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Deep Learning News
Medium
44m
106
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The Deepfake Deception: How to Spot AI-Manipulated Videos and Audio Before They Fool You
- Deepfake technology is being used to create AI-generated videos and audio recordings that manipulate reality with near-perfect precision, posing significant threats to various sectors including journalism, finance, and politics.
- Common uses of deepfakes include political manipulation, corporate fraud, fake celebrity endorsements, and revenge tactics like non-consensual pornography, with 96% of deepfake videos targeting women.
- Detection methods for spotting deepfake videos and audio involve examining details like unnatural eye movements, facial expressions, lighting inconsistencies, robotic voice characteristics, and verifying video sources using tools like InVID.
- Tools like Microsoft's Video Authenticator, Deepware Scanner, and Reality Defender are aiding in the fight against deepfakes by providing deepfake probability scores, real-time video analysis, and identification of subtle distortions.
- To protect oneself, questioning the source before sharing, staying informed about AI advancements, reporting suspicious content to fact-checking organizations and social media platforms, and promoting media literacy are crucial steps.
- The battle against deepfakes requires continued vigilance, education, and regulatory measures to hold creators accountable and safeguard the truth from being manipulated for deceptive purposes.
- Critical thinking remains a vital tool in combating the spread of deepfake deception and ensuring that individuals, organizations, and society at large are equipped to identify and counteract these manipulations.
- As deepfake technology advances, it is essential for individuals to be proactive in understanding and addressing this digital threat to prevent financial losses, reputational damage, and the erosion of trust in media and information.
- By being informed, vigilant, and actively engaging in efforts to detect and combat deepfakes, individuals can play a crucial role in safeguarding against the harmful impact of AI-manipulated content on personal and societal levels.
- Defending the truth against deepfake deception requires a collective effort to promote awareness, accountability, and critical thinking to mitigate the detrimental effects of falsified information and safeguard the integrity of digital communication.
- Empowering individuals with the knowledge and tools to identify and address deepfake threats is essential in preserving trust, authenticity, and transparency in an increasingly interconnected and digitally mediated world.
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Medium
1h
256
Image Credit: Medium
How LLMs Learn: The Pre-Training Phase Explained
- Large language models (LLMs) learn during the pre-training phase by being fed a huge amount of text to understand language rules and context.
- Common Crawl provides data from 250 billion web pages for pre-training, but preprocessing to remove noise is crucial.
- Tokenization breaks text into manageable tokens for numerical processing, with methods like Byte Pair Encoding (BPE) being common.
- Models like GPT-4o use subword-based tokenization to handle large vocabularies more efficiently.
- Training involves Next Token Prediction and Masked Language Modeling to learn language structure and relationships between tokens.
- Base models learn to generate text one token at a time and serve as a starting point for further fine-tuning.
- Base models can memorize text patterns but may struggle with reasoning tasks due to limited structured understanding.
- In-context memory allows base models to adjust responses based on the provided context, demonstrating versatility without fine-tuning.
- Base models excel in replicating text based on memorized patterns but may lack originality and deep reasoning abilities.
- In the pre-training phase, LLMs develop foundational skills by learning from raw data before advanced techniques are applied for post-training.
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Medium
2h
189
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Understanding Sentiment Analysis: A Simple Guide
- Sentiment analysis involves analyzing text to determine whether it conveys a positive, negative, or neutral sentiment.
- There are three main approaches to sentiment analysis: rule-based systems, traditional machine learning, and deep learning.
- Rule-based systems follow predefined rules and word lists to identify sentiment based on certain keywords.
- Machine learning algorithms learn from data and identify sentiment based on patterns and examples.
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Medium
20h
77
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Evolution of Computer Vision Trends You Need To Know
- Computer vision trends leading to 2025 involve advancements in Vision Transformers, Edge AI, Generative AI, 3D computer vision, Ethical AI, Explainable AI, AGVs, Multi-Modal AI, deepfake detection, and Self-Supervised Learning.
- Vision Transformers (ViTs) have been revolutionary, surpassing CNNs with self-attention, enabling processing of entire images, improving object classification, and segmentation.
- Edge AI facilitates real-time processing on devices like smart cameras and drones, reducing latency, improving privacy, and enhancing efficiency for applications like self-driving cars and security systems.
- Generative AI generates synthetic data for training models, automates labeling, and drives advancements in healthcare, entertainment, and research by creating realistic outputs from various data sources.
- 3D computer vision enhances depth perception and object recognition using technologies like LiDAR and depth sensors, crucial for industries like robotics, self-driving cars, and digital twins.
- Ethical AI addresses bias issues in computer vision, focusing on fairness, transparency, and data privacy to ensure AI systems are less biased and more equitable.
- Explainable AI (XAI) increases transparency in AI decision-making, providing logical explanations for actions, crucial in industries like health services, financial activities, and security.
- AGVs automate logistic operations in warehouses and factories, using onboard computers and sensors to enhance speed, accuracy, and utility with advanced vision technology.
- Multi-Modal AI combines computer vision with text, speech, and other data, improving systems' intelligence and context-awareness, leading to applications like visual question answering.
- Computer vision plays a critical role in detecting deepfakes to combat the spread of misinformation and propaganda by identifying alterations in images.
- Self-supervised learning advances AI models like Vision Transformers, reducing human bias and powering real-time applications in robotics, surveillance, and healthcare.
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Medium
1d
158
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Building an AI Powered Resume Analyzer — Phase 01 Implementation
- An AI-powered Resume Analyzer is being developed to streamline resume analysis process efficiently.
- Phase 01 implementation highlights the development of a Full Stack Tool with AI integration using openAI GenAI and LLM.
- Key features released in this phase include Smart AI dashboard, resume parsing with AI-powered NLP, real-time AI guide for improvements, and skills extraction.
- The system is designed to offer resume recommendations, improvements, and data visualizations, enhancing recruitment processes.
- Tech stack includes React TS, Vite, Tailwind CSS for the frontend, Python, FastAPI/Flask, OpenAI API, spaCy, pdfplumber for the backend.
- The architecture includes components for file processing, AI analysis, authentication, and data storage using PostgreSQL and Firebase Firestore.
- Third-party services like OpenAI API and Firebase Authentication aid in resume analysis and real-time updates.
- Future phases will introduce advanced features like resume-to-job matching, AI-driven recommendations, and recruiter-friendly tools.
- Expected outcomes include improved efficiency in applicant filtering and personalized AI-driven resume enhancements for job seekers.
- The platform aims to become a fully automated hiring assistant, leveraging AI for smarter and faster recruitment processes.
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Medium
1d
345
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Building a Bidirectional Code Converter: Pseudocode ↔ C++ Using Transformers and Streamlit
- Building a bidirectional code converter using Transformers and Streamlit.
- Two separate Transformer-based models were trained for conversion between pseudocode and C++.
- Custom tokenization strategy was designed to handle pseudocode constructs and C++ syntax.
- The models were deployed using Streamlit for user-friendly interaction.
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20 Likes
Medium
53m
30
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Liquid Neural Network: Putting the Network to Test in the Chaotic World
- The article discusses the Liquid Neural Network (LNN) as an improvement to Recurrent Neural Networks (RNN), focusing on the training algorithm Backpropagation through Time (BPTT).
- The LNN proposes using the vanilla BPTT algorithm over the adjoint method to address memory consumption and calculation errors during training.
- The article highlights the importance of testing the stability of the LNN model regarding gradients, rapid changes, non-linear dynamics, and bounded hidden states.
- Testing for exploding or vanishing gradients showed stable results, followed by testing rapid changes and non-linear dynamics using the Lorenz System equations.
- The Lorenz System demonstrated chaotic behavior, but the LNN model showed stability and ability to process non-linear dynamics effectively.
- Further testing on the bounds of hidden states ensured stability over longer time steps and the ability to process complex patterns with greater stability compared to a standard RNN.
- Training the LNN against the Lorenz input involved ensuring the model's capability to predict values accurately without divergence in the curves.
- The results indicated the LNN's capacity to process chaotic and dynamic system inputs effectively, promising applications in dynamic AI scenarios.
- Future exploration may focus on the LNN architecture's challenges and further enhancements in subsequent parts of the study.
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1 Like
Medium
2h
65
Gpt Top 5 Rankings along with use
- GPT-4: A multimodal AI model that processes text and images, with a 40% accuracy improvement over GPT-3.5.
- ChatGPT: A chatbot that offers real-time web browsing and integrations, ideal for brainstorming and drafting.
- Gemini Advanced: Google's AI model integrated with Google Workspace, excelling in data analysis and multilingual tasks.
- Claude 2: An AI model suitable for legal contracts and academic papers, prioritizing safety and ethics.
- LLaMA 2: A customizable, open-source model for developers, beneficial in niche applications.
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3 Likes
Medium
3h
239
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AI in Education 2025: Transform Learning with Personalized AI Teaching Tools & Adaptive Technology
- Artificial Intelligence in education is at the forefront of revolutionizing how students learn and engage with content.
- AI offers customized learning experiences that cater to individual needs and preferences.
- In 2025, AI in education is expected to provide personalized teaching tools and adaptive technology.
- AI platforms like DreamBox act as personal tutors, guiding students through their educational journey.
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14 Likes
Medium
14h
101
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Exploring Alternative Mobile Phones: A Guide for Non-Mainstream Users
- Non-mainstream mobile phones hold a promising future filled with numerous possibilities.
- These devices will utilize advanced technologies including flexible screens and AI interfaces to improve user experience.
- Businesses can find abundant opportunities through offering customized designs and unique aesthetics that meet unmet consumer needs while distinguishing their products from the competition.
- Non-mainstream mobile phones need to adopt innovative solutions to tackle their specific challenges in order to establish a clear market segment.
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6 Likes
Medium
23h
166
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Data Science Or Artificial Intelligence: What Comes First?
- At the heart of every AI system lies Data Science, which focuses on collecting, processing, and analysing data to extract meaningful insights.
- Machine Learning (ML) is a part of Data Science. ML algorithms use structured data to learn patterns, make predictions, and automate decision-making processes.
- AI goes beyond pattern recognition and prediction, aiming to mimic human intelligence through advanced techniques such as Deep Learning, Neural Networks, and NLP.
- While AI relies on Data Science, it also enhances and automates many Data Science workflows, improving data cleaning, model selection, and insight generation.
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9 Likes
Medium
1d
138
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Neural Networks for Stock Prediction: How LSTMs & GNNs Transform Investment Strategy
- Neural networks are transforming stock prediction strategies through advanced technology.
- Investors are utilizing deep learning models to analyze stock trends for more accurate predictions and improved market timing.
- The application of LSTM and GNN models are gaining traction in stock prediction.
- These advancements offer investors a smarter way to navigate the financial markets.
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8 Likes
Medium
1d
120
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Neural Networks in Image Recognition: How AI Sees and Understands Visual Data
- Neural networks are revolutionizing image recognition and expanding possibilities.
- An individual reflects on their experience with image recognition in a gallery and ponders if machines can perceive art like humans.
- The story narrates how digital neurons mimic the intricacies of the human brain, leading to transformative discoveries.
- The article explores the magic of machine vision.
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7 Likes
Medium
1d
203
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How we decide among the best Large Language models
- MMLU(EM) and its variants, MMLU-Redux and MMLU-Pro, assess language models across multiple subjects using thousands of questions, highlighting the models' world knowledge and problem-solving skills.
- The DROP dataset challenges models with discrete reasoning tasks over paragraphs, with '3-shot' guiding prompts and 'F1' metric measuring accuracy in reading comprehension.
- IF-Eval (Prompt Strict) evaluates the model's adherence to instructions, focusing on strict prompt compliance to test the model's ability to follow guidelines precisely.
- GPQA benchmark features difficult questions in science fields, emphasizing advanced reasoning, with 'Pass@1' metric measuring accuracy in the model's initial responses.
- SimpleQA tests factual accuracy in language models across various topics, utilizing the 'Correct' metric to measure the model's accuracy in providing correct answers.
- FRAMES benchmark evaluates RAG systems on complex, multi-hop questions, focusing on factual accuracy, retrieval effectiveness, and reasoning skills.
- LongBench v2 assesses large language models on in-depth understanding tasks, with models like OpenAI’s o1-preview exceeding human performance.
- HumanEval-Mul and LiveCodeBench evaluate LLMs in code generation tasks across different programming languages, emphasizing accuracy on the first attempt.
- Codeforces ranks users based on performance in competitive programming, indicating proficiency levels with assigned titles and color codes.
- SWE-bench Verified ensures practical coding challenge evaluation by validating tasks curated from GitHub repositories, enhancing benchmark reliability.
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Towards Data Science
1d
335
Image Credit: Towards Data Science
Debugging the Dreaded NaN
- To implement a NaN capturing solution in PyTorch, one can use PyTorch Lightning's callback interface.
- A NaNCapture Lightning callback is created to handle NaN events during training.
- The callback stores corrupted models and halts training upon encountering NaN values.
- Reproducibility is ensured by including NaNCapture state in the checkpoints for debugging.
- Loading the stored training batch for debugging relies on Lightning's LightningDataModule.
- Testing the callback involves creating a problematic model to trigger NaN occurrences.
- Runtime performance is minimally impacted by the NaNCapture callback, providing valuable debug capabilities.
- Enhancements like capturing and restoring random states for reproducibility are also discussed.
- Encountering NaN failures in machine learning can be challenging and indicate model issues.
- The proposed approach using Lightning callback streamlines NaN error debugging.
- This solution can save developers significant time and effort in debugging NaN errors.
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