#model-training

[ follow ]
#ai-efficiency

Balancing training data and human knowledge to make AI act more like a scientist

Informed machine learning involves incorporating rules and tips, like the laws of physics, to enhance AI efficiency.
Assessing the value of different rules and data in AI training is essential for improving predictive capability.

A popular technique to make AI more efficient has drawbacks | TechCrunch

Quantization of AI models is efficient but has limits, especially with models trained on extensive data.

Balancing training data and human knowledge to make AI act more like a scientist

Informed machine learning involves incorporating rules and tips, like the laws of physics, to enhance AI efficiency.
Assessing the value of different rules and data in AI training is essential for improving predictive capability.

A popular technique to make AI more efficient has drawbacks | TechCrunch

Quantization of AI models is efficient but has limits, especially with models trained on extensive data.
moreai-efficiency
#machine-learning

How to Stand Out in Machine Learning Interviews: A Framework for ML System Design | HackerNoon

ML System Design is a crucial focus area in MLE interviews; prioritize clarifying questions, understanding data, and avoiding random splitting.

What kind of bug would make machine learning suddenly 40% worse at NetHack?

NetHack is used for machine learning experimentation, showing challenges in model performance consistency.

Improving Text Embeddings with Large Language Models: Model Fine-tuning and Evaluation | HackerNoon

Fine-tuning models with synthetic and public datasets optimizes performance while managing computational resources effectively.

Improving Text Embeddings with Large Language Models: Instructions for Training and Evaluation | HackerNoon

Synthetic data generation can enhance training models for multilingual retrieval tasks significantly.
Contrastive pre-training may not always be necessary based on task context.

Improving Text Embeddings with Large Language Models: Is Contrastive Pre-training Necessary? | HackerNoon

Weakly-supervised contrastive pre-training is essential for effective text embedding models.

Should you discretize features for Machine Learning?

Discretization can be used with continuous numeric features to convert them into categorical features for model input.
Although discretization can benefit linear models by aiding in learning non-linear trends, it might not be recommended due to its drawbacks.

How to Stand Out in Machine Learning Interviews: A Framework for ML System Design | HackerNoon

ML System Design is a crucial focus area in MLE interviews; prioritize clarifying questions, understanding data, and avoiding random splitting.

What kind of bug would make machine learning suddenly 40% worse at NetHack?

NetHack is used for machine learning experimentation, showing challenges in model performance consistency.

Improving Text Embeddings with Large Language Models: Model Fine-tuning and Evaluation | HackerNoon

Fine-tuning models with synthetic and public datasets optimizes performance while managing computational resources effectively.

Improving Text Embeddings with Large Language Models: Instructions for Training and Evaluation | HackerNoon

Synthetic data generation can enhance training models for multilingual retrieval tasks significantly.
Contrastive pre-training may not always be necessary based on task context.

Improving Text Embeddings with Large Language Models: Is Contrastive Pre-training Necessary? | HackerNoon

Weakly-supervised contrastive pre-training is essential for effective text embedding models.

Should you discretize features for Machine Learning?

Discretization can be used with continuous numeric features to convert them into categorical features for model input.
Although discretization can benefit linear models by aiding in learning non-linear trends, it might not be recommended due to its drawbacks.
moremachine-learning
#ai

This Week in AI: Tech giants embrace synthetic data | TechCrunch

OpenAI's Canvas feature harnesses synthetic data to enhance user interactions with its chatbot, demonstrating the growing importance of synthetic data in AI development.

How AI Learns from Human Preferences | HackerNoon

The RLHF pipeline enhances model effectiveness through three main phases: supervised fine-tuning, preference sampling, and reinforcement learning optimization.

This Week in AI: Tech giants embrace synthetic data | TechCrunch

OpenAI's Canvas feature harnesses synthetic data to enhance user interactions with its chatbot, demonstrating the growing importance of synthetic data in AI development.

How AI Learns from Human Preferences | HackerNoon

The RLHF pipeline enhances model effectiveness through three main phases: supervised fine-tuning, preference sampling, and reinforcement learning optimization.
moreai

Textbooks Are All You Need: Abstract and Introduction | HackerNoon

phi-1 is a compact 1.3B parameter language model for code, achieving notable accuracy despite its smaller size.

Direct Preference Optimization: Your Language Model is Secretly a Reward Model | HackerNoon

Achieving precise control of unsupervised language models is challenging, particularly when using reinforcement learning from human feedback due to its complexity and instability.

AI models can't learn as they go along like humans do

AI algorithms cannot learn from new data after initial training, forcing companies to retrain models from scratch, which is costly and inefficient.

Evaluating Startup Predictions with Backtesting and Portfolio Simulation | HackerNoon

Backtesting model with periodic retraining to ensure integrity and avoid future influence.
#ai-models

This is AI's brain on AI

Data from AI models is increasingly used to train other AI models through synthetic data, aiding chatbots but also posing risks of destabilization.

DatologyAI is building tech to automatically curate AI training data sets | TechCrunch

Biases can emerge from massive data sets, hindering AI models.
Data preparation challenges, including cleaning, are significant obstacles for AI initiatives.

This is AI's brain on AI

Data from AI models is increasingly used to train other AI models through synthetic data, aiding chatbots but also posing risks of destabilization.

DatologyAI is building tech to automatically curate AI training data sets | TechCrunch

Biases can emerge from massive data sets, hindering AI models.
Data preparation challenges, including cleaning, are significant obstacles for AI initiatives.
moreai-models

OpenAI's CriticGPT Catches Errors in Code Generated by ChatGPT

CriticGPT improves code feedback and bug detection, enhancing model evaluation and training.

EU's new AI rules ignite battle over data transparency

New EU laws on AI transparency will require companies to disclose data used for training models, challenging industry practices.
[ Load more ]