AI Glossary

Llama 2

Llama 2 represents an ambitious stride forward in the field of artificial intelligence. Developed through a collaborative effort between Meta and Microsoft, this state-of-the-art, second-generation large language model has been designed to redefine the boundaries of open-source AI technology. Below, we examine the key aspects of Llama 2 and what makes it an important tool in today's AI landscape.

Llama 2 Overview: Open Source and Democratization

Llama 2's open-source nature stands as a testament to Meta's continued dedication to democratizing cutting-edge AI technology. By offering this model to the general public without charge, Llama 2 aims to level the playing field and promote innovation in numerous applications.

Availability and Accessibility

Llama 2 is readily accessible to both consumers and businesses. Its availability in different parameter options, namely 7B, 13B, and 70B, ensures that it can cater to various needs. Those interested can download Llama 2 from Meta's official site or access cloud-hosted instances via Hugging Face, making it both convenient and versatile.

Open-Source Advantage

The open-source model empowers users to not only utilize Llama 2 but also to refine and adjust the pre-trained design with specific user data. This customization feature ensures that Llama 2 can be tailored to particular applications, ranging from language generation to research and AI-powered tools.

Applications and Utilization

Llama 2's potential extends across multiple domains. Let's explore some of the primary areas where Llama 2 is making a mark:

Creating Chatbots

Llama 2 has emerged as a valuable tool in the creation of sophisticated chatbots that offer user-friendly interactions.

Research and AI-Powered Tools

Researchers and developers are leveraging Llama 2's capabilities to drive innovation in AI research and the development of advanced AI-powered tools.

Performance and Accuracy

While Llama 2's comparative performance against other models like GPT-4 is still under exploration, it has demonstrated some intriguing trends.

Success with Coding Questions

Llama 2 has shown an aptitude for answering coding-related queries, surpassing its performance in more general questions.

Safety Considerations

Though designed to be safe, it's worth noting that potential misuse of user data can occur. Users should proceed with caution, particularly when integrating Llama 2 into business processes where sensitive data is involved.

Conclusion: Llama 2 and the Future of Open-Source AI

Llama 2 signifies a noteworthy evolution in open-source AI technology. Its development by Meta and Microsoft brings to the forefront a model that is not only innovative but also seeks to engage a broader community in AI's promising future. Its range of applications, flexibility, and alignment with Meta's mission to democratize AI establishes Llama 2 as a central figure in the ongoing dialogue around responsible and accessible AI.

Back to Glossary Home

AI and Medicine Grounding Probabilistic Models in Machine Learning Knowledge Distillation Inference Engine Emergent Behavior Double Descent Bayesian Machine Learning Batch Gradient Descent Voice Cloning Homograph Disambiguation Grapheme-to-Phoneme Conversion (G2P)Deep Learning Articulatory Synthesis AI Voice Agents AI Agents Text-to-Speech Models Neural Text-to-Speech (NTTS)Pooling (Machine Learning)Pretraining Machine Learning in Algorithmic Trading Test Data Set Bias-Variance Tradeoff Learning Rate Logits Inductive Bias Continuous Learning Systems Supervised Learning Autoregressive Model Auto Classification Hidden Layer Multitask Prompt Tuning Multi-task Learning Machine Learning Neuron Semi-Supervised Learning Rectified Linear Unit (ReLU)Validation Data Set Incremental Learning Diffusion Clustering Algorithms Few Shot Learning Machine Learning Life Cycle Management Named Entity Recognition AI Robustness Information Retrieval Augmented Intelligence Collaborative Filtering Cognitive Architectures AI Prototyping AI and Big Data AI Scalability AI Literacy Machine Learning Bias Image Recognition AI Resilience Synthetic Data for AI Training Objective Function Data Drift Self-healing AI Spike Neural Networks Human-centered AI Federated Learning Uncertainty in Machine Learning Parametric Neural Networks Limited Memory AI Naive Bayes Classifier AI Transparency Human-in-the-Loop AI Machine Learning Preprocessing AI Privacy Multi-Agent Systems Generative Teaching Networks AI Interpretability AI Regulation Human Augmentation with AI Feature Store for Machine Learning Decision Intelligence Chatbots Quantum Machine Learning Algorithms Computational Phenotyping Counterfactual Explanations in AI Context-Aware Computing Instruction Tuning AI Simulation Ethical AI AI Oversight AI Safety Symbolic AI AI Guardrails Composite AI Gradient Clipping Generative Adversarial Networks (GANs)Rule-Based AI AI Assistants Activation Functions Dall-E Prompt Engineering Hyperparameters AI and Education Chess bots Midjourney (Image Generation)DistilBERT Mistral XLNet Benchmarking Llama 2 Sentiment Analysis LLM Collection ChatGPT Mixture of Experts Latent Dirichlet Allocation (LDA)RoBERTa RLHF Multimodal AI Transformers Winnow Algorithm k-Shingles Flajolet-Martin Algorithm CURE Algorithm Online Gradient Descent Zero-shot Classification Models Curse of Dimensionality Backpropagation Dimensionality Reduction Multimodal Learning Gaussian Processes AI Voice Transfer Gated Recurrent Unit Prompt Chaining Approximate Dynamic Programming Adversarial Machine Learning Deep Reinforcement Learning Speech-to-text models Feedforward Neural Network BERT Gradient Boosting Machines (GBMs)Retrieval-Augmented Generation (RAG)Perceptron Overfitting and Underfitting Machine Learning Large Language Model (LLM)Graphics Processing Unit (GPU)Diffusion Models Classification Tensor Processing Unit (TPU)Natural Language Processing (NLP)Google's Bard OpenAI Whisper Sequence Modeling Precision and Recall Semantic Kernel Fine Tuning in Deep Learning Gradient Scaling AlphaGo Zero Cognitive Map Keyphrase Extraction Multimodal AI Models and Modalities Hidden Markov Models (HMMs)AI Hardware Natural Language Generation (NLG)Natural Language Understanding (NLU)Tokenization Word Embeddings AI and Finance AlphaGo AI Recommendation Algorithms Binary Classification AI AI Generated Music Neuralink AI Video Generation OpenAI Sora Hooke-Jeeves Algorithm Mamba Central Processing Unit (CPU)Generative AI Representation Learning AI in Customer Service Conditional Variational Autoencoders Conversational AI Packages Models Fundamentals Datasets Techniques AI Lifecycle Management AI Monitoring Machine Translation MLOps Monte Carlo Learning Principal Component Analysis Reproducibility in Machine Learning Restricted Boltzmann Machines Support Vector Machines (SVM)Topic Modeling Vanishing and Exploding Gradients Data Labeling F1 Score in Machine Learning Expectation Maximization Beam Search Algorithm Embedding Layer Differential Privacy Data Poisoning Causal Inference Capsule Neural Network Attention Mechanisms Domain Adaptation Evolutionary Algorithms Contrastive Learning Explainable AI Affective AI Semantic Networks Data Augmentation Convolutional Neural Networks Cognitive Computing End-to-end Learning Prompt Tuning Model Drift Neural Radiance Fields Regularization Natural Language Querying (NLQ)Foundation Models Forward Propagation F2 Score AI Ethics Transfer Learning AI Alignment Whisper v3 Whisper v2 Semi-structured data AI Hallucinations Matplotlib NumPy Scikit-learn SciPy Keras TensorFlow Seaborn Python Package PyTorch Natural Language Toolkit (NLTK)Pandas Ego 4D The Pile Common Crawl Datasets SQuAD Intelligent Document Processing Hyperparameter Tuning Markov Decision Process Graph Neural Networks Neural Architecture Search Ablation Model Interpretability Out-of-Distribution Detection Recurrent Neural Networks Active Learning (Machine Learning)Imbalanced Data Loss Function Unsupervised Learning AdaGrad Acoustic Models Concatenative Synthesis Candidate Sampling Computational Creativity AI Emotion Recognition Knowledge Representation and Reasoning Metacognitive Learning Models AI Speech Enhancement Eco-friendly AI Metaheuristic Algorithms Statistical Relational Learning Deepfake Detection One-Shot Learning Semantic Search Algorithms Artificial Super Intelligence Computational Linguistics Computational Semantics Part-of-Speech Tagging Random Forest Neural Style Transfer Neuroevolution Association Rule Learning Autoencoder Data Scarcity Decision Tree Ensemble Learning Entropy in Machine Learning Corpus in NLP Confirmation Bias in Machine Learning Confidence Intervals in Machine Learning Cross Validation in Machine Learning Accuracy in Machine Learning Clustering in Machine Learning Boosting in Machine Learning Epoch in Machine Learning Feature Learning Feature Selection Genetic Algorithms in AI Ground Truth in Machine Learning Hybrid AI AI Detection AI Standards AI Steering ImageNet Learning To Rank Applications

AI Glossary Categories

Categories

AlphabeticalAlphabetical

Alphabetical