- AI in vaccine development
Developing a vaccine is a complex process that usually takes years or decades to achieve. However, with modern technologies, it is easy to sift through millions of materials and get the information needed to develop vaccines. An example of the usefulness of AI was witnessed during the COVID-19 crisis, where a record speed of vaccine development was achieved. AI models allowed researchers to analyze vast amounts of data about coronaviruses of the past and how they work.
- Automated driving and robotaxis
Autonomous driving has been gaining ground over the past decade. In 2022, it has continued to be one of the areas that major companies such as Baidu and Uber are focusing on. These two are currently testing driverless cars and opening more robotaxi services to the public in different cities worldwide. Fully automated driving enables taxis to drive safely without human intervention, which will be necessary for the scalability and commercialization of autonomous vehicles. Over the years, companies like Baidu have invested heavily in driverless vehicles and launched some of them, including Apollo Go Robotaxi service, which operates in the cities of Beijing, Cangzhou and Changsha.
- Natural language processing
The natural language processing systems have become more advanced over the past two years. NLP is the ability of a computer system to understand the meaning of text or speech. This ability has revolutionized the way humans interact with machines. This is evident from the widespread use of AI assistants like Siri, Alexa and Cortana. The natural language processing systems understand what people say and act or respond accordingly. NLP has many to offer than just communication. For instance, it can help scale business operations in an organization.
- Quantum computing
Quantum computing is a new area of competition among organizations and countries. At the beginning of this decade, quantum computing has made some crucial achievements of quantum supremacy. Quantum computing has so much significance for AI since it has the potential to supercharge AI applications compared to binary-based traditional computers. For example, quantum computing can be used to run a generative machine learning model via a generative dataset, which a classical computer will take too much time to do. The use of quantum computing increases accuracy in the real world. On the other hand, advanced technologies like deep learning algorithms also play a critical role in the development of quantum computing. Deep learning algorithms also play a critical role in quantum computing development.
- AI chips
With the proliferation of AI, AI hardware has continued to develop in 2022. There has been a launch of several AI chips customized to perform specialized operations. Although ordinary processors can support AI tasks, AI-specific processors are modified to do tasks like deep learning. As AI applications increase, there is a need for performance or reduction of cost. This can unlock value for companies with a network of data centers meant for cloud services.
