Dive into the world of artificial intelligence â build a deep reinforcement learning gym from scratch. Gain hands-on experience and develop your own gym to train an agent to solve a simple problem, setting the foundation for more complex environments and systems.
Talent.com collaborates with AWS to develop a job recommendation engine using deep learning, processing 5 million daily records in less than 1 hour. The system includes feature engineering, deep learning model architecture design, hyperparameter optimization, and model evaluation, all run using Python.
Text-to-image generation is a rapidly growing field of AI, with Stable Diffusion allowing users to create high-quality images in seconds. The use of Retrieval Augmented Generation (RAG) enhances prompts for Stable Diffusion models, enabling users to create their own AI assistant for prompt generation.
ICL, a multinational manufacturing and mining corporation, developed in-house capabilities using machine learning and computer vision to automatically monitor their mining equipment. With support from the AWS Prototyping program, they were able to build a framework on AWS using Amazon SageMaker to extract vision from 30 cameras, with the potential to scale to thousands.
Amazon SageMaker Studio now offers a fully managed Code Editor based on Code-OSS, along with JupyterLab and RStudio, allowing ML developers to customize and scale their IDEs using flexible workspaces called Spaces. These Spaces provide persistent storage and runtime configurations, improving workflow efficiency and allowing for seamless integration of generative AI tools.
Amazon Comprehend offers pre-trained and custom APIs for natural-language processing. They have developed a pre-labeling tool that automatically annotates documents using existing tabular entity data, reducing the manual work needed to train accurate custom entity recognition models.
Dropbox faces backlash after enabling a default setting that shares user data with OpenAI for AI-powered search, but assures data is only shared when actively used and is deleted within 30 days. CEO Drew Houston apologizes for customer confusion and emphasizes that no customer data is automatically sent to third-party AI services.
Spectral clustering is a complex machine learning technique that uncovers patterns in data. Implementing it involves computing affinity and Laplacian matrices, eigenvector embeddings, and performing k-means clustering.
GeForce NOW adds 17 new games, including The Day Before and Avatar: Frontiers of Pandora, with over 500 games now supporting RTX ON. Ultimate members can experience cinematic ray tracing and stream at up to 4K resolution, while Priority members can build and survive at 1080p and 60fps.
Tesla releases demo video of its Optimus Gen 2 humanoid robot, showcasing significant hardware improvements. Skepticism remains after recent AI demonstration controversies.
The US Federal Trade Commission warns against QR code scams that can take control of smartphones, make fraudulent charges, or obtain personal information. Scammers are targeting QR codes on parking lot kiosks, leading to look-alike sites that funnel funds to fraudulent accounts.
Data projects often fail to deliver real-life impact due to macro-elements such as data availability, skillset, timeframe, organizational readiness, and political environment. The availability and accessibility of relevant data are fundamental, and if data is unattainable, the feasibility of the project should be reconsidered.
Vodafone is transforming into a TechCo by 2025, with plans to have 50% of its workforce involved in software development and deliver 60% of digital services in-house. To support this transition, Vodafone has partnered with Accenture and AWS to build a cloud platform and engaged in an AWS DeepRacer challenge to enhance their machine learning skills.
LM Studio is a tool that allows local machine usage of large language models like GPT-x, LLaMA-x, and Orca-x, offering a clean and intuitive UI for exploring models and conducting reasoning tasks. However, its creator and potential connections with other companies remain unclear.
This article explores the importance of classical computation in the context of artificial intelligence, highlighting its provable correctness, strong generalization, and interpretability compared to the limitations of deep neural networks. It argues that developing AI systems with these classical computation skills is crucial for building generally-intelligent agents.