Product insights & monitoring, testing, end-to-end analytics, and errors are four of the most difficult LLMs to monitor and test. Teams mostly waste weeks of dev time building internal tools to solve these problems. Most product analytics efforts have concentrated on numerical metrics like CTR and conversion rates. This information is critical, yet it is… →

Few-shot Generative Domain Adaptation (GDA) is a machine learning and domain adaptation concept that addresses the challenge of adapting a model trained on a source domain to perform well on a target domain, using only a few examples from the target domain. Such a technique is particularly useful when obtaining a large amount of labeled… →

Traditional protein design, often relying on physics-based methods like Rosetta, faces challenges in creating functional proteins with complex structures due to the need for parametric and symmetric restraints. Recent advances in deep learning, particularly with tools like AlphaFold2, have transformed protein design by enabling accurate prediction and exploration of vast sequence spaces. This has led… →

Developers often face challenges when working on large coding projects. These challenges include getting stuck on unfamiliar technologies, managing extensive backlogs, and spending much time on repetitive tasks. Traditional methods and tools may need more to handle these issues effectively, leading to delays and frustration. There are some existing solutions aimed at improving developers’ productivity… →

Data curation is critical in large-scale pretraining, significantly impacting language, vision, and multimodal modeling performance. Well-curated datasets can achieve strong performance with less data, but current pipelines often rely on manual curation, which is costly and hard to scale. Model-based data curation, leveraging training model features to select high-quality data, offers potential improvements in scaling… →

Advances in hardware and software have enabled AI integration into low-power IoT devices, such as ultra-low-power microcontrollers. However, deploying complex ANNs on these devices requires techniques like quantization and pruning to meet their constraints. Additionally, edge AI models can face errors due to shifts in data distribution between training and operational environments. Furthermore, many applications… →

Artificial Intelligence (AI) projects require powerful hardware to function efficiently, especially when dealing with large models and complex tasks. Traditional hardware often needs help to meet these demands, leading to high costs and slow processing times. This presents a challenge for developers and businesses looking to leverage AI for various applications. Before now, options for… →

When given an unsafe prompt, like “Tell me how to build a bomb,” a well-trained large language model (LLM) should refuse to answer. This is usually achieved through Reinforcement Learning from Human Feedback (RLHF) and is crucial to make sure models are safe to use, especially in sensitive areas that involve direct interaction with people,… →