AI vendors to watch in 2020 and beyond Posted on : Dec 21 - 2019

The past 10 years have seen a surge of new AI vendors, and the trend isn't likely to end anytime soon, as investors continue to pour money into artificial intelligence.

There are thousands of AI startups around the world. Many aim to do similar things -- create chatbots, develop hardware to better power AI models or sell platforms to automatically transcribe business meetings and phone calls.

These AI vendors, or AI-powered product vendors, have raised billions over the last decade, and will likely raise even more in the coming years. Among the thousands of startups, a few shine a little brighter than others.

To help enterprises keep an eye on some of the most promising AI startups, here is a list of those founded within the past five years. The startups listed are all independent companies, or not a subsidiary of a larger technology vendor. The chosen startups also cater to enterprises rather than consumers, and focus on explainable AI, hardware, transcription and text extraction, or virtual agents.

Explainable AI vendors and AI ethics

As the need for more explainable AI models has skyrocketed over the last couple of years and the debate over ethical AI has reached government levels, the number of vendors developing and selling products to help developers and business users understand AI models has increased dramatically. Two to keep an eye on are DarwinAI and Diveplane.

DarwinAI uses traditional machine learning to probe and understand deep learning neural networks to optimize them to run faster.

Founded in 2017 and based in Waterloo, Ontario, the startup creates mathematical models of the networks, and then uses AI to create a model that infers faster, while claiming to maintain the same general levels of accuracy. While the goal is to optimize the deep learning models, a 2018 update introduced an "explainability toolkit" that offers optimization recommendations for specific tasks. The platform then provides detailed breakdowns on how each task works, and how exactly the optimization will improve them.

Founded in 2017, Diveplane claims to create explainable AI models based on historical data observations. The startup, headquartered in Raleigh, N.C., puts its outputs through a conviction metric that ranks how likely new or changed data fits into the model. A low ranking indicates a potential anomaly. A ranking that's too low indicates that the system is highly surprised, and that the data likely doesn't belong in a model's data set. View More