Challenges And Opportunities Of Business In Artificial Intelligence
From numerous years technical advancements are the major drivers of monetary development. The most vital universally useful technology of our period is Artificial Intelligence. AI is very nearly turning into a basic piece of each business foundation, settling on it crucial for company leaders to see how this technology can, and will, upset traditional business model. The impact of AI is increasing in manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education. This paper offers a point of view on how these advances are affecting business and society, and presents a system for seeing how Artificial Intelligence can convey an incentive for the association and industry. This paper talks about how AI is spurring workforce change, data management, privacy, cross-entity collaboration, and generating new ethical challenges for business. This paper underscores on the requirement for business to change its core process and business models to take advantage of AI. And it tries to enable managers to comprehend and follow up on the gigantic opportunity from the blend of human and machine knowledge. The paper also focus on challenges that should be tended to while grasping AI.
INTRODUCTION
AI technology is evolving faster than expected and it is proving to be most effective in producing dramatic results in business today. AI is on the verge of becoming a critical part of every business infrastructure, making it vital for company decision-makers to understand how this technology can, and will, disrupt traditional business models. Adopting to artificial intelligence can make business cost-effective , more productive by cutting down the time we spend doing basic administrative tasks and better customer engagement. Artificial Intelligence has the potential to streamline business processes, improve customer services and leverage sensor-driven data for marketing and advertising. Today, there are numerous applications of artificial intelligence in the consumer and business spaces, from Apple’s Siri to Google’s DeepMind. Siri, for example, uses natural language processing (NLP) to interpret voice commands and respond accordingly. Google’s DeepMind, on the other hand, uses deep learning.
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ARTIFICIAL INTELLIGENCE AND ITS TECHNOLOGIES
AI is defined as an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.[3] The term 'artificial intelligence' commonly applies to devices or applications capable of carrying out specific tasks in human ways, by mimicking cognitive functions such as learning reasoning problem-solving, visual perception and language-understanding. Artificial Intelligence (AI) is defined as intelligence exhibited by an artificial entity to solve complex problems and such a system is generally assumed to be a computer or machine. Accenture defines AI as information systems and applications that can sense, comprehend and act which captured the attention of Business Executives along with technologists and research scientists. AI can be broadly classified into Applied AI & Generalized AI. Applied AI includes systems designed to intelligently carry out a single task, eg move a driverless vehicle, or trade stocks and shares. Generalized AI includes systems or devices that can theoretically handle any task, as they carry enough intelligence to find solutions to unfamiliar problems. [1] The important AI Technologies are Natural Language Generation Speech Recognition Virtual Agents Machine Learning Platforms AI-optimized Hardware: Decision Management Deep Learning Platforms Biometrics Robotic Process Automation Text Analytics and NLP:
- Machine Learning (ML) – uses computer algorithms based on mathematical models using probability to make assumptions and can make predictions about similar data sets.
- Cognitive Computing – builds upon ML using large data sets with the goal to simulate human thought process and predictive decisions.
- Deep Learning – builds on ML using neural nets to make predictive analysis. The use of neural nets is what is differentiating Deep Learning from Cognitive Computing right now. Deep Learning is also helping improve image and speech recognition.
- Predictive application programming interfaces (APIs) – A predict Order this paper