Main Topic: AI Glossary
Section Summaries:
1. Accelerator: A type of microprocessor designed to accelerate AI applications.
2. Agents: Software that can perform tasks independently without human intervention.
3. AGI (Artificial General Intelligence): AI that is as capable as a human at any intellectual task.
4. Alignment: Ensuring that the goals of an AI system align with human values.
5. ASI (Artificial Super Intelligence): AI that surpasses the capabilities of the human mind.
6. Attention: Mechanisms in neural networks that help focus on relevant parts of input.
7. Back Propagation: Algorithm used in training neural networks to compute the gradient of the loss function.
8. Bias: Assumptions made by an AI model about the data and the balance needed between assumptions and predictions.
9. Chain of Thought: The sequence of reasoning steps an AI model uses to make decisions.
10. Chatbot: A computer program that simulates human conversation.
11. ChatGPT: A large-scale AI language model developed by OpenAI.
12. CLIP (Contrastive Language-Image Pretraining): An AI model that connects images and text.
13. Compute: The computational resources used in training or running AI models.
14. Convolutional Neural Network (CNN): A deep learning model used for image recognition tasks.
15. Data Augmentation: Increasing the amount and diversity of data used for training by adding modified copies of existing data.
16. Deep Learning: Training neural networks with many layers to learn complex patterns.
17. Diffusion: A technique for generating new data by adding random noise to existing data.
18. Double Descent: A phenomenon in machine learning where model performance improves, worsens, then improves again.
19. Embedding: The representation of data in a new form, often a vector space.
20. Emergence/Emergent Behavior: Complex behavior arising from simple rules or interactions in AI.
21. End-to-End Learning: Machine learning model that does not require hand-engineered features.
22. Expert Systems: AI applications that provide solutions to complex problems within a specific domain.
23. Explainable AI (XAI): Creating transparent models that provide clear explanations of their decisions.
24. Fine-tuning: Adapting a pre-trained model for a different task or domain.
25. Forward Propagation: The process in a neural network where input data is passed through each layer to produce the output.
26. Foundation Model: Large AI models trained on broad data, meant to be adapted for specific tasks.
27. General Adversarial Network (GAN): A model used to generate new data similar to existing data.
28. Generative AI: Creating models that can generate new content based on existing data.
29. GPT (Generative Pretrained Transformer): A large-scale AI language model developed by OpenAI.
30. GPU (Graphics Processing Unit): A specialized microprocessor for rendering images and training neural networks.
31. Gradient Descent: An optimization method that adjusts a model's parameters based on the direction of improvement in the loss function.
32. Hallucinate/Hallucination: AI models generating content not based on actual data or significantly different from reality.
33. Hyperparameter Tuning: Selecting appropriate values for the hyperparameters of a machine learning model.
34. Inference: Making predictions with a trained machine learning model.
35. Instruction Tuning: Fine-tuning models based on specific instructions in the dataset.
36. Large Language Model (LLM): AI models that generate human-like text and are trained on a broad dataset.
37. Latent Space: The compressed representation of data created by a model.
38. Loss Function: The function a machine learning model seeks to minimize during training.
39. Machine Learning: AI that learns and improves from experience without explicit programming.
40. Mixture of Experts: Training several specialized submodels and combining their predictions.
41. Multimodal: Models that can understand and generate information across different types of data.
42. Natural Language Processing (NLP): AI focused on interaction between computers and humans through language.
43. NeRF (Neural Radiance Fields): A method for creating 3D scenes from 2D images using a neural network.
44. Neural Network: AI model inspired by the human brain, consisting of connected units or neurons.
45. Objective Function: The function a machine learning model seeks to maximize or minimize during training.
46. Overfitting: Modeling error when a function is too closely fit to a limited set of data points.
47. Parameters: Internal variables in a machine learning model used to make predictions.
48. Pre-training: Initial phase of training a model to learn general features and patterns from data.
49. Prompt: The initial context or instruction that sets the task for the model.
50. Regularization: Technique to prevent overfitting by adding a penalty term to the model's loss function.
51. Reinforcement Learning: Learning to make decisions by taking actions to maximize reward.
52. RLHF (Reinforcement Learning from Human Feedback): Training an AI model using feedback from humans.
53. Singularity: Hypothetical future point when technological growth becomes uncontrollable and irreversible.
54. Supervised Learning: Machine learning with labeled training data.
55. Symbolic Artificial Intelligence: AI that uses symbolic reasoning to solve problems and represent knowledge.
56. TensorFlow: Open-source machine learning platform developed by Google.
57. TPU (Tensor Processing Unit): Microprocessor developed by Google for accelerating machine learning.
58. Training Data: Dataset used to train a machine learning model.
59. Transfer Learning: Using a pre-trained model on a new problem.
60. Transformer: Neural network architecture used for processing sequential data like natural language.
61. Underfitting: Modeling error when a statistical model or algorithm cannot capture the underlying structure of data.
62. Unsupervised Learning: Machine learning without labeled training data.
63. Validation Data: Subset of the dataset used to tune the hyperparameters of a model.
64. XAI (Explainable AI): Creating transparent models with clear explanations of their decisions.
65. Zero-shot Learning: Making predictions for conditions not seen during training without fine-tuning.
Subjective Opinions Expressed:
The article does not express any subjective opinions. It is a glossary of AI terms provided by the investment firm.
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