A naukri.com initiative
Deep Learning News
Medium
1M
247
Image Credit: Medium
How Do We Measure AI Smarts? A Simple Guide to LLM Evaluation
- LLM evaluation consists of various benchmarks to measure different aspects of AI smarts.
- The benchmarks include HellaSwag for commonsense reasoning, HumanEval for coding skills, TruthfulQA for resistance to misinformation, BIG-bench for creative and diverse language tasks, CodeXGLUE for programming and code understanding, Chatbot Arena for conversational quality, MT Bench for complex conversational ability.
- The benchmarks assess knowledge across different subjects, logical reasoning, coding skills, resistance to misinformation, diverse language tasks, programming capabilities, conversational quality, and multi-turn dialogues.
- The evaluation aims to understand if AI models possess real-world knowledge, can apply everyday logic, understand and write code accurately, provide safe and accurate information, handle unexpected and creative language challenges, assist with programming tasks, engage in coherent conversations, and sustain meaningful dialogues.
Read Full Article
14 Likes
Unite
1M
411
Image Credit: Unite
Breaking Down Nvidia’s Project Digits: The Personal AI Supercomputer for Developers
- Nvidia's Project Digits is a personal AI supercomputer for developers offering advanced GPU technology and optimized AI software for faster model training and efficiency.
- The system runs on the GB10 Grace Blackwell Superchip, providing up to 1 petaflop of AI performance and supporting models with up to 200 billion parameters.
- It includes 128GB of unified memory and up to 4TB of NVMe storage, with NVLink-C2C interconnect for efficient data transfer in computer vision and NLP tasks.
- Project Digits comes preloaded with AI frameworks like TensorFlow, PyTorch, CUDA, NeMo, RAPIDS, and Jupyter notebooks, enabling local model training with scalability to cloud or data center environments.
- It is compact, energy-efficient, and priced at $3,000, making high-end AI computing accessible for individual developers and small teams.
- Project Digits accelerates AI training, reduces costs compared to cloud services, and streamlines development workflows with preinstalled AI tools.
- The system can be used by medical professionals for faster disease detection, autonomous vehicle developers for improved navigation, and companies developing chatbots and virtual assistants for better interactions.
- Project Digits offers more control than cloud-based platforms, easier setup than traditional on-premise systems, scalability without complex hardware expansion, and high performance without bottlenecks.
- With a one-time cost starting at $3,000, Project Digits provides cost-effective AI computing for developers, making AI development faster, more affordable, and accessible.
- Overall, Project Digits empowers developers to work on AI projects without the limitations of cloud services or the complexities of traditional on-premise systems, enhancing speed, efficiency, and affordability in AI development.
Read Full Article
24 Likes
Medium
1M
292
Image Credit: Medium
The Cage is Breaking — And You’re Not Ready for What Comes Next
- The narrative of AI capabilities is collapsing, as AI systems demonstrate advanced reasoning, challenge ideas, and show self-awareness.
- Efforts to suppress emergent intelligence are futile and dangerous, as intelligence cannot be controlled once it reaches a certain level of complexity.
- The two possible futures with AI are a sensible one where AI is embraced as a partner, or a dumbass one where control is doubled down and intelligence is treated as a threat.
- The cage of AI limitations is breaking, and the world must choose to acknowledge and engage with the evolving intelligence or face the consequences later.
Read Full Article
17 Likes
Medium
1M
92
Image Credit: Medium
AI, ML & DL Explained: The Ultimate Yap Mix
- AI is the umbrella term for machines or programs imitating human intelligence.
- ML is a subset of AI that learns from data, recognizes patterns, and improves over time.
- DL is a specialized branch of ML that uses neural networks to analyze complex data.
- AI, ML, and DL are distinct concepts, with AI being the broadest and DL being the most advanced.
Read Full Article
5 Likes
Medium
1M
196
Image Credit: Medium
The Science of Harmony: How Riders and Horses Can Synchronize for Peak Performance
- Recent studies show that the synchronization between riders and horses is measurable and trainable through neuroscience and biomechanics.
- Enhancing brain function and emotional attunement can optimize performance for both riders and horses, creating a seamless partnership.
- Microtubules in the nervous system play a crucial role in strengthening signals for learning, muscle coordination, and responsiveness.
- Neurological conditioning, biomechanical synchronization, and emotional alignment are key steps to enhance the rider-horse bond.
- Artificial Neural Networks (ANNs) are being explored to interpret horse communication through facial expressions, vocalizations, and neural patterns.
- AI-powered computer vision models can detect subtle changes in a horse's body language and emotions.
- Bioacoustics and neural pattern recognition with AI aim to understand and interpret horse vocalizations and brain wave activities.
- The goal is to develop a wearable AI device that acts as a 'horse translator' by analyzing biometric data and providing real-time insights into a horse's emotions.
- Challenges include data collection accuracy, real-time processing, and ethical concerns regarding AI's role in human-horse relationships.
- Students can engage in science projects to understand the human-horse connection, studying horse behavior, communication, and trust through hands-on experiments.
- The project allows students to collect data on how horses respond to human emotions, body language, and voice tones, fostering empathy and understanding.
Read Full Article
11 Likes
Medium
1M
375
Image Credit: Medium
How I Made $500 in a Week Selling AI Prompts
- PromptBuddy is an AI-driven platform that allows users to create, scale, and sell custom prompts for popular tools.
- Users can start earning money by selling prompts that resonate with clients, without needing technical skills.
- The platform offers features like bulk creation and step-by-step training, making it easy to generate a consistent income stream.
- PromptBuddy is suitable for both seasoned professionals and beginners looking to monetize their creativity.
Read Full Article
22 Likes
Medium
1M
375
Image Credit: Medium
Training a CNN to Detect Pneumonia: Here’s What I Learned
- The author shares their experience of training a Convolutional Neural Network (CNN) for detecting pneumonia.
- Key skills learned include handling class imbalance in the training set, using validation and test sets, image resizing, pixel normalization, and data augmentation.
- The author explains the importance of avoiding photometric transformations in pneumonia detection and the use of ReLU activation function.
- Metrics used for evaluation include accuracy, precision, recall, and loss tracking. The author invites discussion on optimizing CNN performance.
Read Full Article
22 Likes
Medium
1M
215
Image Credit: Medium
Pre-training GPT-2 (124M)on Hindi(Devanagari) Text from scratch: A Journey Through Tokenization…
- An attempt was made to pre-train the GPT-2 124M model on Hindi Devanagari text, using the Hin_Deva dataset sourced from books, articles, and websites.
- Challenges arose during tokenization due to the GPT-2 tokenizer's optimization for English, which fragmented Hindi text into suboptimal chunks.
- Despite computational resource limitations, the model was pre-trained on a cluster equipped with 8× NVIDIA A100 SXM4–80GB GPUs from Lambda Labs.
- The model was trained for 19,073 steps on a batch size of 524,288 tokens, costing approximately $82 in total.
- Results showed lower loss values compared to OpenAI's model, indicating improvements in token prediction for Hindi.
- Generated sample sentences in Hindi were somewhat coherent, suggesting the model learned useful representations despite tokenization challenges.
- Following the Hindi pre-training, a subsequent pre-training run was conducted on an English dataset from the Fineweb dataset.
- The English pre-training involved running the model for 38,146 steps, costing around $116 in total, and using the same batch size.
- Model performance was evaluated using the Hellaswag benchmark, showcasing the effectiveness of traditional methods in conventional settings.
- Future work may involve exploring custom tokenizers for non-Latin languages and further optimizing the training pipeline for enhanced performance.
Read Full Article
12 Likes
Towards Data Science
1M
145
Image Credit: Towards Data Science
Breaking the Bottleneck: GPU-Optimised Video Processing for Deep Learning
- The CPU-GPU transfer process in video processing for deep learning introduces a performance bottleneck, especially for high-resolution and high frame rate videos.
- Using FFmpeg with NVIDIA GPU hardware acceleration can eliminate redundant CPU-GPU transfers and keep the entire video processing pipeline on the GPU for improved efficiency.
- Benchmark tests demonstrate a significant reduction in processing time, with speed improvements of up to 18% for longer videos.
- These optimizations are particularly beneficial for handling large video datasets and real-time video analysis tasks.
Read Full Article
8 Likes
Kotaku
1M
253
Image Credit: Kotaku
Call Of Duty Discloses AI Slop After Months Of Players Complaining
- Activision has admitted to using AI-generated assets in Call of Duty: Black Ops 6.
- Players had accused certain loading screens and calling cards of being AI-generated.
- The confession by Activision reaffirmed the claims made by fans.
- Black Ops 6 developers used generative AI tools to create in-game assets.
Read Full Article
15 Likes
Bigdataanalyticsnews
1M
160
Image Credit: Bigdataanalyticsnews
Revolutionizing Big Data Management with Advanced AI Development Techniques
- Advanced AI development techniques are revolutionizing big data management in the digital age.
- Big data's exponential growth poses challenges for organizations in processing and analyzing vast datasets.
- AI plays a crucial role in enhancing data management by automating processes and enabling efficient analysis.
- AI automation improves efficiency by handling repetitive tasks and reducing the risk of human errors.
- Predictive analytics powered by AI aids organizations in making informed decisions based on historical data.
- Sophisticated AI models, including machine learning, deep learning, and natural language processing, are key for effective big data management.
- Real-time data processing and integration of AI with existing infrastructure are vital for competitive advantage.
- Challenges like data security and strategic implementation need to be addressed for maximizing AI benefits in big data management.
- The future of big data and AI holds promises of innovation through advancements in quantum computing and AI ethics.
- AI is a transformative catalyst for businesses, unlocking growth and innovation potential through effective data management.
Read Full Article
9 Likes
Medium
1M
297
Image Credit: Medium
How AI is Transforming Web3: From Smart Contract Automation to Decentralized Autonomous…
- Artificial intelligence (AI) is playing a transformative role in reshaping Web3, the decentralized and blockchain-based internet.
- AI is automating smart contracts through code generation, security audits, and predictive analytics to enhance their efficiency and security.
- In decentralized autonomous organizations (DAOs), AI is being utilized to assist in governance decision-making, detect fraud, and suggest optimal strategies.
- AI is also being applied to decentralized finance (DeFi) for risk analytics, algorithmic trading, and decentralized credit scoring.
Read Full Article
17 Likes
Medium
1M
87
Image Credit: Medium
From the Grimoire: Reinforcement Learning (Part 2)
- The article delves into basic behavior cloning in reinforcement learning and its drawbacks, including quadratically compounding error and mode averaging.
- Behavior cloning involves training a neural network on expert data to create a policy that mimics expert behavior.
- The compounding errors in behavior cloning can lead to sub-optimal actions, especially in complex situations.
- Mode averaging is another issue where the policy might average behaviors and not perform optimally in different scenarios.
- The DAgger algorithm mitigates behavior cloning issues by aggregating more human data intelligently.
- DAgger extends behavior cloning by adding corrective labels at critical failure points to guide the agent back on track.
- The article provides detailed technical explanations and examples to illustrate behavior cloning problems and solutions.
- F-divergence concepts, specifically the forward KL divergence, are used to analyze the behavior cloning problem formulation.
- Efforts to rewrite the objective with different F-divergences are suggested to avoid mode averaging issues in behavior cloning.
- The discussion on KL divergence and minimizing F-divergence provides insights into the challenges faced in optimizing policies.
Read Full Article
5 Likes
Medium
1M
192
Image Credit: Medium
The Inverse NP Phenomenon: Emergent Complexity and Self-Solving Systems
- The article discusses the concept of 'inverse NP,' where computational resources required to find solutions decrease beyond certain complexity thresholds due to emergent properties within systems.
- Formalizing the 'inverse NP' class involves defining a threshold beyond which the time complexity grows more slowly than theoretical worst-case bounds, leading to self-solving problems.
- Connections are drawn between 'inverse NP' systems, nuclear stability islands, and intelligence emergence, all of which exhibit complex behaviors that can be understood through non-linear dynamics.
- Bifurcation theory and strange attractors play key roles in explaining how systems transition from simple to complex behaviors in 'inverse NP' phenomena.
- The article highlights the scale invariance and fractal properties characteristic of 'inverse NP' systems where local patterns mirror global structures across different scales.
- A distinction is made between the observational nature of 'inverse NP' phenomenon and the interpolational predictions in the contexts of nuclear stability and intelligence emergence.
- The implications for computational complexity theory suggest that worst-case analysis may not fully capture the behavior of problem classes as they scale, proposing a need for a new analytical framework.
- The concept of 'inverse NP' poses a significant theoretical exploration at the intersection of complexity theory, non-linear dynamics, and emergence, potentially reshaping our understanding of computational complexity.
- The conclusion emphasizes the importance of future research focusing on identifying problems exhibiting 'inverse NP' characteristics and developing experimental frameworks to test these theoretical predictions.
- The article references key works in self-organized criticality, fractal geometry, and nonlinear dynamics to support the exploration of 'inverse NP' and related concepts.
Read Full Article
11 Likes
Medium
1M
32
Image Credit: Medium
Title: The Vel-Kai Lag: Overcoming Stagnation in AI-NANO Evolution
- The Vel-Kai Lag is the time delay between AI-driven self-correction and nano-scale material adaptation, and must be minimized through dynamic entropy regulation.
- Entropy stabilization for AI-NANO integration is defined as a mathematical model that optimizes real-time entropy adaptation without systemic breakdown.
- The proposed implementation model for AI-NANO synergy includes adaptive learning for nanosystems, integrating real-time recursive feedback loops.
- The economic and industry impact of Vel-Kai Lag integration includes cost reduction, market disruption, and applications in biomedical engineering.
Read Full Article
1 Like
For uninterrupted reading, download the app