The most hype-worthy AI trends of 2020, according to Gartner

• Gartner evaluated more than 1,700 emerging technologies and selected 30 technologies with real promise in enterprise

Earlier this year, Gartner published its famous hype cycle for emerging technologies — The Gartner’s Hype Cycle for Emerging Technologies 2020 listed a host of artificial intelligence (AI) technologies with different functionalities that will dramatically transform industries and business sectors.

The Hype Cycles guide that is produced each year aims to break down the initial hype new technologies receive and the communal life cycle they will follow throughout the years. Most technologies take years to evolve from a new contender in the market to a nice-to-have tool, and then finally into an indispensable must — or they may be resigned to an altogether different fate (RIP, iPods, BlackBerry, Zune).

This year, Gartner evaluated over 1,700 emerging technologies and narrowed down the contenders to only 30 with the highest ‘transformational’ value in the next decade.

The Gartner Hype Cycle for Emerging Technologies, 2020 highlights 5 unique trends. Find out what they are here: https://t.co/u5Yubewcol. #GartnerSYM #CIO #TechTrends pic.twitter.com/LBatEAZbY6

— Gartner (@Gartner_inc) August 19, 2020

AI-specific technologies were found to make the first appearance in the Hype Cycle. We take a closer look at some of these highly-anticipated finalists.

Embedded AI 

Embedded AI is defined as the use of AI/ML (machine learning) techniques within embedded systems to analyze locally captured data. This specific AI technology offers greater functional advantages since it will increase the accuracy, insights, and intelligence of data analysis from existing and future sensors.

A major application of embedded AI will most likely be seen in manufacturing as its capabilities will provide more in-depth insights needed to maintain machinery and hardware systems. Predictive maintenance will be more efficient with improved insights powering decision making, further minimizing the risks of machinery breakdowns and slashing production losses.

Generative AI

Generative AI is a variety of ML methods that can alter existing content and create new novel content while preserving some likeness to the original training module but (and that’s a big but) it’s not the same. This is the technology used to create “deepfakes,” aka digital content like images, audios, and videos that bad actors can exploit in order to prank, spread misinformation, and/or cause dangerous disruptions. 

Even though 31 AI experts from a UCL study ranked deepfakes as the biggest AI crime threat, deepfakes are also increasingly used for less devious intentions including drug discovery and synthetic data generation.

For instance, the UK-based autonomous vehicles software company Oxbotica enlisted the use of deepfake technology to train its autonomous driving systems in realistic scenarios. The company noted that the technology can generate thousands of hyper-realistic images within minutes to expose the autonomous vehicle systems to “near-infinite variations of the same situation – without real-world testing of a location having ever taken place.”

Responsible AI

Responsible AI emerges as an ethical compass that will steer businesses to make more ethical and balanced business decisions with reduced bias.

Gartner envisioned responsible AI to encourage businesses to increase trust and transparency in their daily operations and decision making while also reducing bias mitigation with AI. The most likely use cases of responsible AI include identifying deepfakes and leading the applications of AI for good.

Last year, UK regulators pushed forward a regulation that requires businesses and organizations to explain the decision made by AI or face multimillion-dollar fines if they fail to do so. This comes at a time where a growing number of firms in the UK are employing AI to execute critical business decisions like shortlisting and hiring candidates. These critical business decisions are not completely immune to potential AI bias. Hence, responsible AI may be paving the way for enterprises to take on more accountability in their decision to embrace AI in core business decisions and have a responsibility to ensure the safety and privacy of partners, employees, and customers.