Monica Anderson Email and Phone Number

Monica Anderson is CTO of Syntience Inc, an AI Research company in Silicon Valley founded by her in 2004. She has a Master's degree in Computer Science from Linköping University in Sweden.She has been researching Large Language Model (LLM) based on Deep Neural Networks (DNN) since January 1, 2001. Her LLMs are very different from competing systems from OpenAI and others. The learning algorithm is called Organic Learning and uses a network of discrete neuron-like objects. We call this a DDNN, for Deep Discrete Neuron Network.Anderson approaches AI and Machine Learning theory starting from Epistemology rather than from logic, math, neuroscience, or programming. Her research focuses on fundamentals like Understanding, Learning, Reasoning, Epistemic Reduction/Abstraction, and the Binding Problem of AI.Syntience released a demo of UM1 (Understanding Machine One) in December 2020. It was one of the first ever Large Language Models in the cloud. UM1 cannot do dialog like ChatGPT; it only does message classification and filtering. These were the main use cases for Industrial Natural Language Understanding until the release of ChatGPT in 2022, which refocused the industry to dialog based systems. Syntience is currently researching next-generation ChatBot technologies by enhancing the UM1 algorithms to handle dialog.Anderson's main publishing site is https://experimental-epistemology.ai which has many links at the top of the page, such as to videos of her talks at https://vimeo.com/showcase/5329344 .Her brand is "Zeroth Principles of AI" and she has a Substack for political essays at https://zerothprinciples.substack.com as well as a MeetUp group and YouTube channel by the same name. She organized over 100 AI Meetups, twice monthly, over five years starting in 2006 and is available to lead corporate workshops on AI Epistemology and other fundamentals.Syntience is seeking to cooperate with corporations that are considering AI/ML/LLM solutions from OpenAI, Microsoft, or Google and want to explore orders-of-magnitude cheaper alternatives.