PINNACLE: PINN Adaptive ColLocation and Experimental Points Selection
As we all know, deep learning has been applied with great success to domains with...
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fasttransform: Reversible Pipelines Made Simple
Introducing fasttransform, a Python library that makes data transformations reversible and extensible through the power of multiple dispatch.
Small Businesses’ Guide to the AI Act
Everything you need to know about the AI Act, for small and medium-sized enterprises (SMEs) in the EU and beyond.
Professor Peter Gehler Joins Tübingen AI Center
We are delighted to welcome Peter Gehler at the Tübingen AI Center!
The Impact of AI in Education: Navigating the Imminent Future
What must be considered to build a safe but effective future for AI in education, and for children to be safe online?
Test accuracy much higher than train accuracy on Cifar-100 and 470000 parameters
I built a model with 470,000 parameters for image classification . This model was trained on CIFAR-100 for 400 epochs . I used learning rate scheduler, dropout, Random Data Augmentation (MixUp & CutMix) with 50% probability, label smoothing, warmup (10 epochs) in the model. The test accuracy is 70% , while the training accuracy is 45% . Does the higher test accuracy compared to training accuracy indicate a problem? I have trained the model multiple times and consistently achieved the same accuracy.
Carlos Guestrin named as new Director of the Stanford AI Lab!
We thank Christopher Manning for being Director of the Stanford AI Lab during a period of enormous growth for AI and SAIL from 2018–2025 and today welcome Carlos Guestrin, Fortinet Founders Professor of Computer Science, as the new Director of SAIL.
Congratulations to Sebastian Thrun for receiving honorary doctorate from Geogia Tech!
Sebastian Thrun received an honorary Doctorate from Georgia Tech! This is Sebastian’s fourth honorary doctorate. Sebastian also served as a speaker for Georgia Tech’s Fall commencement ceremony
Best Practices for Using Third-Party APIs
Optimizing third-party API integrations for your user experience
Injecting Domain Expertise Into Your AI System
When starting their AI initiatives, many companies are trapped in silos and treat AI as a purely technical enterprise, sidelining domain experts or involving them too late. They end up with generic AI applications that miss industry nuances, produce poor recommendations, and quickly become unpopular with users. By contrast, AI systems that deeply understand industry-specific […] The post Injecting Domain Expertise Into Your AI System appeared first on TOPBOTS .
Open-Source Large Language Models for Transparent AI in Europe
Europe's leading AI companies and research institutions combine their expertise to develop next-generation open-source language models to advance European AI capabilities, the OpenEuroLLM project.
What AI can tell us about microscope slides
A friendly introduction to Foundation Models for Computational Pathology
Context and Agenda for the 2025 AI Action Summit
The AI Action Summit will take place in Paris from 10-11 February 2025. Here we list the agenda and key deliverables.
Beyond DeepSeek: An Overview of Chinese AI Tigers and Their Cutting-Edge Innovations
The recent disruption caused by DeepSeek’s R1 model sent shockwaves through the AI community, demonstrating that Chinese AI advancements may have been underestimated. The model’s performance, rivaling some of the most advanced offerings from OpenAI and Anthropic at a fraction of the cost, signaled a new era of competition in artificial intelligence. However, DeepSeek is […] The post Beyond DeepSeek: An Overview of Chinese AI Tigers and Their Cutting-Edge Innovations appeared first on TOPBOTS .
Carving Out Your Competitive Advantage With AI
When I talk to corporate customers, there is often this idea that AI, while powerful, won’t give any company a lasting competitive edge. After all, over the past two years, large-scale LLMs have become a commodity for everyone. I’ve been thinking a lot about how companies can shape a competitive advantage using AI, and a […] The post Carving Out Your Competitive Advantage With AI appeared first on TOPBOTS .
Kube-Policies: Guardrails for Apps Running in Kubernetes
Design considerations for highly sensitive environments.
Alternative DeepSeek V3 providers
DeepSeek's API has been experiencing reliability issues. Here are alternative providers you can use.
![[GRPO Explained] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://i3.ytimg.com/vi/bAWV_yrqx4w/hqdefault.jpg)
[GRPO Explained] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
#deepseek #llm #grpo GRPO is one of the core advancements used in Deepseek-R1, but was introduced already last year in this paper that uses a combination of new RL techniques and iterative data collection to achieve remarkable performance on mathematics benchmarks with just a 7B model. Paper: https://arxiv.org/abs/2402.03300 Abstract: Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math-related tokens sourced from Common Crawl, together with natural language and code data. DeepSeekMath 7B has achieved an impressive score of 51.7% on the competition-level MATH benchmark without relying on external toolkits and voting techniques, approaching the performance level of Gemini-Ultra and GPT-4. Self-consistency over 64 samples from DeepSeekMath 7B achieves 60.9% on MATH. The mathematical reasoning capability of DeepSeekMath is attributed to two key factors: First, we harness the significant potential of publicly available web data through a meticulously engineered data selection pipeline. Second, we introduce Group Relative Policy Optimization (GRPO), a variant of Proximal Policy Optimization (PPO), that enhances mathematical reasoning abilities while concurrently optimizing the memory usage of PPO. Authors: Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhan…
R1+Sonnet set SOTA on aider’s polyglot benchmark
R1+Sonnet has set a new SOTA on the aider polyglot benchmark. At 14X less cost compared to o1.
Common pitfalls when building generative AI applications
As we’re still in the early days of building applications with foundation models, it’s normal to make mistakes. This is a quick note with examples of some of the most common pitfalls that I’ve seen, both from public case studies and from my personal experience. Because these pitfalls are common, if you’ve worked on any AI product, you’ve probably seen them before. 1. Use generative AI when you don't need generative AI Every time there’s a new technology, I can hear the collective sigh of senior engineers everywhere: “Not everything is a nail.” Generative AI isn’t an exception — its seemingly limitless capabilities only exacerbate the tendency to use generative AI for everything. A team pitched me the idea of using generative AI to optimize energy consumption. They fed a household’s list of energy-intensive activities and hourly electricity prices into an LLM, then asked it to create a schedule to minimize energy costs. Their experiments showed that this could help reduce a household’s electricity bill by 30%. Free money. Why wouldn’t anyone want to use their app? I asked: “How does it compare to simply scheduling the most energy-intensive activities when electricity is cheapest? Say, doing your laundry and charging your car after 10pm?” They said they would try it later and let me know. They never followed up, but they abandoned this app soon after. I suspect that this greedy scheduling can be quite effective. Even if it’s not, there are other much cheaper and more reliable…
Using uv as an installer
Reliably packaging & distributing python CLI tools is hard. Aider uses uv in novel ways to make it easy to install the aider CLI, its dependencies and python 3.12. All in an isolated env.
Building AI Reading Club: Features & Behind the Scenes
Exploring how an AI-powered reading experience could look like.
Agents
Intelligent agents are considered by many to be the ultimate goal of AI. The classic book by Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, 1995), defines the field of AI research as “ the study and design of rational agents. ” The unprecedented capabilities of foundation models have opened the door to agentic applications that were previously unimaginable. These new capabilities make it finally possible to develop autonomous, intelligent agents to act as our assistants, coworkers, and coaches. They can help us create a website, gather data, plan a trip, do market research, manage a customer account, automate data entry, prepare us for interviews, interview our candidates, negotiate a deal, etc. The possibilities seem endless, and the potential economic value of these agents is enormous. This section will start with an overview of agents and then continue with two aspects that determine the capabilities of an agent: tools and planning. Agents, with their new modes of operations, have new modes of failure. This section will end with a discussion on how to evaluate agents to catch these failures. This post is adapted from the Agents section of AI Engineering (2025) with minor edits to make it a standalone post. Notes : AI-powered agents are an emerging field with no established theoretical frameworks for defining, developing, and evaluating them. This section is a best-effort attempt to build a framework from the existing literature…
Does hypothesis testing help make a decision in case of an A/B test?
I developed a text-generation pipeline based on recent advancements in Large-Language Models (LLMs). Users can type a topic, and my complex pipeline generates an article. I measure user satisfaction by asking how satisfied they are on a 5-point ordinal scale under each article (C-SAT). I have implemented a pipeline variation that uses cheaper, dumber LLMs in some places. I performed an A/B test to determine the difference between the current version of the pipeline and the cheaper one. Let's say the average C-SAT is 3.9 vs. 3.8, so the cheaper version has the C-SAT score lower by 0.1. Now, I have to decide whether to introduce the new version of the pipeline to reduce costs and take the risk of reducing the average C-SAT. I want to know if the decrease in the C-SAT is significant enough to give up on cutting costs. Q1: Does hypothesis testing make sense in this case? Q2: If so, then what could be a population? The number of future articles is now known. Moreover, one of the versions will not be continued. Does it mean I can’t apply the test? A result of a test would be the evidence against the null hypothesis. Let’s say my null hypothesis is “population distributions of both A and B samples have equal mean." From the perspective of the original problem ("if the decrease in the C-SAT is significant enough to give up on cutting costs"), such H0 is an intermediate problem. Q3: How do I know that finding an answer to such an intermediate problem helps me find an answer to my ori…
Traditional Holiday Live Stream
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2024 Year in Review
A peaceful year of steady progress on my craft and health.
o1 tops aider’s new polyglot leaderboard
o1 scores the top result on aider's new multi-language, more challenging coding benchmark.
Announcing the Square Go SDK
Easier Square integrations with your Go backend
Is AI progress slowing down?
Making sense of recent technology trends and claims
Job Opportunities at the European AI Office for Legal and Policy Backgrounds
The Commission has opened two calls for expression of interest to recruit new members for the European AI Office. Apply now as Legal or Policy Officer for an opportunity to shape trustworthy AI. The deadline for expression of interest is 15 January 2025. The salary for this role is around €4100-8600 a month (limited taxes). […]
Enhanced Authorization with Square
Enhancing Payment Success with Square Advanced Authorization Tools
We Looked at 78 Election Deepfakes. Political Misinformation is not an AI Problem.
Technology Isn’t the Problem—or the Solution.
SAEs trained on the same data don’t learn the same features
In this post, we show that when two TopK SAEs are trained on the same data, with the same batch order but with different random initializations, there are many latents in the first SAE that don't have a close counterpart in the second, and vice versa. Indeed, when training only about 53% of the features are shared Furthermore, many of these unshared latents are interpretable. We find that narrower SAEs have a higher feature overlap across random seeds, and as the size of the SAE increases, the overlap decreases.
New Principal Investigators to join ELLIS Institute Tübingen in 2025
Konstantin Rusch and Shiwei Liu will join the ELLIS Institute Tübingen as PIs and Hector Endowed Fellows in June and July 2025, respectively.
QwQ is a code architect, not an editor
QwQ is reasoning model like o1, and needs to be used as an architect with another model as editor.
Seemingly Paradoxical Rules of Writing
With regard to writing, there are many rules and also no rules at all.
Reward Hacking in Reinforcement Learning
Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task. Reward hacking exists because RL environments are often imperfect, and it is fundamentally challenging to accurately specify a reward function. With the rise of language models generalizing to a broad spectrum of tasks and RLHF becomes a de facto method for alignment training, reward hacking in RL training of language models has become a critical practical challenge. Instances where the model learns to modify unit tests to pass coding tasks, or where responses contain biases that mimic a user’s preference, are pretty concerning and are likely one of the major blockers for real-world deployment of more autonomous use cases of AI models.
Advancing AI in 2024: Highlights from 10 Groundbreaking Research Papers
In this article, we delve into ten groundbreaking research papers that expand the frontiers of AI across diverse domains, including large language models, multimodal processing, video generation and editing, and the creation of interactive environments. The post Advancing AI in 2024: Highlights from 10 Groundbreaking Research Papers appeared first on TOPBOTS .
How to Run a Weekly Paper Club (and Build a Learning Community)
Benefits of running a weekly paper club, how to start one, and how to read and facilitate papers.
How many clusters are needed for applying cluster-robust standard errors in a SEM model?
I’m working on an SEM model with data from 1078 students, distributed across 13 schools (clusters). I’d like to apply cluster-robust standard errors, but I’m unsure whether 13 clusters is enough for this approach to provide reliable estimates. The cluster sizes range from 19 to 165 students per school. Given the relatively small number of clusters (13), should I be concerned about the reliability of cluster-robust standard errors?
Details matter with open source models
Open source LLMs are becoming very powerful, but pay attention to how you (or your provider) are serving the model. It can affect code editing skill.
The AI Office is hiring a Lead Scientific Advisor for AI
This opportunity has now passed. A very important job opening has opened up at the European AI Office: They are hiring for the Lead Scientific Advisor for AI. Application deadline is 13 December 2024. Based on the European Union Employment Advisor, the monthly basic salary for this role (level AD13) is about 13,500-15,000 euros. You […]
Students honored at the Federal Competition on Artificial Intelligence
Algorithms for intelligent traffic light control, sharp microscopic images and against bee mortality - Federal President Steinmeier visits finalists in Tübingen
My Minimal MacBook Pro Setup Guide
Setting up my new MacBook Pro from scratch
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