• Financial Market Applications of LLMs

    The AI revolution drove frenzied investment in both private and public companies and captured the public’s imagination in 2023. Transformational consumer products like ChatGPT are powered by Large Language Models (LLMs) that excel at modeling sequences of tokens that represent words or parts of words [2]. Amazingly, structural

  • A Brief Overview of Gender Bias in AI

    A brief overview and discussion on gender bias in AI

  • Mamba Explained

    Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.

  • Car-GPT: Could LLMs finally make self-driving cars happen?

    Exploring the utility of large language models in autonomous driving: Can they be trusted for self-driving cars, and what are the key challenges?

  • Do text embeddings perfectly encode text?

    'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.

  • Why Doesn’t My Model Work?

    Have you ever trained a model you thought was good, but then it failed miserably when applied to real world data? If so, you’re in good company.

  • Deep learning for single-cell sequencing: a microscope to see the diversity of cells

    On the the pivotal role that Deep Learning has played as a key enabler for advancing single-cell sequencing technologies.

  • Salmon in the Loop

    On fish counting – a complex sociotechnical problem in a field that is going through the process of digital transformation.

  • Neural algorithmic reasoning

    In this article, we will talk about classical computation: the kind of computation typically found in an undergraduate Computer Science course on Algorithms and Data Structures [1]. Think shortest path-finding, sorting, clever ways to break problems down into simpler problems, incredible ways to organise data for efficient retrieval and updates.

  • The Artificiality of Alignment

    This essay first appeared in Reboot. Credulous, breathless coverage of “AI existential risk” (abbreviated “x-risk”) has reached the mainstream. Who could have foreseen that the smallcaps onomatopoeia “ꜰᴏᴏᴍ” — both evocative of and directly derived from children’s cartoons —

  • An Introduction to the Problems of AI Consciousness

    Once considered a forbidden topic in the AI community, discussions around the concept of AI consciousness are now taking center stage, marking a significant shift since the current AI resurgence began over a decade ago.

  • Text-to-CAD: Risks and Opportunities

    In the realm of AI-powered text-to-CAD, there's promise, but also a surge in subpar designs. Can we steer this technology towards better outcomes?

  • Interpretability Creationism

    On “interpretability creationism” – interpretability methods that only look at the final state of the model and ignore its evolution over the course of training

  • What Do LLMs Know About Linguistics? It Depends on How You Ask

    On the phenomenon of LLM sensitivity to prompting choices through two core linguistic tasks and categorize how specific prompting choices can affect the model's behavior.

  • Why transformative artificial intelligence is really, really hard to achieve

    A collection of the best technical, social, and economic arguments Humans have a good track record of innovation. The mechanization of agriculture, steam engines, electricity, modern medicine, computers, and the internet—these technologies radically changed the world. Still, the trend growth rate of GDP per capita in the world&


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