QAI (Quantum Artificial Intelligence)
Introducing QAI Digital Physics
The Standard Model of particle physics is the theory describing the known fundamental forces. QAI is a machine learning digital physics system built on the premise that the standard model is synonymous with information and by extension, computation. The patent pending system implements human intuition and emotion without rules, continuously learns and is unbiased. The system was validated during life science drug discovery where it resulted in what we understand to be the first “smart molecule,” i.e. one that makes decisions about its biological activity. The molecule showed good activity, was patented, and is now the property of Genentech. QAI has a great many applications.
Resolving the Artificial Intelligence ML Stalemate
Application of current Machine Learning (ML) cannot accurately and reliably analyze the enormous volumes of data captured and processed today, nor reliably convert it to predictive actionable intelligence. The reason ~ we all have access to the same old fundamental Machine Learning (ML) algorithms, resulting in an Artificial Intelligence ML stalemate.
The logic to this assertion is quite simple: current ML algorithms are over 30+ years old and have not fundamentally changed in decades, i.e. everyone has what everyone has. The only difference today is computer processing speed has drastically increased and proportionately reduced in cost ubiquitously. The current ML algorithms are the same as decades past and their known flaws, including bias, fundamental to all supervised ML algorithms and performance challenges with reinforcement ML algorithms and other challenges, are existentially systemic.
The result is we all fully understand these flawed ML algorithms and their exploitable counter measures. Metaphorically, current ML is like building construction technology. Using it and faster computer processing, taller and taller buildings are constructed, yet they will always just be tall buildings with their fundamental ML algorithmic flaws and will never reach the moon.
We realized long ago that ML must change to address these limitations, and we have changed the game.
"It's all about QAI Digital Physics quantum signaling." --Lars Wood
QAI requires no training; is unbiased
BIAS is the biggest challenge in machine learning
View links below to understand the value of solving this problem
Forget Killer Robots—Bias Is the Real AI Danger (MIT Technology Review)
Inspecting Algorithms for Bias (MIT Technology Review)
Google fined $21.1M for search bias in India (TechCrunch)
Training AI to be unbiased must be a priority, not an afterthought (City A.M.)
Why it’s so hard to create unbiased artificial intelligence (TechCrunch)