💡🤖😎10 AI Terms and Aspects That Everyone Needs to Understand and Be Aware of Today
🧐Today, we’ll look at 10 aspects that most broadly cover the field of AI in its various manifestations:
✅ Reasoning/Planning: Modern AI systems can solve problems by using patterns they’ve learned from historical data to understand the information, which is similar to the process of reasoning. The most advanced systems can go further, tackling more complex problems by creating plans and determining a sequence of actions to achieve a goal.
✅ Learning/Inference: There are two stages to creating and using an AI system: learning and inference. Learning can be compared to the process of educating an AI, where it’s given a set of data and it learns to perform tasks or make predictions based on that data.
Inference is the process by which an AI uses learned patterns and parameters to, for example, predict the price of a new home that will soon go on sale.
✅ Small Language Models (SLMs): Compact versions of Large Language Models (LLMs). Both of these types use machine learning techniques to recognize patterns and relationships, allowing them to generate realistic and natural language responses. However, unlike LLMs, which are huge and require a lot of computing power and memory, SLMs like Phi-3 are trained on smaller, curated datasets and have fewer parameters.
✅ Grounded: Generative AI systems can create stories, poems, jokes, and answer research questions. However, they sometimes have difficulty separating fact from fiction or use outdated data, leading to erroneous answers called “hallucinations.” Developers aim to make AI interactions with the real world more accurate through a process called grounding, where the model is connected to current data and specific examples to improve accuracy and produce more relevant results.
✅ Retrieval Augmented Generation (RAG): When developers give AI access to external data sources to make it more accurate and relevant, a technique called Retrieval Augmented Generation (RAG) is used. This approach saves time and resources by adding new knowledge without having to retrain the AI.
✅ Orchestration: AI programs perform many tasks when processing user requests, and an orchestration layer manages their actions in the right order to get the best response. The orchestration layer can also follow the RAG pattern, searching the web for fresh information and adding context.
✅ Memory: Modern AI models technically do not have memory. However, they may have orchestration instructions that help them “remember” information by performing specific steps with each interaction.
✅ Transformers and Diffusion Models: Humans have been training AI systems to understand and generate language for decades, but one of the breakthroughs that has accelerated progress is the Transformer model. Among generative AIs, Transformers are the ones that understand context and nuance the best and fastest.
Diffusion models are typically used to generate images. These models continue to make small adjustments until they create the desired output.
✅ Frontier Models: Frontier models are large-scale systems that push the boundaries of AI and can perform a wide range of tasks with new and advanced capabilities. They are becoming key tools for a variety of industries, including healthcare, finance, scientific research, and education.
✅ GPU: A graphics processing unit is a powerful computing unit. Initially created to improve the graphics in video games, they have now become the real “muscles” of the computing world. And since AI essentially deals with a huge number of computational problems in order to understand language and recognize images or sounds, GPUs are indispensable for AI both at the training stage and when working with finished models.