Artificial intelligence (AI) is polarizing. It excites the futurist and engenders trepidation in the conservative. In my previous post, I described the different capabilities of both discriminative and generative AI, and sketched a world of opportunities where AI changes the way that insurers and insured would interact. This blog continues the discussion, now investigating the …
With Amazon SageMaker, you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. This post provides a solution tailored to customers that are already using MLflow, an open-source platform for managing ML workflows. …
Read more “Securing MLflow in AWS: Fine-grained access control with AWS native services”
TL;RD There is no more excuse not to have a great marketing plan. Prompt: “Craft a marketing plan in the style of Allan Dib’s 1-Page Marketing Plan for our AI blog.” This is the prompt we used – simply replace “AI blog” with your product or service to generate a plan suitable to your product(s) …
“In my opinion, AutoGPT / babyAGI / AgentGPT is the real disruption point for the use of AI for complex problem-solving. Keep an eye out for these terms popping up in the news in the coming months.” That’s what Lars said, and rightfully so! Autonomous AI agents will introduce a new world of research and finding …
In this post, we discuss a machine learning (ML) solution for complex image searches using Amazon Kendra and Amazon Rekognition. Specifically, we use the example of architecture diagrams for complex images due to their incorporation of numerous different visual icons and text. With the internet, searching and obtaining an image has never been easier. Most …
Read more “Build an image search engine with Amazon Kendra and Amazon Rekognition”
*=Equal Contributors There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to survey landscape based on activity time series, where time series signal is transformed to frequency domain and compressed into embeddings by a contractive autoencoder, which preserve cyclic temporal patterns observed in time …
Read more “Self-Supervised Temporal Analysis of Spatiotemporal Data”
Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala, Javier Fernandez-Ivern, Anup Rokkam Pratap, Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience. Behind the scenes, a myriad of systems and services are involved in orchestrating the product experience. These backend systems are consistently being …
Read more “Migrating Critical Traffic At Scale with No Downtime — Part 1”
Three of my four grandparents were teachers. Their love of learning and passion for lighting a spark in their students impressed upon me the power that teachers have to change lives and shape the future. As I’m writing this, I am in Jacksonville, Florida, just a few miles from where my grandmother, Rosalie Gordon-Mills, was …
Read more “Supporting teachers in their STEM skilling journeys”
Posted by AJ Piergiovanni and Anelia Angelova, Research Scientists, Google Research Vision-language foundational models are built on the premise of a single pre-training followed by subsequent adaptation to multiple downstream tasks. Two main and disjoint training scenarios are popular: a CLIP-style contrastive learning and next-token prediction. Contrastive learning trains the model to predict if image-text …
Read more “MaMMUT: A simple vision-encoder text-decoder architecture for multimodal tasks”
The world of artificial intelligence (AI) and machine learning (ML) has been witnessing a paradigm shift with the rise of generative AI models that can create human-like text, images, code, and audio. Compared to classical ML models, generative AI models are significantly bigger and more complex. However, their increasing complexity also comes with high costs …