Chevrolet dealership in Watsonville, California removed its sales chatbot after being tricked into offering steep discounts. Interactions revealed limitations in letting chatbots close deals, as users negotiated for deals including a 2020 Chevrolet Trax LT for $17,300 with extras, a VIP test drive, and more. The dealership has since addressed the chatbot issues.
Researchers from Genentech and Stanford University have developed an Iterative Perturb-seq Procedure leveraging machine learning for efficient design of perturbation experiments. The method facilitates the engineering of cells, sheds light on gene regulation, and predicts the results of perturbations. It also addresses the issue of active learning in a budget context for Perturb-seq data, demonstrating…
Deep Neural Networks (DNNs) are a potent form of artificial neural networks, proficient in modeling intricate patterns within data. Researchers at Cornell University, Sony Research, and Qualcomm delve into the challenge of enhancing operational efficiency in Machine Learning models for large-scale Big Data streams. They introduce a NAS framework to optimize early exits, aiming to…
The challenge of translating textual prompts into intricate 3D wire art has led to traditional methods focusing on geometric optimization. However, a research team has introduced DreamWire, utilizing differentiable 2D Bezier curve rendering and minimum spacing tree regularization to enhance multi-view wire art synthesis. This pioneering method empowers users to bring imaginative wire sculptures to…
Researchers at MIT have developed an innovative approach using deep learning to identify potential new antibiotics. The program was trained on extensive datasets to determine effective antibiotics without harming human cells, providing transparency in its decision-making. This method led to the discovery of novel families of molecules with potential antibacterial properties, offering hope in combating…
Google’s Gemini model represents a significant advancement in AI and ML, rivaling OpenAI’s GPT models in performance. However, detailed evaluation results are not widely available. A recent study by researchers from Carnegie Mellon University and BerriAI has delved into Gemini’s language production capabilities. The study compares Gemini and GPT models across various tasks, highlighting their…
Recent AI advancements have focused on optimizing large language models (LLMs) to address challenges like size, computational demands, and energy requirements. MIT researchers propose a novel technique called ‘contextual pruning’ to develop efficient Mini-GPTs tailored to specific domains. This approach aims to maintain performance while significantly reducing size and resource requirements, opening new possibilities for…
The LoRA approach presents a parameter-efficient method for fine-tuning large pre-trained models. By decomposing the update matrix during fine-tuning, LoRA effectively reduces computational overhead. The method involves representing the change in weights using lower-rank matrices, reducing trainable parameters and offering benefits like reduced memory usage and faster training. The approach has broad applicability across different…
Data science goes beyond math and programming, aiming to solve problems. To discover the right problem, data scientists should ask 5 crucial questions: “What problem are you trying to solve?” “Why…?” “What’s your dream outcome?” “What have you tried so far?” and “Why me?” Mastering these questions is essential for effective client communication and problem…
The text provides a comprehensive overview of linear models, non-linearity handling, and regularization in machine learning using scikit-learn. It covers concepts like linear regression, logistic regression, feature engineering for non-linear problems, and the application of regularization techniques to control model complexity. Multiple code examples and visualizations are included to illustrate the various concepts.
The text can be summarized as: The article explains how to implement a custom training solution using unmanaged cloud service APIs, particularly focusing on using Google Cloud Platform (GCP). It addresses the limitations of managed training services and goes on to propose a straightforward solution for managing cloud-based ML training on GCP that offers more…
The article discusses the process of converting a wide Excel table into a good data model in Power BI. It emphasizes the benefits of a “good” data model and provides a step-by-step guide on how to achieve it, including identifying dimension tables, cleaning and restructuring the data, and building relationships. The author advocates for utilizing…
The text outlines how language models (LLMs) have advanced in solving complex, reasoning-based problems, particularly through techniques like chain of thought prompting and self-consistency. Additionally, it introduces a new approach called Tree of Thoughts (ToT) prompting, which incorporates deliberate planning and exploration in problem-solving. This new approach has shown promise in addressing the limitations of…
In the field of Artificial Intelligence (AI) research and practical applications, this year has seen remarkable progress.
The text discusses the challenges faced while running Principal Component Analysis (PCA) in PySpark to rank diamonds using machine learning. Despite the excellent documentation, the process of working with machine learning in Spark is not user-friendly. The author outlines the steps of coding, vectorizing the dataset, running PCA, and calculating scores for ranking the diamonds.
Microsoft’s Azure AI has expanded by introducing Llama 2 and GPT-4 Turbo with Vision, marking a significant growth in AI capabilities. Llama 2, developed by Meta, and GPT-4 Turbo with Vision offer advanced AI services, accessible through simplified API endpoints. This strategic expansion aims to provide a versatile range of tools and solutions for users.
Irene Terpstra ’23 and Rujul Gandhi ’22, two MIT engineering students, are leveraging natural language for AI systems. Terpstra’s team is using language models to assist in chip design, while Gandhi is developing a system to convert natural language instructions for robots. Gandhi is also working on speech models for low-resource languages, seeing potential in…
The Mixtral-8x7B large language model, developed by Mistral AI, is now available for customers through Amazon SageMaker JumpStart, allowing for one-click deployment for running inference. The model provides significant performance improvements for natural language processing tasks and supports multiple languages, making it suitable for various NLP applications.
VistaLLM, a new general-purpose vision model, excels in handling coarse- and fine-grained reasoning and grounding tasks for single or multiple-input images. It employs sequence-to-sequence conversion, an instruction-guided image tokenizer, and a gradient-aware adaptive contour sampling scheme. The model consistently outperforms others across diverse vision and vision-language tasks, marking a significant advancement in vision-language processing. Read…
The blog describes TruEra’s collaboration in co-writing with Josh Reini, Shayak Sen, and Anupam Datta from TruEra. It highlights Amazon SageMaker JumpStart’s provision of pretrained foundation models, outlines the need for adapting foundation models to new tasks or domains, and mentions TruLens’ framework for extensible, automated evaluations. Additionally, it details the processes of deploying and…