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Deep Learning News
Medium
2w
260
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The Bubble Continuum: A Unified Model of Energy via Void Collapse with NumPy Demonstration
- The Bubble Continuum introduces a theoretical model reinterpreting energy dynamics through void collapse events, offering insights into various disciplines like soft matter physics and clean energy theory.
- The model proposes that energy is released through the resolution of voids under pressure differentials, challenging traditional views of energy derived solely from matter transformation.
- The Bubble Continuum bridges diverse domains including soft matter physics, fluid dynamics, acoustics, and materials science, showcasing various applications from material engineering to clean energy solutions.
- It presents an energy continuum framework with stages like weak collapse from soap bubbles, cavitational collapse like in pistol shrimps, and resonant collapse through harmonized standing waves.
- Innovations include energy harvesting from void collapses, material engineering applications, and a fusion-like reactor model based on collapse-resonance rings.
- Proof-of-concept systems in nature like the pistol shrimp and sonoluminescence chambers demonstrate the practicality of void collapse energy phenomena.
- The model philosophically reframes entropy as a mechanism of creative tension resolved through structured collapse, where even the void contributes to energy emergence.
- Future work involves experimental design, simulations, and applications testing to further explore the Bubble Continuum model.
- A Python code snippet using NumPy and Matplotlib is provided to simulate the Bubble Continuum Energy Model, showcasing how the theoretical concepts can be implemented.
- The Bubble Buddy Continuum offers a unified field theory of void collapse energy, presenting new insights into physics, energy, and material design, highlighting the role of voids in energy generation.
- The model emphasizes the poetic notion that energy can be born from voids, suggesting a new perspective on the contributions of emptiness to energy dynamics.
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Hackernoon
2w
190
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A Survey of Machine Learning Approaches for Predicting Hospital Readmission
- Several studies have focused on machine/deep learning applications in healthcare and predicting hospital readmissions.
- Different approaches such as clustering procedures, Bayesian networks, and deep learning algorithms have been used to predict patient readmission rates.
- Studies have achieved accuracies ranging from 66% to 85% in predicting patient readmissions using machine learning techniques.
- Predictive models leveraging patient data and clinical information have shown promising results in forecasting hospital readmissions, contributing to the optimization of healthcare outcomes.
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Hackernoon
2w
195
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The Future of Patient Care: Text Mining Discharge Notes to Slash Readmissions
- Hospital readmission is a concerning issue affecting patient outcomes and healthcare costs.
- This study focuses on predicting patient readmission within 30 days using text mining on discharge notes.
- Machine learning and deep learning methods, including Bio-Discharge Summary Bert (BDSS), were utilized in the model.
- The model combining BDSS with a multilayer perceptron (MLP) outperformed existing methods with a 94% recall rate and 75% AUC.
- Integration of text mining and deep learning improves patient outcomes and resource allocation in healthcare.
- Utilization of EHR for readmission rate monitoring is crucial for enhancing treatment quality and cost savings in healthcare.
- Text mining and AI predictive approaches play a significant role in preventing rapid readmissions to hospitals.
- Various machine learning and deep learning models were employed to predict patient readmission based on clinical notes.
- The study compared different models, utilized advanced text representation techniques, and analyzed the entire dataset without data balancing.
- The research contributes to enhancing predictive modeling in healthcare by leveraging text mining and deep learning techniques.
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Medium
2w
4
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Weird Science: A Cosmic Card Trick, To Demystify Bell’s Theorem, Entanglement, and Non-locality.
- Quantum mechanics defies classical intuition with concepts like entanglement, superposition, and non-locality, explained through Bell’s Theorem.
- An analogy is presented in the form of a cosmic card trick to illustrate quantum mechanics, with entangled particles resembling a 'stacked deck' and changing rules during play.
- The analogy delves into the idea of patterns emerging in quantum mechanics results, showcasing statistical correlations between measurements on entangled particles.
- Bell's Theorem formalizes the paradox in quantum mechanics, proving violations of classical notions of locality and realism, challenging our understanding of the universe.
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Medium
2w
238
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Synthetic Data Revolution: A Personal Journey into AI’s Next Frontier
- Synthetic data is transforming industries by offering privacy-friendly solutions and addressing data scarcity.
- The journey into understanding synthetic data began at a tech conference where initial skepticism turned into curiosity.
- Experts discussed the potential of AI-generated data, highlighting its game-changing capabilities over real data.
- Industries like healthcare are using synthetic data for training AI models, ensuring privacy compliance and data richness for accurate diagnostics.
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Medium
2w
234
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No Data, No Problem: How This AI Learns Without Us
- Absolute Zero Reasoner (AZR) is an AI that teaches itself without human-made training data and outperforms models trained on human examples.
- AZR can develop unique reasoning strategies, potentially leading to innovative solutions in mathematics, coding, and other fields.
- Understanding AZR's reasoning processes is challenging due to its self-devised methods, raising concerns about transparency and trust in AI decisions.
- The ethical considerations of ensuring self-taught AI systems align with human values become more complex when their learning pathways are self-directed and less transparent.
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Medium
2w
160
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AWS vs Azure vs Google Cloud: How We Chose Our ML Platform
- Choosing between AWS, Azure, and Google Cloud for machine learning projects is crucial for innovation in cloud computing.
- A company's transition to a cloud-based machine learning platform can be a significant undertaking with various considerations.
- Each platform - AWS, Azure, and Google Cloud - offers unique strengths and complexities in the realm of machine learning.
- AWS provides SageMaker for simplified model building and deployment, Azure offers a robust ML platform, and Google Cloud focuses on seamless ecosystem integration.
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Medium
2w
56
The Data Trap Post One: I Pay for Data—So Why Am I Paying to Watch Ads?
- Paying for data on the internet includes watching ads, leading to an unexpected cost to the user.
- Users often unknowingly spend a significant part of their data plan on viewing promotional content without explicit consent.
- This situation acts as a hidden tax on users, especially impacting those with limited data plans, creating a form of exploitation.
- Platforms benefit from this system as they profit from both advertisers seeking attention and users paying for data to receive ads.
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Medium
2w
82
Model Quantization for Scalable ML Deployment
- Model quantization involves converting model weights and activations from float32 to lower precision formats like float16 or int8.
- Quantization to float16 is straightforward, while quantization to int8 involves mapping the wide range of float32 values to 256 integer values.
- Two main quantization schemes are used: Affine Quantization Scheme for non-zero offset data and Symmetric Quantization Scheme for zero-centered data.
- Different quantization methods like Dynamic Quantization, Static Quantization, and Quantization Aware Training are used to reduce model size, improve efficiency, and enable real-time AI on limited-resources.
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Medium
2w
373
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Classifying Invasive Weeds with Deep Learning, Yet Another Classification Problem
- Model was optimized by switching to ResNet18 and utilizing pre-trained ImageNet weights to reduce training time.
- Number of epochs was reduced to 6, with 3 frozen and 3 unfrozen, resulting in training time dropping to around 2 hours with 88% accuracy.
- Batch size was doubled to 64 to speed up learning process.
- Mixed Precision with to_fp16() was enabled to cut memory usage and increase speed by around 20% with minimal accuracy loss.
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Medium
2w
60
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A Balanced Framework for Ethical AI: Empathy, Civics, and Symbiosis w/ PyTorch proof.
- This article proposes a framework for ethical AI design based on empathy, civics, and symbiosis to ensure AI operates in partnership with humanity.
- The framework aims to address challenges like misuse, societal harm, and erosion of human autonomy, preventing extreme outcomes and fostering multidimensional human collaboration.
- The three pillars of ethical AI include empathy, civics, and symbiosis, enabling respectful recognition of humanity, ethical reasoning, and a balanced partnership between AI and humans.
- The triad of empathy, civics, and symbiosis forms a dynamic equilibrium for ethical AI, preventing coercive systems, utilitarian harm, and tolerance of unethical behavior.
- Practical applications of the framework include preventing misuse, fostering human growth, and promoting societal harmony through ethical decision-making and mediation.
- A PyTorch-based proof of concept is presented, demonstrating how the framework can be implemented in neural computation to evaluate ethical actions based on the three pillars.
- The AI prototype utilizes Empathy, Civics, and Symbiosis modules to simulate ethical decision-making, showcasing the potential for AI to model triadic ethical reasoning.
- Future steps involve training modules, adding safeguards against misuse, enhancing explainability, and integrating the framework into AI agents for real-world interactions with humans.
- By integrating empathy, civics, and symbiosis, AI can progress as a compassionate and responsible partner in human development, promoting wisdom and balance in decision-making.
- The article concludes that ethical AI development involves not only philosophical considerations but also practical implementation, as shown through the PyTorch prototype.
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Bigdataanalyticsnews
2w
287
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AI and the Future of Quantitative Finance
- AI is reshaping quantitative finance by introducing new levels of speed, precision, and adaptability, revolutionizing how quants operate.
- Machine learning and natural language processing are key AI technologies transforming quant finance, enabling advanced analysis and decision-making.
- AI-driven quant strategies include sentiment-driven trading, smart portfolio optimization, and enhanced risk management through dynamic adaptation.
- Challenges in AI implementation include model transparency, data quality issues, and the risk of overfitting.
- Quantum computing, a future ally to AI, offers the potential for real-time optimization and precise risk assessments in finance.
- Despite AI advancements, the human element remains crucial in quantitative finance, with AI likely to augment rather than replace human quants.
- Finance professionals need to adapt by learning AI programming languages, machine learning frameworks, and staying informed on emerging trends like quantum computing.
- AI and quantum computing together hold the promise of accelerating financial model development and giving firms a competitive edge in trading and risk management.
- The future of quantitative finance will be led by those who effectively harness AI, machine learning, and quantum computing to drive innovation and strategic decision-making.
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Medium
2w
39
Image Credit: Medium
Google’s AI Max for Search Campaigns
- Google's AI Max for Search campaigns leverage AI-powered broad match and intent-based targeting to double conversions and reduce ad costs significantly.
- AI Max combines broad match keywords with intent-based targeting for more effective ad delivery that resonates with user search queries.
- AI Max redefines ad targeting by understanding user intent beyond keyword matching and focusing on capturing searchers' true preferences.
- Results from testing AI Max for Search campaigns have been impressive, leading to a paradigm shift in how search advertising is approached.
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Medium
2w
204
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Meta’s AI Data Crisis: Copyright Battles & Ethical Solutions
- Controversy surrounds Meta's data practices, leading to debates on copyright infringement, data ethics, and legal boundaries of AI development.
- Allegations against Meta include using pirated data for training AI models, involving copyright infringement and stripping of copyright management information.
- The legal battles faced by Meta have broader implications for AI development and have raised concerns among authors in the U.S.
- The issue goes beyond Meta itself, highlighting the need for ethical considerations in AI development and shaping the future of AI.
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Medium
2w
117
Image Credit: Medium
Understanding Artificial General Intelligence AGI
- Artificial General Intelligence (AGI) is set to revolutionize industries and daily life with human-like intelligence.
- Discovering the concept of AGI felt like glimpsing into a futuristic realm bridging science fiction and reality.
- Encountering a TED Talk by a renowned AI expert showcased the potential of AGI in solving intricate problems ranging from medical breakthroughs to space exploration.
- AGI transcends conventional AI by aspiring to replicate the vast spectrum of human intelligence, prompting exploration into cutting-edge machine learning and neural network advancements.
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