February 13, 2018 — Oracle demonstrated artificial intelligence and machine learning advances in the Oracle Cloud Platform at Oracle CloudWorld in New York.

Autonomous capabilities for application development, mobile and bots, integration, analytics, security and system management are scheduled for availability in the first half of this year.

Oracle announced a slew of new Platform as a Service features and capabilities, including the following:

Mobile and bots —

Self-learning chatbots that observe interaction patterns and preferences to automate frequently performed end-user actions, freeing up time for higher-productivity tasks;
Unsupervised smart bots that use machine learning to learn from user conversations, enabling fluid, contextual conversations; and
Automated caching of API calls to the nearest data center in real time for lowest-latency responses based on the end user’s location.

Security and management —

Machine learning-driven user and entity behavior analytics to isolate and eliminate suspicious and malicious users automatically;
Preventative controls to intercept data leaks across both structured and unstructured data repositories; and
A unified data repository across log, performance, user experience, and configuration data with applied AI and ML to automate setting and managing performance and security monitoring metadata.

The Oracle Digital Assistant
Oracle also demonstrated an Oracle Digital Assistant, which will provide centralized communications across CRM, ERP, HCM, custom applications and business intelligence data.

The assistant will use AI to correlate data and automate user behavior.

Among its capabilities:

Integration with speech-based devices and software like Amazon Echo (Alexa), Apple Siri, Google Home and Speech, Harman Kardon (Cortana) and Microsoft Cortana;
Deep neural net-based machine learning algorithms to process messages from voice-based devices to understand end-user input and take action;
Intelligent routing to Oracle Bots with the knowledge to process end users input; and
Deep insights into user behavior, context, preferences and routines the Assistant uses to self-learn, in order to recommend and automate across all data sets on behalf of the user.