Idea Learning combines the power of human knowledge with machine learning.
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Idea
Learning
Gamalon gets smarter faster by bringing together machine learning with domain-specific human understanding.
Gamalon was built to be explainable and easily editable.
Idea Learning is the fastest, most accurate approach to help you gain rich insights from your unstructured data.
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Import Real-time Data
You can import your real-time or batch data from any other system. Idea Learning incorporates human grammar automatically, and it rapidly learns to understand data in any database or machine format.
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Our AI Summarizes your Customers’ Ideas
After importing data, Idea Learning finds the initial set of categories to populate your machine learning model and surfaces a first summary of all the customer’s ideas.
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A Person Updates the Model
A person, typically a subject matter expert for the business, reviews what the machine learning has come up with in terms of categories and so forth, and provides any feedback/guidance they may have for what they would prefer.
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Our AI Updates the Model
After an SME or analyst is done editing the model, our Idea Learning algorithm will then iterate again on the data set, learning more, and the model will grow deeper.
6 Key
Principles
We have developed our approach to machine learning, which we call Idea Learning, with 6 key principles in mind. Inspired by the work of Pedro Domingos in the Master Algorithm, we combined different aspects of each approach to machine learning.
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People
Editable & Auditable AI; Human-machine interaction using ideas, not just labeling data -
Deep Learning
Neural / Connectionist; Deep learning finds patterns that people cannot find themselves. -
Programs
The ability to learn rules/programs that humans can read and understand -
Probabilistic
System is self aware internally about where it is uncertain -
Populations
Inspiration from evolutionary algorithms -
Compositional
Train and then recombine modules
Related
Articles
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When Machines Have Ideas
A lab-to-market examination of AI solutions putting corporate data to work.
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Academic Literature Relating To Gamalon
Reviewing the emergence of several core architectural principles or axioms that seem necessary for machine learning systems to progress.
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MIT Media Lab Presentation
Ben Vigoda, Gamalon CEO, speaks at MIT Media Lab about Gamalon and how Gamalon helps you understand your customers.
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TEDx Boston: When Machines Have Ideas
Why building “stories” (i.e. Bayesian generative models) into machine intelligence systems can be very powerful.
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Talking Machines Interview
Listen to Katherine Gorman interview our CEO, Ben Vigoda, on Talking Machines.