AI/ML News & Innovations Hub

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

Dubai Police's 24x7 Smart Home Security System: How to protect your home while travelling this summer

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

RLHF and RLAIF in GPT-NeoX

GPT-NeoX now supports post-training thanks to a collaboration with SynthLabs.

Iteratively Reweighted Least Squares (IRLS) and Gauss-Newton

I am studying nonlinear regression optimization methods. I want to show that the Gauss-Newton method can be seen as an IRLS. We want to maximize $$ - \sum_{i = 1}^{n} (y_i - h(\mathbf{x}_i^T \mathbf{\beta}))^2 $$ Approximating $h(\mathbf{x}_i^T \mathbf{\beta})$ we have $$ - \sum_{i = 1}^{n} [y_i - h(\mathbf{x}_i^T \mathbf{\beta}^{(t)}) - h'(\mathbf{x}_i^T \mathbf{\beta}) \mathbf{x}_i^T (\mathbf{\beta} - \mathbf{\beta}^{(t)}) ]^2 $$ and here I don't know what to do. The books I have read do not show what to do when you have any function $h$ , or that explain the Gauss-Newton method in a direct way. I would appreciate any kind of help or suggestions. I am also looking for references on the subject in this regard.

FAQ about the book and our writing process

What's in the book and how we wrote it

Llama 3.2 Vision-Instruct Inference Speed on A100 or H100 GPU

Can anyone provide an estimated time of how long does it take for Llama-3.2 Vision-Instruct 11-B model to: process an image size of 1-MB and prompt size of 1000 words and generate a response of 500 words The GPUs used for inference could be A100, A6000, or H100.

Challenges in Aggregating Outputs from Classifiers Trained on Subsets of Classes

I’m currently working on a project involving several classifiers, each trained on a subset of classes. These classifiers are designed to handle different aspects of the classification task, but I’m facing a challenge when it comes to aggregating their outputs into a single prediction. For example, if one classifier is responsible for distinguishing between classes 0 and 1, and another handles classes 2 and 3, how can we effectively combine their results when the correct answer belongs to class 1? Our initial approach was to use an "other" class to indicate when an input doesn’t belong to a classifier’s assigned classes, but this did not yield the desired results. We are now exploring the possibility of implementing an additional head for detecting out-of-distribution classes, but we’re looking for a more efficient and streamlined solution. Has anyone encountered a similar issue or have any suggestions for effectively aggregating outputs from multiple classifiers? Thank you for your assistance!

Batch LLM Inference on Anyscale slashes AWS Bedrock costs by up to 6x

Announcing RayTurbo

Anyscale on Kubernetes: Simplifying AI Workloads on User-Managed Infrastructure

Enhancing Data Quality Using Better Designed ETLs

Sharing an ETL design doc template

First AI Certification for Design & Media Practitioners Launched

In a bold initiative that merges technology with creativity, Nanyang Polytechnic (NYP) and AI Singapore (AISG) have joined forces to introduce the Certified AI Practitioners for Design & Media...

Separating code reasoning and editing

An Architect model describes how to solve the coding problem, and an Editor model translates that into file edits. This Architect/Editor approach produces SOTA benchmark results.

BadEdit: Backdooring Large Language Models By Model Editing

Large Language Models (LLMs) exemplified by ChatGPT continue to gain widespread usage in addressing a...

Low validation loss from the first epoch?

The initial validation loss is low from the first epoch and then decreases slightly. What does this actually mean? Does it indicate that the model can effectively and quickly identify patterns for this task? I can see that the model works in practice, but the results (some image restoration) aren’t ideal yet, so I want to improve its performance even further. Given this low loss from the first epoch, should I focus on training with more data or on adjusting the architecture and layers to be even more complex, etc.? Given the small differences between the first epoch and the last, is it more likely that the model was barely able to improve performance during these epochs, or could the difference in loss still be meaningful? The dataset count was of 10,000 images - 0.9 for training, 0.1 for validation. First epoch loss: Epoch [1/50], Training Loss: 0.026428, Validation Loss: 0.023727 Last epoch and plateau: Epoch [34/50], Training Loss: 0.020682, Validation Loss: 0.020651

The Practitioner's Guide to the Maximal Update Parameterization

Exploring the implementation details of muTransfer

Can AI automate computational reproducibility?

A new benchmark to measure the impact of AI on improving science

I'm trying to train an AI, but I have low accuracy using Rust and PyTorch

I'm just starting out in the world of machine learning, and I really like Rust. I've been testing and learning more. I took the example of transfer training and did some tests, but I can't understand why I have high accuracy in training and low accuracy in testing using the same validation base. Why? I studied overfit, but it doesn't seem to be the case, because I'm using the same validation base without new data. use std::env; use std::error::Error; use std::path::PathBuf; use anyhow::{ bail, Result }; use tch::nn::{ self, ModuleT, OptimizerConfig, VarStore }; use tch::vision::{ imagenet, resnet }; use tch::{ Device, Kind, Tensor }; pub fn bee_test() -> Result > { tch::manual_seed(123); let manifest_dir = env::var("CARGO_MANIFEST_DIR")?; let project_dir = PathBuf::from(manifest_dir); let dataset_path = project_dir.join("data/hymenoptera_data"); let dataset = imagenet::load_from_dir(dataset_path)?; println!("{dataset:?}"); let model_path = project_dir.join("data/bee.ot"); println!("Caminho do modelo: {:?}", model_path); let device = Device::cuda_if_available(); let mut vs = VarStore::new(device); vs.load(model_path.as_path()).map_err(|op| { format!("Erro ao carregar o modelo: {:?}", op); op })?; let net = resnet::resnet34_no_final_layer(&vs.root()); let linear = nn::linear(vs.root(), 512, 2, Default::default()); let net2: nn::Sequential = nn ::seq() .add_fn(move |xs| net.forward_t(xs, false)) .add(linear); let predicted = net2.forward_t(&dataset.test_images, false); let prob…

o1-preview is SOTA on the aider leaderboard

Preliminary benchmark results for the new OpenAI o1 models.

Fine-tuning Llama-3, Mistral and Mixtral with Anyscale

Start reading the AI Snake Oil book online

The book was published September 2024

From Code to Robots: The Top AI Trends Transforming Business and Life

Artificial intelligence is no longer a concept of the distant future – it’s here, evolving at a rapid pace and reshaping industries in real time. From healthcare to entertainment, AI’s influence is everywhere, sparking innovation, efficiency, and even ethical debates. But with so much happening at once, where exactly is the industry heading? To make […] The post From Code to Robots: The Top AI Trends Transforming Business and Life appeared first on TOPBOTS .

Towards Robust and Expressive Whole-body Human Pose and Shape Estimation

Introduction Given a single RGB image, 3D human pose and shape estimation aims to reconstruct...

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

Advancing Customizable Benchmarking in HELM via Unitxt Integration

The Holistic Evaluation of Language Models (HELM) framework is an open source framework for reproducible and transparent benchmarking of language models that is widely adopted by academia and industry. To meet HELM users’ needs for more powerful benchmarking features, we are proud to announce our collaboration with Unitxt, an open-source community platform developed by IBM Research for data preprocessing and benchmark customization. The integration of Unitxt into HELM gives HELM users access to the vast Unitxt catalog of benchmarks, and allows users to run sharable and customizable evaluation pipelines with greater ease.

Scaling Embedding Generation Pipelines From Pandas to Ray Data

Breaking Barriers, Building Bridges: Increasing Language Representation in Southeast Asia

Held outside of Singapore for the very first time, the third Languages Summit...

Réinventer l’éducation : quand le numérique transforme les apprentissages

Réinventer l’éducation : quand le numérique transforme les apprentissages mtestari lun, 09/02/2024 - 09:00 La transformation numérique de l’école doit être une opportunité pour mieux apprendre et individualiser l’apprentissage grâce au développement de nouvelles technologies associées à la formation de la communauté enseignante et des élèves aux compétences du 21e siècle. Autant d’enjeux sociétaux et scientifiques que relèvent, depuis plus de dix ans, les équipes pluridisciplinaires du Centre Inria de l’université de Bordeaux avec des chercheurs et chercheuses en intelligence artificielle, en interaction Humain - Machine, en neurosciences, en psychologie développementale et en sciences de l’éducation. © freepik /Photo Pch.Vector Mieux comprendre les processus d’apprentissage L’une des caractéristiques la plus importante pour l’ensemble des équipes impliquées reste leur approche systémique, positionnant l’apprenant toujours au centre de l’étude en considérant l’ensemble de ses interactions avec l’environnement dans lequel il évolue. « Ces recherches ne peuvent être menées sans l’appui des acteurs de terrain que sont les enseignants et les professionnels de l’éducation. Elles restent indissociables des avancées des sciences humaines pour ancrer la transformation numérique de l’école sur les besoins des apprenants, des éducateurs et de leurs environnements » souligne Nicolas Roussel, directeur du Centre Inria de l’université de Bordeaux. Le premier défi consiste à mieux compren…

Clean-image Backdoor: Attacking Multi-label Models with Poisoned Labels Only

Multi-label learning Multi-label learning is commonly exploited to recognize a set of categories in an...

The AI Investment Landscape: Who’s Funding the Next Wave of Innovation?

Artificial Intelligence (AI) is reshaping the tech landscape, attracting a diverse array of investors eager to capitalize on its potential. In this article, we explore the key players driving investments in AI, from tech giants and venture capital funds to angel investors, and examine the companies they are betting on. The post The AI Investment Landscape: Who’s Funding the Next Wave of Innovation? appeared first on TOPBOTS .

Why use the MLP Gaussian Decoder in a VAE if it’s limited in modeling complex data?

My question concerns the paper arxiv.org/pdf/1312.6114. I want to know why they proposed to use the MLP Gaussian decoder with parameters given by the MLP transformation of the z variable as the likelihood model $p_{\theta}(x|z)$ . If they are using NNs to perform inference on the best set of parameters, why use a Gaussian if it is very limiting in modeling complex data?

The AI Act: Responsibilities of the European Commission (AI Office)

If you are unsure who is implementing and enforcing the new digital law and what the specific time frames are, you might find this post—and our post on the responsibilities of the EU Member States—very helpful. The tables below provide a comprehensive list of all obligations and tasks that the AI Act places upon to the […]

The AI Act: Responsibilities of the EU Member States

If you are unsure who is implementing and enforcing the EU AI Act and what the specific time frames are, you might find this post—and our post on the responsibilities of the European Commission (AI Office)—very helpful. The tables below provide you with a comprehensive list of all obligations and tasks that the AI Act places […]

Understanding the embeddings model (dunzhang/stella_en_400M_v5) by Alibaba. The details about the retrieve task and the s2s task

The model I am talking about is hosted here : From the documentation: We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.Prompt of s2p task(e.g. retrieve task): ..., Prompt of s2s task(e.g. semantic textual similarity task): ... Question: What is S2P task? How is retrieve task different from semantic textual similarity task? Difference in the usage and also in the training steps would be much appreciated in the answer

Is the implementation equivalent to the formulation of the estimate of the Policy Gradient from spinningup?

In Part 3: Intro to Policy Optimization from spinningup documentation, there is a formula to compute the estimate of the policy gradient: This is an expectation, which means that we can estimate it with a sample mean. If we collect a set of trajectories $ D = > \{\tau_i\}_{i=1,...,N} $ where each trajectory is obtained by letting the agent act in the environment using the policy $\pi_{\theta}$ , the policy gradient can be estimated with $$ \hat{g} = \frac{1}{|D|} \sum_{\tau \in D} \sum_{t=0}^{T} > \nabla_{\theta} \log \pi_{\theta}(a_t |s_t) R(\tau), $$ where $|D|$ is the number of trajectories in $D$ (here, $N$ ). Also in the code on the same page to compute loss: # make loss function whose gradient, for the right data, is policy gradient def compute_loss(obs, act, weights): logp = get_policy(obs).log_prob(act) return -(logp * weights).mean() Here the mean() is calculated over all the (state, action) pairs. However, the formula above calculates the mean over trajectories. There seems to be a disconnect. Are these two forms equivalent?

Direct Preference Optimization with Synthetic Data on Anyscale

"repeated measures ANOVA" vs "ANOVA with subject as random effect" vs "repeated measures ANOVA with subject as random effect"

What is the difference between running a repeated measures ANOVA (the measure is repeated across time for each subject) vs an ANOVA with a subject as a random effect?` I have also seen mention of a "repeated measures ANOVA with subject as random effect". For context I have 6 groups and 3 post-baseline time points per group/subject. Each subject is a member of one group. I would like to compare means of both different groups at the same time point (ex group 1 vs group 3 at time point B) and means of the same group at different time points (ex. group 1 at time point B vs group 1 at time point C). I will use ls-means to compare means, but I don't really understand the differences between the above three model categories. If there even is a difference! Thank you very much!

AI companies are pivoting from creating gods to building products. Good.

Turning models into products runs into five challenges

Humanoid Robots on the Rise: Industry Advances, Key Players, and Adoption Timelines

The robotics industry stands on the brink of a significant transformation, with many experts – including NVIDIA CEO Jensen Huang – suggesting that we might be approaching a "ChatGPT moment" for robotics. The post Humanoid Robots on the Rise: Industry Advances, Key Players, and Adoption Timelines appeared first on TOPBOTS .

Utilizing Symbolic Regression to discover a larger class of splits for Decision Trees

This work is based on the paper: K.S. Fong and M. Motani, “Symbolic Regression Enhanced...

Any relation between two KL divergences?

I am using a KL divergence to measure the difference between distributions, but I would like to compare the results to each one another, because the probability distributions I'm measuring are correlated. What is a good measure for comparing two KL divergences on related distributions? Ex. If I'm looking at some characterization data for a test, let's say X-ray diffraction patterns for a metal sample. I get an XRD pattern of intensity vs. diffraction angle, and can create a probability distribution for that in a reference state (room temp, new sample). I then run the same XRD test on the same sample while I vary temperature, and for that temperature I get a new distribution of my XRD data, and I can calculate the KL divergence for that distribution relative to my reference one. Let's say I then cold-roll my metal sample, and run the same XRD experiment, and then calculate the KL divergence with the same reference state for those distributions. I have two KL divergence results, and I know that there are variables for the metal sample, temperature vs. strength, that are correlated. Is there any comparison technique between divergences to tell me how much one distribution diverged relative to another? Especially if the variables are interrelated? Edit: Changing the example to be more specific.

Lower bound of KL divergence of Gaussian mixture with Gaussian (univariate)

I'm interested in a non-zero Kullback-Leibler divergence lower bound between a Gaussian mixture $q$ and a standard Gaussian $p(x) = \mathcal N(x \mid 0,1)$ , both univariate : $D_{KL}(q \parallel p)$ . I'm aware of this question , but I conjecture that with some constraints the problem might become easier. My constraints: The Gaussian mixture is guaranteed to have mean zero The Gaussian mixture is guaranteed to have variance lower bounded by $\gamma^2$ . I skim over Hershey & Olsen (2007) , which mentions a pretty trivial Gaussian approximation of the KL divergence in Section 4, where $q$ is approximated by a Gaussian $\bar q(x) = \mathcal N(x \mid \mu, \sigma^2)$ , (here, $\mu \triangleq \mathbb E_q[x] = 0, \sigma^2 \triangleq \mathbb E_q[x^2] - \mathbb E_q[x]^2 \ge \gamma^2$ ); and then the target KL is approximated by $D_{KL}(\bar q \parallel p)$ . However, the paper says this is a poor approximation, and does not mention whether it's a bound either. In Melbourne et al. (2018) , the authors propose an upper bound on the entropy of a mixture distribution. Since $D_{KL}(q \parallel p) = -\mathbb H(q) - \mathbb E_q[\log p(x)]$ , I'm able to take advantage of it, provided that I know how to upper bound the expected log density term , which is outside my expertise again.

Bayesian hierarchical model for comparing reviewers

I am working on a research paper about reviewing people's expertise. I have 21 respondents, each answered 10 questions. Now I have asked 4 groups of reviewers (each group consists of 3 people) to grade the answers between 1 and 5 (5 best). I have used Fleiss' Kappa to measure inter-rater reliability between reviewers in the same group. Unfortunately one reviewer asked me to include a Bayesian hierarchical model to account for the variability among raters and to provide a probabilistic measure of rater consistency. This would allow for a more nuanced understanding of rater behavior and the inherent uncertainties in human judgment, which are oversimplified by the use of a linear scale in the current methodology. In my opinion it is an over-complication. I wanted to check whether people from different backgrounds can assess people in the same topic. I do not see any benefits from using Bayesian hierarchical model for comparing reviewers. I am trying to perform this test, but nonetheless I would like to respond to the reviewer POLITELY that this is not a use-case for this model and Fleiss kappa is sufficient. Could you help me, or prove me wrong?

AI in Finance Global Challenge Startup Grant Awardee

In June 2023, the Monetary Authority of Singapore (MAS) launched the 8th edition...