward Modeling (RM), and Proximal Policy Optimization (PPO). TRL is an extension of Hugging Face’s transformers collection and supports various language models. It…
This article discusses the development of a GPT-based virtual assistant for Enefit, an energy company in the Baltics. It highlights the importance of data/information governance in ensuring accurate responses from the virtual assistant. It also emphasizes the need for guidance and training to customize the behavior and style of the assistant. The article concludes that…
Researchers from Peking University, UCLA, Beijing University of Posts and Telecommunications, and Beijing Institute for General Artificial Intelligence have developed JARVIS-1, a multimodal agent for open-world tasks in Minecraft. JARVIS-1 combines pre-trained multimodal language models to interpret visual observations and human instructions, generating plans for control. It achieves nearly perfect performance in over 200 tasks…
Researchers from the University of Washington and Duke University have developed Punica, a multi-tenant serving framework for LoRA models on a shared GPU cluster. By utilizing a new CUDA kernel called SGMV, Punica enables efficient batching of requests from multiple LoRA models, resulting in improved GPU usage and throughput. The paper details the contributions and…
This blog post discusses the options and benefits of parallelizing Python code on Spark when working with Pandas. It compares Pandas UDFs and the ‘concurrent.futures’ module as two approaches to concurrent processing in order to determine their use cases. The post also covers the challenges of working with large datasets and the performance results of…
The data job market has been challenging, with a significant decrease in job postings from Big Tech companies (FAANG) but slight improvement in hiring by other companies. The overall job market seems to be recovering after a dip in May. There is a higher demand for data engineers compared to data scientists or data analysts.…
Researchers from Shanghai Jiao Tong University and China University of Mining and Technology have developed TransLO, a LiDAR odometry network that combines CNNs and transformers to enhance global feature embeddings and outlier rejection. TransLO outperforms existing methods on the KITTI odometry dataset with superior accuracy and efficiency. Components like WMSA and MCFA were evaluated through…
SPHINX is a multi-modal large language model that addresses the limitations of existing models in understanding visual instructions and performing diverse tasks. It integrates model weights, tuning tasks, and visual embeddings to excel in tasks like human pose estimation and object detection. SPHINX’s fine-grained visual understanding and collaboration with other models make it a frontrunner…
Amazon researchers have developed KD-Boost, a knowledge distillation technique, to address the challenges of real-time semantic matching in web search and e-commerce product search. KD-Boost uses ground truth and soft labels from a teacher model to train low-latency, accurate student models. The technique has shown significant improvements in relevance, query-to-query matching, and product coverage.
Apple is sponsoring the EMNLP conference in Singapore from December 6 to 10. EMNLP is a prominent conference on natural language processing. Apple will host workshops and events during the conference.
Learn how to accelerate feature selection, which typically involves creating multiple models and can be sluggish, thanks to the tips provided in the article on Towards Data Science.