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Medium
2h
42
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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
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TechBullion
7h
305
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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.
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Medium
11h
193
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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.
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Medium
12h
133
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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.
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Medium
1d
3
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Joyce Shen’s picks: musings and readings in AI/ML, December 23, 2024
- Musings and Readings: AI/ML news highlights
- 1. Research papers: 'Towards Friendly AI', 'Jet: A Modern Transformer-Based Normalizing Flow', 'Large Language Models and Code Security'
- 2. Discussion on enterprise AI product management and value creation with Nadiem von Heydebrand at Mindfuel
- 3. Deals: Seed financing raised by Triton Anchor, NeuroKaire, Chargezoom, Anatomy Financial, Starboard, Indapta Therapeutics, Slip Robotics, Salt AI, Basis, Hamming.ai, Simulation Theory, Kurrent, Sotelix Endoscopy, Portal Biotechnologies
- 4. Sunairio secures $6.4 million financing round for leveraging high-resolution climate data for energy asset risk simulation
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Medium
1h
290
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.
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Medium
2h
195
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.
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Medium
12h
170
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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.
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Marktechpost
13h
94
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.
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Siliconangle
15h
426
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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.
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Medium
15h
120
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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.
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Marktechpost
16h
168
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.
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Medium
21h
364
What You Should Know About Getting Into Data Science
- To get into data science, it is important to have a strong foundation in mathematics and statistics, as well as programming knowledge.
- Data engineering skills, including knowledge of databases and technologies like Apache Spark or Hadoop, are crucial for managing and processing large amounts of data.
- Data science plays a significant role in the creation of artificial intelligence systems, utilizing machine learning and deep learning for advanced technology applications.
- In addition to technical skills, communication, critical thinking, and data visualization abilities are important for a data scientist.
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Medium
21h
163
The Role of AI in Software Engineering
- AI brings automation, predictive insights, and adaptive solutions to traditional software engineering methods.
- AI enhances the software development lifecycle by analyzing code, automating testing, and processing requirements.
- AI optimizes DevOps pipelines, aids in anomaly detection, and assists architects in designing scalable software.
- Challenges include bias in AI systems, talent gap, ethical concerns, and the need for interpretability and quantum algorithms.
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Medium
23h
172
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Classifier-free guidance for LLMs performance enhancing
- Classifier-free guidance for LLM text generation using conditional and unconditional score estimates has been developed as a simpler version of classifier guidance.
- CFG is used to update predicted scores of generated LLM text in a direction of some predefined class without applying gradient-based updates.
- An alternative implementation approach to CFG for LLM text generation without severe degradation of generated sample quality has been suggested.
- The original CFG approach may cause unexpected artefacts and degradation of LLM text quality, but the artefacts depend on multiple factors such as the model and prompts.
- The suggested alternative implementation has been shown to prevent the degradation of generated LLM sample quality in both manual and automatic tests.
- Examples of artefacts and degradation of generated LLM sample quality have been demonstrated through tests for different CFG coefficients on a GPT2 model.
- The problem arises from the logarithm component in the original CFG implementation, which treats probabilities unequally and can cause low-probability tokens to receive high scores after applying CFG.
- The suggested alternative implementation removes the logarithm component and aligns the text-CFG with diffusion-models CFG that only operate with model predicted scores without gradients.
- The suggested alternative implementation introduces minimal changes to the HuggingFace Transformers' UnbatchedClassifierFreeGuidanceLogitsProcessor function.
- The suggested alternative implementation has improved text quality in manual tests and has not deteriorated performance on automatic tests compared to the original implementation.
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