Alliance Seeks Greater Clarity for Artificial Intelligence

As businesses seek to bring artificial intelligence and machine learning (AI/ML) into the mainstream, challenges emerge. Scaling from pilot projects to production can be difficult. Earlier this year, the issue got new attention and a group that seeks to address the problems got noticed.

“Band of AI startups launch ‘rebel alliance’ for interoperability”

That headline in VentureBeat certainly caught my eye. Rebel alliances within the technology sector certainly help move things forward. In the 1990s, a rebel alliance – led by Sun Microsystems – took a shot at the Microsoft-centric C++ establishment with the Java language and associated frameworks. Around the same time, IBM and Oracle led a rebel alliance to unseat the dominance of Microsoft Windows PCs with their own versions of “thin-client” workstations. More recently, a group of companies led by Google and Sony took on the dominance of Apple in the smartphone space with the Android operating system.

There has been a lot of concern about the dominance of large tech players in shaping artificial intelligence, and in this case, a group of startups is pitching a different philosophy and approach. The AI Infrastructure Alliance is developing a canonical stack for machine learning, bringing together a number of vendors, communities, and other organizations.

“When a true canonical stack forms in the marketplace, it creates a rock-solid foundation for future software to build on, letting developers and researchers move up the stack to solve bigger, more challenging and more rewarding problems,” according to the alliance. The alliance seeks to bring “striking clarity” to AI efforts “by highlighting the strongest platforms and establishing clean APIs, integration points, and open standards for how different components of a complete enterprise machine learning stack can and should interoperate.”

Along with the creation of a Canonical Stack, the alliance seeks to accomplish the following:

• Develop ideal best practices and architectures for doing AI/ML at scale in enterprise organizations
• Foster openness for algorithms, tooling, libraries, frameworks, models and datasets in AI/ML
• Advocate for technologies, such as differential privacy and homomorphic encryption, that helps anonymize data sets and protect privacy
• Work towards universal standards to share data between AI/ML applications

The alliance is supported by numerous AI vendors, including Determined AI, Algorithmia, Fiddler, and Iguazio.