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We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering.
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering Read More »
We’ve analyzed how ChatGPT responds to users based on their name, using AI research assistants to protect privacy.
Evaluating fairness in ChatGPT Read More »
Translator Copilot is Unbabel’s new AI assistant built directly into our CAT tool. It leverages large language models (LLMs) and Unbabel’s proprietary Quality Estimation (QE) technology to act as a smart second pair of eyes for every translation. From checking whether customer instructions are followed to flagging potential errors in real time, Translator Copilot strengthens
Introducing Translator Copilot: Bridging Customers and Translators with AI Read More »
Updated February 9, 2024 to include the newest iteration of Tower models. We are thrilled to announce the release of Tower, a suite of multilingual large language models (LLM) optimized for translation-related tasks. Tower is built on top of LLaMA2 [1], comes in two sizes — 7B and 13B parameters —, and currently supports 10
Announcing Tower: An Open Multilingual LLM for Translation-Related Tasks Read More »
Malicious software (malware) causes much harm to our devices and life. We are eager to understand the malware behavior and the threat it made. Most of the record files of malware are variable length and text-based files with time stamps, such as event log data and dynamic analysis profiles. Using the time stamps, we can
Sequence Feature Extraction for Malware Family Analysis via Graph Neural Network Read More »
The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies. Some are made specifically for the web, others are for the desktop
Scientific Visualization: Python + Matplotlib Read More »
In this survey, we connect several lines of work from the pre-neural and neural era, by showing how hybrid approaches of words and characters as well as subword-based approaches based on learned segmentation have been proposed and evaluated. We conclude that there is and likely will never be a silver bullet singular solution for all
In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The
Federated Learning: Issues in Medical Application Read More »
«AutoQML, self-assembling circuits, hyper-parameterized Quantum ML platform, using cirq, tensorflow and tfq. Trillions of possible qubit registries, gate combinations and moment sequences, ready to be adapted into your ML flow. Here I demonstrate climatechange, jameswebbspacetelescope and microbiology vision applications… [Thus far, a circuit with 16-Qubits and a gate sequence of [ YY ] – [ XX ] – [CNOT]
Auto Quantum Circuits Read More »
Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS.
