AI has rapidly emerged as a transformative technology across all sectors, leading to the creation of a wide variety of new buzzwords. But what do these terms actually mean?
Several keywords are essential to understanding the latest developments in AI. This article, by Entelect Business Analyst Kyle Gray, explores some of the most prominent terms and concepts that have shaped the AI landscape this year.
Foundation Models
Massive machine learning models trained on diverse data sources—such as text or images—serving as the backbone for specialised AI tools. Think of them like building blocks that can be adapted to fit multiple applications.
Machine Learning
A core branch of AI where algorithms improve through repeated exposure to data. With feedback, these models learn and become more accurate over time.
Human-in-the-Loop
This approach blends human input with machine learning, ensuring models improve through human guidance during training or operations. It’s key to building reliable, adaptable AI.
Explainability vs. Black Boxes
- Black Box: An AI model whose internal workings are not easily understood.
- Explainable AI (XAI): Systems designed to make AI decisions transparent, crucial in industries like insurance or banking to avoid discrimination.
Generative AI
AI tools that create content—whether text, images, code, or videos—based on user prompts. This technology powers chatbots, creative platforms, and even music generation.
Edge AI
AI algorithms deployed on local devices (like smartphones) instead of the cloud, enabling real-time processing and better privacy. Think of smart home systems or IoT automation running directly on your devices.
Deep Learning
A subfield of machine learning where AI mimics the way our brains work, enabling systems to recognise patterns and perform complex tasks—like image recognition or language translation.
AI Hallucination
When AI fabricates false information that sounds plausible. This challenge highlights the need for careful oversight, as AI models can prioritise coherence over factual accuracy.
Multimodal Learning
AI models that combine data types—like text, images, and audio—to better understand complex situations. This integration unlocks richer insights, such as analysing video with both sound and visuals in mind.
Quantum AI
A future-facing field combining quantum computing with AI. While still early, it promises breakthroughs in areas like cryptography and optimisation, but we're far from practical applications today. (See Silicon Valley, Season 6 for a laugh.)
Reinforcement Learning (RL)
AI learns by interacting with environments and receiving feedback—like rewards or penalties—similar to how people learn through experience. Human-in-the-loop systems often play a role here.
Future Horizons: General and Sentient AI
- General AI: Hypothetical AI with human-like problem-solving across any task.
- Sentient AI: A still-distant concept—AI that feels emotions and perceives the world like humans. Whether we can or should achieve this remains an open ethical debate.
The Bottom Line
Whether you're working with AI systems today or just curious about their future, understanding these terms helps you keep up with the technology reshaping our world. If you’ve mastered this, give Jarvis a wave—or brace yourself for Skynet. Either way, the AI journey is just beginning.