The annual Hype Cycle for Artificial Intelligence, 2020 is just published! Yay! AI as a concept is rolling off the top of the Peak of Inflated Expectations. (Yay!) It is time for production deployments, and that reveals new challenges. Some of these challenges are not really unheard of – they reflect a lack of capabilities fully expected in software engineering, for example, quality assurance and tools for managing AI in production. But some challenges are novel: With AI advances come greater concerns about responsible development of AI systems.
What’s new this year? What captures the imagination and promise new solutions to tough problems? Five innovation profiles debuted on the hype cycle 2020:
- Composite AI refers to the combination of different AI techniques to achieve the best results. Our recent survey about AI in the organizations confirmed that machine learning is only one technique among others.
- Generative AI is the next frontier compared to the AI methods that directly extract numeric or categorical insights from data. It creates original artifacts or reconstructed content and data mainly thanks to the notable progress of GANs, BERT and GPT-2/GPT-3.
- Small Data as a concept indicates both the issue and approach to help those clients who ask us, “How should we get data for AI if we are not Google?” Different strategies to overcome the problem are getting visible this year – synthetic data, transfer learning, federated learning, self-supervised learning, few-shot learning and knowledge graphs.
- Responsible AI: The broader AI adoption is, the more enterprises learn about their responsibility for the AI solutions and technologies they implement. For example, in June 2020, Amazon, IBM and Microsoft stopped selling AI services for facial recognition to the law enforcement.
- Things as Customers: Customer experience is at the top of corporate AI agendas. But what if some customer chores are offloaded to virtual personal assistants, smart appliances, connected cars and IoT-enabled factory equipment?
We designed the Hype Cycle for Artificial Intelligence to give a high-level picture of AI innovations and disciplines for CIOs, AI leaders, and data and analytics leaders. Its two companions expand the high-level picture:
- Hype Cycle for Data Science and Machine Learning depicts the progress of ML tools, algorithms and data science techniques.
- Hype Cycle for Natural Language Technologies is dedicated to advances in this rapidly expanding field.
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