Latest AI/ML News
27943 matching items
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
https://ykilcher.com/discord Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/ BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n
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.
How to Plot a Sigmoidal Curve to a Dose-Response to Find the Saturation Point and EC50
Hi there, I have injected voltage (mV) on the x-axis (not a log scale) and the neural response of the network (as area under the curve (AUC))) on the y. The data also includes a sample size of n = 8 wells for each voltage and a SE of the area for each data point. While I can visualise approximately where the saturation point is, I would like something more mathematically/statistically rigorous. Cheers! Hi @EdM and @whuber I actually got ChatGPT to help me write a Python code using that formula, and I got this: (!)
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
Exciting Partnership Announcement: AISG and Hippocratic AI
We’re thrilled to announce the signing of a Memorandum of Understanding (MOU) between Hippocratic AI and AI Singapore (AISG)! This MOU brings together the complementary strengths of both organisations...
What distribution describes the duration until a first poisson event occurs?
Imagine you have 100 individuals observed for 100 days. Every day, each individual has a 10% chance of an event occurring. What is the distribution in duration until the first event? Empirically it can be observed as follows (this is R code): n
European leadership in innovation with AI and Science
ELIAS presents pan-European network ELIAS ALLIANCE at Falling Walls event in Berlin
Innovative Solutions to Improve Health and Patient Outcomes Featured at Inaugural AI in Healthcare Technology Showcase
At the inaugural AI in Healthcare Technology Showcase today, AI Singapore exhibited 16 AI projects that are set to improve healthcare processes and patient outcomes in Singapore. The event...
Does the UK’s liver transplant matching algorithm systematically exclude younger patients?
Seemingly minor technical decisions can have life-or-death effects
Unclear points about the nature of hypothesis sets in Hoeffding Inequality-based generalization theory
In machine learning, we use a hypothesis set $\mathcal{H}$ and Hoeffding Inequality for generalization theory. I have the following question: Case 1: When we have a finite and fixed hypothesis set $\mathcal{H}$ , do we have the parameter of hypotheses in the set known or unknown? Case 2: When we have an infinite hypothesis set $\mathcal{H}$ , do we have the parameter of hypotheses in the set known or unknown?
Partially rewriting an LLM in natural language
Using interpretations of SAE latents to simulate activations.
RMSprop approach applied to Q-learning for adaptive dynamic learning rate
I am new to this group, Anybody familiar with Q-learning algorithm and RMSprop approach ? i have a question regarding the application of RMSprop approach into Q-Learning to adapt dynamically the learning rate for each Q(s,a). I am confused how to compute the average square gradient in tabular Q-Learning. Is it E[g^2] = beta * E[g^2]+ (1-beta) * td_error**2. ? and what would be the formula to update Q(s,a) ? Is it Q(s,a)
Overview of all AI Act National Implementation Plans
This post gives an overview of the national authorities to be designated under the AI Act and what we know about the national implementation plans.
HELM Safety: Towards Standardized Safety Evaluations of Language Models
*Work done while at Stanford CRFM
Deep RL problem: Loss decreases but agent doesn't learn
I'm implementing a basic Vanilla Policy Gradient algorithm for the CartPole-v1 gymnasium environment, and I don't know what I'm doing wrong. No matter what I try, during the training loop the loss decreases (so the model is actually learning something), but the episode total reward also decreases until it reaches around 9-10 steps (which I imagine is the minimum number of steps needed to make the pole fall). So it's learning to do it bad! In this algorithm there are a few moving parts that influence each other, and I run out of ideas to see where the problem could be. For the discounted rewards I'm using $ Q_{k,t} = \sum_{i=0}{\gamma^{i-t} r_i} $ for $k$ = all episodes and $t$ = all steps in an episode. And for the loss: $ L = -\sum_{k,t}Q_{k,t}log\pi_{\theta}(a_t | s_t)$ The code is a mix from Maxim Lapan's Deep RL Hands-On book, Karpathy's Pong example (blog, code), and personal tweaks. Here's my code: import gymnasium as gym import torch from torch import nn import torch.nn.functional as F from torch.nn.init import xavier_uniform_ import numpy as np GAMMA = 0.99 LEARNING_RATE = 0.001 BATCH_SIZE = 4 DEVICE = torch.device('mps') class XavierLinear(nn.Linear): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None) -> None: super().__init__(in_features, out_features, bias, device, dtype) xavier_uniform_(self.weight) class VPG(nn.Module): def __init__(self, input_size, output_size): super(VPG, self).__init__() self.net = nn.Sequenti…
A New Chapter for fast.ai: How To Solve It With Code
fast.ai is joining Answer.AI, and we’re announcing a new kind of educational experience, ‘How To Solve It With Code’
Is it possible to achieve both detection and image classification by training the model on the custom dataset?
I am beginning to work on a non-linear navigation system for educational videos as a part of my undergrad coursework project. As a part of it, I need to classify the unique frames (frame which is slide, handwritten, only figure, with both text and figure, only text, blank frame) and also need to segment (here also I have a question, is it a segmentation task or should I go with multi-object detection) the content of the frame (text, figure, title, figure title). I would like to do this using a single model trained on a single custom dataset. But I am not sure about the practicality of this. I have some questions regarding this: I am not sure how I should prepare the dataset for this task. Whether I could use annotation &its labels and class labels in the same dataset? Can I train the model on the dataset (if the answer to 1 is yes) only once to achieve both tasks? Do I need to make any other changes to the model except some changes to the final layer? Thank you in advance!
Is there an error in Russell & Norvig (4th edition)'s claim about A* with a consistent heuristic?
In "AI: A Modern Approach" (Russell & Norvig, 4th edition), section 3.5.2 "A* search" includes this sentence on page 88: In addition, with a consistent heuristic, the first time we reach a state it will be on an optimal path, so we never have to re-add a state to the frontier, and never have to change an entry in reached . There are three claims here: The first time we reach a state it will be on an optimal path. We never have to re-add a state to the frontier. We never have to change an entry in reached . I believe that claims (1) and (3) are false. To see this easily, consider the heuristic $h(n) = 0$ for all $n$ . Clearly it is consistent. With this heuristic, A* is just Dijkstra's algorithm. But in Dijkstra's algorithm, the first time we reach a state (i.e. add it to the frontier, as defined on p. 72) it certainly might not be on an optimal path, and we may have to update its entry in reached (i.e. the parent pointer, as seen in the pseudocode in Figure 3.7 on p. 73) as soon as we discover a shorter path to it. So is this simply an error in the book, or am I missing something here?
Stock Market LSTM predictions request models
I am looking for more advanced models for my stock market software can anyone either point me in the right direction for the latest research on LSTM or others or any experts collab/provide models for me in this specific area? Models must be convertible to onnx format currently.
Third-party evaluation to identify risks in LLMs’ training data
An overview of the minetester and preliminary work
In defense of screen time
Pundits say my husband and I are parenting wrong.
AI Singapore launches AIAP Foundation, a new AI-powered online course to equip aspiring AI Engineers with fundamental AI and software engineering knowledge and skills
AI Singapore today announced the launch of AIAP Foundation, a new online course aimed at equipping aspiring AI engineers with a strong foundation in industry-relevant AI and software engineering...
Mechanistic Anomaly Detection Research Update 2
Interim report on ongoing work on mechanistic anomaly detection