Artificial Intelligence
Researchers at Osaka University mapped human facial expressions’ mechanics to enhance androids’ emotional recognition. Analyzing 44 facial actions using 125 markers, they studied muscle and skin interactions. The findings may improve robotics, facial recognition, and medical diagnostics by providing data to recreate nuanced expressions in androids, mitigating the ‘uncanny valley’ effect.
A new study led by Hugging Face indicates considerable energy and carbon footprint in AI tasks, with image generation as the most intensive, equivalent to driving 4.1 miles. Text generation is less intensive. Research suggests choosing specialized AI models for tasks to reduce emissions, as day-to-day use significantly surpasses the carbon cost of AI training.
Deep machine learning, especially with neural networks, faces a challenge balancing interpretability and efficiency. White box probabilistic models are interpretable but outperformed by less interpretable deep neural networks. Tensor networks (TNs) offer a promising solution, enhancing both interpretability with quantum theories and efficiency on quantum and classical computers. Researchers at Capital Normal University and the…
Colleagues utilized Dask for partitioning data efficiently in training XGBoost models, allowing parallel processing across cores without overloading RAM. Experimentation indicated optimal partition size depends on dataset size, CPU, and RAM, with recommendations for handling data in small servers. Tips include averaging execution times and preferring smaller partitions if uncertain.
Human Machine Interfaces (HMIs) facilitate user interaction with various devices and technologies. Innovations are enhancing their intuitiveness and efficiency. A Spanish research team has created a structured dataset from human-machine interactions using custom-built UIs, aiding in the development of adaptive interfaces. The open-source dataset and analysis tools support advancements in personalized UIs, while highlighting future…
Particle Swarm Optimization (PSO) is a nature-inspired algorithm used to find optimal solutions in complex, high-dimensional spaces, like supply chain problems. It utilizes ‘particles’ that represent candidate solutions, influenced by personal and global bests. PSO efficiently outperforms brute-force grid searches, requiring significantly fewer computations, and can handle problems impractical for grid searches. The article demonstrates…
Google’s Duet AI enhances G-Suite productivity by simplifying complex tasks in Sheets, personalizing Meet backgrounds, generating images in Slides, improving writing in Docs, and drafting emails in Gmail. These AI-powered features streamline analysis, meetings, visualization, writing, and email management across various applications.
Part 2 of an article on Wave Data Feature Engineering focuses on spectral features. Techniques like FFT help convert time-domain signals into frequency-domain, providing insights on dominant frequencies and power distribution through features such as spectral entropy, kurtosis, PSD, and Harmonic Ratios. The next part will discuss Wavelet Transform, Demodulation, RQA, and signal generation for…
Researchers critically evaluated foundational models scGPT and Geneformer for single-cell biology, assessing zero-shot performance on tasks like cell clustering and batch effect correction. Despite efforts, both models demonstrated suboptimal performance, often underperforming compared to baseline models. The study suggests future research focus on the relationship between pretraining and downstream task performance.
The study explores the environmental impact of deep learning in pathology, advocating for the use of simpler models and model pruning to reduce CO2 emissions. Strategies include minimizing data inputs and selecting specific tissue regions. Findings suggest pruned models maintain accuracy while offering sustainability, promoting a balance between technological growth and ecological care in healthcare…
Caching stores function call results to optimize repeated computations, saving time and resources. Strategies include LRU, LFU, FIFO, LIFO, MRU, and RR. Considerations are memory footprint, access, insertion, and deletion times. Python’s functools.lru_cache and other libraries facilitate caching implementation, offering features like maximum cache size, hit/miss stats, and expiration times.
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.