AWS

AI Breakthrough: New Method Predicts and Prevents Biological Invasions Researchers have developed a groundbreaking AI-based methodology to more accurately predict which plant species are likely to become invasive, aiming to prevent biological invasions before they occur. By combining decades of ecological data with advanced machine learning algorithms, the new models achieve over 90% accuracy in predicting invasion success. This data-driven approach is designed to remove biases from ecological risk assessments and increase their predictive power, helping authorities identify high-risk species before importation. Initially focused on Caribbean islands, the research will expand by training the models with data from other global regions to enhance their applicability. ...

NVIDIA Unveils RLP: A New Era of Reinforcement Learning for AI Pretraining NVIDIA researchers have developed a groundbreaking training objective called Reinforcement Learning Pretraining (RLP), which integrates reinforcement learning directly into the pretraining stage of AI model development. The core concept of RLP treats a short chain-of-thought as an action that is rewarded based on the information gain it provides for the model’s next-token prediction. This innovative method creates a verifier-free and dense reward system that can be applied at scale to standard text streams. Unlike previous methods that depend on sparse signals or external verifiers, RLP’s approach allows for updates at every single token position. Experiments demonstrate that RLP can yield significant improvements in a model’s reasoning capabilities, with one model showing a substantial increase in its average score on scientific reasoning benchmarks. ...

OpenAI & Broadcom Announce Landmark 10GW Custom AI Accelerator Partnership OpenAI and Broadcom have announced a strategic multi-year collaboration to develop and deploy 10 gigawatts of custom AI accelerators. Under this partnership, OpenAI will lead the design of the AI accelerators and systems, while Broadcom will be responsible for their development and deployment. This collaboration will also involve the co-development of systems incorporating Broadcom’s Ethernet and other connectivity solutions to enhance the scalability of next-generation AI clusters. The deployment of these AI accelerator and network system racks is scheduled to commence in the latter half of 2026, with a target completion by the end of 2029. Sam Altman, CEO of OpenAI, stated that this partnership is a crucial step in building the necessary infrastructure to unlock the full potential of AI. ...

Weekly Tech News: AI, Robotics, Autonomous Vehicles & Cloud Innovations Google’s Gemini 2.5 AI Wins Gold Medal in Global Programming Contest A specialized version of Google DeepMind’s Gemini 2.5 AI model, known as Deep Think, has achieved a gold medal-level ranking at the prestigious International Collegiate Programming Contest (ICPC). Held in Baku, Azerbaijan, the competition featured 139 of the world’s top university programming teams. Gemini 2.5 Deep Think successfully solved 10 of the 12 complex algorithmic problems, placing it second overall compared to the human teams. ...

Equifax Launches AI Innovation Lab in Ireland for Global R&D Equifax has officially opened a new AI Innovation Lab in Wexford, Ireland, on September 11, 2025, establishing a global hub for its artificial intelligence and machine learning research. Supported by the Irish Government through IDA Ireland, the lab will unite data scientists and tech specialists to pioneer advanced AI solutions. The facility’s primary focus is on developing sophisticated AI and machine learning algorithms to improve credit risk assessment, enhance fraud detection, and promote greater financial inclusion. This launch is a key part of Equifax’s overarching EFX.AI strategy, building on its decade of AI innovation. ...

Microsoft Challenges OpenAI with New Proprietary AI Models: MAI-Voice-1 and MAI-1 Preview Microsoft has announced its first two proprietary artificial intelligence models, MAI-Voice-1 and MAI-1 Preview, marking a significant strategic shift towards developing its own foundational AI technologies. MAI-Voice-1 is an advanced speech generation model capable of creating one minute of audio in under a second on a single GPU. The large language model, MAI-1 Preview, is now available for public testing. This move signals Microsoft’s ambition to compete directly with major AI players, including its partner OpenAI, by building a proprietary AI stack for its future product ecosystem. ...

MIT Report: 95% of Enterprise GenAI Projects Fail to Deliver ROI A new report from MIT’s NANDA initiative reveals that a staggering 95% of enterprise generative AI pilot programs are failing to produce a significant return on investment. Despite investments totaling between $35 and $40 billion in the US, only 5% of organizations have successfully deployed AI tools into production at scale. The report, titled “The GenAI Divide: State of AI in Business 2025,” is based on interviews with enterprise leaders, analysis of public AI initiatives, and a survey of business professionals. The primary reasons for failure are not a lack of infrastructure or talent, but rather the inability of AI systems to retain data, adapt, and learn over time. The study also found that purchasing AI tools from vendors and forming partnerships has a significantly higher success rate than in-house development. ...

When you choose to run your kubernetes cluster on AWS, there are 2 easy ways: The almost valina way using Kops The amazon way using EKS The common thing between both ways, is using Amazon ECR for storing the docker images and have a worry free push, pull to and from Amazon ECR which requires a IAM Role that allows the worker nodes pulling the images saftly. Amazon ECR Amazon Elastic Container Service is one of the cheapst ways to store docker images and safer due to the nature of Amazon IAM. To pull from ECR you first need to authenticate using you AWS credentials, or role, get a token, do docker login to your ECR with the server address, and pull from the repository the docker image. ...

This is a short post on log rotating AWS Elasticsearch indices easily using curator In the past I’ve made a simple docker container with curator and with a simple actions file for managing indices. This worked great on my own installation of Elasticsearch that managed our logs on a small scale. The actions file was: --- actions: 1: action: delete_indices description: >- Rolling indeces according to filters options: ignore_empty_list: True #disable_action: True disable_action: False filters: - filtertype: pattern kind: prefix value: filebeat- - filtertype: age source: name direction: older timestring: '%Y.%m.%d' unit: days unit_count: ${DAYS_TO_KEEP:14} And it was controlled in the docker run command, which host to work on and what old indices should be deleted, with environment variables. ...

Using AWS Lambda has become very popular. Most of the time using it is fast and just (but not always). When you start having a lot of lambda functions the complexity of managing them can be hard. When a developer can just edit the code inline, it can become the worse manage service use ever chose. So again, the most important thing to do when you start working with Lambda is to have a proper way to deliver the code from git to aws. ...