Machine Intelligence Applications - Hypergiant

***

Hypergiant uses Machine Intelligence to evolve how humans work.

We take the AI learning of the past 80 years -representing the best simulations of human decision-making, use it to define the most effective machine intelligence approach, add your data, and invent your intelligent machine that changes the world.

Input data for immediate correspondence.

“Intelligent machines don’t take jobs away. They create better jobs and better people. There is nothing artificial about trying to make people better.”
- Ben Lamm

Hyper
Giant

The main difference between Artificial Intelligence and Machine Intelligence is that Artificial Intelligence uses existing data to simulate human behavior, and Machine Intelligence determines how best to augment human behavior.


THE INTELLIGENT MACHINE
  • LEARNING

    SUPERVISED— the machine answers from a defined dataset. UNSUPERVISED— the machine finds structure in an undefined dataset. REINFORCEMENT— the machine learns by trial and error.

  • VISION

    The intelligent machine sees objects or reads content, analyzes and interprets the input, and may create images or text. It makes operational decisions based on the conclusions it draws from the data.

  • SPEECH

    The intelligent machine hears human language—in its natural form, despite its imperfections—To analyze and interpret the input, and may speak, translate, or transcribe to text.

Machine Learning

Machine Intelligence Applications

Machine learning is not the future. it is now. The coin sorter at your bank operates on a supervised learning model that identifies and categorizes currency. Health insurance analysts rely on unsupervised learning to identify patterns in patient behaviors that lead to more or fewer claims. The publishing world employs intelligent software to read content and create original work and summaries. Reinforcement learning pilots your automated vacuum whenever it hits a wall and informs the artificial intelligence behind a tense chess match.

COMPUTER VISION

Machine Intelligence Data Analytics

As important as sight is to logical and psychological human development, so is computer vision to an intelligent machine. A common example is software that analyzes a photograph—identifying and classifying the objects and people within it—to suggest labels and tags. Higher-tech applications of computer vision pilot autonomous vehicles and run facial recognition algorithms for search-and-rescue missions [executed by humans and machines]. The more visual data an intelligent machine has, the greater its reliability and decision-making power.

NATURAL LANGUAGE & SPEECH

Machine Intelligence Techniques

Like computer vision, natural language processing/generation and speech recognition/synthesis are key components of machine intelligence applications that augment how humans work. Hypergiant applies a mix of Markov models and deep learning to extract text from audio, and machine learning to generate WAVs from scratch. NLP and speech technologies are not new, and they are evolving at exponential light speed. interactive phone systems have been around nearly two decades. Siri and Alexa are family members. A recent development is an earpiece that translates spoken foreign language into the wearer’s preferred language.

Machine Intelligence Data Analytics

Hypergiant relies on two models:

PREDICTIVE ANALYTICS & CORRELATIVE ANALYTICS

Predictive Analytics

Predictive Analytics

  • EXTRACTING MEANINGFUL INFORMATION FROM DATASETS TO CREATE A MODEL THAT CAN PREDICT FUTURE PROBABILITIES, I.E. NAÏVE BAYES.
Correlative Analytics

Correlative Analytics

  • EXTRACTING MEANINGFUL INFORMATION FROM DATASETS TO CREATE A MODEL THAT IDENTIFIES CORRELATIONS TO MAKE PREDICTIONS, I.E. CLUSTERING.
Machine Intelligence Techniques