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Dev
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Amazon Comprehend for Text and Document Analysis
- Amazon Comprehend is an AWS natural language processing (NLP) service that analyzes text and documents.
- Key features of Amazon Comprehend include entity detection, key phrases identification, personally identifiable information (PII) identification and redaction, language detection, sentiment analysis, and syntax analysis.
- Amazon Comprehend allows customization with pre-trained models or creating custom models for document classification, entity detection, and document topic modeling.
- Accessing Amazon Comprehend is straightforward through the AWS Console or Comprehend APIs, supporting real-time or asynchronous analysis for various file formats.
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Analyticsindiamag
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Google DeepMind Just Made Small Models Irrelevant with RRTs
- Google DeepMind, in collaboration with KAIST AI, proposes a method called Relaxed Recursive Transformers (RRTs) that reduce the cost, computing, and resources required for a LLM to function.
- RRTs allow LLMs to be programmed to behave like small language models yet outperform many of the standard SLMs present today.
- Layer Tying, an RRT technique, allows input to pass through a small number of layers recursively, cutting down memory requirements and significantly reducing computational resources.
- RRTs introduce low-ranking adaptation that adjusts the shared weights with a slight amount of variation which guarantees distinct behaviour in processing the input.
- Recursive RRT models provide substantial accuracy improvements and performance parity with full-size models trained on 3 trillion tokens.
- This method introduces LoRA or low-ranking adaptation. Low-rank matrices are set up leading to substantial energy savings by increasing inference throughput.
- Compared to other models, the RRT uptrained on 60 billion tokens achieved performance parity with full-size models trained on 3 trillion tokens.
- RRTs may contribute to impactful energy savings by making LLMs smarter without adding significantly to their footprint.
- Quantisation and Layer Skip are other ways explored to scale down LLMs without compromising on performance, but RRTs involve parameter sharing and real-time verification during draft token generation.
- Further research is needed to determine the uptraining cost associated with scaling to larger models before RRTs are deployed in real-world applications.
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VentureBeat
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ByteDance’s AI can make your photos act out movie scenes — but is it too real?
- ByteDance has developed an AI system, X-Portrait 2, that can transform photographs into realistic video performances.
- The system captures subtle expressions and emotions, making still images mirror scenes from famous movies.
- However, concerns arise regarding the ability to create realistic videos from any photograph, which may contribute to misinformation.
- ByteDance's unique access to a massive collection of user-generated videos provides training data at a scale unavailable to most AI companies.
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Analyticsindiamag
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Bengaluru-based Spacetech GalaxEye Secures $10 Mn in Series A Funding
- Spacetech startup GalaxEye has raised $10 million in its Series A funding round.
- The funding will support the development of a constellation of multi-sensor satellites.
- These satellites will provide high-resolution, all-weather data for sectors like defence, agriculture, maritime, and insurance.
- The funding round was led by MountTech Growth Fund – Kavachh (MGF-Kavachh).
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Dev
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The Stack Overflow Error
- The Stack Overflow Error occurs when the stack memory is filled with multiple function calls, leading to a memory overload.
- A stack is a data structure where the first item added is the last to execute (FILO).
- Recursion is a way to create loops by calling a function within itself.
- The Stack Overflow Error is commonly encountered in Java when using recursion with a large number of function calls.
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Medium
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Top 5 Books on Artificial Intelligence Every Enthusiast Should Read
- In this thought-provoking book, Nick Bostrom explores the future of AI and its potential to surpass human intelligence. Bostrom delves into the concept of superintelligence, discussing its possible paths of development and its existential risks. He raises critical questions about how humanity can prepare for and manage this transformative technology.
- This book serves as a comprehensive introduction to AI's core principles and techniques. Negnevitsky covers expert systems, neural networks, fuzzy logic, and evolutionary computation. The book emphasizes practical applications and provides numerous examples to help readers understand how AI is used in the real world.
- Max Tegmark examines how AI might evolve and its potential to shape the future of life on Earth. The book discusses AI's societal, economic, and ethical implications, pondering questions about consciousness, employment, and the broader impact of intelligent systems on humanity.
- This practical guide offers hands-on experience building machine learning models using popular frameworks like Scikit-Learn, Keras, and TensorFlow. Géron covers essential concepts, techniques, and tools, making it an excellent resource for those interested in machine learning and AI development.
- Pedro Domingos explores the five major machine learning paradigms and the quest for a “master algorithm” to unify them all. The book provides a deep dive into how machine learning works, its real-world applications, and its transformative potential across industries.
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Medium
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Will LLMs Become A Censoring Weapon?
- Anthropic, OpenAI’s biggest rival, has released research on experimenting with feature engineering in language models (LLMs).
- Feature engineering allows researchers to manipulate the behavior of LLMs by strengthening or clamping specific concepts or 'features'.
- The study reveals that LLMs could potentially be turned into censoring machines.
- This raises concerns, especially given the extreme polarization in recent US elections.
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Medium
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Unlocking the Power of Long Short-Term Memory (LSTM) with Time-Series Sequences
- Despite LSTM’s potential, accurately predicting dynamic wildfire indices remains challenging due to the complex and volatile nature of environmental factors influencing wildfire risk.
- Using synthetic time-series data, an LSTM model was developed and trained to predict a wildfire risk index based on weather-related features. Cross-validation and hyperparameter tuning were employed to optimize model performance.
- The trained model demonstrated underfitting, producing near-constant predictions that failed to reflect the true variability in the data. There was a discrepancy between actual and predicted values, and the loss values were stable but suboptimal.
- To improve the model's ability to capture patterns in complex wildfire prediction, enhancements in model complexity, feature engineering, and data preprocessing are recommended.
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Medium
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My Incredible Learning Journey in Python and Data Science.
- The author initially started coding with Grasshopper app for JavaScript but later got drawn towards Python.
- Taking an introductory Python course on Udemy sparked the author's interest in data science.
- Learning Python has been exciting and challenging, especially in the field of data science.
- The author feels grateful for being selected in the Arewa Data Science Program and is excited to deepen their knowledge in machine learning.
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VentureBeat
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Mistral AI takes on OpenAI with new moderation API, tackling harmful content in 11 languages
- French startup Mistral AI has launched a new content moderation API to compete with OpenAI and address concerns about AI safety and content filtering.
- The API, powered by Mistral's Ministral 8B model, detects harmful content across nine categories and supports 11 languages.
- Mistral's approach focuses on both edge computing and safety features, making it attractive to European companies subject to data protection regulations.
- The company's moderation API is already being used in its Le Chat platform and is available through Mistral's cloud platform.
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Medium
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From Networks to Data: An Anthropologist’s Journey into the Culture of Venture Capital
- Anthropology researcher Emily Tao delves deep into Venture Capital (VC) to understand its culture and practices.
- VCs largely rely on networking to create deal flow, whereby investors get around 75% of their investments from network-driven sources.
- Robust networks with domain-specific operators, investors and experts become crucial during the due diligence stage.
- Data-driven venture capital (DDVC) is an emerging practice reshaping the VC landscape.
- DDVC firms integrate data and machine learning into every aspect of their business.
- VC traditionally thrives on secret information, but DDVCs aims to change that by unlocking the black box with data.
- Diversity is still an issue in the VC industry, and data and AI exhibit bias. For instance, data's created by humans and algorithms mirror historical biases.
- DDVC is not a panacea for the human issues within VC, but it represents a cultural transformation.
- DDVC is not the only answer for the VC industry and is one that's still in its early stages.
- Interviewees such as Jenny Tooth, Rob Kniaz and John Spindler provided essential insights into the VC industry.
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VentureBeat
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‘Unrestricted’ AI group Nous Research launches first chatbot — with guardrails
- AI research group Nous Research has launched its first user-facing chatbot called Nous Chat.
- The chatbot provides access to its large language model, Hermes 3-70B, in a familiar format.
- Nous Chat offers suggested prompts and serves up quick answers with source citations.
- While it has some guardrails, researchers have already found ways to bypass restrictions.
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Medium
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441
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The 1st Person Dataset: A Personalized Shopping Revolution
- The 1st person dataset refers to the vast amount of data generated by individual consumers, including their needs, preferences, and purchase history.
- Personalized shopping experiences offered by SAAS platforms help consumers save time and money through tailored recommendations and efficient checkout processes.
- Artificial intelligence and machine learning analyze 1st person datasets to provide accurate and relevant recommendations.
- Data privacy and ethical considerations are essential in leveraging 1st person datasets for personalized shopping.
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Analyticsindiamag
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AWS, Anthropic and Palantir Join Forces to Bring Generative AI to US Defense and Intelligence
- AWS, Anthropic, and Palantir have partnered to bring generative AI to US defense and intelligence agencies.
- The partnership aims to enable rapid data analysis, pattern recognition, and document review.
- The collaboration leverages Palantir's AI Platform (AIP) and AWS's SageMaker for secure AI deployment.
- This partnership may prove beneficial, as Claude models are highly regarded for programming and coding.
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Medium
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Protect Your Personal Data from E-Waste Hackers
- E-waste hacking poses serious cybersecurity risks as discarded electronics can contain sensitive data.
- Proper disposal of electronics is crucial to prevent data breaches and protect personal and corporate information.
- Securely erasing data from storage devices before disposal is essential, but traditional methods may not be effective against modern data recovery techniques.
- Fidelity Height's Opal Lock solution offers a state-of-the-art method to securely erase data without generating e-waste.
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