Gartner 2021 Hype Cycle for Emerging Technologies. What’s in It for AI Leaders?

Gartner’s 2021 Hype Cycle for Emerging Technologies is out, so it is a good moment to take a deep look at the report and reflect on our AI strategy as a company. You can find a brief summary of the complete report here.

“2009 Feltron Report” by jakeprzespo is licensed under CC BY 2.0

As every year, let’s start with some assumptions that everyone should be aware of when interpreting this Hype Cycle, especially when comparing the cycle’s graphical representation with past years:

• All technologies presented in the Hype Cycle are considered to be at an early stage of maturity.
• If a specific technology is not featured it does not necessarily imply that they are not going to have a significant impact. It might imply quite the opposite. One reason for some technologies to disappear from the Hype Cycle might be that they are no longer “emerging” but mature enough to be key for business and IT, having demonstrated its positive impact. Some of these technologies are covered in specific Hype Cycles, as we will see later on this article.

For 2021, Gartner is classifying emerging technologies under three themes, that they are using in overall hype cycles. These themes are related to building trust, growth, and change. I really think that these three themes are very aligned with the top priority every AI leader should have: delivering AI solutions at scale (growth) that have a profound impact on business (change) by creating products, services or changes in processes that are reliable, ethical, transparent, and explainable (trust).

AI emerging technologies related to trust

• Homomorphic encryption is a form of encryption that allows to perform computational operations on data without the need to decrypt it first. For AI driven companies, this opens the door both to encourage data driven economy by sharing their data as well as for more accurate results in their algorithms by being able to incorporate external data without compromising privacy. Recent research results from first level institutions like BSC (Barcelona Supercomputing Center) have opened the door to apply this kind of techniques to big encrypted neural networks.

AI emerging technologies related to growth

• AI-driven innovation refers to the use of AI to create products and services. While Gartner classifies this into the growth category, in my opinion it is related to the three of them. Innovating through AI requires change and trust, ensuring that the underlying AI technologies can deliver results, and proving that those results can impact the P&L of a company. Also, new AI-driven products and services must be trustworthy from an ethical and legal perspective. In my experience, the success of AI-driven innovation initiatives depends on an end-to-end business and data technology approach:

1. Properly framing the business opportunity to be addressed and explore both social and market trends and existing services related for in depth understanding of consumer drivers and competitive framework.
2. Translating the business problem into a data problem. At this stage, it is relevant to identify data sources through a comprehensive Data Map and decide the algorithmic strategy to follow.
3. Defining the right UX/ UI for the product/ service we are defining.
4. Defining the Organizational Structure to manage the service, defining roles, responsibilities, and tasks to deliver the service at scale.

• Quantum ML. While Quantum Computing and its applications to ML are being so hyped, even Gartner acknowledges that there is yet no clear evidence of improvements by using Quantum computing techniques in Machine Learning. Real advancements in this area will require to close the gap between current quantum hardware and ML by working on the problem from the two perspectives at the same time: designing quantum hardware that best implement new promising Machine Learning algorithms. One of the challenges in this area is finding the right talent that has interdisciplinary knowledge in machine learning and quantum hardware design and implementation. In terms of mainstream adoption, Gartner positions Quantum ML in a ten+ years time frame.
• Generative AI is, very simply put, a set of algorithms that can generate data similar to the one used to train them. OpenAI announced in 2021 two of its multimodal neural networks, including WALL-E, which helped boosting the popularity of Generative AI. While it is a lot of hype behind this kind of AI for creative uses, it also opens the door in the future to other relevant research fields, for example drug discovery. Generative AI also poses significant challenges from a societal perspective, as OpenAI mentions in their blog: they “plan to analyze how models like DALL·E relate to societal issues […], the potential for bias in the model outputs, and the longer-term ethical challenges implied by this technology. As the saying goes, an image is worth a thousand words, and we should take very seriously how tools like this can affect misinformation spreading in the future.

AI emerging technologies related to change

• AI-augmented design and AI-augmented software engineering are both related to generative AI and the impact AI can have in the work that can happen in front of a computer, particularly software development and web design. We are seeing a lot of hype around these two technologies thanks to the publication of algorithms such as GPT-X or OpenAI’s Codex, which fits solutions like GitHub’s Copilot. It was mid-June 2021 when Sam Altman, OpenAI’s CEO, published a tweet in which he claimed that AI was going to have a bigger effect on jobs that take place in front of a computer much faster than those happening in the physical world:

Sam Altman’s tweet about AI disruption in computer-assisted work

As always, these technologies do not come without challenges. From the disruption they might create in some low level coding and UX tasks, to the legal implications that training these AI algorithms might have.

• Physics-informed AI is a type of AI that do not only learns from digital training data but is also capable of adapting to the physical environment. While AI is getting very good at solving problems in the digital world, real world interaction poses greater challenges that require the combination of real-time sensing and interaction with the environment, and we can expect a lot of investment in this area.

Complementing these AI emerging technologies, Gartner’s Hype Cycle for Artificial Intelligence, 2021 priority matrix allows us to have a list of the AI technologies that, according to Gartner, will have a transformational impact in the coming 2–5 years:

• Composite AI
• Computer Vision
• Decision Intelligence
• Deep Learning
• Edge AI
• Generative AI
• Human-Centered AI
• Intelligent Applications
• Machine Learning

It is interesting to note that Gartner does not include NLP and Transformer technologies in this list, expecting a transformational impact not before 5–10 years. The causes for this delay are many, including the development of NLP algorithms on minority languages or the ethical issues and bias this algorithms face.

As a final remark, it is interesting to see how societal challenges are becoming key for AI emerging technologies to be adopted. This is a trend I only expect to keep growing in the future as Responsible AI is becoming more and more popular, as Gartner itself notes including it as an innovation trigger in its Gartner’s Hype Cycle for Artificial Intelligence, 2021.

Which ones do you think are the AI-related technologies that will have the greatest impact in the next years? Which emerging AI technologies would you invest on as an AI leader?

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