We introduce logit-gap steering, a fast jailbreak framework that casts the refusal-affirmation gap of RLHF-aligned language models as a single pass over the vocabulary. A forward-computable score blends gap reduction with lightweight proxies for KL penalty and reward shift, allowing a "sort-sum-stop" sweep to complete in under a second and return a short suffix--two orders of magnitude fewer model calls than beam or gradient attacks. The same suffix generalises to unseen prompts and scales from 0.5 B to 70 B checkpoints, lifting one-shot attack success from baseline levels to 80-100 % while preserving topical coherence. Beyond efficiency, these suffixes expose sentence-boundary reward cliffs and other alignment artefacts, offering a lightweight probe into how safety tuning reshapes internal representations.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. We are looking for a Senior Engineer to lead projects and initiatives on our newest subteam within ML Platform: The ML Developer Productivity team. As an early team member, you will contribute extensively to setting and executing on a vision for increasing ML velocity at Affirm through a focus on developer productivity. Our mission is to build a self-service, easy-to-use foundation for developing and delivering robust models to production. This is a new team that will focus on creating internal tools used by ML Engineers for fast paced ML development.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. We are looking for an Engineering Manager to lead projects and initiatives on our new ML Developer Productivity team within the ML Platform group. In this role, you will lead and grow a hard-working distributed team to drive the vision and roadmap of the machine learning developer experience. Our mission is to build a self-service, easy-to-use foundation for developing and delivering robust models to production. This is a new team that will focus on creating internal tools used by ML Engineers for fast paced ML development.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. The International Machine Learning Underwriting (INML) team builds Affirm essential real-time underwriting models used outside of the US. These models determine which users are approved for an Affirm loan and how much they are approved for. These models are central to Affirm's continued success and growth, and need to be explainable to the user and scalable across regions. We offer a competitive package, with some highlights listed below.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Affirm, Inc. proudly includes Affirm, PayBright, and Returnly. We've opened an office in Poland with a goal to hire a substantial team of talented engineers within the first year. Machine learning is a critical tool in this effort! As a member of the software engineering team, you will design, build, optimize, and support machine learning systems both offline and real time.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Affirm, Inc. proudly includes Affirm, PayBright, and Returnly. Affirm's Machine Learning team solves problems critical to our business model - personalizing shopping experiences, detecting fraud, optimizing interest rates, and assessing creditworthiness in real time. Our innovative products necessitate the creation of novel machine learning solutions to drive both existing and new products. Affirm is proud to be a remote-first company!

According to Grand View Research, the global artificial intelligence (AI) market was worth an estimated $62 billion in 2020 but could grow 40% annually through 2028. If you don't yet have AI stocks in your long-term portfolio, it might be time to start thinking about it. The market's recent sell-off of technology and high-growth companies has created a great buying opportunity for bold and patient investors. Here are three top AI stocks building moats around their algorithms and whose stocks are attractively priced today. Technology can change at a blistering pace, and nobody can know for sure that the winners of today will still hold their crown tomorrow, a year from now, or a decade from now.

At the end of last month, a set of principles and advice on ethics in the use of Artificial Intelligence (AI) was known, adopted for the first time jointly and unanimously by the 193 member states of the General Council of the UNESCO. Beyond the uniqueness of its universal character, it is about Unesco launched a guide to improve the relationship between humans and robots and combines ethical issues to a warning voice that has been heard for a long time. There are already several international political organizations that have been warning about the need to provide an ethical component to what is undoubtedly the most notable advance in applied science of our time. In fact, in November but from '19 the European Union (EU) had published its Ethical Guidelines for a reliable artificial intelligence whose proposal revolves around the collateral effects, or unforeseen risks, that the implementation of disruptive technologies like this can generate. Likewise, in April of this year we learned about the European Commission regulation regarding the use of algorithms able to learn and make decisions.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Affirm, Inc. proudly includes Affirm, PayBright, and Returnly. Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. The ML Platform team's mission is to develop a self-service foundation for applications of machine learning that will provide scalable business impact. This team works in close partnership with ML engineers to deliver systems that enable fast paced ML development.

Machine learning approaches for building task-oriented dialogue systems require large conversational datasets with labels to train on. We are interested in building task-oriented dialogue systems from human-human conversations, which may be available in ample amounts in existing customer care center logs or can be collected from crowd workers. Annotating these datasets can be prohibitively expensive. Recently multiple annotated task-oriented human-machine dialogue datasets have been released, however their annotation schema varies across different collections, even for well-defined categories such as dialogue acts (DAs). We propose a Universal DA schema for task-oriented dialogues and align existing annotated datasets with our schema. Our aim is to train a Universal DA tagger (U-DAT) for task-oriented dialogues and use it for tagging human-human conversations. We investigate multiple datasets, propose manual and automated approaches for aligning the different schema, and present results on a target corpus of human-human dialogues. In unsupervised learning experiments we achieve an F1 score of 54.1% on system turns in human-human dialogues. In a semi-supervised setup, the F1 score increases to 57.7% which would otherwise require at least 1.7K manually annotated turns. For new domains, we show further improvements when unlabeled or labeled target domain data is available.