AI meets climate: MIT Energy and Climate Hack 2023
The Energy and Climate Hack presented opportunities for students and companies to collaborate and develop innovative solutions.
How AWS Prototyping enabled ICL-Group to build computer vision models on Amazon SageMaker | Amazon Web Services
This is a customer post jointly authored by ICL and AWS employees. ICL is a multi-national manufacturing and mining corporation based in Israel that manufactures products based on unique minerals…
Improve your Stable Diffusion prompts with Retrieval Augmented Generation | Amazon Web Services
Text-to-image generation is a rapidly growing field of artificial intelligence with applications in a variety of areas, such as media and entertainment, gaming, ecommerce product visualization, advertising and marketing, architectural…
Streamlining ETL data processing at Talent.com with Amazon SageMaker | Amazon Web Services
This post outlines the ETL pipeline we developed for feature processing for training and deploying a job recommender model at Talent.com. Our pipeline uses SageMaker Processing jobs for efficient data…
Automate PDF pre-labeling for Amazon Comprehend | Amazon Web Services
Amazon Comprehend is a natural-language processing (NLP) service that provides pre-trained and custom APIs to derive insights from textual data. Amazon Comprehend customers can train custom named entity recognition (NER)…
Three MIT students selected as inaugural MIT-Pillar AI Collective Fellows
The graduate students will aim to commercialize innovations in AI, machine learning, and data science.
Deep neural networks show promise as models of human hearing
Study shows computational models trained to perform auditory tasks display an internal organization similar to that of the human auditory cortex.
Create summaries of recordings using generative AI with Amazon Bedrock and Amazon Transcribe | Amazon Web Services
Meeting notes are a crucial part of collaboration, yet they often fall through the cracks. Between leading discussions, listening closely, and typing notes, it’s easy for key information to slip…
Fine-tune Llama 2 using QLoRA and Deploy it on Amazon SageMaker with AWS Inferentia2 | Amazon Web Services
In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We use the AWS Neuron software…
Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions | Amazon Web Services
Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design…