- Between 2018 and 2019, organizations that have deployed artificial intelligence (AI) grew from 4% to 14%, according to Gartner’s 2019 CIO Agenda survey.
- Conversational AI remains at the top of corporate agendas spurred by the worldwide success of Amazon Alexa, Google Assistant, and others.
- Enterprises are making progress with AI as it grows more widespread, and they’re also making more mistakes that contribute to their accelerating learning curve.
These and many other new insights are from Gartner Hype Cycle For AI, 2019 published earlier this year and summarized in the recent Gartner blog post, Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019. Gartner’s definition of Hype Cycles includes five phases of a technology’s lifecycle and is explained here. Gartner’s latest Hype Cycle for AI reflects the growing popularity of AutoML, intelligent applications, AI platform as a service or AI cloud services as enterprises ramp up their adoption of AI. The Gartner Hype Cycle for AI, 2019, is shown below:
Details Of What’s New In Gartner’s Hype Cycle For AI, 2019
- Speech Recognition is less than two years to mainstream adoption and is predicted to deliver the most significant transformational benefits of all technologies on the Hype Cycle. Gartner advises its clients to consider including speech recognition on their short-term AI technology roadmaps. Gartner observes, unlike other technologies within the natural-language processing area, speech to text (and text to speech) is a stand-alone commodity where its modules can be plugged into a variety of natural-language workflows. Leading vendors in this technology area Amazon, Baidu, Cedat 85, Google, IBM, Intelligent Voice, Microsoft, NICE, Nuance, and Speechmatics.
- Eight new AI-based technologies are included in this year’s Hype Cycle, reflecting Gartner enterprise clients’ plans to scale AI across DevOps and IT while supporting new business models. The latest technologies to be included in the Hype Cycle for AI reflect how enterprises are trying to demystify AI to improve adoption while at the same time, fuel new business models. The new technologies include the following:
- AI Cloud Services – AI cloud services are hosted services that allow development teams to incorporate the advantages inherent in AI and machine learning.
- AutoML – Automated machine learning (AutoML) is the capability of automating the process of building, deploying, and managing machine learning models.
- Augmented Intelligence – Augmented intelligence is a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making, and new experiences.
- Explainable AI – AI researchers define “explainable AI” as an ensemble of methods that make black-box AI algorithms’ outputs sufficiently understandable.
- Edge AI – Edge AI refers to the use of AI techniques embedded in IoT endpoints, gateways, and edge devices, in applications ranging from autonomous vehicles to streaming analytics.
- Reinforcement Learning – Reinforcement learning has the primary potential for gaming and automation industries and has the potential to lead to significant breakthroughs in robotics, vehicle routing, logistics, and other industrial control scenarios.
- Quantum Computing – Quantum computing has the potential to make significant contributions to the areas of systems optimization, machine learning, cryptography, drug discovery, and organic chemistry. Although outside the planning horizon of most enterprises, quantum computing could have strategic impacts in key businesses or operations.
- AI Marketplaces – Gartner defines an AI Marketplace as an easily accessible place supported by a technical infrastructure that facilitates the publication, consumption, and billing of reusable algorithms. Some marketplaces are used within an organization to support the internal sharing of prebuilt algorithms among data scientists.
- Gartner considers the following AI technologies to be on the rise and part of the Innovation Trigger phase of the AI Hype Cycle. AI Marketplaces, Reinforcement Learning, Decision Intelligence, AI Cloud Services, Data Labeling, and Annotation Services, and Knowledge Graphs are now showing signs of potential technology breakthroughs as evidence by early proof-of-concept stories. Technologies in the Innovation Trigger phase of the Hype Cycle often lack usable, scalable products with commercial viability not yet proven.
- Smart Robots and AutoML are at the peak of the Hype Cycle in 2019. In contrast to the rapid growth of industrial robotics systems that adopted by manufacturers due to the lack of workers, Smart Robots are defined by Gartner as having electromechanical form factors that work autonomously in the physical world, learning in short-term intervals from human-supervised training and demonstrations or by their supervised experiences including taking direction form human voices in a shop floor environment. Whiz robot from SoftBank Robotics is an example of a SmartRobot that will be sold under the robot-as-a service (RaaS) model and originally be available only in Japan. AutoML is one of the most hyped technology in AI this year. Gartner defines automated machine learning (AutoML) as the capability of automating the process of building, deploying, or managing machine learning models. Leading vendors providing AutoML platforms and applications include Amazon SageMaker, Big Squid, dotData, DataRobot, Google Cloud Platform, H2O.ai, KNIME, RapidMiner, and Sky Tree.
- Nine technologies were removed or reassigned from this years’ Hype Cycle of AI compared to 2018. Gartner has removed nine technologies, often reassigning them into broader categories. Augmented reality and Virtual Reality are now part of augmented intelligence, a more general category, and remains on many other Hype Cycles. Commercial UAVs (drones) are now part of edge AI, a more general category. Ensemble learning had already reached the Plateau in 2018 and has now graduated from the Hype Cycle. Human-in-the-loop crowdsourcing has been replaced by data labeling and annotation services, a broader category. Natural language generation is now included as part of NLP. Knowledge management tools have been replaced by insight engines, which are more relevant to AI. Predictive analytics and prescriptive analytics are now part of decision intelligence, a more general category.
Hype Cycle for Artificial Intelligence, 2019, Published 25 July 2019, (Client access reqd.)
Top Trends in the Gartner Hype Cycle for Artificial Intelligence, 2019 published September 12, 2019