Description of AI and Algorithms - Key Concepts

To better understand this rapidly evolving field, let’s explore some key terms and concepts:

• Narrow AI. Current AI technology is “narrow” because it can solve only the tasks for which it is trained. It can’t extrapolate from its experience to entirely different areas of intelligence or understand broader concepts without specific training. Narrow AI systems may also suffer from “catastrophic forgetting,” losing the ability to solve previous tasks when they are trained for new ones.

• Artificial General Intelligence (AGI). AGI refers to an intelligent agent that can understand or learn any intellectual task a human can do. It is a longstanding goal in AI development, exemplified by the Turing Test, designed by Alan Turing in 1950.

• Machine learning. Instead of programming machines with explicit instructions, machine learning enables machines to deduce concepts or outcomes directly from data. This process includes building models for machines to learn.

• Neural nets. A type of machine-learning model loosely based on human brain neurons. Neural nets are digital operating units that produce outputs based on inputs. They are arranged in layered networks, with more neurons enabling a greater capacity to learn complex concepts and relationships.

• Deep learning. A type of machine learning based on artificial neural networks that uses multiple layers of processing to extract progressively higher-level features from data. Deep learning can lead to superior results in applications such as computer vision, speech recognition, natural language processing, drug design, and medical image analysis.

• Generative adversarial networks (GANs). GANs are neural network models where a generator and a discriminator serve as adversaries. The generator creates synthesized outputs to fool the discriminator while the discriminator tries to detect if the output is fake.

• Natural language processing (NLP). NLP focuses on creating computers and digital agents that can interact with humans using regular human language. It aims to develop devices that can analyze vast amounts of natural-language data in near-real-time.

• Federated learning. Federated learning is a decentralized, distributed machine-learning technique that trains AI models across multiple devices holding local data samples, such as smartphones or IoT devices, without sharing the actual data. This approach allows the AI model to learn from a wide range of data sources while preserving privacy and reducing the need for centralized data storage. In federated learning, devices collaboratively update a shared global model by sending only the model updates, such as gradients or parameters, to a central server, which then aggregates these updates to improve the global model. Federated learning unlocks information for AI applications without requiring devices to surrender and share data, making it a standard for meeting privacy regulations.

• Large language models (LLMs). LLMs are advanced machine-learning models designed for natural-language processing tasks. They excel in understanding and generating human-like text based on context, thanks to innovations in deep-learning techniques, particularly the Transformer architecture. Trained on massive text datasets, LLMs like OpenAI’s GPT-4, Google’s Bard, and Stability AI have demonstrated remarkable capabilities in various applications, such as summarization, translation, and question-answering. Their ability to generate coherent and contextually accurate text has made them a powerful tool in the world of Artificial Intelligence.

By understanding these key terms and concepts, we can appreciate the ongoing advancements in AI and its potential to revolutionize various aspects of our lives.