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How to Start a Million-Dollar Home Service Business (Make $1.3m in 19 Months)
The article discusses how to start a successful home service business, using the example of a pool cleaning service. The authors share their framework, which involves choosing a service, learning the necessary skills, finding customers through Next Door app, using text messaging to convert leads, scaling with Google Ads, and eventually hiring and scaling the…
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A New Deep Learning Research Identifies Antimalarial Drug as a Possible Treatment for Osteoporosis
Scientists have discovered a potential treatment for osteoporosis by reprogramming bone marrow cells using deep learning algorithms. They found that administering dihydroartemisinin (DHA), a derivative of a malaria treatment component, reduced bone loss in mice and encouraged the production of bone-building cells. This breakthrough offers hope for developing a therapeutic agent to address the root…
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Constrained Optimization and the KKT Conditions
The text provides an insight into the Lagrangian function and its application in constrained optimization problems. It explains how the Lagrangian function is used to incorporate constraints into optimization and introduces the Karush-Kuhn-Tucker (KKT) conditions for optimality. The text also discusses the application of constrained optimization in Support Vector Machines (SVM).
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What is Prompt Architecture in LLMs?
The article discusses prompt engineering techniques and introduces the concept of prompt architecture for interacting with Large Language Models (LLMs). It highlights the importance of specific prompts and explores different prompt architectures such as role prompting, chain of thought prompting, self-consistency prompting, step-back prompting, and chain of verification prompting. The article also suggests choosing the…
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Meet 3D-GPT: An Artificial Intelligence Framework for Instruction-Driven 3D Modelling that Makes Use of Large Language Models (LLMs)
The article discusses the use of 3D content production in the metaverse age and the challenges faced by designers in the 3D modeling process. It introduces 3D-GPT, a framework designed to facilitate instruction-driven 3D content synthesis using Large Language Models (LLMs). The framework empowers LLMs to act as problem-solving agents and provides accurate and customizable…
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Digital colonialism and culture in the age of machine learning and AI
Digital colonialism refers to the dominance of tech giants and powerful entities over the digital landscape, influencing the flow of information, knowledge, and culture. This has implications for AI, as it reflects the data it’s trained on. Biases in datasets and language representation pose challenges to creating truly inclusive and representative AI models. Additionally, exploitative…
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This AI Research from China Introduces Character-LLM that Teaches LLMs to Act as Specific People such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
Character-LLM is a trainable agent designed to simulate specific individuals, such as Beethoven, Queen Cleopatra, and Julius Caesar, by editing profiles and training models. Researchers in China introduced a training framework involving Experience Reconstruction, Upload, and Protective Experiences to train these simulacra. The evaluation involved interviewing the trained agents in a test playground to assess…
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Meta AI Researchers Introduce GenBench: A Revolutionary Framework for Advancing Generalization in Natural Language Processing
A group of researchers from Meta has introduced a new framework called GenBench, which aims to enhance generalization in Natural Language Processing (NLP) models. GenBench includes a taxonomy to categorize NLP generalization research, a meta-analysis of related papers, evaluation tools, and cards. The framework allows for better model evaluation and development, improving the resilience and…
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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies
Researchers at McMaster University have developed online machine learning models to predict wastewater influent flow rates, particularly during the COVID-19 pandemic. The models outperformed conventional batch learning models in terms of accuracy, exhibiting high R2 values and low errors. The team believes these models can provide reliable decision support for wastewater operators in coping with…
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Researchers at Northwestern University have Proposed a Groundbreaking Machine-Learning Framework for off-grid Medical Data Classification Cutting AI Energy Use by 99%
Researchers at Northwestern University have developed a machine learning framework using mixed-kernel transistors based on dual-gated van der Waals heterojunctions for off-grid medical data classification and diagnosis, specifically for electrocardiogram (ECG) interpretation. The solution offers a more energy-efficient and practical approach compared to traditional methods, addressing the challenges of power consumption and complexity. The paper…