AI & ML Research 3 Days

Last updated: June 24, 2026, 11:30 PM ET

AI Model Development & Architecture

OpenAI and Broadcom unveiled Jalapeño, a custom AI chip engineered for Large Language Model (LLM) inference, aiming to boost performance, efficiency, and scalability within AI systems. This collaboration signals a move towards specialized hardware for AI workloads. Meanwhile, Google Deep Mind detailed computer use in Gemini 3.5 Flash, expanding the model's capabilities for real-time interaction and data processing. Research into Gemma models has revealed a three-phase factual recall circuit within Gemma-2B and Gemma-12B-IT, where activation patching indicates the residual stream plays a significant role in storing and retrieving facts. Further exploration into LLMs by Google AI suggests that reasoning unlocks parametric knowledge, enabling generative AI to access and utilize stored information more effectively.

Data Engineering & Pipeline Management

A practical approach to onboarding as a data engineer involves making ETL pipelines testable, according to new guidance. This workflow emphasizes environment setup, automated testing, and leveraging AI for development assistance. For those working with agents, a shift from single-agent systems to multi-agent pipelines is recommended, particularly for tasks like text-to-SQL translation, offering a more robust and adaptable solution. In the realm of Retrieval-Augmented Generation (RAG), a mental model suggests that retrieval is filtering, not search. This perspective advocates for filtering structured tables and table of contents data before engaging embeddings, and prioritizing smaller "anchor" contexts for expanded retrieval. An additional RAG strategy proposes using parallel detectors before a final LLM call for anchor detection, with retrieval stages prioritizing keywords, then table of contents, and finally embeddings. For handling vague user queries in RAG, the advice is to clarify once and learn the default, prompting for a single focused clarification and then defaulting to that learned behavior for future interactions.

AI Infrastructure & Web Data

The burgeoning AI sector is driving the emergence of a web data infrastructure layer, essential for enterprises needing to access and process data at scale. This infrastructure aims to overcome challenges where relevant information is often blocked or inaccessible. The development of AI has also spurred interest in specialized hardware, with OpenAI and Broadcom unveiling a custom chip optimized for LLM inference. This initiative is part of a broader trend toward developing hardware tailored to AI's computational demands. Beyond specialized chips, efforts are underway to build shared standards for advanced AI through organizations like the Appia Foundation, focusing on evaluation frameworks and safety practices to promote global cooperation.

Model Interpretability & Factual Recall

Recent research is shedding light on how LLMs store and access information. A study on Gemma models revealed a three-phase factual recall circuit, indicating specific mechanisms for how facts are routed and read out across transformer layers, with the residual stream performing a substantial part of this function. This work complements findings that reasoning unlocks parametric knowledge in LLMs, suggesting that the ability to reason is intrinsically linked to the model's capacity to recall and utilize its learned information. These advancements are crucial for understanding and improving the reliability of AI-generated content.

AI Tools & Development Platforms

The AI development landscape is expanding with new tools and platforms designed to democratize access and enhance productivity. OpenAI introduced Daybreak tools, including Codex Security and GPT-5.5-Cyber, aimed at helping organizations identify, validate, and patch vulnerabilities at scale. A complementary initiative, Patch the Planet, supports open-source maintainers by using AI and expert review to find and fix vulnerabilities. For local AI development, a guide demonstrates how to build a local AI coding agent using Gemma 4 and Open Code, detailing the installation of Ollama and launching Open Code with a local model. Developers can also learn to create powerful loops in Claude Code to enhance coding agents, and how to use Claude Code in their browser to verify work. The rise of the "no-code AI" movement suggests that programmers may feel less unique as AI tools become more accessible, requiring a reevaluation of skill sets.

Credit Scoring & Data Analysis

New methodologies are emerging for transforming machine learning outputs into practical financial tools. A guide details how to build a credit scoring grid from a logistic regression model, explaining how to convert model coefficients into a 0-1000 score, incorporating risk classes and stability checks. In data analysis, a common challenge for outlier detection involves encoding categorical data, where alternative encoding methods are explored as potentially superior to one-hot encoding. Furthermore, best practices for data preprocessing are being reevaluated, with an example showing how Gemini solved a Pandas problem in seconds after a data scientist spent an hour on the task, though the importance of data science fundamentals for spotting suboptimal AI solutions remains.

AI Applications & Safety

AI's potential applications are expanding into diverse fields. OpenAI and partners are backing efforts to stop respiratory infections, leveraging AI to combat common illnesses. In wildlife conservation, AI warning systems aim to avoid deadly clashes between elephants and humans in India, where a significant portion of elephant habitat lies outside protected areas. Efforts to improve AI safety and standards are ongoing, with OpenAI helping build shared standards for advanced AI through initiatives focused on evaluation frameworks and safety practices. The development of specialized AI hardware, such as the LLM-optimized inference chip by OpenAI and Broadcom, is also critical for scaling AI capabilities responsibly. Safety and security are further addressed by OpenAI's Daybreak initiative to support open source maintainers, aiming to secure the open-source ecosystem.

Robotics & Human Augmentation

Advancements in robotics are enabling more sophisticated human-like capabilities. Ultrasound imaging allows a robot hand to mimic human dexterity, with the technology helping robots achieve nuanced movements previously exclusive to human hands. In medical applications, engineered "mini livers" could be injected as an alternative to transplantation, offering new hope for patients with chronic liver disease. Elsewhere, an adaptable fastener developed at CSAIL could reinvent everyday items like zippers, simplifying tasks from pitching tents to adjusting casts. Diagnostic capabilities are also improving, with a breath test being developed to diagnose pneumonia in minutes, utilizing a portable sensor for rapid lung condition assessment.

AI in Education & Learning

The role of AI in education is a subject of national conversation, with discussions on its risks and positive potential. Regardless of perspective, sharing a love for calculus is presented as a way to engage with foundational mathematical concepts. The complexity of video games is also being explored through a mathematical lens, with "Super Mario" being described as mathier than it appears. On a more personal level, AI is also being used to open doors to mental health help online, providing resources for individuals seeking coping strategies and vocabulary for mental well-being.

Energy & Infrastructure

Extreme weather events are straining energy infrastructure, with Europe experiencing power plant shutdowns due to record heat waves. This situation highlights the vulnerability of grids to climate-related challenges and increased demand for cooling. In contrast, advancements in infrastructure include the development of flying solar-powered platforms to deliver better internet and the construction of record-breaking subsea tunnels, such as the world’s deepest and longest subsea road tunnel. The technology powering the future of chipmaking involves a massive, multi-million dollar machine underscoring the complex engineering required for advanced manufacturing.