Towards AI

DECEMBER 31, 2023

However, as we progress towards creating superintelligence — AI that surpasses even the smartest humans in complex tasks — human supervision inevitably becomes weaker. Is there additional work we can do to make weak supervision more efficient?

AI 108

AI AI AI Modeling Artificial Intelligence 108

Marktechpost

JANUARY 5, 2024

So, researchers have been investigating the use of supervised learning models, which use fewer labeled points. After this, they used a weakly supervised deep-learning method for plant organ segmentation. Also, quantitative results showed notable gains over baseline techniques, particularly in less supervised environments.

Deep Learning 132

Deep Learning Artificial Intelligence Artificial Intelligence AI 132

Mlearning.ai

MARCH 26, 2023

Google’s new video to sequence multimodal model Continue reading on MLearning.ai »

ML 52

ML Data Science Artificial Intelligence Artificial Intelligence 52

Marktechpost

DECEMBER 18, 2023

For example, a superhuman model could generate millions of lines of complicated code for which a human cannot provide reliable supervision. The researchers created the weak supervisors by finetuning small-sized pre-trained models on ground truth labels. The results also show promising weak-to-strong generalization on chess puzzles.

OpenAI 102

OpenAI NLP ChatGPT AI Researcher 102

Marktechpost

JANUARY 5, 2024

To align the performance of such models with desirable behavior, they are fine-tuned using techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). In conclusion, SPIN is a novel framework that converts a weak LLM to a strong one without the need for an expert human annotator.

LLM 117

LLM Machine Learning Natural Language Processing Large Language Models 117

Machine Learning Research at Apple

APRIL 24, 2024

This paper presents a novel weakly supervised pre-training of vision models on web-scale image-text data. Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings.

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Marktechpost

JANUARY 31, 2024

Early examples have demonstrated weaknesses in red teaming, including the production of discriminating statements and unintentional disclosure of personal information. The team has used Supervised Fine-tuning (SFT) with RTVLM to apply red teaming alignment to LLaVA-v1.5. The RTVLM dataset on LLaVA-v1.5,

Large Language Models 128

Large Language Models AI AI Artificial Intelligence 128

Towards AI

APRIL 8, 2024

With the use of machine learning, people find out about the 2 main types of machine learning: Supervised and Unsupervised learning. Supervised Learning First, what exactly is supervised learning? In supervised machine learning, the machine learning algorithm is trained on a labeled dataset. Let’s get right into it.

Machine Learning 96

Machine Learning Algorithm AI AI 96

Marktechpost

FEBRUARY 22, 2024

By adopting a two-stage training process that includes contrastive pre-training on multilingual text pairs followed by supervised fine-tuning, the models aim to balance inference efficiency and embedding quality, making them highly versatile for various multilingual applications.

Natural Language Processing 131

Natural Language Processing NLP ML Large Language Models 131

Marktechpost

JANUARY 3, 2024

More sophisticated multi-stage training paradigms are used by state-of-the-art techniques like E5 and BGE, which pre-train on weakly-supervised text pairs and fine-tune on labeled datasets to improve resilience and performance.

Artificial Intelligence 110

Artificial Intelligence Artificial Intelligence Large Language Models BERT 110

Marktechpost

DECEMBER 7, 2023

The research team adopts a pre-training strategy with weak supervision to overcome the scarcity of regional caption data. This weak supervision pre-training is a practical solution to transfer general knowledge of visual concepts beyond the limited regional captioning data available.

Computer Vision 104

Computer Vision Natural Language Processing Large Language Models AI Researcher 104

Marktechpost

FEBRUARY 15, 2024

They introduce a self-supervised attention-based method for weakly supervised regression, predicting continuous biomarkers from 11,671 patient images across nine cancer types. The study employs the CAMIL regression method based on attention-based multiple-instance learning and self-supervised pretraining of the feature extractor.

Deep Learning 118

Deep Learning Categorization ML Artificial Intelligence 118

Eugene Yan

JULY 31, 2021

How to generate labels from scratch with semi, active, and weakly supervised learning.

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Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. So, we’re interested in looking at the intersection of these two concepts: foundation models and weak supervision.

BERT 52

BERT NLP Machine Learning AI 52

Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. So, we’re interested in looking at the intersection of these two concepts: foundation models and weak supervision.

BERT 52

BERT NLP Machine Learning AI 52

Marktechpost

NOVEMBER 28, 2023

Weakly supervised sticker-like images are used for domain alignment. The introduction of Style Tailoring aims to optimize fast alignment, visual diversity, and technique conformity for generating visually appealing stickers. It also emphasizes the importance of transparency and scene diversity in the generated stickers.

Prompt Engineer 124

Prompt Engineer Prompt Engineering AI AI 124

Marktechpost

MARCH 11, 2024

These contributions provide valuable tools for evaluating Mamba model performance, fairness, and robustness and open avenues for weakly supervised downstream tasks. This insight enables the development of explainability techniques for Mamba models, enhancing understanding of their inner representations. Check out the Paper and Github.

Machine Learning 136

Machine Learning Explainability Neural Network Computer Vision 136

Unite.AI

FEBRUARY 16, 2024

Machine learning operates through supervised , unsupervised , and reinforcement learning. Supervised learning involves machines learning from labeled data to predict or classify new data. Hybrid AI combines symbolic and connectionist AI to leverage its strengths and overcome weaknesses, aiming for more robust and versatile systems.

Neural Network 195

Neural Network Algorithm Machine Learning Natural Language Processing 195

Marktechpost

JANUARY 18, 2024

To determine how visually similar the two images are, the team employs a vision-only self-supervised encoder like DINOv2. They focus on improving MLLMs’ visual grounding capabilities by integrating a vision-only self-supervised model, like DINOv2. Such a set of pictures is known as a CLIP-blind pair.

AI Researcher 112

AI Researcher AI Research Large Language Models AI 112

Marktechpost

JUNE 19, 2023

However, there are vast differences in supervision when it comes to learning from base and novel categories. Their proposed method, The Mask-free OVIS pipeline, takes advantage of weak supervision by utilizing pseudomask annotations derived from a vision-language model to learn base and novel categories.

AI Researcher 58

AI Researcher AI Research Convolutional Neural Networks Computer Vision 58

Snorkel AI

AUGUST 8, 2023

Snorkel enables increased accuracy, scalability, and security with: Labeling functions and weak supervision. Labeling functions and weak supervision let users create large training sets quickly and programmatically, allowing labeling schemas and labeling logic to shift with business needs. Cross-team collaboration.

Data Scientist 52

Data Scientist Artificial Intelligence Artificial Intelligence AI 52

Snorkel AI

AUGUST 8, 2023

Snorkel enables increased accuracy, scalability, and security with: Labeling functions and weak supervision. Labeling functions and weak supervision let users create large training sets quickly and programmatically, allowing labeling schemas and labeling logic to shift with business needs. Cross-team collaboration.

Data Scientist 52

Data Scientist Artificial Intelligence Artificial Intelligence AI 52

AssemblyAI

MARCH 15, 2023

This 60TB dataset, was used to train Conformer-1, making our model the largest-trained supervised speech recognition model for English that is available today. "Robust speech recognition via large-scale weak supervision." "Language models are few-shot learners." 8] Radford, Alec, et al. "Robust

Convolutional Neural Networks 364

Convolutional Neural Networks Large Language Models Neural Network OpenAI 364

Marktechpost

JULY 19, 2023

This approach generates many candidate functions and ensembles their extractions using weak supervision. While weak supervision is traditionally applied to human-generated functions, EVAPORATE-CODE+ operates with machine-generated functions and addresses the challenges of this setup to enable quality improvements.

Large Language Models 74

Large Language Models LLM AI Tools AI 74

Explosion

MARCH 24, 2022

skweak: A software toolkit for weak supervision applied to NLP tasks - GitHub - NorskRegnesentral/skweak: skweak: A software toolkit for weak supervision applied to NLP tasks

NLP 40

NLP 40

TheSequence

MAY 19, 2023

With over 30 live talks spanning two days, The Future of Data-Centric AI brings together the largest gathering of the data-centric AI community to explore the latest research and advancements in data-centric AI, including sessions on LLMs, GenAI, prompt engineering, and weak supervision. REGISTER NOW

Data Scientist 52

Data Scientist Prompt Engineer Prompt Engineering AI 52

Marktechpost

DECEMBER 3, 2023

The chatbot can easily respond to humans, generate content, answer queries, and perform a range of tasks by utilizing the capabilities of Reinforcement Learning from Human Feedback (RLHF) and instruction-tuning through supervised fine-tuning.

Large Language Models 126

Large Language Models ChatGPT LLM Artificial Intelligence 126

Snorkel AI

SEPTEMBER 29, 2023

That makes data labeling a foundational requirement for any supervised machine learning application—which describes the vast majority of ML projects. Some machine learning algorithms, such as clustering and self-supervised learning , do not require data labels, but their direct business applications are limited. What is data labeling?

Large Language Models 59

Large Language Models Machine Learning Data Science Data Scientist 59

Mlearning.ai

APRIL 18, 2023

Understanding the DINOv2 Model, its Advantages, and its Applications in Computer Vision Introduction : Meta AI, has recently open-sourced DINOv2, a self-supervised learning method for training computer vision models. In this article, we will discuss what DINOv2 is, its advantages, applications, and conclusions. What is DINOv2?

Computer Vision 52

Computer Vision Metadata AI Modeling AI 52

Marktechpost

NOVEMBER 25, 2023

Large Language Models (LLMs), although highly competent in a wide array of language tasks, often display weak reasoning capabilities by making very simple mistakes. Researchers have tried to tackle these issues by increasing the supervised training data or through reinforcement learning strategies.

LLM 133

LLM Large Language Models AI AI 133

Aiiot Talk

MARCH 31, 2024

Play on the strengths of each individual and guide them toward skills to overcome weaknesses. Perhaps the employee has begun caring for an ailing parent, requiring more of their time and little recourse for daytime supervision. You might also have staff who are bold and daring. Only 14% of employees feel leadership listens to them.

Robotics 130

Robotics Artificial Intelligence Artificial Intelligence Explainability 130

Marktechpost

OCTOBER 16, 2023

These models have been trained on large-scale unsupervised or weakly supervised text-to-image datasets. The paper discusses the recent emergence of text-to-image diffusion models at the forefront of image generation.

AI 128

AI AI AI Researcher AI Research 128

Unite.AI

JANUARY 23, 2024

AI can supercharge security teams by allowing them to detect suspicious activity in real-time, even without human supervision. You can use AI tools to identify weaknesses in your cybersecurity measures through penetration testing to reduce the chances of bad actors compromising your systems.

Automation 182

Automation AI Tools Machine Learning AI 182

ODSC - Open Data Science

JANUARY 3, 2024

Weak-to-strong generalization  — Explores how a “ weaker ” AI model can supervise and guide a “ stronger ” model to achieve superior performance beyond the limitations of the weaker model’s supervision. MagicAnimate  — Temporally Consistent Human Image Animation using Diffusion Model.

Large Language Models 52

Large Language Models Artificial Intelligence Artificial Intelligence LLM 52

Mlearning.ai

MAY 29, 2023

Standard Finetuning (with labels) A common practice to train a task-specific model is to finetune a pre-trained model with supervised data (ala ULMFiT ). Next, it uses the rationale for training small models in a supervised manner. There’s a twist; the approach frames supervised learning with rationales as a multi-task problem.

LLM 52

LLM Large Language Models Robotics AI 52

Snorkel AI

MAY 4, 2023

The sessions at this year’s conference will focus on the following: Data development techniques: programmatic labeling, synthetic data, active learning, weak supervision, data cleaning, and augmentation. Enterprise use cases: predictive AI, generative AI, NLP, computer vision, conversational AI.

Computer Vision 52

Computer Vision Large Language Models AI AI 52

Snorkel AI

MAY 4, 2023

The sessions at this year’s conference will focus on the following: Data development techniques: programmatic labeling, synthetic data, active learning, weak supervision, data cleaning, and augmentation. Enterprise use cases: predictive AI, generative AI, NLP, computer vision, conversational AI.

Computer Vision 52

Computer Vision Large Language Models AI AI 52