A new chapter of industrial AI has begun. Industry experts already anticipate some of the main artificial intelligence trends in 2020. What’s clear is that the role of this technology in the manufacturing sector will continue to grow. In fact, according to a recent PwC report, by 2030 artificial intelligence will come to contribute up to 15 trillion euros to the world economy.
Artificial intelligence: the most anticipated trends of 2020
1. Growing harmony and convergence with other technologies
As one of AI trends of 2020, artificial intelligence will interact and converge with other technologies. From blockchain to analytics, up to ERP solutions, the goal will be to contribute more and more to the creation of IIoT systems, so as to make the workflow more efficient and automated.
2. Greater efficiency and precision of neural networks
The architectures of artificial neuron networks that simulate the functioning of neurons within a computer system will continue to grow in size and depth. In 2020, more improvements will happen, resulting in increasingly accurate results. Furthermore, the efficiency of neural networks will also increase, both from an energy and an operational point of view.
3. AI and predictive maintenance
AI will play a greater role in predictive maintenance and safety. It will be possible to exploit artificial intelligence models to identify anomalous behaviors in the production plant, so as to speed up maintenance programming and repair before any error occurs, preventing downtime or accidents.
4. Automated AI development
“AI for AI”: this will be one of the main artificial intelligence trends in 2020. In other words, AI will be used to promote the automation of the phases and processes involved in creating and managing artificial intelligence solutions, as well as to scale them within the company more extensively and efficiently.
Some experts believe that AI will become an actual operating system for companies. In 2020, this technology will take further steps in conquering this challenge, with important steps forward in the development of the infrastructures necessary to support the implementation of this solution.
6. Less “hunger” for data
Regular artificial intelligence algorithms used to rely on high volumes of data. Being based on deep learning, these technologies were only able to work accurately if formed and validated by huge amounts of data. Which lead to several limits, including the difficulty and cost of gaining access to the types and quantities of data needed. For this reason, researchers aim to develop AIs capable of using data synthesis methodologies.
For example, in the automotive industry this approach will make it possible to obtain driver information more efficiently (for example by analyzing a video). This will simplify and reduce the costs of developing new advanced safety features and customizing the driving experience.