The development of AI is transforming nearly every field. Already, machine learning algorithms are being incorporated into analytics and customer relationship management systems. Chatbots have been incorporated into websites, providing instant assistance to customers. And, the automation of job positions has become a topic of discussion among IT analysts and academics. For example, AI-driven tutors are helping students in classes by assessing student needs and providing extra support. AI may even replace some teachers.
AI designers must incorporate ethical principles into their algorithms, ensuring that they respect human values and correspond to human concerns. To ensure that AI solutions are not detrimental to humanity, organizations should create a code of ethics for AI developers. They should also implement an audit trail of their AI solutions and train their users on its use. Moreover, organizations should create remediation mechanisms should their AI solutions cause harm to people. These are just a few examples of AI ethics.
When an AI is created with the theory of mind, it will understand human emotions and be able to infer their intentions. Ultimately, it will be able to predict human behavior and predict future outcomes. Moreover, it would have a sense of self. Self-aware AI would know its current state and avoid making mistakes. It may even be able to detect potential problems. This type of AI is considered to be the future of intelligent machines.
Humans have long been fascinated with the idea of building machines that mimic the human brain. John McCarthy coined the term artificial intelligence in 1955, and it was not until 1956 that AI research began to take off. The project, which began at Dartmouth College, led to the development of machine learning, deep learning, predictive analytics, and even chatbots. Today, AI research is being done at many universities around the world. But, for the time being, AI research remains a highly controversial topic.
AI is already changing the way we live. Companies are able to automate repetitive learning and discovery through data. AI allows businesses to focus on customer needs instead of dealing with customer service representatives. Uber, for instance, employs advanced machine learning algorithms to predict customer needs and proactively dispatch drivers when they are needed. Companies like Google are also using AI-driven algorithms to improve their services. These systems can even be used to identify investment opportunities. This technology is becoming the future of work.
Advances in AI have enabled AI to play an increasingly important role in modern software. Companies that apply machine learning to diagnose patients have an advantage over human doctors. IBM Watson understands natural language, mines data for answers, and even offers a confidence score. Chatbots can help patients find medical information, schedule appointments, and even explain the billing process. In recent years, AI technologies have been used to combat the COVID-19 pandemic.
While AI is becoming a reality, many challenges remain. IT and the C-suite need to support data science teams. There is no standardization of data science processes, and senior executives may not understand the potential of AI investments. This means that the organization may not lend enough resources to the AI ecosystem to achieve its full potential. So, how can AI teams support these efforts? In a few short years, there is likely to be a lot of growth.
