Automatic realtime machine learning

for tomorrow's intelligent discovery services

How is it different from conventional machine-learning?

  • The Engine learns automatically and so there is no notion of training and re-training cycles. It learns as it goes along.

  • From Inside Facebook's Biggest AI Project Ever

  • “To do this Facebook run tens of trillions queries per day to make about six million predictions per second. Facebook trains the algorithms that power its News Feed within hours, using trillions of data points. The company updates its learning models every 15 minutes to two hours so that it can react quickly to current events.”

    The re-training of learning models is endemic to every machine learning system in the world and the cost in infrastructure, resources and time are enormous.

    It tricks people into thinking that AI services are dynamic when in fact, they are static.

  • Add, update and delete data items individually or in bulk at any time. The Engine is the analogical equivalent of an in-memory SQL DBMS.

  • Design a UI/UX for a responsive and interactive AI with a flexible query API. Options includes multiple-items-per-query, more-like-these, less-like-these, relevance feedback, serendipity and anomaly detection.

  • A query is formed with one or more items and the Engine will find other relevant items in ranked order.

  • All data types are supported which includes text, images, audio and composite data.

  • Scales effortlessly on a single server and across distributed machines.

  • For all types of discovery services including Personal Discovery, Autonomous Discovery, Intentional Discovery, Feeds and Recommendation Systems and so on.

  • Some use-cases:

  • End-to-end automated machine intelligence. Plug-in data at one end and query at the other, all with a simple API. Opens up powerful AI for developers with no previous machine learning experience.

  • Personal discovery with privacy. Today we have intentional discovery (“I want this”) and social discovery (“My friends think I want that”) yet personal discovery (“I don’t know what I want but I will when I see it”) remains largely an unsolved problem.

  • Transform relational databases into intelligent databases.

  • Job seekers search a corpus of job descriptions with their resume. Conversely, employers search for candidates with a ‘fake’ resume or job description (or both) as the query.

  • ... and many more.

Bayesian machine learning. Automatic learner. Deep learning. Distributed. Scalable. Interactive. API.

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