Artificial intelligence has accelerated its progress amid the coronavirus pandemic. While 2020 was an extremely challenging year for individuals and companies, AI applications played a crucial role in saving lives and promoting economic flexibility.
We’ll talk about significant advances which are transforming our economic future and our society.
AI in vaccine development
Vaccine developments usually take years and decades. However, Covid-19 vaccine candidates were already in the process of testing only three months after the first surge of the pandemic. Such incredibly speedy developments were achieved with AI models’ help in analyzing vast amounts of data about Covid-19. Machine learning models can sort the data and predict which subcomponents are capable of producing an immune response.
Automated driving – robotaxis
Autonomous driving technology resumed evolving last year. The industry’s leading companies tested driverless cars and made robotaxi services available to the public in several cities.
Applied natural language processing
Natural language systems developed remarkably last year. They stepped forward in generating language that adjusts with human speaking and writing patterns. They even progressed in visual understanding. These natural language models are delivering more accurate search results, which lead to more solid user experiences.
Quantum computing made significant advancements in 2020. One of them is the Jiuzhang computer’s achievement of quantum supremacy.
This is important for AI, as quantum computing can power AI applications compared to classical binary-based computers. For example, quantum computing could be applied to run a generative machine learning model through a more extensive data set than a classical computer can process. Thus, it could make the model more accurate and valid in real-world environments. Advanced technologies such as deep learning algorithms also play an increasingly critical role in quantum computing research.
AI hardware resumed development in 2020, with the launch of several AI chips for specialized tasks. While an ordinary processor can support AI tasks, specific AI processors are modified with specific systems to optimize the performance of tasks like deep learning.