Large language model
MeshGPT is a novel AI method developed for directly generating high-fidelity triangle meshes without conversion. It uses a GPT-based architecture with a geometric vocabulary, outperforming existing mesh generation techniques. Users prefer MeshGPT for its quality and realistic triangulation, as proven in studies against other prominent methods.
The tutorial discusses efficient dataset sampling techniques in Python. It compares three methods: uniform, random, and Latin Hypercube Sampling (LHS). Uniform sampling is simple but scales poorly with dimensions. Random sampling is straightforward, better for large dimensions, yet may form clusters. LHS offers stratified random samples, preferable for high dimensions with fewer samples, albeit more…
Generative AI is rapidly transforming customer experiences, with many companies launching applications on AWS, including major brands and startups. AWS is democratizing advanced generative AI technology, making it more accessible and secure across three layers of infrastructure, model building, and applications, such as Amazon CodeWhisperer and the newly introduced Amazon Q for professional assistance. Upcoming…
The Foobar Challenge is a five-level coding challenge by Google completed within a time limit in Python or Java. The author describes their experience with the complexity of Level 3, involving binary numbers, dynamic programming, and Markov chains, emphasizing the necessity of research for unfamiliar concepts to achieve elegant solutions.
A research team has proposed Relational Deep Learning, an end-to-end technique for Machine Learning that processes data across multiple relational tables without manual feature engineering. They introduced RELBENCH, a framework with benchmark datasets for relational databases, facilitating efficient data handling, predictive model building, and performance evaluation using Graph Neural Networks.
Amazon SageMaker is a fully managed service that simplifies building, training, and deploying ML models. It offers API deployment, containerization, and various deployment options including AWS SDKs and AWS CLI. New Python SDK improvements and SageMaker Studio interactive experiences streamline model packaging and deployment. Features include multi-model endpoints, price-performance optimization, and deployment without prior SageMaker…
Amazon SageMaker has launched two new features to streamline ML model deployment: the ModelBuilder in the SageMaker Python SDK and an interactive deployment experience in SageMaker Studio. These features automate deployment steps, simplify the process across different frameworks, and enhance productivity. Additional customization options include staging models, extending pre-built containers, and custom inference specification.
Recent research highlights concerns about Large Language Models (LLMs), such as biased outputs and environmental impacts. Further details are available on Towards Data Science.
Microsoft President Brad Smith stated Sam Altman’s temporary departure from OpenAI was not due to AI safety issues. Amid speculation and internal concerns over Altman’s management style, Microsoft, a close partner, has secured a non-voting observer seat on OpenAI’s board. Altman has since been reinstated, pledging to advance OpenAI’s mission and safety.
Microsoft plans to invest £2.5 billion in the UK tech industry, focusing on AI infrastructure and development. The investment will expand data centers, introduce 20,000 GPUs by 2026, and train over a million people in AI skills. This move aims to reinforce the UK as a leading science and AI hub.
Digital publishers use machine learning for faster content creation, ensuring relevant images match articles. Amazon’s Titan Multimodal Embeddings model generates image and text embeddings for semantic search. This streamlines finding appropriate images, without keywords, by comparing metadata similarity—enhancing media workflows while maintaining quality. Amazon Bedrock simplifies AI application development for various modalities.
The paper explores Transformers’ capabilities in length generalization on algorithmic tasks and proposes a framework to predict their performance in this area. Accepted at NeurIPS 2023’s MATH workshop, it addresses the paradox of language models’ emergent properties versus their struggles with simple reasoning.
Researchers use knowledge graphs to enhance neural models in Natural Language Processing (NLP) and Computer Vision, grounding them in organized data. However, non-English languages face a scarcity of quality textual data. A new task, automatic Knowledge Graph Enhancement (KGE), has been introduced to improve non-English textual data’s quantity and quality.
This study, presented at NeurIPS 2023’s UniReps Workshop, introduces an efficient approach to combine vision foundation models (VFMs) like CLIP and SAM into a single model that leverages their respective semantic and spatial understanding strengths through multi-task learning techniques.
This work confirms that multigroup fairness concepts yield strong omniprediction—loss minimization across diverse loss functions. The study establishes a reciprocal link, showing that multicalibration and omniprediction are equivalent. New definitions are proposed. (47 words)
This paper, accepted for the NeurIPS 2023 workshop, discusses the overlooked potential of automatic speech recognition (ASR) in federated learning (FL) and differential privacy (DP), highlighting ASR’s suitability as a benchmark due to its data distribution and real-world relevance.
Daniel Bakkelund suggests three heuristics to evaluate AI project viability: First, ensure you can clearly articulate the problem in writing. Second, ascertain if an informed human could theoretically solve the problem, given unlimited resources and time. Third, confirm that all necessary context for the AI to learn and give answers is available. If all conditions…
Data and machine learning professionals are wrapping up the year by enhancing skills and preparing for career progression. November’s popular reads in Towards Data Science (TDS) included guides on knowledge graphs, hardware benchmarks, job search tips, and Markov models. New insights and projects explored human’s role in ML, AI bias, and personal data tracking. A…
At the DealBook summit, Nvidia CEO Jensen Huang predicted that AI could rival human intelligence within five years, emphasizing Nvidia’s crucial role in AI’s growth due to the increased demand for their GPUs. Despite current AI limitations, Nvidia’s advancements are significant, amidst calls for robust governance in AI companies.
Researchers at TUS and collaborating institutes have created a deep learning binary classifier that identifies an unknown quasicrystalline phase in materials with over 92% accuracy, revolutionizing material analysis with wide-ranging technological implications.