A naukri.com initiative
ML News
Marktechpost
1d
256
Redesigning Datasets for AI-Driven Mathematical Discovery: Overcoming Current Limitations and Enhancing Workflow Representation
- Current datasets used to train and evaluate AI-based mathematical assistants are limited in scope and design, lacking representation of critical aspects of mathematical workflows and proof-based reasoning.
- To overcome these limitations, there is a need to redesign datasets to include elements like 'motivated proofs' and workflows that capture the nuanced tasks involved in mathematical research.
- Researchers advocate for improving AI models to serve as effective 'mathematical copilots' by integrating specialized modules and developing universal models for theorem discovery.
- The study highlights the need for new datasets that encompass a wide range of mathematical research activities and provide comprehensive evaluation methods to guide the development of more capable AI systems.
Read Full Article
15 Likes
Medium
1d
268
Image Credit: Medium
AGI: Is Deliberation the New intelligence? Paper Summary.
- The Deliberative Alignment paper explains how OpenAI effectively tackled jailbreaks, safety protocols, and induced deliberation into their latest models.
- The paper focuses on making the model learn safety specifications and achieve systematic thinking.
- The training process involves generating training data from safety specifications and prompts, filtering them using a reward model, and using reinforcement learning to reward Chain of Thought-based learning.
- The O1 model shows promise in improving true positives and decreasing false negatives, signaling a step towards AGI.
Read Full Article
16 Likes
Medium
1d
311
Image Credit: Medium
Day 58: Attention Mechanisms — Foundation of Transformer Models
- An attention mechanism is a computational process that helps models prioritize the most important parts of the input data when making predictions.
- Attention mechanisms address challenges faced by traditional models like RNNs by enabling models to attend to relevant parts of the input.
- The attention mechanism has three key components: score calculation, normalization, and weighted sum.
- Attention mechanisms are widely used in various fields including natural language processing, computer vision, speech recognition, and healthcare.
Read Full Article
18 Likes
Medium
1d
69
General Overview :Time Value Function
- Present value (PV) is the dollar amount now of a sum expected to be received or paid in the future, adjusted for a specified interest rate.
- Future value (FV) is the dollar sum that an amount will grow into at a specified point in time, given a specific interest rate.
- Interest rate (r) refers to the rate at which money accrues over a certain period of time, whether simple or compound.
- Period of time (n) is the duration for which money has been lent or borrowed.
Read Full Article
4 Likes
Medium
1d
350
Understanding K-Means Clustering and PCA: Unraveling the Power of Data Science Techniques
- K-Means Clustering and PCA are powerful data science techniques used for dimensionality reduction and data exploration.
- K-Means Clustering groups data points into clusters based on similarity, while PCA reduces features for easier analysis and visualization.
- K-Means Clustering helps identify patterns in data, while PCA reveals variance and improves machine learning algorithms.
- Both techniques have limitations, such as assuming certain data structures or relationships.
Read Full Article
21 Likes
Medium
1d
50
Image Credit: Medium
Scaling Smarter: An Overview of Large Language Models (LLMs) and Their Compression Techniques Part…
- Part 1 provides an overview of LLMs, discussing their advantages, disadvantages, and use cases.
- Some important LLM models/frameworks/tools with pros, cons, and use cases are listed below. The ones given are GPT-3.5, GPT-4, GPT-2, LLaMA 2, Alpaca, DistilBERT, MiniLM, TinyBERT, BERT, Sentence-BERT, RoBERTa, Faiss (Facebook AI Similarity Search), ONNX Runtime, TensorRT, Hugging Face Transformers, Transformers.js, and ggml.
- Type: Large Transformer-based LLM
- Type: Medium-sized LLM
- Type: LLM
- Type: Fine-tuned LLaMA
- Type: Transformer-based LLM
- Type: Transformer-based LLM
- Type: Transformer-based LLM
- Type: Sentence Embedding Model
Read Full Article
3 Likes
Medium
1d
227
What Goes Beyond the Prompt?
- Tokenization provides structure for the AI to process the input.
- Transformers use self-attention to handle different cases.
- Transformers process words in parallel, enabling faster computations and improved context analysis.
- The Transformer architecture consists of two main parts.
Read Full Article
13 Likes
TechBullion
1d
337
Image Credit: TechBullion
Can Machine Learning Models Truly Revolutionize Retail Sales Forecasting?
- Traditional methods such as linear regression (LR) have been widely used for decades in retail sales forecasting, but these models often fail to address the complexities of modern datasets.
- Recent research by Priyam Ganguly and Isha Mukherjee delves into the transformative potential of machine learning (ML) models in overcoming these challenges, offering a nuanced perspective on their efficacy and exploring new techniques for improving forecasting accuracy.
- The researchers evaluated a suite of advanced ML techniques to uncover the most effective solution for retail sales forecasting.
- The outcome was a landmark R-squared value of 0.945 with an optimized Random Forest model, which far outperformed traditional LR, demonstrating its inability to effectively capture the intricate patterns and seasonal trends inherent in retail data.
- While Random Forest stood out in this study, the other techniques also provided valuable insights. Gradient Boosting excelled in strong relationships between the features and target variables, while SVR excelled in smaller, non-linear patterns.
- The research underscores the superior capabilities of Random Forest in handling complex datasets with a high degree of accuracy.
- Accurate sales forecasting enables retailers to optimize inventory management, reduce waste, and enhance customer satisfaction by ensuring product availability.
- As businesses increasingly turn to data-driven strategies, the adoption of advanced machine learning models could become a defining factor for competitive advantage in the retail sector.
- The researchers emphasize the importance of addressing biases in historical data, which, if left unchecked, could perpetuate inequities in decision-making.
- Transparency, fairness, and ethics must be at the core of the design and deployment of machine learning solutions, particularly when they influence business practices that affect consumers’ lives.
Read Full Article
20 Likes
Medium
2d
210
Image Credit: Medium
The Ethical Dilemma of AI and Automation
- AI and automation have brought about an ethical dilemma in society.
- One challenge is the issue of bias, as AI systems can perpetuate or amplify existing biases in data.
- For example, AI in hiring processes might favor certain demographics, raising questions about fairness.
- Ensuring fair and just AI decision-making processes is crucial in critical areas like employment and criminal justice.
Read Full Article
12 Likes
Medium
2d
144
Image Credit: Medium
World’s most expensive Indo-US NISAR satellite likely to be launched in March: Nasa
- NISAR is a cutting-edge satellite equipped with a twin-frequency synthetic aperture radar.
- With an estimated cost of $1.5 billion, NISAR is the most expensive satellite of its kind.
- Key features of NISAR include global observation, wide applications, and unprecedented accuracy.
- NISAR aims to address global challenges such as climate change, natural disasters, and food security.
Read Full Article
8 Likes
Medium
2d
183
Image Credit: Medium
Feature Importance: Unveiling the Heroes of Machine Learning
- Feature importance techniques help identify the most valuable features in a machine learning model.
- Understanding feature contributions improves model explainability.
- Knowing which features drive results enables actionable insights.
- Feature importance can be applied in various domains, such as healthcare.
Read Full Article
11 Likes
Marktechpost
2d
101
Meet OREO (Offline REasoning Optimization): An Offline Reinforcement Learning Method for Enhancing LLM Multi-Step Reasoning
- Large Language Models (LLMs) face challenges in multi-step reasoning tasks.
- Traditional reinforcement learning methods have limitations in improving LLM reasoning.
- OREO (Offline REasoning Optimization) is an offline RL approach designed to enhance LLM reasoning capabilities.
- OREO optimizes the soft Bellman Equation for precise credit assignment and improved performance.
Read Full Article
6 Likes
Siliconangle
2d
489
Image Credit: Siliconangle
Coralogix acquires Aporia to enhance AI and machine learning observability
- Coralogix has acquired Aporia Technologies, a machine learning observability startup.
- Aporia offers a machine-learning observability platform for monitoring and controlling defects in machine learning models.
- Coralogix aims to combine AI and software insights to provide end-to-end visibility and actionable insights.
- The acquisition includes the launch of Coralogix AI research center to focus on solving fundamental problems in AI.
Read Full Article
1 Like
Medium
2d
129
Image Credit: Medium
Robots Gain Superhuman Vision: Seeing Through Walls with Radio Waves
- Radio frequency (RF) sensing or through-wall imaging technology has enabled robots to see through walls or objects.
- The technology works via emitting radio waves and analysing the signals that bounce back from walls or obstacles.
- By measuring the time it takes for the waves to return and their changes in frequency and amplitude, various components create a detailed 3D map of the environment, including hidden objects and structures.
- It offers several advantages over traditional vision-based systems: penetration and see-through capability in challenging environments; robustness and reliability in adverse conditions; ability to generate detailed 3D maps with object recognition; capability to cover large areas with a single sensor.
- The potential applications of radio wave imaging are vast and span a wide range of sectors, from security and surveillance, construction and infrastructure, manufacturing and logistics, to healthcare, archaeology and cultural heritage, and autonomous vehicles.
- Several companies and research institutions are actively developing and deploying radio wave imaging technology such as Vayyar Imaging, Walabot, MIT researchers and the University of Utah.
- While the technology holds immense promise, there are still some challenges to overcome such as cost, complexity, and regulation.
- However, ongoing research and development efforts are focused on improving resolution and increasing range, reducing cost, and developing new applications.
- As the technology continues to advance, experts predict even more exciting applications will emerge, transforming our lives in ways we can only imagine.
Read Full Article
7 Likes
Marktechpost
2d
180
ConfliBERT: A Domain-Specific Language Model for Political Violence Event Detection and Classification
- Researchers have developed ConfliBERT, a specialized language model for processing political and violence-related texts.
- ConfliBERT outperforms general-purpose large language models such as Google's Gemma 2, Meta's Llama 3.1, and Alibaba's Qwen 2.5 in accuracy, precision, and recall.
- The model demonstrates superior performance in classifying terrorist attacks using the Global Terrorism Dataset, particularly in identifying bombing and kidnapping events.
- ConfliBERT combines domain-specific knowledge with computational techniques and shows promise in conflict research and event data processing.
Read Full Article
10 Likes
For uninterrupted reading, download the app