Algorithm: Today’s algorithms are sets of instructions that computers follow, and they have a long history dating back to Babylonian times. For example, a Euclidean algorithm for division is still used today. Even simple tasks like brushing your teeth could be considered algorithms due to the complex movements involved. Machine Learning: a branch of AI that allows computers to learn from data they process. Instead of programming knowledge directly into a computer, ML systems can be trained with millions of labeled images to recognize patterns and classify new examples. ML systems excel at recognizing patterns but struggle with complex planning and reasoning. Natural Language Processing: a form of machine learning that enables computers to interpret and respond to human language. NLP techniques select words based on their probability of accomplishing a goal, such as summarization or translation. Computers now make language associations themselves, whereas in the past, scientists had to code the rules. Neural Networks: a machine learning technique that emulates the behavior of neurons in the human brain. Artificial neurons send and receive information to form neural networks. Unlike older machine learning methods, neural networks learn from new data and adapt. For instance, Pinterest uses neural networks to personalize content for users based on their interests. Deep Learning: a form of AI that employs neural networks with multiple layers of nodes. Unlike neural nets, deep learning algorithms can handle more complex tasks due to their interconnected layers. However, they are less efficient than the human brain. Large Language Models: deep learning algorithms that can process vast amounts of data and generate text, images, or other content. These models are trained on open-source data sets and can operate in different modalities, like language, images, and audio. Generative AI: an AI type that creates content such as text, images, video, and audio. Generative AI is based on prompts given to foundation models, which then generate outputs. This technology has led to applications like chatbots, code writing assistants, and design tools. Interest in generative AI surged with the release of OpenAI’s ChatGPT and Dall-E. Chatbots: computer programs that engage in conversations with people using human language. Modern chatbots rely on generative AI and large language models to provide responses and generate new content. Hallucination: when a foundation model produces responses that are not grounded in fact, presenting them as if they were real. Since generative AI can sometimes produce hallucinations, experts advocate for human oversight. Artificial General Intelligence: a theoretical form of AI that can learn and think like humans. AGI aims to create machines capable of general problem-solving and adaptation.
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