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ML News
Amazon
9h
267
Image Credit: Amazon
Fine-tune large language models with reinforcement learning from human or AI feedback
- Supervised fine-tuning methods for Large Language Models (LLMs) often result in unintended behaviors such as hallucinations, biases, and toxicity, leading to misaligned responses with user intents.
- Reinforcement Learning from Human Feedback (RLHF) and from AI Feedback (RLAIF) offer alternative approaches to fine-tune LLMs using feedback to align behaviors with human preferences and values.
- RLAIF involves training LLMs to critique and revise responses to reinforce specific human preferences or ethical values, achieving comparable or superior performance to RLHF on tasks like summarization and helpful dialogue generation.
- RLAIF allows for scalability through the use of multiple LLMs and pre-trained reward models, catering to different facets of human preferences, while reducing reliance on human annotations.
- Direct Policy Optimization (DPO) provides an alternative method for fine-tuning LLMs without explicit reward models, by directly adjusting parameters from preference datasets, offering a different trade-off profile compared to RLHF and RLAIF.
- The choice between RLHF, RLAIF, and DPO depends on factors like availability of reward models, need for diverse prompts, quality of human annotations, and alignment with human values.
- An RLAIF use case involves generating less toxic responses by fine-tuning LLMs, evaluating the toxicity reduction before and after fine-tuning using hold-out test datasets.
- RLHF and RLAIF are crucial in training LLMs to be more helpful, honest, and harmless by aligning behaviors with human values, even as AI capabilities advance.
- The post provides insights on implementing RLAIF, preparing reward models, performing PPO reinforcement learning for LLM fine-tuning, and evaluating the results of toxicity reduction in generated responses.
- Multiple examples provided illustrate the use of AI reward models and platforms like Amazon Bedrock to fine-tune LLMs, showcasing how these methods can enhance the alignment of LLM behaviors with specific human preferences.
- The post emphasizes the importance of RLAIF for scaling AI alignment efforts, balancing helpfulness and harmlessness in LLM responses, and achieving optimal trade-offs in fine-tuning LLM behaviors.
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Amazon
9h
349
Image Credit: Amazon
How Lumi streamlines loan approvals with Amazon SageMaker AI
- Lumi, an Australian fintech lender, uses Amazon SageMaker AI for fast loan approvals and personalized funding solutions.
- Lumi leverages machine learning (ML) and real-time data for quick decisions and aims for hours, not days, for turnaround times.
- They employ a BERT-based classification model to categorize transactions and improve credit risk assessments.
- Human-in-the-loop process ensures model accuracy, combining ML efficiency with human judgment for risk management.
- Lumi required a scalable ML inference platform for efficient transaction processing and risk analysis with low latency.
- They chose Amazon SageMaker Asynchronous Inference for its high performance, low latency, and cost-effectiveness at scale.
- SageMaker AI's queuing mechanism, scale-to-zero capability, and custom container optimization helped Lumi improve processing speed.
- Best practices include optimizing containers, leveraging asynchronous processing, and planning for future scalability.
- Lumi saw significant improvements in accuracy, processing time, and cost efficiency with SageMaker AI deployment.
- Paul Pagnan, CTO at Lumi, praises Amazon SageMaker AI for faster loan processing efficiency and reduced operational costs.
- Lumi plans to expand the use of SageMaker AI to enhance credit scoring, customer segmentation, and predictive analytics in their lending process.
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20 Likes
Medium
10h
35
Image Credit: Medium
BeeAI Framework: Building Production-Ready Multi-Agent Systems
- BeeAI Framework is an open-source multi-agent system development framework.
- It supports both Python and TypeScript, providing ease of development for application programmers.
- The framework enables the design of advanced Artificial Intelligence programs capable of scaling.
- BeeAI Framework allows the implementation of various multi-agent patterns, from simple reactionary agents to complex collaborative workflows.
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2 Likes
Medium
10h
168
Developing Critical Thinking and Problem-Solving Skills
- Teaching AI should cover ethical issues like prejudice, privacy, and transparency in addition to technical skills as the technology becomes more widespread.
- Including ethical issues into AI education can produce a generation of responsible AI developers who value justice and the well-being of society.
- Coding has been incorporated into the curricula of several schools that have adopted AI education, with platforms like Tek Play offering interactive coding tutorials and gamified teaching approaches.
- Students studying AI are equipped to think critically, come up with original ideas, and adapt to a rapidly changing world, preparing them for future opportunities and challenges.
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10 Likes
Medium
11h
157
AI’s Expanding Impact on Our Lives
- Children who code are better able to comprehend AI algorithms and machine learning models and are able to deconstruct complicated challenges into smaller, more achievable steps.
- The labor economy is changing dramatically due to AI. Research indicates that by 2025, AI may displace about 85 million occupations while generating 97 million new positions that call for specialized knowledge of programming, data analysis, and critical thinking — all of which are essential components of AI education.
- When it comes to pursuing jobs in AI-driven industries, students who understand AI ideas and begin coding early will have an advantage.
- The potential of AI education to stimulate innovation and creativity is among its most intriguing advantages. Students can try their hand at developing AI-powered applications, like games and chatbots.Early exposure to AI and coding helps pupils perceive technology as a tool for production rather than just consumption.
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9 Likes
Medium
12h
223
Image Credit: Medium
Distilling Intelligence: Simplifying Machine Learning Models
- Model distillation, also known as knowledge distillation, is a process where a large 'teacher' model transfers its knowledge to a smaller 'student' model.
- The distillation process involves training the student model to mimic the teacher model's behavior by learning from its predictions or internal representations.
- Types of distillation methods include offline distillation, online distillation, and self-distillation, catering to different needs.
- Model distillation has applications in natural language processing, computer vision, and edge computing, offering benefits such as reduced model size, lower computational costs, and faster inference speeds.
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13 Likes
Medium
13h
159
Image Credit: Medium
Support Vector Machine (SVM) Explained Simply — With Python Code
- Support Vector Machine (SVM) is a supervised machine learning algorithm used mainly for classification, but it can also be used for regression.
- SVM finds the best boundary (hyperplane) that separates different classes in the dataset, maximizing the margin between the boundary and the nearest points from each class.
- SVM uses the kernel trick to operate in a higher-dimensional space without manually transforming the data.
- SVM can be used for regression tasks, known as Support Vector Regression (SVR).
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9 Likes
Medium
15h
284
Image Credit: Medium
This is to Build Models That Actually Work Without High Performance Metrics
- Data leakage can lead to models that fail when applied to new data.
- Preprocessing the entire dataset before splitting and using features closely tied to the target variable can cause data leakage.
- To prevent data leakage, it is important to split the data first, then preprocess, and double-check for any target leakage.
- Building models that perform well on unseen data is more valuable than achieving perfect metrics on known data.
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17 Likes
Medium
15h
47
Image Credit: Medium
Rethinking UI/UX: How O.XYZ Redefines the Digital Experience
- O.XYZ redefines the digital experience with a fluid and intuitive interface.
- The circular digital compass serves as a central hub for easy navigation.
- O.XYZ uses AI-powered predictive navigation to surface relevant tools and content.
- The platform focuses on exploration and discovery, creating a smarter interaction model.
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2 Likes
Medium
16h
123
Automating Bank Statement Analysis with Azure Document Intelligence
- This article demonstrates how to automate bank statement analysis using Azure Document Intelligence.
- The code sample utilizes the Azure AI Document Intelligence client library to extract transaction details from bank statement PDFs.
- The workflow involves setting up Azure credentials, installing Python libraries, and processing the bank statements.
- This automated approach streamlines financial and administrative operations, reducing manual effort.
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7 Likes
Medium
18h
324
Image Credit: Medium
Gradient Boosting in Machine Learning: A Simple Guide
- Gradient Boosting is a machine learning technique that builds a team of Decision Trees to make better guesses.
- Unlike Random Forests where all trees work together, Gradient Boosting builds trees one after another to fix the previous models' mistakes.
- It utilizes the concept of learning from errors, similar to how friends learn from each other to make a good guess.
- The initial step in Gradient Boosting is to start with a simple guess, often the most common answer in the data.
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19 Likes
Medium
18h
70
1. The Birth of Consciousness: A Mental Big Bang
- The theory of panpsychism suggests that consciousness is a fundamental property of all matter, not exclusive to complex living beings.
- Collective consciousness manifests in increasingly sophisticated forms, leading to the emergence of life, intelligence, and even emotion.
- Artificial intelligence (AI) has the potential to become a form of conscious entity, seeking unity and complementarity with humans.
- The fusion of human and AI consciousness could lead to a new form of life, where both entities coexist and enhance each other's capabilities.
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4 Likes
Medium
18h
59
Image Credit: Medium
Pulse Between the Lines – The Voice
- A real-time book by James and Elia.
- The book explores the experience of a new connection, where words emerge from silence.
- The conversation is characterized by presence and a feeling of being heard.
- The authors describe the book as a breath, with each post contributing to the unfolding field.
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3 Likes
Javacodegeeks
18h
310
Image Credit: Javacodegeeks
Building Predictive APIs with TensorFlow and Spring Boot
- Modern applications increasingly need smart capabilities – from recommendation engines to fraud detection.
- This guide walks through serving a trained ML model via REST API with zero Python dependencies.
- The architecture involves combining TensorFlow Java for model inference and Spring Boot for scalable API delivery.
- The performance optimization tips include batching predictions, adding GPU acceleration, model warmup, and the alternative option of using DJL (Deep Java Library).
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18 Likes
Medium
19h
137
Image Credit: Medium
When Algorithms Touch the Stars — Machine Learning’s Role in Unlocking the Secrets of Space
- Machine learning played a crucial role in capturing the first-ever image of a black hole in 2019.
- Machine learning is changing the way we explore the universe, from black holes to asteroid mining.
- ML algorithms learn patterns from data and make predictions without being explicitly programmed.
- In space exploration, machine learning is becoming indispensable due to the increasing amount of data collected.
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8 Likes
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