Artificial Intelligence
What Is AI?
Artificial intelligence is transforming scientific research as well as everyday life, from communications to transportation to health care and more. Explore what defines AI, how it has evolved since the Turing Test, and the future of artificial intelligence.
What Is the Difference Between "Artificial Intelligence" and "Machine Learning"?
The term "artificial intelligence" is older and broader than "machine learning." Learn how the terms relate to each other and to the concepts of "neural networks" and "deep learning."
How Do Computers Learn?
Machine learning applications power many features of modern life, including search engines, social media, and self-driving cars. Discover how computers learn to make decisions and predictions in this illustration of two key machine learning models.
How Is AI Applied in Everyday Life?
While scientists and engineers explore AI's potential to advance discovery and technology, smart technologies also directly influence our daily lives. Explore the sometimes surprising examples of AI applications.
What Is Big Data?
The increase in available data has fueled the rise of artificial intelligence. Find out what characterizes big data, where big data comes from, and how it is used.
Will Machines Become More Intelligent Than Humans?
Whether or not artificial intelligence will be able to outperform human intelligence—and how soon that could happen—is a common question fueled by depictions of AI in movies and other forms of popular culture. Learn the definition of "singularity" and see a timeline of advances in AI over the past 75 years.
How Does AI Drive Autonomous Systems?
Learn the difference between automation and autonomy, and hear from Caltech faculty who are pushing the limits of AI to create autonomous technology, from self-driving cars to ambulance drones to prosthetic devices.
Can We Trust AI?
As AI is further incorporated into everyday life, more scholars, industries, and ordinary users are examining its effects on society. The Caltech Science Exchange spoke with AI researchers at Caltech about what it might take to trust current and future technologies.
What is Generative AI?
Generative AI applications such as ChatGPT, a chatbot that answers questions with detailed written responses; and DALL-E, which creates realistic images and art based on text prompts; became widely popular beginning in 2022 when companies released versions of their applications that members of the public, not just experts, could easily use.
Ask a Caltech Expert
Where can you find machine learning in finance? Could AI help nature conservation efforts? How is AI transforming astronomy, biology, and other fields? What does an autonomous underwater vehicle have to do with sustainability? Find answers from Caltech researchers.
Terms to Know
A set of instructions or sequence of steps that tells a computer how to perform a task or calculation. In some AI applications, algorithms tell computers how to adapt and refine processes in response to data, without a human supplying new instructions.
Artificial intelligence describes an application or machine that mimics human intelligence.
A system in which machines execute repeated tasks based on a fixed set of human-supplied instructions.
A system in which a machine makes independent, real-time decisions based on human-supplied rules and goals.
The massive amounts of data that are coming in quickly and from a variety of sources, such as internet-connected devices, sensors, and social platforms. In some cases, using or learning from big data requires AI methods. Big data also can enhance the ability to create new AI applications.
An AI system that mimics human conversation. While some simple chatbots rely on pre-programmed text, more sophisticated systems, trained on large data sets, are able to convincingly replicate human interaction.
A subset of machine learning. Deep learning uses machine learning algorithms but structures the algorithms in layers to create "artificial neural networks." These networks are modeled after the human brain and are most likely to provide the experience of interacting with a real human.
An approach that includes human feedback and oversight in machine learning systems. Including humans in the loop may improve accuracy and guard against bias and unintended outcomes of AI.
A computer-generated simplification of something that exists in the real world, such as climate change, disease spread, or earthquakes. Machine learning systems develop models by analyzing patterns in large data sets. Models can be used to simulate natural processes and make predictions.
Interconnected sets of processing units, or nodes, modeled on the human brain, that are used in deep learning to identify patterns in data and, on the basis of those patterns, make predictions in response to new data. Neural networks are used in facial recognition systems, digital marketing, and other applications.
A hypothetical scenario in which an AI system develops agency and grows beyond human ability to control it.
The data used to "teach" a machine learning system to recognize patterns and features. Typically, continual training results in more accurate machine learning systems. Likewise, biased or incomplete datasets can lead to imprecise or unintended outcomes.
An interview-based method proposed by computer pioneer Alan Turing to assess whether a machine can think.
