Build and scale the next wave of AI innovation on AWS

Content recommendations

AI powers recommendation engines for leading streaming services like Netflix, analyzing user preferences to deliver personalized content suggestions. By keeping customers engaged, AI helps businesses improve retention and boost customer satisfaction.

Personalized shopping

E-commerce platforms use AI to provide personalized product recommendations based on customers’ browsing history and preferences, driving higher sales and better shopping experiences.

Medical Analysis and Diagnostics

AI is reshaping healthcare with advanced diagnostics, treatment planning, and patient monitoring. AI systems can analyze medical images to detect diseases early and help customize treatment plans based on patient history and data.

Traffic management

AI optimizes traffic flows by analyzing real-time data, predicting traffic patterns, and suggesting alternate routes. This improves transportation efficiency, reduces congestion, and helps lower emissions.

Conservation

AI is a powerful tool in conservation efforts, helping monitor wildlife, combat deforestation, and prevent poaching with AI-powered drones and satellite imagery. AI’s real-time monitoring capabilities are transforming environmental protection strategies.

Generative AI, or gen AI, is a component of AI that can create new content and ideas, like images and videos, and also reuse what it knows to solve new problems. Like all artificial intelligence, generative AI works by using very large models that are pre-trained on vast amounts of data. The models try to predict features given a certain label, quickly learning the distribution of different data features and their relationships. Generative AI is able to demonstrate human-like creative capabilities such as creating new content—such as images, videos, and text—from simple prompts, representing a major leap in AI’s creative capabilities. Learn more about gen AI.

While you may see the terms artificial intelligence and machine learning being used interchangeably in many places, machine learning is technically one among many other branches of artificial intelligence. It is the science of developing algorithms and statistical models to correlate data. Computer systems use machine learning algorithms to process large quantities of historical data and identify data patterns. In the current context, machine learning refers to a set of statistical techniques called machine learning models that you can use independently or to support other more complex AI techniques. Learn more about ML.

Deep learning takes machine learning one step further. Deep learning algorithms use neural networks that work together to learn and process information. They comprise millions of software components that perform micromathematical operations on small data units to solve a larger problem. For example, they process individual pixels in an image to classify that image. Modern AI systems often combine multiple deep neural networks to perform complex tasks like writing poems or creating images from text prompts. Learn more about deep learning in AI.

Neural Networks

At the core of AI, neural networks mimic the human brain, processing data through interconnected nodes to solve complex problems. Learn about neural networks

Natural Language Processing (NLP)

NLP enables machines to understand and generate human language, powering applications like chatbots, document summarization, and sentiment analysis. Learn about natural language processing (NLP)

Computer Vision

AI extracts insights from images and videos, used in everything from content moderation to autonomous vehicles, where split-second decisions are critical. Learn about computer vision

Speech Recognition

Deep learning models transcribe and interpret human speech, driving applications like virtual assistants and call center software. Learn about speech-to-text

AI stands for Artificial Intelligence, which refers to machines' capability to mimic human intelligence, including learning, reasoning, and problem-solving.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human cognitive functions, such as understanding language, recognizing patterns, and making decisions.

To start using AI in your business, identify areas where AI can improve efficiency, such as automating customer service with chatbots, analyzing data for better decision making, or personalizing marketing efforts. Tools like predictive analytics, AI-driven content generation, and recommendation systems can help drive business growth.

You can start using AI in daily life through virtual assistants like Alexa or smart home devices that automate tasks. Additionally, AI-powered apps for fitness tracking, language learning, and budgeting can make everyday activities more efficient and tailored to your needs.

The history of Artificial Intelligence (AI) spans over seven decades, beginning with Alan Turing's groundbreaking work in 1950. Early developments from 1943 to 1969 focused on neural networks and the creation of the first AI chatbot. The 1970s saw the first "AI winter" due to funding cuts and technological limitations, followed by a renewed interest in the 1980s with a focus on expert systems and neural networks. A second "AI winter" occurred in the late 1980s and 1990s, leading to fragmented research. Significant achievements marked the period from 1997 to 2006, including IBM's Deep Blue defeating a world chess champion. From 2007 to 2018, advancements in deep learning and increased accessibility through cloud computing propelled AI forward. The widespread adoption of AI chatbots in 2022 renewed interest in AI development. Currently, AI technologies are specialized and function within pre-determined parameters. Artificial General Intelligence (AGI) , which aims to create software with human-like intelligence and self-learning abilities, remains a theoretical concept and future research goal.