1. It is not accessible to millions of developers
2. It doesn’t operate in the here and now
3. It is not interactive
Xyggy solves all 3 problems for discovery services and we talk more about each below:
Conventional AI is not accessible to millions of developers because they don’t have the requisite knowledge and skills.
Open source AI frameworks are of little use to most developers if they don’t know what’s what.
Instead, a high-priesthood has grown up that harks back to the mainframe era of experts in the glass room.
Xyggy automates the complexity end-to-end to make it easy for all developers to build intelligent discovery services.
Plug-in data at one end and build at the other.
The automated black-box in the middle looks after all the complexity.
It opens up powerful AI for all developers.
Discovery services include Personal Discovery, Autonomous Discovery, Intentional Discovery, Feeds and Recommendation Systems.
Conventional machine learning delivers realtime scoring (against a static data model) but not realtime learning (on static or changing data) which is its Achilles heel.
It is like learning to eat spaghetti for the first time by watching someone else do it. But, you can’t put it into practice until the next day after your brain has been updated with the knowledge while sleeping. Imagine if real life was like that. It is for AI 1.0.
A real world example demonstrates this - 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.”
Each time a model is re-trained and re-tested, the cost in time and resources accumulate.
The Xyggy Engine automatically learns as it goes along.
There are no training and re-training cycles.
The world changes.
With conventional AI, when new or update data arrives, the model is re-trained and re-tested. Again, the cost in time and resources accumulate.
The Engine is the analogical equivalent of an in-memory SQL DBMS with equivalent CRUD operations and query statements all available through the API.
Add, update and delete data items individually or in bulk at any time.
Design a UI/UX for a responsive and interactive AI with the API.
Options includes multiple-items-per-query, more-like-these, less-like-these, relevance feedback, serendipity, anomaly detection and active learning.
A query is formed with one or more items and the Engine will find other relevant items in ranked order. A query can be modified mid-flight.
For all data types including text, images, audio and composite. Structured and unstructured data.
Scales effortlessly on a single server and loosly-coupled distributed machines.
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.
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