The vast majority of business apps use databases. Our customers frequently integrate with Microsoft SQL, NoSQL, SQLite, Oracle, SAP and more. Recently, CIOs we're speaking with have started talking about the rise of Artificial Intelligence as a data source, and they're beginning to look at how to embed artificial intelligence in mobile apps.
We’re written recently about chatbots and sensors becoming key to mobile app development. At the recent MIT CIO Symposium, CIOs were talking to us about artificial intelligence and machine learning. As CIOs, IT leaders and developers begin to consider how to incorporate Artificial Intelligence in their app projects, here are two articles we thought our readers would find particularly helpful.
First, Janakiram & Associates Analyst Janakiram MSV recently wrote an article on 3 Steps to Embedding Artificial Intelligence in Enterprise Applications. The article stressed the importance of developers to build a roadmap to intelligent business apps, and offered practical advice developers could start using today.
"Artificial Intelligence is all set to become the new database for the next generation applications.”He predicts:
“Like databases, Artificial Intelligence (AI) is moving towards becoming a core component of modern applications. In the coming months, almost every application that we use will depend on some form of AI....Artificial Intelligence is all set to become the new database for the next generation applications.”
In his article, Janakiram outlines 3 steps developers can take to begin polishing their knowledge of AI for app development:
1. Start Consuming Artificial Intelligence APIs Janakiram explains that this is the least disruptive way to get started – by turning existing apps intelligent by integrating with APIs for text-to-speed, speech-to-text, natural language processing, video search, language understanding, image processing and more. He includes a sample list of Artificial Intelligence Platforms that expose their APIs at an affordable price point:
- Amazon AI Services
- Google Cloud ML Services
- IBM Watson Services
- Microsoft Cognitive Services
- Amazon ML
- Azure ML Studio
- Ersatz Labs
4. Finally, Janakiram lists open source platforms for Machine Learning and Deep Learning that developers should begin exploring to get into more sophisticated artificial intelligence efforts:
In a second piece by AI vendor Neura, writers proposed 6 ways developers can start embedding artificial intelligence into their apps to improve user experiences:
- Moment Based Alerts – typically, alerts are based on the clock, but AI generates alerts or reminder based on the user’s context or real-time activities.
- Tailored Messages – apps can generate intelligent, personalized messages based on behaviors that are captured and analyzed
- Self-Knowledge/Awareness – apps that capture behaviors can then tell users more about themselves, their physical trends and prompt action or in-app behavior
- Proactive Service – smart apps that have proactive services take action based on a user’s behavior, such as turning off a thermostat or lights when a user leave the house.
- Smart Logins – who isn’t frustrated with login processes? Getting users into and out of systems as needed, or as required by security or safety requirements, can make for better user experiences.
- Next-Level Gamification and Incentives – apps that can assess user behavior and reward them -- or encourage them - to take action, can help make apps a fun, routine part of the day.
Read Janakiram MSV’s article3 Steps to Embedding Artificial Intelligence in Enterprise Applications or Neuva’s article Avoiding Churn with AI: Six Ways to Boost Engagement of Your IoT Device or App with Artificial Intelligence