The intelligence demonstrated by computers in making decisions in a human-like way with the ability to iteratively improve themselves. While weak Al systems are built. to simulate human intelligence, strong Al is built to copy human intelligence.
A set of well-defined rules that a machine goes through to solve a problem or complete a task. Typically used for computations and data processing, algorithms form the basis of computing.
Natural Language Processing
NLP refers to the interactions between computers and humans. It ensures that Al can understand and interpret natural human languages we speak, thus bridging the gap between humans and electronic devices.
A subset of machine learning that mimics the neurons in the human brain and how they signal to one another. Neural networks pass data through interconnected layers of nodes, until the network creates the output. Neural networks are at the heart of deep learning algorithms.
How Al decides the order of words in a sentence it creates, based on the probability of the words to make sense
A step up this neural network is clever enough to elude the programm the data passes through severa layers of processing before issu a response. ChatGPT and Stable Diffusion use deep learning techniques.
OpenAl’s third-generation language model and forerunner of ChatGPT
Google’s deep learning model that can fine-tune images created through diffusion. It can even generate new pictures of specific people based on their existing photos. Not for public use.
OpenAl’s text-to-image tool uses GPT-3 to interpret users’ requests. Available to the public in beta form, users can construct up to 50 images for free.
This image editing app for iOS and Android launched in 2018 has gone viral in recent weeks, thanks to a new ‘Magic Avatar’ feature. Its overly sexualised images, particularly of women, and accidental nudes have stirred controversy.
A method for creating images from text prompts that is used by popular text-to-image tools, like DALL-E and Stable Diffusion. Such image generation models, trained to reverse the diffusion process on natural images, start with a random noise image and then generate new natural images.
Teaching computers to recognise patterns using data and algorithms. Unlike for traditional programming, the computer doesn’t need to be explicitly coded to handle all conceivable scenarios.
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