Artificial Intelligence (AI)
The Latest Gemini 2.5 Flash-Lite Preview is Now the Fastest Proprietary Model (External Tests) and 50% Fewer Output Tokens
Google released an updated version of Gemini 2.5 Flash and Gemini 2.5 Flash-Lite preview models across AI Studio and Vertex AI, plus rolling aliases—gemini-flash-latest and gemini-flash-lite-latest—that always point to the newest preview in each family. For production stability, Google advises pinning fixed strings (gemini-2.5-flash, gemini-2.5-flash-lite). Google will give a two-week…
New AI system could accelerate clinical research
Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images. For instance, to determine how the size of the brain’s hippocampus changes as patients age, the scientist first outlines each hippocampus in a series of…
Sakana AI Released ShinkaEvolve: An Open-Source Framework that Evolves Programs for Scientific Discovery with Unprecedented Sample-Efficiency
Sakana AI has released ShinkaEvolve, an open-sourced framework that uses large language models (LLMs) as mutation operators in an evolutionary loop to evolve programs for scientific and engineering problems—while drastically cutting the number of evaluations needed to reach strong solutions. On the canonical circle-packing benchmark (n=26 in a unit square), ShinkaEvolve reports a new SOTA…
AI system learns from many types of scientific information and runs experiments to discover new materials
Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience…
How to Build an End-to-End Data Science Workflow with Machine Learning, Interpretability, and Gemini AI Assistance?
In this tutorial, we walk through an advanced end-to-end data science workflow where we combine traditional machine learning with the power of Gemini. We begin by preparing and modeling the diabetes dataset, then we dive into evaluation, feature importance, and partial dependence. Along the way, we bring in Gemini as our AI data scientist to…
Push Notifications Deep Dive: The Ultimate Technical Guide to APNs & FCM
Push notifications are the lifeline of modern mobile apps, delivering real-time updates, alerts, and personalized experiences — even when the app isn’t running. But how do these tiny messages travel across the globe from your server to a user’s screen within milliseconds?If you’re a developer eager to master the internal mechanics of Apple Push Notification Service (APNs)…
How are MIT entrepreneurs using AI?
The Martin Trust Center for MIT Entrepreneurship strives to teach students the craft of entrepreneurship. Over the last few years, no technology has changed that craft more than artificial intelligence. While many are predicting a rapid and complete transformation in how startups are built, the Trust Center’s leaders have a more nuanced view. “The fundamentals…
How to Create Reliable Conversational AI Agents Using Parlant?
Parlant is a framework designed to help developers build production-ready AI agents that behave consistently and reliably. A common challenge when deploying large language model (LLM) agents is that they often perform well in testing but fail when interacting with real users. They may ignore carefully designed system prompts, generate inaccurate or irrelevant responses at…
New tool makes generative AI models more likely to create breakthrough materials
The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials. But when it comes to designing materials with…
Building a Hybrid Rule-Based and Machine Learning Framework to Detect and Defend Against Jailbreak Prompts in LLM Systems
In this tutorial, we introduce a Jailbreak Defense that we built step-by-step to detect and safely handle policy-evasion prompts. We generate realistic attack and benign examples, craft rule-based signals, and combine those with TF-IDF features into a compact, interpretable classifier so we can catch evasive prompts without blocking legitimate requests. We demonstrate evaluation metrics, explain…