The AWS Generative AI Innovation Center, launched in June 2023, has assisted numerous clients in creating custom AI solutions. Starting Q1 2024, the new Custom Model Program will enable customers to fine-tune Anthropic Claude models with their own data through Amazon Bedrock. The program offers specialized support from AI experts for tailored model optimization.
The author shares their insights from the fourth week of the #30DayMapChallenge, where participants create daily thematic maps, offering analysis on their experience. Read more at Towards Data Science.
The #30DayMapChallenge concluded with participants creating compelling geo-visualizations, demonstrating the power of community and data storytelling. The challenge encompassed various themes like Oceania’s wildlife, global migration flows, traffic patterns, and diamond extraction visualization techniques, highlighting unique data interpretations and the significance of collective creativity throughout the event.
Researchers have discovered 2.2 million new crystals, using GNoME, a deep learning tool that predicts material stability, accelerating discovery time equivalent to 800 years of research.
Google’s DeepMind introduced GNoME, a deep learning tool for fast material discovery, facilitating the prediction and lab creation of thousands of new materials. Partnered with Lawrence Berkeley National Laboratory’s autonomous lab, the tool uses AI to optimize material engineering, potentially accelerating technological innovation across various sectors.
Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by combining external data retrieval with generative AI, ensuring accurate, current information and greater transparency. It reduces computational costs and risk of misinformation, integrating databases into a searchable knowledge base for reliable, context-rich communication. RAG improves AI-powered applications and user trust.
MLOps integrates machine learning development and deployment to facilitate continuous delivery of high-performance models. It enhances deployment speed, model quality, and reduces operation costs by automating the transition from development to production using CI/CD pipelines and tools like ML frameworks, cloud platforms, and MLOps systems. Enterprises can begin with MLOps by selecting suitable tools, establishing…
Recent AI research introduced FollowNet, a benchmark for car-following behavior modeling, addressing limitations like non-standardized data and evaluation criteria. It consolidates data from five driving datasets and evaluates classic and data-driven models, aiming to reflect mixed-traffic scenarios more accurately and enhance dataset features for future algorithmic improvements.
Stability AI introduces SDXL Turbo, an AI text-to-image generator that creates images in milliseconds, updating in real-time with prompt edits. It uses Adversarial Diffusion Distillation, blending diffusion model quality and GAN speed, saving computing resources and potentially enabling live animation creation. Model resources are accessible via Hugging Face.
Engineers have developed a method to teach robots to recognize uncertainty by quantifying the vagueness of human instructions, prompting them to request clarification when necessary, such as when multiple objects are present but only one is needed.
Amazon introduced Amazon Q, an AI chatbot for workplace assistance from AWS, focusing on streamlining office tasks while prioritizing data security. Competing with Microsoft and Google, it’s priced at $20/user/month. Amazon also plans to enhance AI infrastructure in partnership with Nvidia.
The Nordic AI Summit in Oslo will showcase how Norwegian business leaders utilize AI for company transformation. The event includes expert talks, such as by Simplifai’s Erik Leung, and discussions on practical AI applications, aiming to bridge the understanding between engineers and decision-makers. Networking opportunities with key AI figures are also highlighted.
Improve code efficiency with these five language-agnostic methods: extract loop-invariants to reduce CPU cycles; use enums instead of strings for state representation to avoid errors and enhance performance; replace conditional statements with algebraic or boolean operations when possible; utilize memoization to store function outputs for repeated calls; and select the optimal data structure for your…
Scientists from universities in Victoria and Copenhagen applied AI to the Free Ocean Wave Dataset, successfully predicting rogue waves using a neural network. Employing symbolic regression, they derived an equation revealing the causal factors of these waves, aiding forecasts and enhancing maritime safety.
Researchers from various universities in China and Hong Kong developed GS-SLAM, a 3D Gaussian-based SLAM system, to balance accuracy with efficiency. It uses innovative rendering and adaptive strategies to enhance pose tracking, demonstrating competitive performance on standard datasets. GS-SLAM offers improvements in dense visual SLAM, but faces challenges with memory usage and dependency on high-quality…
Seoul National University researchers have advanced AI in art by training an AI agent to create authentic collages via reinforcement learning. Their model eschews pixel-based methods for a process that mirrors human techniques, showing promise in crafting AI-generated art with depth and creativity as confirmed by user studies and CLIP-based assessments.
GAIA, a benchmark by FAIR Meta and partners, tests AI assistants on real-world tasks that demand reasoning and multi-modal skills. It evaluates LLMs with practical, non-gameable questions reflecting actual use cases, aiming to bridge the gap between AI and human performance. GAIA benchmarks show humans outperforming GPT-4, guiding future AI advancements.
Amazon Q, an AI-powered assistant by AWS, offers customized support tailored to specific business needs and workflows, with high security and privacy standards. It assists developers with AWS insights, automates feature development, integrates with company systems, and offers administrative control, enhancing workplace productivity and innovation.
Researchers from Meta AI introduced “Style Tailoring,” improving Latent Diffusion Models (LDMs) for sticker generation with better visual quality, alignment, and diversity. It employs multi-stage fine-tuning, human-in-the-loop adjustments, and achieves 14-16.2% enhancements over the base Emu model, with room for broader research applications.
Researchers have created a composite material that alters its behavior with temperature changes, aiming to advance autonomous robotics that interact dynamically with their surroundings.