Experts regard artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries. For instance, this PWC articles predicts that AI could potentially contribute $15.7 trillion to the global economy by 2035. China and the United States are primed to benefit the most from the coming AI boom, accounting for nearly 70% of the global impact.
Artificial Intelligence is the basis for mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment. Stated simply, AI is trying to make computers think and act like humans.
To achieve it requires three key components :
Computational systems.
Data and Data management.
Advanced AI algorithms.
The more humanlike the desired outcome, the more data and processing power required. At least since the first century BCE, humans have been intrigued by the possibility of creating machines that mimic the human brain. In modern times, the term artificial intelligence was coined in 1955 by John McCarthy. In 1956, McCarthy and others organized a conference titled the “Dartmouth Summer Research Project on Artificial Intelligence.” This beginning led to the creation of machine learning, predictive analytics, and now to prescriptive analytics. It also gave rise to a whole new field of study, data science. Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision-making. As an example, most humans can figure out how to not lose at tic-tac-toe (naughts and crosses), even though there are 255,168 unique moves, of which 46,080 ends in a draw. Far fewer folks would be considered grand champions of checkers, with more than 500 x 1018, or 500 quintillion, different potential moves. Computers are extremely efficient at calculating these combinations and permutations to arrive at the best decision. AI (and its logical evolution of machine learning) and deep learning are the foundational future of business decision-making.
Applications of AI can be seen in everyday scenarios such as financial services fraud detection, retail purchase predictions, and online customer support interactions. Here are just a few examples:
Fraud detection:The financial services industry uses artificial intelligence in two ways. Initial scoring of applications for credit uses AI to understand creditworthiness. More advanced AI engines are employed to monitor and detect fraudulent payment card transactions in real time. Virtual customer assistance:Call centers use to predict and respond to customer inquiries of human interaction. Voice recognition, coupled with simulated human dialogue, is the first point of interaction in a customer service inquiry. Higher-level inquiries are redirected to a human. When a person initiates dialogue on a webpage via chat (chatbot), the person is often interacting with a computer running specialized AI. If the chatbot can’t interpret or address the question, a human intervenes to communicate directly with the person. These non-interpretive instances are fed into a machine-learning computation system to improve the AI application for future interactions. Advancements in AI for applications like natural language programming and computer vision are helping industries like financial services, healthcare, and automotive accelerate innovation, improve customer experience, and reduce costs. Gartner estimates that up to 70% of people will interact with conversational AI platforms on a daily basis by the year 2022. NLP and CV provide a valuable link between humans and robots: NLP helps computer programs understand human speech, and CV applies machine learning models to images, and is perfectly suited for everything from selfie filters to medical imaging.
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