April 28, 2023
AI, or Artificial Intelligence, is technically not artificial at all, and refers to the human development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing objects in images, and making decisions based on data.
In the past year AI has become a rapidly growing field that is taking the world by storm. However this comes a plethora of technical jargon and acronyms, which are typically confusing to people unfamiliar with the field. Here we will cover in a nutshell the most common modern day AI terminologies that are useful to be aware of.
ChatGPT is an AI-powered chatbot that uses machine learning to generate human-like responses to user queries. GPT stands for 'generative pre-trained transformer', and accordingly is ChatGPT is trained on a large corpus of text data with extensive training before being released to the public. Although weak at computation, it's impressive language prowess means it can be used across a wide variety of applications, from customer services to building websites.
AutoGPT is an extension of ChatGPT that starts out from a human submitted high-level prompt or goal, then re-prompts itself in an iterative fashion depending on the results it finds from each individual prompt. The purpose of this auto-prompting technique is to use more a creative discovery learning process to simplify and potentially surpass human prompting, or to produce unpredictably novel solutions to problems.
TruthGPT is Elon Musk's AI-powered fact-checking tool that uses machine learning to identify false information in text-based content. Truth GPT is trained on a large corpus of fact-checked information and can be used to help combat misinformation and fake news.
Bard is a conversational artificial intelligence chatbot developed by Google using large language models (LLMs). It was developed as a direct response to the rise of OpenAI's ChatGPT, and was released in a limited capacity in March 2023 and was met with lukewarm responses.
Stable Diffusion is a generative model that uses deep learning to create images from text. The model is based on a neural network architecture that can learn to map text descriptions to image features. This means it can create an image matching the input text description.
Stable diffusion is also a AI technique used in some machine learning algorithms to help prevent overfitting. Overfitting occurs when a model performs well on the data it was trained on, but performs poorly on new data. Stable diffusion helps to prevent this by introducing random noise into the training data.
Midjourney is a service created and hosted by the independent research lab Midjourney Inc. The platform generates images from natural language prompts, similar to Stable Diffusion.
Similar to Stable Diffusion and Midjourney, DALL-E is a deep learning model developed to generate digital images from prompts. DALL-E was revealed by OpenAI in January 2021. It leverages a modified version of GPT to generate realistic images at high resolution, and can covert combinations of language based concepts, attributes, and styles.
Google DeepMind is an AI research organization that has developed a range of applications, including AlphaGo, an AI program that defeated the world champion at the game of Go, and DeepMind Health, which uses AI to help doctors diagnose and treat patients more effectively.
IBM Watson is an AI platform that provides a range of cognitive services and tools to help businesses and organizations analyze data, build chatbots, and develop other AI-powered applications.
Amazon Go is an AI-powered shopping experience that allows customers to enter a store, grab what they want, and leave without waiting in line or checking out, thanks to computer vision and machine learning algorithms.
Natural Language Processing (NLP) is a branch of interpretative AI that deals with the interaction between computers and human language. NLP is used in a variety of applications such as language translation, sentiment analysis, and chatbots.
LLM stands for Large Language Model, and LDM stands for Language Data Model. These are terms used to describe AI models that have been trained on vast amounts of text data. LLMs and LDMs are used in Natural Language Processing (NLP) tasks such as language translation and sentiment analysis, and provide core elements of some of the latest AI applications.
Predictive analytics is a type of AI that uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Predictive analytics is used in a variety of industries, including finance, healthcare, and marketing.
Machine intelligence refers to the ability of machines to learn, reason, and recognize patterns. Machine intelligence is the foundation of AI and is used in a wide range of applications, including image recognition, natural language processing, and predictive analytics.
Machine learning is a subset of AI that involves teaching machines to learn from data without being explicitly programmed. Machine learning algorithms can be used to identify patterns in data and make predictions based on that data.
AGI stands for Artificial General Intelligence. AGI refers to AI systems that have the ability to independently learn and reason beyond human abilities (but not necessarily by mimicking them). AGI is the holy grail of AI research, but also brings with it threatening scenarios of dramatic disruptions to modern life or potentially existential risks to humanity. Though many experts believe it is still a long way off, others suggest that the latest developments in AI are showing some initial indications of AGI characteristics.
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