- Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research
What is the Role of Mathematics in Modern Machine Learning?The past decade has witnessed a shift in how progress is made in machine learning. Research involving carefully designed and mathematically principled architectures result in only marginal improvements while compute-intensive and engineering-first efforts that scale to ever larger training sets
- What's Missing From LLM Chatbots: A Sense of Purpose
LLM-based chatbots’ capabilities have been advancing every month. These improvements are mostly measured by benchmarks like MMLU, HumanEval, and MATH (e.g. sonnet 3.5, gpt-4o). However, as these measures get more and more saturated, is user experience increasing in proportion to these scores? If we envision a future
- We Need Positive Visions for AI Grounded in Wellbeing
IntroductionImagine yourself a decade ago, jumping directly into the present shock of conversing naturally with an encyclopedic AI that crafts images, writes code, and debates philosophy. Won’t this technology almost certainly transform society — and hasn’t AI’s impact on us so far been
- 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?
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