Artificial intelligence (AI) is rapidly transforming how we interact with technology, from everyday tools like ChatGPT and Google Gemini to advanced applications like Microsoft Copilot. To stay informed in this AI-driven era, here are 30 key terms you need to know:
- Artificial General Intelligence (AGI): A hypothetical AI capable of surpassing human intelligence across all tasks, a major milestone yet to be achieved.
- Agentive: AI systems that autonomously pursue goals without constant oversight, such as self-driving cars.
- AI Ethics: Guidelines that ensure AI operates responsibly, addressing issues like fairness, safety, and privacy.
- Alignment: Adjusting AI systems to produce desired outcomes and avoid harmful behavior.
- Anthropomorphism: Attributing human characteristics to AI, often leading to misconceptions about its capabilities.
- Autonomous Agents: AI systems designed to complete tasks independently, such as warehouse robots.
- Bias: Errors caused by unrepresentative training data, often resulting in skewed or unfair outputs.
- Chatbot: Software designed to simulate human-like conversations, such as customer support bots.
- Cognitive Computing: A synonym for AI focused on emulating human decision-making.
- Deep Learning: A subfield of machine learning that uses neural networks to analyze complex patterns in data.
- Diffusion: A technique in AI where noise is added to data and then removed to generate new outputs.
- Emergent Behavior: Unexpected capabilities that arise in advanced AI models during training.
- GANs (Generative Adversarial Networks): Two neural networks working together to generate realistic new data, like images.
- Generative AI: AI systems capable of creating text, images, videos, or code based on training data.
- Guardrails: Safeguards ensuring AI does not produce harmful or inappropriate content.
- Hallucination: Incorrect or nonsensical outputs generated by AI systems, often due to limitations in training.
- Inference: The process by which AI generates new content based on patterns in its training data.
- LLM (Large Language Model): AI models like GPT-4 trained on vast amounts of text to generate human-like language.
- Machine Learning (ML): A field within AI where algorithms learn from data to make predictions or decisions.
- Multimodal AI: AI capable of processing various input types, such as text, images, and audio simultaneously.
- Neural Network: A computational model inspired by the human brain, used for recognizing patterns in data.
- Overfitting: When an AI model learns too closely from its training data, making it less effective with new inputs.
- Parameters: The numerical values within an AI model that guide its behavior and decision-making.
- Prompt: User input that guides AI responses, forming the basis of interaction with chatbots.
- Stochastic Parrot: A critique of AI’s lack of true understanding, likening it to parroting human language.
- Style Transfer: AI’s ability to apply the style of one image to the content of another.
- Tokens: Small segments of text AI processes to generate outputs, often a few characters or words at a time.
- Training Data: The datasets used to teach AI models, ranging from text and images to videos and code.
- Transformer Model: An AI architecture enabling better contextual understanding of relationships in data, crucial for LLMs.
- Weak AI: Narrowly focused AI designed for specific tasks, like voice assistants or recommendation systems.
Helping SMEs automate and scale their operations with seamless tools, while sharing my journey in system automation and entrepreneurship
1 个月Understanding terms like AGI and autonomous agents is crucial as AI continues to evolve. These concepts give us a glimpse of how advanced and transformative AI can become. ??
Chief of staff at DSHG Sonic & A Digital Agency
1 个月?Thank for sharing understanding these key AI terms is crucial ?