Artificial intelligence (AI) technology is progressing rapidly and extensively across a variety of fields. From self-driving cars to virtual tutors, AI is revolutionizing many products and services we use every day.
Despite the rapid advancements, there remain numerous obstacles that need to be addressed before AI technologies can be fully utilized and beneficial for society as a whole. For instance, healthcare is seeing the implementation of AI as remote health checks and follow-up tools that improve diagnoses, anticipate diseases, and optimize clinical decision-making.
As AI develops, there are pressing concerns about its responsible implementation that safeguards human rights, democracy, and the environment. To address these issues, the United States is a founding member of the Global Partnership for AI Innovation (GPAI), an effort to foster trustworthy and inclusive AI technologies.
One of the most frequently asked questions about AI is its potential to address socio-economic inequalities and racial biases. For instance, during the COVID-19 pandemic that has displaced millions worldwide, some AI models used for disease prediction failed to take into account that certain people are more vulnerable than others due to their race and economic background.
Another major concern is the quality of data used to train AI systems. With all the data being generated by the Internet of Things – including through sensors attached to assets and devices – it must be properly analyzed and contextualized for it to be effective.
This process necessitates an intricate series of computations performed by neural networks or ‘deep learning’ algorithms. These programs are capable of discovering regularities and structure in data, which allows them to ‘learn’ new skills which can be applied in future situations.
In the transportation industry, AI-powered driver assistance systems and semi-autonomous vehicles can learn from other drivers’ experiences on the road and use that data to help prevent accidents or traffic congestion. Furthermore, these technologies help identify potential issues before they arise so humans are alerted about them beforehand.
Furthermore, AI can also contribute to safety and security at work by anticipating potentially hazardous conditions before they happen. This can save lives and guarantee employees’ protection.
Machine learning is a foundational aspect of AI, enabling the collection and analysis of large amounts of data in an efficient manner. This can lead to faster, more precise decisions with better information.
For instance, smart energy management systems that collect information from sensors attached to assets can utilize machine learning algorithms for analysis and delivery of this data to a company’s decision-makers so they can make better choices regarding the direction of their business and infrastructure.
Other examples of AI applications include cybersecurity, customer relationship management, internet searches, personal assistants, and voice-to-text applications. Furthermore, it’s being employed to create artificially intelligent robots that can perform tasks previously handled only by humans.
