HypeCycle for AI 2021
Every year, I look forward to the updated HypeCycle from Gartner. The Gartners survey a large number of companies about what they are using and what they plan to use, and…
Every year, I look forward to the updated HypeCycle from Gartner. The Gartners survey a large number of companies about what they are using and what they plan to use, and from this, they can discern certain trends. Predicting the future is extraordinarily difficult.
When I attended their conference a few years ago, they proudly predicted that today people would communicate more with chatbots than with real humans. Fortunately, that did not come to pass. Chatbots are currently in the so-called valley of disillusionment, and practice has already revealed the weaknesses of traditional tree-based chatbots.
What trends do the Gartners see in AI today?
1. Operationalisation of AI – today, it takes the average organisation eight months to deploy a finished model into a production environment. This is far too slow, and so most companies are considering how to shorten this time through better architecture. One solution is ModelOps – which reduces this time and can also include a system for managing and overseeing the entire AI lifecycle.
2. Efficient use of data, models, and computations. This group includes my favourite composite (hybrid) models. That is, combinations of strong models, typically deep neural networks, with something that is well-explained, such as an expert system. This also encompasses generative AI, which enhances datasets with synthetic data.
3. Responsible AI – AI is increasingly assisting people in decision-making, and with this comes a growing emphasis on reducing bias. There is often talk of discrimination based on race, gender, age, the neighbourhood where you live, and so on. In the EU and the USA, there is an ever-stronger focus on fairness, transparency, security, and privacy.
4. Data – more attention is being directed towards new analytical techniques known as “small data” and “wide data.” This concerns how to utilise the data we have more effectively. For example, how can we predict the course of an epidemic when we have only a short time series (small data)? How can we extract more information from various unstructured and diverse data (wide data)?
Sources:
– https://www.gartner.com/…/the-4-trends-that-prevail-on…
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